Spiral spraying path optimization method and system for large-diameter steel pipe pile

Abstract

The application provides a spiral spraying path optimization method and system for large-diameter steel pipe piles, and relates to the technical field of spraying path optimization. The method comprises: for a target steel pipe, performing equidistant planning of a straight line and three-dimensional inverse mapping in a two-dimensional mapping plane; performing decoupling of the spiral spraying path based on spiral pitch, spray gun movement speed and spray gun attitude angle, and performing iterative optimization based on fixed individuals and wandering individuals classification under initial population expansion; generating a spraying parameter control sequence according to the optimized spiral spraying path, and controlling the spiral spraying of the target steel pipe; collecting wet film thickness, atomized particle size and atomized cone angle, and performing spraying speed adjustment amount decision-making and optimal parameter combination decision-making based on atomized particle size deviation in parallel, and updating to the optimized spiral spraying path. Through the application, dynamic optimization control of the spiral spraying path and deposition state under the complex curved surface of the steel pipe can be realized, and the technical effects of improving film thickness uniformity and spraying stability are achieved.

Description

Technical Field

[0001] This application relates to the field of spraying path optimization technology, and in particular to a method and system for spiral spraying path optimization for large-diameter steel pipe piles. Background Technology

[0002] With the continuous expansion of marine engineering, cross-sea bridges and port infrastructure construction, the demand for large-diameter steel pipe piles in marine corrosion protection, heavy-load support and long-term service in complex environments continues to increase. The requirements for the uniformity of coating on the outer surface of steel pipe piles, coating adhesion stability and automated spraying efficiency are also constantly improving.

[0003] Currently, most existing large-diameter steel pipe spraying methods employ fixed pitch trajectory control or spray gun movement modes based on empirical parameters. In actual spraying processes, the spray gun trajectory is usually preset based on an ideal cylindrical model, lacking dynamic adaptability to changes in the taper of the steel pipe surface, undulations in the weld area, and local curvature variations. Furthermore, the spraying path parameters and spraying condition parameters are mostly controlled by a fixed ratio, lacking an effective linkage adjustment mechanism between the spray gun movement speed, spiral coverage density, and spray deposition state. When there are local structural differences on the steel pipe surface or fluctuations in spraying conditions, uneven film thickness, imbalanced spray overlap rate, and decreased coverage stability can easily occur in local areas.

[0004] In summary, the existing technology suffers from a lack of dynamic analysis and adaptive adjustment mechanisms for the relationship between the actual curved surface characteristics of the steel pipe and the spraying deposition state. This makes it difficult to optimize the spiral spraying path in real time according to changes in the spraying state, which further affects the uniformity of film thickness and the stability of spraying quality during the spraying process of large-diameter steel pipe piles. Summary of the Invention

[0005] The purpose of this application is to provide a spiral spraying path optimization method and system for large-diameter steel pipe piles, in order to solve the technical problem in the prior art that the lack of dynamic analysis and adaptive adjustment mechanism for the correlation between the actual curved surface characteristics of the steel pipe surface and the spraying deposition state makes it difficult to optimize the spiral spraying path in real time according to the changes in the spraying state, which further affects the uniformity of film thickness and the stability of spraying quality in the spraying process of large-diameter steel pipe piles.

[0006] In view of the above problems, this application provides a method and system for optimizing the spiral spraying path for large-diameter steel pipe piles.

[0007] Firstly, this application provides a spiral spraying path optimization method for large-diameter steel pipe piles, implemented through a spiral spraying path optimization system for large-diameter steel pipe piles. The method includes: for the target steel pipe, performing equidistant planning and three-dimensional inverse mapping of a straight line path in a two-dimensional mapping plane using line laser surface scanning and Riemann mapping to obtain a spiral spraying path; performing decoupling based on spiral pitch, spray gun movement speed, and spray gun attitude angle on the spiral spraying path, employing a dual-objective fitness function, and performing iterative optimization based on fixed and wandering individual classification under initial population expansion to obtain an optimized spiral spraying path; generating a spraying parameter control sequence based on the optimized spiral spraying path to drive the spray gun head to perform spiral spraying control on the target steel pipe; and synchronously collecting wet film thickness, atomized particle size, and atomization cone angle during the spiral spraying process, triggering an online judge to execute in parallel a decision on the spraying speed adjustment based on the thickness offset vector and an optimal parameter combination decision based on the atomized particle size deviation, updating the optimized spiral spraying path.

[0008] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: using a line laser to scan the surface of the target steel pipe to generate a three-dimensional point cloud and curvature distribution map marked with taper, elliptical angle and local concavity and convexity, as the steel pipe surface scanning data; mapping the steel pipe surface scanning data to a two-dimensional plane rectangle through Riemann mapping to obtain a two-dimensional mapping plane; performing equidistant planning of straight-line routes in the two-dimensional mapping plane to obtain a planned path; and inversely mapping the planned path back to three-dimensional space as the spiral spraying path.

[0009] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: decoupling and encoding the spiral spraying path to obtain a spiral pitch sequence, a spray gun moving speed sequence, and a spray gun attitude angle sequence, wherein the elements of each sequence correspond to discretized segments along the length of the target steel pipe; constructing a dual-objective fitness function, wherein the total spraying time is the first optimization objective and the estimated paint consumption is the second optimization objective; determining an initial population based on the dual-objective fitness function using the spiral pitch sequence, the spray gun moving speed sequence, and the spray gun attitude angle sequence, and performing iterative optimization to obtain the optimized spiral spraying path.

[0010] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: using the spiral pitch sequence, spray gun moving speed sequence, and spray gun attitude angle sequence as reference individuals, and expanding the population based on the reference individuals to obtain the initial population; according to the initial population, labeling fixed individuals and wandering individuals according to a preset ratio; using wandering individuals within a preset range of each fixed individual as optimization groups, and using cross-mutation iteration based on binary crossover operators and polynomial mutation operators to determine the first optimized individual; traversing the first optimized individual, comparing it with the fixed individuals in the group, and replacing the fixed individuals according to fitness to obtain the first iterative population; and optimizing the spiral spraying path through multiple rounds of iterations until convergence based on the first iterative population.

[0011] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: the first optimization objective in the dual-objective fitness function is obtained by dividing the path length of each segment based on the sequence segment by the sum of the corresponding spray gun movement speeds; the second optimization objective is obtained by integrating the segment path length, spiral pitch, and instantaneous deposition efficiency function of each sequence segment; the instantaneous deposition efficiency function is the dynamic mapping relationship between the proportion of the mass of the coating actually adhering to the surface of the steel pipe and the mass lost due to atomization and dispersion in the spraying working parameters per unit time.

[0012] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: detecting the wet film thickness of the upper spraying area based on an optical thickness sensor deployed at the rear of the spray gun head; monitoring the paint mist particle size distribution and atomization cone angle in real time using a laser diffractometer as atomization data; inputting the wet film thickness, atomization data, and real-time operating parameters into an online judgment unit to perform two-way spraying analysis and spraying parameter control optimization to determine the spraying speed adjustment amount and the optimal parameter combination; and integrating the spraying speed adjustment amount and the optimal parameter combination to update the optimized spiral spraying path.

[0013] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: the first judgment branch of the online judge receives the wet film thickness and real-time operating parameters, performs thickness target value calculation and comparison based on the instantaneous deposition efficiency function, and determines the thickness offset vector; according to the thickness offset vector, the spraying speed adjustment amount is calculated using speed-thickness inverse mapping, wherein the variable pitch value is determined by the spraying speed adjustment amount, and the thickness and pitch value are positively correlated.

[0014] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: the second judgment branch of the online judge receives the atomization data and real-time operating parameters, and judges whether the atomization particle size deviates from the target range; if it deviates, an atomization coupling relationship is established based on the paint flow rate, atomization pressure, spray gun distance, and atomization particle size, and an extreme value optimization method is adopted, with atomization uniformity as the optimization constraint, and iteratively searches along the gradient direction in the three-dimensional parameter space based on paint flow rate-atomization pressure-spray gun distance to determine the optimal parameter combination that converges the atomization quality to the extreme value.

[0015] Preferably, the spiral spraying path optimization method for large-diameter steel pipe piles further includes: after the target steel pipe is sprayed, obtaining the optimized spiral spraying path after online updates throughout the entire spraying cycle; storing the optimized spiral spraying path in the industrial database, and performing variable parameter control adaptive spiral spraying management under the production line batch spraying conditions.

[0016] Secondly, this application also provides a spiral spraying path optimization system for large-diameter steel pipe piles, used to execute the spiral spraying path optimization method for large-diameter steel pipe piles as described in the first aspect, including: a spiral spraying path obtaining module, used to obtain a spiral spraying path for a target steel pipe by performing equidistant planning and three-dimensional inverse mapping of a straight line in a two-dimensional mapping plane through line laser surface scanning and Riemann mapping; and a spiral spraying path optimization module, used to perform decoupling on the spiral spraying path based on spiral pitch, spray gun moving speed and spray gun attitude angle, and adopt a dual-objective adaptation... The stress function performs iterative optimization based on the classification of fixed and wandering individuals under the initial population expansion to obtain an optimized spiral spraying path; the spiral spraying control module is used to generate a spraying parameter control sequence according to the optimized spiral spraying path, and drive the spray gun head to perform spiral spraying control on the target steel pipe; the judgment trigger module is used to synchronously collect wet film thickness, atomized particle size and atomization cone angle as the spiral spraying process progresses, and trigger the online judge to perform the spraying speed adjustment decision based on the thickness offset vector and the optimal parameter combination decision based on the atomized particle size deviation in parallel, and update to the optimized spiral spraying path.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of coordinated dynamic optimization control of spiral spraying path parameters, spraying motion state and atomization deposition state under complex curved surface conditions of steel pipes, the technical effect of improving the uniformity of spraying coverage, film thickness stability and adaptive spraying control capability under complex working conditions of large-diameter steel pipe piles is achieved.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the spiral spraying path optimization method for large-diameter steel pipe piles proposed in this application.

[0021] Figure 2 This is a schematic diagram of the spiral spraying path optimization system for large-diameter steel pipe piles, as described in this application.

[0022] The attached diagrams are labeled as follows: Module 1 for obtaining the spiral spraying path, Module 2 for optimizing the spiral spraying path, Module 3 for spiral spraying control, and Module 4 for determining and triggering the process. Detailed Implementation

[0023] This application provides a spiral spraying path optimization method and system for large-diameter steel pipe piles. It solves the technical problem in existing technologies where the lack of dynamic analysis and adaptive adjustment mechanisms regarding the correlation between the actual curved surface characteristics of the steel pipe and the spraying deposition state makes it difficult to optimize the spiral spraying path in real time according to changes in the spraying state, further affecting the film thickness uniformity and spraying quality stability during the large-diameter steel pipe pile spraying process. The application achieves the technical goal of collaborative dynamic optimization control of spiral spraying path parameters, spraying motion state, and atomization deposition state under complex curved surface conditions of the steel pipe, thereby improving the uniformity of spraying coverage, film thickness stability, and adaptive spraying control capabilities under complex working conditions for large-diameter steel pipe piles.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a spiral spraying path optimization method for large-diameter steel pipe piles, which is applied to a spiral spraying path optimization system for large-diameter steel pipe piles. The method specifically includes the following steps: For the target steel pipe, the spiral spraying path is obtained by performing equidistant planning of straight lines and three-dimensional inverse mapping in a two-dimensional mapping plane through line laser surface scanning and Riemann mapping.

[0026] Furthermore, this application also includes: using line laser scanning to scan the surface of the target steel pipe to generate a three-dimensional point cloud and curvature distribution map marked with taper, elliptical angle and local concavity and convexity, as the steel pipe surface scanning data; mapping the steel pipe surface scanning data to a two-dimensional plane rectangle through Riemann mapping to obtain a two-dimensional mapping plane; performing equidistant planning of straight line routes in the two-dimensional mapping plane to obtain a planned path; and inversely mapping the planned path back to three-dimensional space as a spiral spraying path.

[0027] Specifically, a line laser is used to scan the surface of the target steel pipe, generating a three-dimensional point cloud and curvature distribution map marked with taper, elliptic angle, and local concavity / convexity. This data serves as the steel pipe surface scanning data. A line laser scanning device, which continuously emits laser beams along a single direction, performs simultaneous circumferential and axial scanning on the outer surface of the target steel pipe. Spatial coordinate information of the steel pipe surface is obtained through changes in laser reflection position, and a discretized three-dimensional point cloud set is constructed according to the sampling order. Taper is used to characterize the diameter gradient along the length of the steel pipe; elliptic angle is used to characterize the degree of ellipticization of the steel pipe cross-section after deviating from the standard circle; and local concavity / convexity is used to characterize the local height fluctuations in the weld area, processing indentation area, or material deformation area. The curvature distribution map describes the spatial variation of the surface curvature at different locations on the steel pipe. By performing surface fitting and normal vector calculation on adjacent point clouds, the local curvature parameters of each region are determined, thus forming steel pipe surface scanning data containing spatial geometric features and surface morphology features. This provides basic geometric constraint information for subsequent surface unfolding and path planning.

[0028] Furthermore, by using Riemann mapping to map the scanned data of the steel pipe surface onto a two-dimensional planar rectangle, a two-dimensional mapping plane is obtained. Based on the conformal mapping principle in complex functions, the three-dimensional surface coordinates of the outer surface of the steel pipe are subjected to surface unfolding processing. This ensures that the local angular relationships of the steel pipe surface remain unchanged during the unfolding process, and the original cylindrical surface is converted into a two-dimensional rectangular region. The horizontal coordinates of the two-dimensional rectangular region correspond to the circumferential unfolding length of the steel pipe, and the vertical coordinates correspond to the axial length of the steel pipe. During the Riemann mapping process, by establishing a continuous mapping relationship between the surface parameter domain and the two-dimensional planar parameter domain, the spatial coupling complexity in the three-dimensional surface path planning process is reduced, while maintaining the continuity of local geometry and direction. This avoids path distortion, local stretching imbalance, or abrupt directional changes during surface unfolding. The two-dimensional mapping plane is used to transform the originally complex surface spiral trajectory planning problem into a planar straight line trajectory planning problem, providing a unified planar coordinate basis for the subsequent generation of ideal spiral paths.

[0029] Then, equidistant planning of straight lines is performed in the two-dimensional mapping plane to obtain the planned path. Within the two-dimensional rectangular plane, according to the preset spray coverage width, the overlap rate of adjacent spray bands, and the target film thickness uniformity requirements, the spacing planning of multiple straight lines along the length of the two-dimensional plane is performed to maintain a constant distance relationship between adjacent paths. The equidistant planning of straight lines is used to represent the planar expression of the ideal spiral after unfolding. Since the steel pipe surface forms a rectangular plane after two-dimensional unfolding, the original spatial spiral trajectory corresponds to a straight line trajectory with a constant slope in the two-dimensional plane. During the equidistant planning process, the interval parameter between adjacent straight lines corresponds to the spray width of the spray gun, thereby ensuring that the spiral spray trajectory formed after inverse mapping meets the continuous coverage requirements and reduces the probability of missed spray areas and repeated spray areas. The planned path is used to represent the initial ideal path model without considering process disturbances, weld seam abrupt changes, local deformation, and spraying dynamic errors.

[0030] Subsequently, the planned path is inversely mapped back to three-dimensional space as a spiral spraying path. Specifically, this involves: based on the corresponding mapping relationship between the two-dimensional planar parameter domain and the steel pipe surface parameter domain, the two-dimensional straight path is converted back into a spatial curve path on the outer surface of the steel pipe, thereby generating a spiral spraying path that extends continuously along the axial direction of the steel pipe and rotates around its circumference. Since the Riemann mapping is a conformal mapping, the local angular relationship of the path can be kept continuous and stable during the inverse mapping process. Therefore, when the spray gun moves along the path, the change in the spray gun's attitude angle remains continuous and smooth, avoiding large-scale attitude changes. At the same time, the coupling relationship between the spray gun's travel speed and the steel pipe's rotation speed remains constant along the path, keeping the coverage per unit area stable, thus forming ideal spiral path parameters that meet the requirements of uniform spraying. The spiral spraying path is used to describe the basic spraying trajectory model under theoretical conditions. The basic spraying trajectory model has not yet considered actual working condition variables such as material abrupt changes in the weld area, local thermal deformation of the steel pipe, environmental disturbances, atomization state fluctuations, and spraying dynamic deviations. Therefore, subsequent adaptive optimization and dynamic adjustment need to be performed in conjunction with online detection results.

[0031] The spiral spraying path is decoupled based on the spiral pitch, spray gun moving speed and spray gun attitude angle. A dual-objective fitness function is used to perform iterative optimization based on the classification of fixed individuals and wandering individuals under the initial population expansion, so as to obtain the optimized spiral spraying path.

[0032] Furthermore, this application also includes: decoupling and encoding the spiral spraying path to obtain a spiral pitch sequence, a spray gun moving speed sequence, and a spray gun attitude angle sequence, wherein the elements of each sequence correspond to discretized segments along the length of the target steel pipe; constructing a bi-objective fitness function, wherein the total spraying time is the first optimization objective and the estimated paint usage is the second optimization objective; determining an initial population based on the bi-objective fitness function using the spiral pitch sequence, the spray gun moving speed sequence, and the spray gun attitude angle sequence, and performing iterative optimization to obtain an optimized spiral spraying path.

[0033] Furthermore, this application also includes: using the spiral pitch sequence, spray gun movement speed sequence, and spray gun attitude angle sequence as reference individuals, and expanding the population based on the reference individuals to obtain the initial population; according to the initial population, and according to a preset ratio, labeling fixed individuals and wandering individuals; using wandering individuals within a preset range of each fixed individual as optimization groups, and using cross-mutation iteration based on binary crossover operators and polynomial mutation operators to determine the first optimized individual; traversing the first optimized individual, comparing it with the fixed individuals in the group, and replacing the fixed individuals according to fitness to obtain the first iterative population; and based on the first iterative population, optimizing through multiple rounds of iteration until convergence to obtain the optimized spiral spraying path.

[0034] Furthermore, this application also includes: the first optimization objective in the dual-objective fitness function is obtained by dividing the path length of each segment based on the sequence segment by the sum of the corresponding spray gun movement speeds; the second optimization objective is obtained by integrating the segment path length, helical pitch and instantaneous deposition efficiency function of each sequence segment; the instantaneous deposition efficiency function is the dynamic mapping relationship between the proportion of the mass of the coating actually adhering to the surface of the steel pipe and the mass lost due to atomization and dispersion in the spraying working conditions per unit time.

[0035] Specifically, the spiral spraying path is decoupled and encoded to obtain a spiral pitch sequence, a spray gun moving speed sequence, and a spray gun attitude angle sequence. Each element of the sequence corresponds to a discretized segment along the length of the target steel pipe. Specifically, this includes dividing the spatial spiral spraying path generated by inverse mapping into multiple continuous discretized path segments according to the axial length of the target steel pipe, and independently parameterizing the spraying motion parameters corresponding to each path segment. The spiral pitch sequence describes the interval variation relationship between adjacent spiral trajectories along the axial direction of the steel pipe, and the spiral pitch characterizes the spatial spiral density formed between the spray gun's axial advance speed and the circumferential rotation speed of the steel pipe. The spray gun moving speed sequence describes the spray gun's axial movement along the target steel pipe. The linear velocity changes during path operation, and there is a corresponding relationship between the spray gun movement speed and the amount of coating deposited per unit area. The spray gun attitude angle sequence is used to describe the change in the angle between the spray gun spray direction and the normal direction of the steel pipe surface. The spray gun attitude angle is used to control the atomization spray coverage and coating adhesion uniformity. Discretization segmentation is used to convert the continuous spatial path into a finite set of parameter nodes, thereby reducing the solution complexity in the continuous optimization process and improving the computability in the multi-parameter joint optimization process. The elements in each sequence correspond to specific segmented regions along the length direction of the target steel pipe, enabling different regions to perform differentiated parameter adjustments according to the changes in steel pipe curvature, weld area undulations, and local spraying requirements.

[0036] Furthermore, a dual-objective fitness function is constructed, with total spraying time as the first optimization objective and estimated paint consumption as the second optimization objective. Specifically, this includes: establishing a dual-objective fitness function to evaluate spraying efficiency and cost based on the helical pitch, spray gun movement speed, and spray gun attitude angle parameters corresponding to the discretized path segments; where total spraying time characterizes the cumulative running time required to complete the spraying operation of the entire target steel pipe, and is obtained by summing the ratios between the path length corresponding to each discretized path segment and the spray gun movement speed; total spraying time reflects spraying production efficiency; estimated paint consumption... The paint consumption calculation characterizes the total paint mass consumed per unit area during the spraying process. The paint consumption estimate is based on a comprehensive calculation of spiral coverage density, spray gun speed, spray overlap rate, and spray deposition efficiency per unit time. The estimated paint consumption reflects the level of spraying material consumption. The dual-objective fitness function is used to simultaneously constrain spraying efficiency and spraying resource consumption. By establishing a multi-objective collaborative optimization relationship, it avoids the problem of insufficient film thickness caused by excessively fast spraying speed due to a single optimization objective, or the problem of paint waste caused by excessively dense spraying coverage, thereby achieving a balance between spraying efficiency and spraying quality.

[0037] Furthermore, the first optimization objective in the dual-objective fitness function is obtained by dividing the path length of each segment based on sequence segmentation by the sum of the corresponding spray gun movement speeds. The second optimization objective is obtained by integrating the segment path length, helical pitch, and instantaneous deposition efficiency function of each sequence segment. Specifically, the dual-objective fitness function is used to quantitatively evaluate the comprehensive performance of the helical spraying path in terms of both spraying efficiency and paint utilization. The first optimization objective describes the cumulative spraying time required to complete the spraying process of the entire target steel pipe, and the second optimization objective describes the expected total amount of paint consumed to complete the spraying process. Sequence segmentation is used to divide the complete helical spraying path along the axial direction of the target steel pipe into multiple continuous discrete path intervals, each discrete path interval corresponding to an independent... The parameters include the spiral pitch, spray gun movement speed, and spray gun attitude angle. The segmented path length represents the length of the spatial curve formed by the actual movement trajectory of the spray gun along the curved surface of the steel pipe within a single discrete path interval. The segmented path length is calculated based on the circumferential rotational displacement and axial propulsion displacement of the steel pipe. The spray gun movement speed represents the linear velocity of the spray gun as it moves along the spiral spraying path. There is a corresponding relationship between the spray gun movement speed and the coating deposition time per unit area. The first optimization objective is obtained by dividing the segmented path length of each discrete path interval by the corresponding spray gun movement speed and then summing the results to form the total spraying time evaluation result for the complete spraying process. The summation process represents the cumulative running time consumed by the spray gun to complete the spraying of all path intervals sequentially. Furthermore, the second optimization objective is obtained by integrating the segment path length, helical pitch, and instantaneous deposition efficiency function of each sequence segment. Specifically, the helical pitch represents the advancement interval of the helical spraying trajectory in the axial direction of the steel pipe, and the size of the helical pitch determines the coverage density and overlap between adjacent spraying trajectories. When the helical pitch decreases, the coverage area of ​​adjacent spraying trajectories increases, and the amount of coating deposited per unit area increases. When the helical pitch increases, the coverage area of ​​adjacent spraying trajectories decreases, and the amount of coating deposited per unit area decreases. The second optimization objective is obtained by integrating the segment path length, corresponding helical pitch, and instantaneous deposition efficiency function in each discrete path interval. The integration operation describes the dynamic change relationship between the coating deposition state and time and spatial position during the continuous movement of the spray gun. The integration result represents the comprehensive material consumption level formed by the total amount of coating actually consumed and effectively adhered to the surface of the steel pipe during the entire spraying process and the total amount of spraying loss. The second optimization objective is used to evaluate the coating utilization efficiency corresponding to different combinations of path parameters, thereby reducing material waste and repeated coverage during the spraying process. Specifically, the first optimization objective in the dual-objective fitness function is used to characterize the total spraying time required to complete the overall spraying process of the target steel pipe. The first optimization objective function can be expressed as: F1 = F1 represents the first optimization objective value; N represents the total number of discretized path segments after dividing the target steel pipe along its length; Li V represents the segment path length corresponding to the i-th path segment; i This represents the spray gun movement speed corresponding to the i-th path segment; This represents the running time required for the spray gun to complete the spraying of the i-th path segment; after performing an accumulation operation on all discrete path segments, the total spraying time corresponding to the complete steel pipe spraying process is obtained. Furthermore, the segment path length is calculated based on the spatial pitch relationship corresponding to the spiral spraying trajectory, and the formula for calculating the segment path length can be expressed as: Li = L i R represents the length of the spatial spiral path corresponding to the i-th path segment; i P represents the local radius of the steel pipe corresponding to the i-th path segment; i Represents the helical pitch corresponding to the i-th path segment; 2πR i This indicates the circumferential unfolded length of the steel pipe after one complete rotation; This represents the actual spatial distance traveled by the spray gun along its spiral trajectory. Furthermore, the second optimization objective in the bi-objective fitness function characterizes the estimated paint usage during the spraying process; the second optimization objective function can be expressed as: F2 = F2 represents the second optimization objective value; Q i P represents the paint spraying flow rate per unit time corresponding to the i-th path segment; i η represents the helical pitch corresponding to the i-th path segment; i (t) represents the instantaneous deposition efficiency function corresponding to the i-th path segment; dl represents the differential and integral unit along the path length direction; the integral operation is used to represent the dynamic accumulation process of the coating deposition state during the continuous spiral path movement of the spray gun; P i η i (t) is used to characterize the actual paint consumption level per unit path length. Wherein, the helical pitch P... i Used to indicate the advancing interval between adjacent spiral spraying trajectories along the axial direction of the steel pipe; when the spiral pitch decreases, the overlap area between adjacent spraying trajectories increases, and the amount of paint deposited per unit area increases; when the spiral pitch increases, the coverage density between adjacent spraying trajectories decreases, and the amount of paint deposited per unit area decreases.

[0038] Then, the instantaneous deposition efficiency function is the dynamic mapping relationship between the proportion of paint actually adhering to the steel pipe surface and the lost mass due to atomization and dispersion in the spraying conditions per unit time. Specifically, it includes: the instantaneous deposition efficiency function describing the real-time change law of paint spraying mass transforming into effective adhered mass during the spraying process; spraying conditions including spray gun movement speed, spray gun attitude angle, spraying distance between the spray gun and the steel pipe surface, paint flow rate, atomization pressure, ambient wind speed, ambient temperature, and atomized particle size distribution; and the paint sprayed per unit time representing the total paint mass sprayed by the spray gun through the nozzle per unit time. The proportion of the mass actually adhering to the steel pipe surface is used to represent the ratio of the mass of the sprayed coating that ultimately deposits and forms an effective wet film layer to the total sprayed mass; the mass lost due to atomization and dispersion is used to represent the mass of coating that fails to adhere to the steel pipe surface and is lost in the form of paint mist drift, air diffusion, or bounce scattering; the dynamic mapping relationship is used to represent the nonlinear variation law between coating adhesion efficiency and atomization loss under different spraying conditions; the instantaneous deposition efficiency function can adjust the function output in real time with changes in spray gun speed, spraying distance, and atomization state, thereby reflecting the dynamic deposition capacity change process in actual spraying conditions. Specifically, the instantaneous deposition efficiency function is used to describe the dynamic change relationship of the effective adhesion process of the sprayed coating to the steel pipe surface under different spraying conditions, and the instantaneous deposition efficiency function can be expressed as: η(t)=M a (t) / [M a (t)+M l [(t)], where η(t) represents the instantaneous deposition efficiency at time t; M a (t) represents the effective coating mass actually adhered to the surface of the steel pipe per unit time; M l (t) represents the loss of coating mass per unit time due to atomization, air diffusion, and rebound loss; M a (t)+M lη(t) represents the total mass of paint sprayed by the spray gun per unit time; the function result is used to represent the mass ratio of effective deposition in the total sprayed paint. Specifically, there is a dynamic mapping relationship between the instantaneous deposition efficiency function and the spraying condition parameters, which can be expressed as: η(t)=f(D(t),θ(t),μ(t),K(t),V(t),Q(t)), where D(t) represents the real-time spraying distance between the spray gun nozzle and the steel pipe surface; θ(t) represents the real-time spray gun attitude angle between the spray gun spraying direction and the normal direction of the steel pipe surface; μ(t) represents the paint viscosity; K(t) represents the local curvature of the steel pipe surface; V(t) represents the spray gun moving speed; Q(t) represents the paint spraying flow rate per unit time; and the function f is used to describe the nonlinear coupling mapping relationship between the spraying condition parameters and the deposition efficiency. Among them, when the spray gun distance increases, the paint mist diffusion range increases, resulting in a decrease in the effective adhesion ratio; when the spray gun attitude angle deviates from the normal direction of the steel pipe surface, the paint rebound loss increases; when the paint viscosity changes, the atomized particle size distribution changes; when the curvature of the steel pipe surface changes, the spray coverage area and local deposition state change; the above parameters together affect the output result of the instantaneous deposition efficiency function, thereby affecting the estimated paint consumption and the evaluation result of spraying quality.

[0039] Then, based on the dual-objective fitness function, an initial population is determined using the spiral pitch sequence, spray gun movement speed sequence, and spray gun attitude angle sequence. Iterative optimization is then performed to obtain the optimized spiral spraying path. Specifically, this includes: combining the spiral pitch sequence, spray gun movement speed sequence, and spray gun attitude angle sequence to form individual path control parameters, and generating multiple different parameter combinations based on a preset parameter perturbation range, serving as a set of candidate solutions in the initial population; each candidate solution in the initial population corresponds to a complete set of spiral spraying path control parameters, used to describe the path execution mode under different spraying conditions; during the iterative optimization process, based on the dual-objective fitness function... The function calculates the total spraying time and estimated paint consumption for each candidate solution, and performs population screening, crossover, and parameter mutation based on the fitness evaluation results to continuously update the path parameter combination. The crossover process is used to reorganize the parameter information between different candidate solutions, and the mutation process is used to enhance the local search capability and avoid the optimization process from getting stuck in a local optimum. After multiple iterations, the fitness results gradually converge, and finally an optimized spiral spraying path is obtained that simultaneously meets the requirements of spraying efficiency, paint consumption, and spraying coverage uniformity. The optimized spiral spraying path is used as the basic execution path for subsequent spray gun motion control and online dynamic correction.

[0040] Furthermore, using the helical pitch sequence, spray gun movement speed sequence, and spray gun attitude angle sequence as baseline individuals, and expanding the population based on these baseline individuals, an initial population is obtained. Specifically, this involves combining the decoupled and encoded helical pitch sequence, spray gun movement speed sequence, and spray gun attitude angle sequence according to a unified parameter dimension to form parameter individuals that describe the complete spraying path control state. The baseline individuals represent the basic path parameter combinations generated based on initial process experience, theoretical spraying models, or historical spraying data. The parameter elements in the helical pitch sequence characterize the axial advance interval variation of the steel pipe and the spray gun movement... The parameter elements in the velocity sequence are used to characterize the velocity distribution state of the spray gun running along the spiral trajectory, and the parameter elements in the spray gun attitude angle sequence are used to characterize the spatial angle state of the spray gun spray direction relative to the normal direction of the steel pipe surface. Population expansion is used to introduce multiple sets of random perturbation parameters around the baseline individual. By performing random increases and decreases on the spiral pitch, spray gun moving speed and spray gun attitude angle, multiple candidate path parameter combinations with differences are generated, thus forming an initial population containing multiple candidate solutions. Different individuals in the initial population correspond to different spraying control schemes, which are used to provide the search space basis for the subsequent multi-objective optimization process.

[0041] Furthermore, based on the initial population and a preset ratio, fixed individuals and wandering individuals are labeled. Specifically, this includes: classifying different candidate individuals according to the total number of individuals in the initial population and a preset ratio. Fixed individuals represent reference individuals that serve as local search centers in the current iteration, while wandering individuals represent dynamic search individuals that perform neighborhood search and parameter perturbation around fixed individuals. The preset ratio controls the distribution between the number of fixed individuals and the number of wandering individuals, achieving a balance between global and local search capabilities by adjusting the proportion of individuals of different categories. Fixed individuals typically correspond to candidate solutions with high fitness or high path parameter stability, while wandering individuals typically correspond to perturbation solutions generated around the neighborhood of fixed individuals. The labeling process includes assigning fixed category or wandering category labels to different individuals based on fitness ranking results, random sampling results, or neighborhood distance relationships, thereby establishing the search structure relationship in the subsequent group optimization process.

[0042] Then, using the wandering individuals within a preset range of each fixed individual as optimization groups, a cross-mutation iteration based on binary crossover operators and polynomial mutation operators is employed to determine the first optimized individual. Specifically, this involves: based on the position of the fixed individual in the parameter space, using the preset range as the neighborhood boundary, dividing multiple wandering individuals within the neighborhood into optimization groups formed by the corresponding fixed individuals. The preset range represents the search neighborhood scale in the path parameter space, and the search neighborhood scale is jointly determined by the range of helical pitch differences, the range of spray gun speed differences, and the range of spray gun attitude angle differences. The optimization groups are used to perform targeted searches within the local parameter space, thereby... To improve the efficiency of path parameter optimization, the binary crossover operator is used to cross-recombine the parameter codes in different individuals according to binary bits, and generate new path parameter combinations by exchanging some parameter fragments, thereby enhancing the diversity of parameter combinations; the multinomial mutation operator is used to perform nonlinear random perturbation on parameter values ​​according to a preset probability, so that the parameter change amplitude meets the multinomial probability distribution relationship, thereby enhancing the local search capability and avoiding the optimization process from getting trapped in a local optimum; the cross-mutation iteration is used to repeatedly execute the parameter recombination and parameter perturbation process, and evaluate and screen the newly generated individuals according to the dual-objective fitness function, and finally obtain the first optimized individual in the current optimization stage.

[0043] Subsequently, the first optimized individuals are traversed and compared with the fixed individuals in the group. Fixed individuals are replaced based on fitness to obtain the first iterative population. Specifically, this involves: evaluating the fitness of each of the multiple first optimized individuals generated in the current optimization stage, and comparing them with the fixed individuals in the corresponding optimization group using the objective function. Fitness characterizes the comprehensive performance level of candidate path parameter combinations in terms of spraying time, paint usage, and spray coverage stability. During the fitness comparison, when the bi-objective evaluation result of the first optimized individual is better than that of the fixed individual, the first optimized individual replaces the original fixed individual, and the updated path parameter combination is retained. When the first optimized individual does not reach the fitness level corresponding to the fixed individual, the original fixed individual remains unchanged. This replacement process is used to improve the overall performance of the population round by round and gradually eliminates low-quality candidate solutions through a winner-preserving mechanism. After traversing all optimization groups, the updated set of fixed individuals and the corresponding wandering individual set together form the first iterative population, providing a new population foundation for subsequent multiple rounds of iterative optimization.

[0044] Finally, based on the first iteration population, the optimized spiral spraying path is obtained through multiple rounds of iterative optimization until convergence. Specifically, this includes: using the first iteration population as the input population for the next round of optimization, repeatedly executing the processes of dividing fixed individuals into wandering individuals, neighborhood optimization grouping, crossover and mutation calculation, and fitness replacement and updating, so that the path parameter combination continuously converges towards the target optimal direction during multiple rounds of iteration. Convergence is used to indicate that during consecutive rounds of iteration, the change in the fitness function is lower than a preset threshold or the optimal individual remains in a stable state, thus indicating that the path parameter search results have become stable. During the multiple rounds of iterative optimization, the spiral pitch parameter gradually adapts to the spraying coverage requirements of different areas, the spray gun moving speed parameter gradually adapts to the film thickness stability requirements of different curvature areas, and the spray gun attitude angle parameter gradually adapts to the surface normal change of the steel pipe and the local undulation characteristics of the weld area. The finally obtained optimized spiral spraying path is used to describe a comprehensive optimized spraying trajectory model that meets the requirements of spraying efficiency, paint utilization, spraying uniformity, and dynamic adaptability.

[0045] Based on the optimized spiral spraying path, a spraying parameter control sequence is generated to drive the spray gun head to perform spiral spraying control on the target steel pipe.

[0046] Furthermore, this application also includes: after the target steel pipe is sprayed, obtaining the optimized spiral spraying path after the online update of the entire spraying cycle; storing the optimized spiral spraying path in the industrial database, and performing variable parameter control adaptive spiral spraying management under the production line batch spraying conditions.

[0047] Specifically, based on the optimized spiral spraying path, a spraying parameter control sequence is generated to drive the spray gun head to perform spiral spraying control on the target steel pipe. This includes: sequentially arranging the motion control parameters and process control parameters during the spraying process according to the spiral spraying path parameters optimized through multi-objective iteration, forming a spraying parameter control sequence. This sequence describes the path motion state and spraying process state of the spray gun at each moment within the complete spraying cycle. The path motion state includes the spray gun's spatial coordinate position, spray gun moving speed, steel pipe rotation angular velocity, and spray gun attitude angle. The process control state includes paint flow rate, atomizing air pressure, spray gun spray distance, and spraying start / stop status. The parameters in the spraying parameter control sequence are arranged in chronological order or path... The spraying system is arranged in a path sequence, enabling it to continuously perform path tracking control and spraying process adjustment control according to a predetermined time sequence. It drives the spray gun head for spiral spraying control, specifically by sending control commands to the spray gun drive mechanism, steel pipe rotation mechanism, and paint supply pressure regulating mechanism via an industrial controller. This causes the spray gun to perform continuous spiral motion around the target steel pipe along an optimized spiral spraying path, simultaneously completing the paint spraying process. The spray gun head performs atomization spraying and path tracking motion, while the target steel pipe represents a large-diameter steel pipe component to be coated or surface-coated. During spiral spraying control, the spray gun forms a continuous spiral coverage trajectory along the circumferential rotation direction and axial advancement direction of the steel pipe, thereby achieving uniform and continuous spraying of the outer surface of the steel pipe.

[0048] Furthermore, after the target steel pipe is coated, the optimized spiral coating path is obtained after the online update of the entire coating cycle. Specifically, after the coating is completed, the wet film thickness, atomized particle size, spray gun operation status, and path offset status collected in real time during the complete coating cycle are comprehensively analyzed to obtain the optimized spiral coating path after the online update of the entire coating cycle. The optimized spiral coating path after the online update of the entire coating cycle is used to represent the final path parameter model after incorporating the actual coating condition changes, local weld area disturbances, and dynamic coating error correction results.

[0049] Furthermore, the optimized spiral spraying path is stored in an industrial database to implement variable parameter control and adaptive spiral spraying management under production line batch spraying conditions. Specifically, this includes: writing the optimized spiral spraying path parameters, after full-cycle dynamic correction, into the industrial database for structured storage. The industrial database stores historical spraying control data corresponding to different steel pipe specifications, different spraying process conditions, and different production batches. The structured storage content includes spiral pitch parameters, spray gun movement speed parameters, spray gun attitude angle parameters, spraying process parameters, wet film thickness distribution results, atomization state parameters, and spraying quality evaluation results. The historical spraying path data in the industrial database can form a spraying experience model for different steel pipe conditions, which can be used for parameter retrieval in subsequent spraying tasks. The optimization reference; production line batch spraying conditions are used to represent the process differences between different batches of steel pipes under the same production line environment in terms of steel pipe diameter, steel pipe length, weld distribution, surface curvature, ambient temperature, and coating characteristics; variable parameter control is used to represent that the spraying control parameters can be dynamically adjusted according to different batch conditions, including changes in spray gun movement speed, spiral pitch, spray gun attitude angle, and spraying process parameters; adaptive spiral spraying control is used to represent that the spraying control system can perform dynamic matching, parameter correction, and online optimization on the current spraying path based on historical path models in the industrial database and real-time operating condition detection results, so that different batches of steel pipes can maintain stable spraying quality and uniform coverage under complex operating conditions.

[0050] As the spiral spraying process progresses, the wet film thickness, atomized particle size, and atomization cone angle are collected synchronously. This triggers the online judgment unit to execute in parallel a decision on the spraying speed adjustment based on the thickness offset vector, and a decision on the optimal parameter combination based on the atomized particle size deviation, updating the optimized spiral spraying path.

[0051] Furthermore, this application also includes: detecting the wet film thickness of the upper spraying area using an optical thickness sensor deployed at the rear of the spray gun head; monitoring the paint mist particle size distribution and atomization cone angle in real time using a laser diffractometer as atomization data; inputting the wet film thickness, atomization data, and real-time operating parameters into an online judgment unit to perform two-way spraying analysis and spraying parameter control optimization to determine the spraying speed adjustment amount and the optimal parameter combination; and integrating the spraying speed adjustment amount and the optimal parameter combination to update the optimized spiral spraying path.

[0052] Furthermore, this application also includes: the first judgment branch of the online judge receives the wet film thickness and real-time operating parameters, performs thickness target value calculation and comparison based on the instantaneous deposition efficiency function, and determines the thickness offset vector; according to the thickness offset vector, the spraying speed adjustment amount is calculated by speed-thickness inverse mapping, wherein the variable pitch value is determined by the spraying speed adjustment amount, and the thickness and pitch value are positively correlated.

[0053] Furthermore, this application also includes: the second judgment branch of the online judge receives the atomization data and real-time operating parameters, and judges whether the atomization particle size deviates from the target range; if it deviates, an atomization coupling relationship is established based on the paint flow rate, atomization pressure, spray gun distance, and atomization particle size, and an extreme value optimization method is adopted, with atomization uniformity as the optimization constraint, and iteratively searches along the gradient direction in the three-dimensional parameter space based on paint flow rate-atomization pressure-spray gun distance to determine the optimal parameter combination that converges the atomization quality to the extreme value.

[0054] Specifically, based on the optical thickness sensor deployed at the rear of the spray gun head, the wet film thickness of the upper spraying area is detected. This includes: during the continuous spraying motion of the spray gun head along the spiral spraying path, optical thickness sensors are spaced apart at the rear of the spray gun head along the spraying motion direction. Online wet film thickness detection is performed on the upper spraying area that has already been sprayed using optical reflection measurement. The spray gun head is used to perform paint atomization spraying and path tracking motion. The upper spraying area represents the steel pipe surface area where a wet film layer has already formed before the current position of the spray gun. The optical thickness sensor is used to detect the wet film thickness of the upper spraying area using laser reflection, white light interference, or optical... The displacement detection method acquires the coating surface height information and calculates the wet film thickness value by combining it with the reference height of the steel pipe substrate surface. The wet film thickness is used to represent the thickness parameter of the liquid coating formed by the coating in the uncured state. The wet film thickness is related to the subsequent dry film thickness, adhesion performance and anti-corrosion effect. The online detection process is used to synchronously acquire the real-time thickness change status during the spraying process, thereby avoiding the path correction lag problem caused by offline detection after the spraying is completed. By continuously acquiring the wet film thickness distribution results corresponding to different path positions, the spatial distribution information of thickness along the steel pipe surface can be formed, which can be used for subsequent spraying uniformity analysis and dynamic speed adjustment.

[0055] Furthermore, based on a laser diffractometer, the paint mist particle size distribution and atomization cone angle are monitored in real time as atomization data. Specifically, this includes: using a laser diffractometer located on the side of the spraying area or near the spray gun to perform real-time particle size detection on the paint mist particles formed during the spraying process; the laser diffractometer is used to calculate the particle size distribution of the paint mist particles by inverting the diffraction intensity distribution formed after the laser beam passes through the paint mist area; the paint mist particle size distribution is used to describe the proportion or volume distribution of paint mist particles of different sizes in the overall sprayed particles, and the particle size distribution directly affects the coating adhesion efficiency, surface smoothness, and spray coverage. Uniformity; when the paint mist particle size is too large, it is easy to cause sagging and local accumulation; when the paint mist particle size is too small, it is easy to cause air drift and atomization dispersion loss; the atomization cone angle is used to represent the diffusion angle range of the paint mist jet sprayed from the spray gun in space. The atomization cone angle is related to the spray coverage width, the deposition density per unit area, and the edge overspray state; real-time monitoring is used to represent the dynamic continuous sampling of the paint mist state during the continuous movement and continuous spraying of the spray gun; atomization data is used to represent the set of spraying state parameters composed of paint mist particle size distribution, atomization cone angle, and corresponding timestamps, which is used to reflect the atomization quality state under the current spraying process.

[0056] Then, the wet film thickness, atomization data, and real-time operating parameters are input into the online judgment unit to perform two-way spraying analysis and spraying parameter control optimization, determining the spraying speed adjustment and optimal parameter combination. Specifically, this includes: the first judgment branch of the online judgment unit receives the wet film thickness and real-time operating parameters, performs thickness target value calculation and comparison based on the instantaneous deposition efficiency function, and determines the thickness offset vector. That is, the first judgment branch inside the online judgment unit is used to perform dynamic analysis of wet film thickness. The first judgment branch is used to receive real-time wet film thickness data output by the optical thickness sensor and real-time operating parameters during the spraying process. The real-time operating parameters include spray gun movement speed, spray gun attitude angle, spray gun spray distance, paint flow rate, atomization pressure, steel pipe rotation speed, local curvature of the steel pipe surface, and ambient temperature. The first judgment branch performs dynamic calculation on the target wet film thickness that can be theoretically formed under the current spraying conditions based on the instantaneous deposition efficiency function. The instantaneous deposition efficiency function is used to describe the spraying speed. The dynamic deposition capability of the spray coating during its effective adhesion to the steel pipe surface is dynamically changed by the function output, which varies with the spray gun distance, spray gun angle, coating viscosity, and steel pipe curvature. The target thickness value represents the theoretical wet film thickness that should be achieved per unit area under the current spraying parameters. The first judgment branch compares the actual wet film thickness obtained in real time with the theoretical target thickness value point by point to determine the thickness deviation at different path positions. The thickness offset vector represents the set of thickness deviations formed on the steel pipe surface along the spiral spraying path, where each element in the vector corresponds to the thickness deviation at a discrete path position. When the actual wet film thickness is lower than the target wet film thickness, the corresponding vector element shows a negative offset state; when the actual wet film thickness is higher than the target wet film thickness, the corresponding vector element shows a positive offset state. The thickness offset vector describes the spatial deviation trend of the overall spray coating thickness distribution on the steel pipe surface relative to the target thickness distribution. Specifically, after receiving the wet film thickness and real-time operating parameters, the first judgment branch of the online judge first calculates the theoretical target thickness value corresponding to the current path position based on the instantaneous deposition efficiency function. The formula for calculating the target thickness value can be expressed as: H t (x)=[Q(x)×η(x)] / [V(x)×P(x)], H t (x) represents the theoretical thickness target value corresponding to the path position x of the steel pipe; Q(x) represents the paint spray flow rate per unit time corresponding to the path position x; η(x) represents the instantaneous deposition efficiency function value corresponding to the path position x; V(x) represents the spray gun moving speed corresponding to the path position x; P(x) represents the spiral pitch corresponding to the path position x; Q(x)×η(x) represents the actual effective coating mass adhering to the surface of the steel pipe per unit time; V(x)×P(x) represents the path advancement amount corresponding to the coverage per unit area; the theoretical thickness target value is used to represent the target wet film thickness that should be formed per unit area under the current spraying conditions.

[0057] The instantaneous deposition efficiency function maintains a dynamic coupling relationship with the aforementioned spraying condition parameters. The instantaneous deposition efficiency function can be further expressed as: η(x)=f(D(x),θ(x),μ(x),K(x),Q(x),V(x)), where D(x) represents the real-time spray gun distance between the spray gun nozzle and the steel pipe surface; θ(x) represents the spray gun attitude angle; μ(x) represents the paint viscosity; K(x) represents the local curvature of the steel pipe surface; Q(x) represents the paint spray flow rate; and V(x) represents the spray gun moving speed. The function f is used to describe the dynamic nonlinear mapping relationship formed by the effective deposition capacity of the paint under different spraying condition parameter changes.

[0058] Furthermore, the first judgment branch determines the thickness offset vector by comparing the theoretical thickness target value with the actual wet film thickness obtained from real-time detection. The formula for calculating the thickness offset vector can be expressed as: ΔH(x) = H a (x)-H t H(x), where ΔH(x) represents the thickness offset corresponding to path position x; H a (x) represents the actual wet film thickness detected in real time by the optical thickness sensor; H t (x) represents the theoretical thickness target value; when ΔH(x) < 0, it indicates that the current region is too thin; when ΔH(x) > 0, it indicates that the current region is too thick; the thickness offsets corresponding to all path positions are arranged in the path order to form a thickness offset vector.

[0059] Furthermore, based on the thickness offset vector, the spraying speed adjustment is calculated using a speed-thickness inverse mapping. The spraying speed adjustment determines the variable pitch value, and thickness and pitch are positively correlated. Specifically, this includes: establishing an inverse mapping relationship between the spray gun movement speed and wet film thickness based on the deviation direction and magnitude in the thickness offset vector, and calculating the spraying speed adjustment using this speed-thickness inverse mapping relationship. The speed-thickness inverse mapping describes the inverse effect of changes in spray gun movement speed on the amount of paint deposited per unit area. Specifically, when the spray gun movement speed decreases, the residence time of the spray gun in the local area increases, the amount of paint deposited per unit area increases, and the wet film thickness increases; when the spray gun movement speed increases... The shorter dwell time of the spray gun in local areas reduces the amount of paint deposited per unit area and decreases the wet film thickness. The spray speed adjustment value represents the dynamic correction value applied to the spray gun movement speed during the current spraying stage. The spray speed adjustment value is mainly used to perform real-time correction control on areas that are already too thin or too thick, thereby achieving rapid compensation for the wet film thickness in local areas. When the thickness offset vector corresponds to a negative offset state, the spray gun movement speed is reduced to increase the amount of paint deposited per unit area; when the thickness offset vector corresponds to a positive offset state, the spray gun movement speed is increased to reduce the amount of paint deposited per unit area. The spray speed adjustment mechanism is used to solve the real-time thickness deviation problem that has already formed at the current path position.

[0060] Then, the variable pitch value is determined based on the spraying speed adjustment amount. Specifically, this includes: dynamically adjusting the helical pitch parameters in the subsequent helical spraying path according to the correction direction and correction amplitude corresponding to the spraying speed adjustment amount, thereby forming a variable pitch control result; the variable pitch value is used to represent the dynamic advancement interval of the helical spraying trajectory along the axial direction of the steel pipe; variable pitch control is used to adjust the spraying coverage density in advance for path areas that have not yet been sprayed; when the current area is too thin, the axial distance between adjacent spraying trajectories is reduced by decreasing the helical pitch value corresponding to the subsequent path, thereby increasing the path overlap rate in the subsequent spraying process; after the path overlap rate is increased, the same area can be covered by more helical trajectories, and the unit The increased cumulative deposition over the area reduces the probability of subsequent insufficient thickness. When the current area is too thick, the axial spacing between adjacent spraying trajectories is increased by increasing the helical pitch value corresponding to the subsequent path, thereby reducing the path overlap rate and the deposition per unit area. Thickness and pitch value are positively correlated, representing the corresponding adjustment relationship between the wet film thickness change trend and the helical path coverage density. Specifically, when the target thickness requirement increases, the pitch value is reduced to increase the coverage density, and when the target thickness requirement decreases, the pitch value is increased to decrease the coverage density. The variable pitch control mechanism is used to establish advance compensation capability for future path areas, thereby reducing the problem of continuous thickness fluctuations in local areas.

[0061] Subsequently, a collaborative control relationship is formed between the spraying speed adjustment and the variable pitch value. Specifically, the spraying speed adjustment is mainly used to solve the immediate thickness deviation problem that has occurred in the current spraying area, which belongs to real-time local compensation control; the variable pitch value is mainly used to adjust the coverage density distribution of the subsequent spiral spraying path, which belongs to future path pre-compensation control; real-time local compensation control can improve the film thickness consistency of the current spraying area, and future path pre-compensation control can reduce the probability of the same thickness deviation recurring in the subsequent spraying process; by simultaneously executing dynamic speed correction and dynamic pitch adjustment, dual-stage thickness collaborative control of the current area and the future area can be achieved, thereby improving the wet film thickness stability and path coverage uniformity in the overall spraying process. Specifically, based on the thickness offset vector, the spraying speed adjustment is calculated using a speed-thickness inverse mapping. The speed-thickness inverse mapping relationship can be expressed as: ΔV(x) = -k1 × ΔH(x), where ΔV(x) represents the spraying speed adjustment corresponding to path position x; k1 represents the speed adjustment gain coefficient; the negative sign indicates that there is an inverse relationship between the spray gun movement speed and the wet film thickness; when the actual wet film thickness is lower than the target thickness, ΔH(x) < 0, corresponding to ΔV(x) > 0, the spray gun movement speed decreases, thereby increasing the deposition per unit area; when the actual wet film thickness is higher than the target thickness, ΔH(x) > 0, corresponding to an increase in the spray gun movement speed, thereby reducing the deposition per unit area; the spraying speed adjustment is mainly used to perform real-time compensation control on the currently formed thickness deviation.

[0062] Furthermore, the pitch value is determined by the spraying speed adjustment, and the formula for calculating the pitch value can be expressed as: P n (x)=P0(x)+k2×ΔH(x), P n (x) represents the updated variable pitch value; P0(x) represents the original helical pitch value; k2 represents the pitch adjustment coefficient; ΔH(x) represents the thickness offset; when the thickness is too thin, ΔH(x) < 0, the strain pitch value decreases, and the axial spacing between subsequent helical paths decreases; when the thickness is too thick, ΔH(x) > 0, the strain pitch value increases, and the axial spacing between subsequent helical paths increases; the variable pitch value is used to adjust the coverage density in the future spraying path area.

[0063] There is an inverse relationship between the spiral path overlap rate and the pitch value. The overlap rate calculation relationship can be expressed as: O(x)=1-P(x) / W(x), where O(x) represents the spray overlap rate corresponding to the path position x; P(x) represents the spiral pitch; W(x) represents the spray width of the spray gun. When the pitch decreases, the spray overlap rate increases, and a higher coverage density is formed between adjacent spray trajectories. When the pitch increases, the spray overlap rate decreases, and the coverage area between adjacent spray trajectories decreases.

[0064] Furthermore, there is a positive relationship between the cumulative deposition thickness per unit area and the spiral overlap rate, which can be expressed as: H c (x)=H s (x)×(1+O(x)), H c (x) represents the cumulative deposition thickness corresponding to path location x; H s (x) represents the base deposition thickness formed by a single spray; where O(x) represents the spray overlap rate; as the spray overlap rate increases, the same area receives more spiral trajectory coverage, and the cumulative deposition thickness increases.

[0065] The second judgment branch of the online judgment unit receives atomization data and real-time operating parameters to determine whether the atomized particle size deviates from the target range. Specifically, the online judgment unit has an internally set second judgment branch for performing spray atomization state analysis. This second judgment branch receives atomization data and real-time operating parameters collected during the spraying process and performs spraying process stability analysis based on the atomization state change results. The atomization data represents the paint mist particle size distribution results, atomization cone angle results, and corresponding time series information obtained by the laser diffraction particle size analyzer. The real-time operating parameters include paint flow rate, atomization pressure, spray gun spray distance, spray gun movement speed, spray gun attitude angle, paint viscosity, ambient temperature, and ambient wind speed. The second judgment branch reads the paint mist particle size distribution state at different time points to determine whether the atomized particle size deviates from the target range during the current spraying process. The atomization stability is dynamically evaluated; the atomization particle size is used to represent the average size or particle size distribution of the paint mist particles formed after spraying from the spray gun. The size of the atomization particle size directly affects the coating adhesion efficiency, surface smoothness, and uniformity of spray coverage; the target range is a pre-set allowable fluctuation range of atomization particle size based on different steel pipe specifications, different spraying process requirements, and different coating types; when the actual atomization particle size exceeds the upper limit of the target range, it indicates that the paint mist particles are too large, which can easily lead to local accumulation, sagging, and surface roughness; when the actual atomization particle size is lower than the lower limit of the target range, it indicates that the paint mist particles are too fine, which can easily lead to air drift, scattering loss, and reduced deposition efficiency; the second judgment branch judges whether there is an atomization abnormality in the current spraying process by continuously comparing the real-time atomization particle size with the target range.

[0066] Furthermore, if a deviation occurs, an atomization coupling relationship is established based on the paint flow rate, atomizing pressure, spray gun distance, and atomized particle size. Specifically, this includes: when the second judgment branch detects that the actual atomized particle size deviates from the target range, an atomization coupling relationship model is established using multiple key process parameters in the current spraying condition; the paint flow rate represents the volume or mass of paint output from the spray gun nozzle per unit time; the atomizing pressure represents the compressed gas pressure parameter used to break up the liquid paint into paint mist particles; the spray gun distance represents the spatial distance between the spray gun nozzle outlet and the steel pipe surface; and the atomized particle size represents the size distribution of the paint mist particles formed after spraying. Atomization coupling... The relationship is used to describe the joint influence of multiple spraying process parameters on the formation of atomization state. When the paint flow rate increases, the output of liquid paint per unit time increases, and larger particles are easily formed under constant air pressure. When the atomization air pressure increases, the breaking ability of liquid paint is enhanced, and the atomized particle size decreases. When the spray gun distance increases, the paint mist diffusion range increases, and some fine particles are prone to drift loss. There are nonlinear coupling effects between different process parameters, so it is necessary to describe the atomization state change law corresponding to different parameter combinations through joint modeling. The atomization coupling relationship model is used as the basic analysis model for subsequent parameter optimization and dynamic process adjustment.

[0067] Then, an extreme value optimization method is adopted, with atomization uniformity as the optimization constraint. An iterative search is performed along the gradient direction in a three-dimensional parameter space based on paint flow rate, atomization pressure, and spray gun distance to determine the optimal parameter combination that converges the atomization quality to the extreme value. Specifically, this includes: after establishing the atomization coupling relationship model, a dynamic optimization search is performed on the spraying process parameters using the extreme value optimization method; the extreme value optimization method is used to find the parameter combination in the continuous parameter space that enables the objective evaluation function to reach its optimal state; atomization uniformity is used to represent the degree of consistency in the distribution of paint mist particles of different sizes in the spatial spraying area. The higher the atomization uniformity, the more concentrated the size distribution of paint mist particles and the more stable the spray coverage; the optimization constraint is used to restrict the atomization state during the parameter search process to meet the preset uniformity requirements, thereby avoiding the problem of unstable spray coverage caused by only pursuing particle size changes; the three-dimensional parameter space is a continuous parameter search space established with paint flow rate, atomization pressure, and spray gun distance as three independent parameter axes, where... Each coordinate point in the parameter space corresponds to a set of spraying process parameters. Iterative search along the gradient direction is used to calculate the search direction of the fastest growth or decline of the objective function based on the direction of change of the objective function corresponding to the current parameter state, and gradually adjust the parameter values ​​along the corresponding direction. The gradient direction is used to represent the local change trend direction of the objective function in the current parameter space. During the iterative search process, the paint flow rate, atomization pressure, and spray gun distance parameters are continuously updated so that the real-time atomization state gradually approaches the target optimal state. Atomization quality is used to represent the comprehensive evaluation result formed by the stability of paint mist particle size, uniformity of spray coverage, and deposition adhesion ability. Convergence to the extreme value is used to indicate that after multiple rounds of parameter adjustment, the change of the atomization quality evaluation result is lower than the preset threshold, thus indicating that the current parameter combination has reached the local optimal or global optimal state. The optimal parameter combination is used to represent the set of spraying process parameters that meet the requirements of target atomization particle size range, target atomization uniformity, and target deposition efficiency.

[0068] Subsequently, the optimal parameter combination is used to update the spraying process control status. Specifically, this includes: sending the paint flow rate parameters, atomization pressure parameters, and spray gun distance parameters obtained through extreme value optimization to the spraying execution control system in real time, so that the spray gun atomization state is restored to the target particle size range and target uniformity range; the spraying execution control system adjusts the output state of the paint supply system, the pressure state of the air supply system, and the spatial position state of the spray gun according to the updated parameter combination, thereby improving the paint mist adhesion efficiency and spray coverage stability in the subsequent spraying area; through continuous execution of parameter feedback updates, the spraying system can maintain a stable atomization state under complex working conditions, reducing the problem of inconsistent coating quality caused by process fluctuations.

[0069] Subsequently, the spraying speed adjustment and the optimal parameter combination are integrated to update the optimized spiral spraying path. Specifically, this includes: dynamically correcting the spray gun movement speed parameters in the original spiral spraying path based on the spraying speed adjustment, and synchronously updating the spray gun attitude angle, spray gun spray distance, and atomization process parameters based on the optimal parameter combination; the integration process is used to map the motion control correction results and process control correction results into the spiral spraying path parameter model, so that the path trajectory control and spraying process control remain consistent; the updated optimized spiral spraying path not only includes spatial motion trajectory information, but also dynamic spraying process state information at the corresponding path position; by continuously updating the path parameters, the spraying system can perform real-time adaptive adjustments to the weld area curvature changes, local film thickness deviations, and atomization state fluctuations, thereby improving the uniformity of spraying coverage and the stability of coating quality; the updated optimized spiral spraying path can serve as the final path control result for the subsequent path execution stage and the historical spraying database storage stage.

[0070] In summary, the spiral spraying path optimization method for large-diameter steel pipe piles provided in this application has the following technical effects: by achieving the technical goal of collaborative dynamic optimization control of spiral spraying path parameters, spraying motion state, and atomization deposition state under complex curved surface conditions of steel pipes, it achieves the technical effects of improving the uniformity of spraying coverage, film thickness stability, and adaptive spraying control capability under complex working conditions for large-diameter steel pipe piles.

[0071] Example 2: Based on the same inventive concept as the spiral spraying path optimization method for large-diameter steel pipe piles in the foregoing examples, this application also provides a spiral spraying path optimization system for large-diameter steel pipe piles. Please refer to the appendix. Figure 2 The system includes: a spiral spraying path acquisition module 1, which, for the target steel pipe, performs equidistant planning and three-dimensional inverse mapping of straight lines in a two-dimensional mapping plane through line laser surface scanning and Riemann mapping to obtain a spiral spraying path; an optimized spiral spraying path acquisition module 2, which performs decoupling based on spiral pitch, spray gun moving speed and spray gun attitude angle on the spiral spraying path, and performs iterative optimization based on fixed individuals and wandering individuals under initial population expansion using a dual-objective fitness function to obtain an optimized spiral spraying path; a spiral spraying control module 3, which generates a spraying parameter control sequence based on the optimized spiral spraying path and drives the spray gun head to perform spiral spraying control on the target steel pipe; and a judgment trigger module 4, which, as the spiral spraying process progresses, synchronously collects wet film thickness, atomized particle size and atomization cone angle, triggers an online judge to perform parallel execution of spraying speed adjustment decision based on thickness offset vector and optimal parameter combination decision based on atomized particle size deviation, and updates the optimized spiral spraying path.

[0072] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: scanning the surface of the target steel pipe with a line laser to generate a three-dimensional point cloud and curvature distribution map marked with taper, elliptical angle and local concavity and convexity, as the steel pipe surface scanning data; mapping the steel pipe surface scanning data to a two-dimensional plane rectangle through Riemann mapping to obtain a two-dimensional mapping plane; performing equidistant planning of straight-line routes in the two-dimensional mapping plane to obtain a planned path; and inversely mapping the planned path back to three-dimensional space as the spiral spraying path.

[0073] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: decoupling and encoding the spiral spraying path to obtain a spiral pitch sequence, a spray gun moving speed sequence, and a spray gun attitude angle sequence, wherein the elements of each sequence correspond to discretized segments along the length of the target steel pipe; constructing a dual-objective fitness function, wherein the total spraying time is the first optimization objective and the estimated paint consumption is the second optimization objective; determining an initial population based on the dual-objective fitness function using the spiral pitch sequence, the spray gun moving speed sequence, and the spray gun attitude angle sequence, and performing iterative optimization to obtain the optimized spiral spraying path.

[0074] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: using the spiral pitch sequence, spray gun movement speed sequence, and spray gun attitude angle sequence as reference individuals, and expanding the population based on the reference individuals to obtain the initial population; according to the initial population, and according to a preset ratio, labeling fixed individuals and wandering individuals; using wandering individuals within a preset range of each fixed individual as optimization groups, and using cross-mutation iteration based on binary crossover operators and polynomial mutation operators to determine the first optimized individual; traversing the first optimized individual, comparing it with the fixed individuals in the group, and replacing the fixed individuals according to fitness to obtain the first iterative population; and optimizing the spiral spraying path through multiple rounds of iterations until convergence based on the first iterative population.

[0075] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: the first optimization objective in the dual-objective fitness function is obtained by dividing the path length of each segment based on the sequence segment by the corresponding spray gun movement speed, and the second optimization objective is obtained by integrating the segment path length, spiral pitch and instantaneous deposition efficiency function of each sequence segment; the instantaneous deposition efficiency function is the dynamic mapping relationship between the proportion of the mass of the coating actually attached to the surface of the steel pipe and the mass lost due to atomization and dispersion in the spraying working conditions per unit time.

[0076] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: detecting the wet film thickness of the upper spraying area based on the optical thickness sensor deployed at the rear of the spray gun head; monitoring the paint mist particle size distribution and atomization cone angle in real time using a laser diffractometer as atomization data; inputting the wet film thickness, atomization data, and real-time operating parameters into an online judgment unit to perform two-way spraying analysis and spraying parameter control optimization, determining the spraying speed adjustment amount and the optimal parameter combination; and integrating the spraying speed adjustment amount and the optimal parameter combination to update the optimized spiral spraying path.

[0077] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: the first judgment branch of the online judge receives the wet film thickness and real-time operating parameters, performs thickness target value calculation and comparison based on the instantaneous deposition efficiency function, and determines the thickness offset vector; according to the thickness offset vector, the spraying speed adjustment amount is calculated by speed-thickness inverse mapping, wherein the variable pitch value is determined by the spraying speed adjustment amount, and the thickness and pitch value are positively correlated.

[0078] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used for: the second judgment branch of the online judge receives the atomization data and real-time operating parameters, and judges whether the atomization particle size deviates from the target range; if it deviates, it establishes an atomization coupling relationship based on the paint flow rate, atomization pressure, spray gun distance, and atomization particle size, and adopts an extreme value optimization method, with atomization uniformity as the optimization constraint, it iteratively searches along the gradient direction in the three-dimensional parameter space based on paint flow rate-atomization pressure-spray gun distance, and determines the optimal parameter combination that converges the atomization quality to the extreme value.

[0079] Furthermore, the spiral spraying path optimization system for large-diameter steel pipe piles is also used to: obtain the optimized spiral spraying path after the target steel pipe spraying is completed online throughout the spraying cycle; store the optimized spiral spraying path in the industrial database, and perform variable parameter control adaptive spiral spraying management under the production line batch spraying conditions.

[0080] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The spiral spraying path optimization method and specific examples for large-diameter steel pipe piles in the foregoing embodiment one are also applicable to the spiral spraying path optimization system for large-diameter steel pipe piles in this embodiment. Through the foregoing detailed description of the spiral spraying path optimization method for large-diameter steel pipe piles, those skilled in the art can clearly understand the spiral spraying path optimization system for large-diameter steel pipe piles in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0081] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the spiral spraying path for large-diameter steel pipe piles, characterized in that, The method includes: For the target steel pipe, the spiral spraying path is obtained by performing equidistant planning of straight lines and three-dimensional inverse mapping in the two-dimensional mapping plane through line laser surface scanning and Riemann mapping. The spiral spraying path is decoupled based on the spiral pitch, spray gun moving speed and spray gun attitude angle. A bi-objective fitness function is used to perform iterative optimization based on the classification of fixed individuals and wandering individuals under the initial population expansion, so as to obtain the optimized spiral spraying path. Based on the optimized spiral spraying path, a spraying parameter control sequence is generated to drive the spray gun head to perform spiral spraying control on the target steel pipe. As the spiral spraying process progresses, the wet film thickness, atomized particle size, and atomization cone angle are collected synchronously. This triggers the online judgment unit to execute in parallel a decision on the spraying speed adjustment based on the thickness offset vector, and a decision on the optimal parameter combination based on the atomized particle size deviation, updating the optimized spiral spraying path.

2. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 1, characterized in that, The spiral spraying path is obtained, including: Line laser scanning is used to scan the surface of the target steel pipe, generating a three-dimensional point cloud and curvature distribution map marked with taper, elliptical angle and local concavity and convexity, which serves as the scanning data of the steel pipe surface; By using Riemann mapping, the scanning data of the curved surface of the steel pipe is mapped onto a two-dimensional plane rectangle to obtain a two-dimensional mapping plane; In the two-dimensional mapping plane, equidistant planning of straight-line routes is performed to obtain the planned path; The planned path is then mapped back to three-dimensional space to serve as a spiral spraying path.

3. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 1, characterized in that, The optimized spiral spraying path is obtained, including: The spiral spraying path is decoupled and encoded to obtain a spiral pitch sequence, a spray gun moving speed sequence, and a spray gun attitude angle sequence, wherein the elements of each sequence correspond to the discretized segments along the length of the target steel pipe. A dual-objective fitness function is constructed, in which the total spraying time is the first optimization objective and the estimated paint consumption is the second optimization objective; Based on the bi-objective fitness function, the initial population is determined by the spiral pitch sequence, the spray gun moving speed sequence, and the spray gun attitude angle sequence. Iterative optimization is then performed to obtain the optimized spiral spraying path.

4. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 3, characterized in that, Using the spiral pitch sequence, spray gun moving speed sequence, and spray gun attitude angle sequence as reference individuals, the initial population is obtained by population expansion based on the reference individuals; Based on the initial population, and according to a preset ratio, fixed individuals and wandering individuals are identified; The first optimal individual is determined by taking the wandering individuals within the preset range of each fixed individual as the optimal group and using crossover and mutation iteration based on binary crossover operator and polynomial mutation operator. Traverse the first optimized individual, compare it with the fixed individuals in the group, and replace the fixed individuals according to their fitness to obtain the first iterative population; Based on the first iterative population, the optimized spiral spraying path is obtained by iterating through multiple rounds until convergence.

5. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 4, characterized in that, The first optimization objective in the dual-objective fitness function is obtained by dividing the path length of each segment based on sequence segmentation by the sum of the corresponding spray gun movement speed, and the second optimization objective is obtained by integrating the segment path length, helical pitch and instantaneous deposition efficiency function of each sequence segment. The instantaneous deposition efficiency function is a dynamic mapping relationship between the proportion of the mass of the coating actually adhering to the surface of the steel pipe per unit time and the mass lost due to atomization and dispersion under spraying conditions.

6. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 1, characterized in that, The online decision-maker is triggered to execute in parallel decisions regarding the spraying speed adjustment based on the thickness offset vector, and the optimal parameter combination decision based on the atomized particle size deviation, including: The wet film thickness of the upper spraying area is detected by an optical thickness sensor deployed at the rear of the spray gun head. The particle size distribution and atomization cone angle of the paint mist are monitored in real time using a laser diffraction particle size analyzer as atomization data. The wet film thickness, atomization data and real-time operating parameters are input into the online judgment unit to perform two-way spraying analysis and spraying parameter control optimization, and to determine the spraying speed adjustment amount and the optimal parameter combination. The optimized spiral spraying path is updated by combining the spraying speed adjustment with the optimal parameter combination.

7. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 6, characterized in that, The two-way spraying analysis and spraying parameter control optimization include: The first judgment branch of the online judge receives the wet film thickness and real-time operating parameters, performs thickness target value calculation and comparison based on the instantaneous deposition efficiency function, and determines the thickness offset vector; Based on the thickness offset vector, the spraying speed adjustment is calculated using the speed-thickness inverse mapping, wherein the variable pitch value is determined by the spraying speed adjustment, and the thickness is positively correlated with the pitch value.

8. The spiral spraying path optimization method for large-diameter steel pipe piles as described in claim 6, characterized in that, The two-way spraying analysis and spraying parameter control optimization include: The second judgment branch of the online judgment device receives the atomization data and real-time operating parameters to determine whether the atomization particle size deviates from the target range. If there is a deviation, an atomization coupling relationship is established based on the paint flow rate, atomization pressure, spray gun distance, and atomization particle size. An extreme value optimization method is adopted, with atomization uniformity as the optimization constraint. The optimal parameter combination that converges to the extreme value is determined by iteratively searching along the gradient direction in the three-dimensional parameter space based on paint flow rate, atomization pressure, and spray gun distance.

9. The method as described in claim 1, characterized in that, After controlling the spray gun head to perform spiral spraying on the target steel pipe, the process includes: Once the target steel pipe is coated, the optimized spiral coating path is obtained after the online update of the entire coating cycle. The optimized spiral spraying path is stored in the industrial database, and variable parameter control adaptive spiral spraying management is performed under the batch spraying conditions of the production line.

10. A spiral spraying path optimization system for large-diameter steel pipe piles, characterized in that, The steps for implementing the spiral spraying path optimization method for large-diameter steel pipe piles according to any one of claims 1 to 9 include: The spiral spraying path acquisition module is used to obtain the spiral spraying path for the target steel pipe by performing equidistant planning of straight lines and three-dimensional inverse mapping in a two-dimensional mapping plane through line laser surface scanning and Riemann mapping. The module for optimizing the spiral spraying path is used to decouple the spiral spraying path based on the spiral pitch, the spray gun moving speed and the spray gun attitude angle. It adopts a dual-objective fitness function and performs iterative optimization based on the classification of fixed individuals and wandering individuals under the initial population expansion to obtain the optimized spiral spraying path. The spiral spraying control module is used to generate a spraying parameter control sequence based on the optimized spiral spraying path, and drive the spray gun head to perform spiral spraying control on the target steel pipe. The judgment trigger module is used to synchronously collect wet film thickness, atomized particle size and atomization cone angle as the spiral spraying process progresses. The online judge executes in parallel the decision of spraying speed adjustment based on thickness offset vector and the decision of optimal parameter combination based on atomized particle size deviation, and updates the optimized spiral spraying path.

Images