Control method of gluing system of wind power mixing tower and gluing system

Abstract

The invention provides a control method for a gluing system of a wind power mixing tower and the gluing system, and relates to the technical field of automatic gluing systems.According to the control method, online collection and PI D closed-loop regulation and control of multi-dimensional physical parameters of colloid are integrated, and dynamic optimization control over the colloid proportion and stirring time is achieved in combination with a colloid outlet flow model based on environment and technological parameters; high-precision three-dimensional point cloud data and a heuristic A Star algorithm are adopted to plan a gluing path, so that the comprehensive coverage of a gluing area and the optimal path are ensured, and manual intervention and construction risks are reduced; through real-time path monitoring and a deviation-sensitive gluing speed adjusting mechanism, path deviation is automatically compensated, and the construction precision and efficiency are improved; intelligent control over the whole process is achieved, the reliability, the automation level and the construction quality of gluing operation are improved, and therefore the actual requirement for gluing of the high-quality wind power tower under the complex working condition is met.

Description

Technical Field

[0001] This invention relates to the field of automatic adhesive application system technology, specifically to a control method and adhesive application system for a wind power hybrid tower adhesive application system. Background Technology

[0002] With the rapid development of the wind power industry, wind turbine concrete-concrete hybrid tower structures are widely used due to their superior mechanical properties and ease of construction. However, as a key process for ensuring structural durability and sealing performance, the quality of the adhesive application significantly impacts the overall performance and service life of the wind turbine tower. Traditional adhesive application systems often rely on empirical parameters, neglecting the dynamic effects of environmental changes and the physical properties of the adhesive. This leads to unstable adhesive quality, resulting in uneven coating thickness, imbalanced adhesive ratios, and low construction efficiency. Furthermore, manual mixing makes it difficult to control the adhesive curing time precisely.

[0003] In the prior art, CN118305051A discloses a control method and a coating system for a coating system. The coating system includes a vision module, a ranging module, and a coating module. The method includes: acquiring and obtaining a first image of the current workpiece using the vision module; determining a first motion path of the coating module based at least on the first image; controlling the coating module to start moving according to the first motion path to perform the coating operation; at each moment during at least a portion of the movement process, determining the displacement of the coating module along a first direction based on a first distance detected by the ranging module at that moment, so that the coating module can coat the target area when the distance between its end and the target area meets a preset distance requirement; the first distance is the distance between the ranging module and the measurement area on the surface of the current workpiece, and the distance between the measurement area and the target area is less than or equal to a first distance threshold. Although it can perform path planning and coating interruption correction based on vision and ranging, it lacks closed-loop optimization for dynamic control of colloid physical parameters and environment, cannot achieve intelligent adjustment of colloid ratio and stirring time, and lacks comprehensive control of the influence of environmental parameters on coating quality, resulting in insufficient intelligence and adaptability to complex working conditions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a control method and a coating system for a wind power hybrid tower coating system, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The control method for the wind power hybrid tower adhesive coating system includes the following specific steps:

[0008] S1: Collect the physical parameters of the colloid and the environmental parameters of the wind turbine tower, and construct the optimal proportion model of the mixing ratio of each component of the colloid based on the PID control algorithm;

[0009] S2: Construct a glue dispensing flow rate model based on the environmental parameters of the wind turbine tower and the process parameters during the glue application operation, and control the glue dispensing flow rate and mixing time during the glue application operation based on the glue dispensing flow rate model and the optimal ratio model respectively;

[0010] S3: Collect 3D point cloud data of the wind turbine tower and the adhesive application area, and generate the optimal path covering the entire adhesive application area based on the A Star algorithm;

[0011] S4: Collect the actual path during the glue application operation, and dynamically adjust the glue application speed based on the actual path and the optimal path until the entire glue application area is covered.

[0012] Preferably, the physical parameters of the colloid include colloidal composition, composition ratio, viscosity, curing time, and rheology;

[0013] In step S1, the logic for constructing the optimal ratio model is as follows:

[0014] S101: Define the key performance indicators that need to be monitored after the colloidal components are mixed, perform real-time monitoring of each performance indicator, and generate real-time monitoring data for each performance indicator.

[0015] S102: After each stirring is completed, the measurement error between the real-time detection data and the preset reference data is generated;

[0016] S103: Calculate the adjustment amount of the component ratio of different colloidal components based on the PID algorithm, and dynamically adjust the component ratio according to the calculated adjustment amount.

[0017] Preferably, in step S102, the measurement error is calculated as follows:

[0018]

[0019] In the formula These represent the measurement error, real-time detection data, and reference data at time t, respectively.

[0020] In step S103, the method for calculating the adjustment amount of the component ratio is as follows:

[0021]

[0022] In the formula K p K i K dThese represent the proportional coefficient, integral coefficient, and differential coefficient, respectively. τ represents the adjustment amount of the component proportion at time t, and τ represents the integral variable;

[0023] In step S103, the calculation method for dynamically adjusting the component ratio is as follows:

[0024]

[0025] In the formula Let represent the component proportions at times t+Δt and t, respectively, where Δt represents the time step and t... mix (t) represents the mixing time of the mixing cycle at time t.

[0026] Preferably, in step S2, the logic for constructing the adhesive flow model is as follows:

[0027] The environmental parameters of the wind turbine tower and the process parameters during the adhesive application were collected. The environmental parameters included wall thickness, ambient temperature, and ambient humidity, while the process parameters included adhesive application height, adhesive application angle, adhesive application speed, and saturation coefficient.

[0028] A glue flow rate model was constructed based on the environmental parameters of the wind turbine tower and the process parameters during the glue application operation. Its expression is as follows:

[0029] Q = H × (WH × cota) × V × K × 10 -6

[0030] In the formula, Q represents the glue flow rate, H represents the glue application height, W represents the wall thickness, a represents the glue application angle, V represents the glue application speed, and K represents the fullness coefficient.

[0031] Preferably, in step S2, the stirring time is calculated as follows:

[0032]

[0033] In the formula t mix (t) represents the stirring time at time t, t0 represents the initial stirring time, w1 and w2 both represent calculation weights, and w1 + w2 = 1, T(t) and RH(t) represent the ambient temperature and ambient humidity at time t, respectively. set RH set These represent the reference temperature and reference humidity, respectively.

[0034] Preferably, in step S3, the logic for generating the optimal path based on the A*Star algorithm is as follows:

[0035] Preprocess and mesh the 3D point cloud data of wind turbine towers to construct a state space diagram;

[0036] Based on the division of the adhesive application area and obstacle information, calculate the shortest coverage path from the start point to the end point of the adhesive application area to ensure complete coverage of the adhesive application area and no collisions along the path.

[0037] Preferably, in step S4, the logic for dynamically adjusting the adhesive application speed is as follows:

[0038] When the actual path matches the optimal path well, maintain the glue application speed unchanged;

[0039] When the deviation between the actual path and the optimal path exceeds a preset deviation threshold, the adhesive application speed is reduced, the adhesive application time within the deviation area is extended, and the adhesive thickness is increased. The formula for adjusting the adhesive application speed is as follows:

[0040]

[0041] In the formula V ′ This indicates the adjusted glue application speed, d. err This represents the deviation distance between the actual path and the optimal path, where γ represents the adjustment coefficient, and γ∈[0.01,0.2].

[0042] When the deviation between the actual path and the optimal path exceeds three times the preset deviation threshold, the optimal path is regenerated according to the A Star algorithm to complete the painting of the missed areas.

[0043] A wind turbine hybrid tower adhesive application system, wherein the adhesive application system is used to perform the above-mentioned adhesive application method, specifically including:

[0044] The data acquisition module is used to collect physical parameters of the colloid, environmental parameters of the wind turbine tower, and real-time path information of the coating equipment.

[0045] The proportional control module, with a built-in PID algorithm, enables dynamic adjustment of the proportion of colloidal components.

[0046] The process control module enables the calculation of the dispensing flow rate model and the control of the stirring time based on environmental and process parameters;

[0047] The path planning module generates the optimal adhesive application path based on the 3D point cloud data of the wind turbine tower and the A* algorithm;

[0048] The speed adjustment module dynamically adjusts the adhesive application speed and path compensation based on the deviation between the actual path and the optimal path.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] This invention achieves dynamic optimization control of colloid ratio and mixing time by integrating online acquisition of multi-dimensional physical parameters of the colloid with PID closed-loop control, combined with a dispensing flow rate model based on environmental and process parameters. This ensures the stability of colloid performance and the consistency of coating quality. High-precision 3D point cloud data and a heuristic A*Star algorithm are used for coating path planning, ensuring comprehensive coverage and optimal path for the coating area on complex wind turbine tower surfaces, effectively reducing manual intervention and construction risks. Furthermore, through real-time path monitoring and a deviation-sensitive coating speed adjustment mechanism, intelligent closed-loop control of the coating process is achieved, automatically compensating for path deviations and improving construction accuracy and efficiency. The overall solution realizes intelligent management and control of the entire process of wind turbine hybrid tower coating system, from dynamic adjustment of colloid performance, precise flow control, intelligent path planning to real-time assurance of construction quality. This improves the reliability, automation level, and construction quality of coating operations, thereby meeting the actual needs of high-quality wind turbine tower coating under complex operating conditions. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0052] Figure 2 This is a schematic diagram of the module structure of the present invention;

[0053] Figure 3 This is a schematic diagram illustrating the viscosity fluctuations of the colloid after stirring under different conditions according to the present invention.

[0054] Figure 4 This is a schematic diagram illustrating the viscosity error fluctuation of the colloid after stirring under different conditions according to the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0056] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0057] Example:

[0058] Please see Figures 1-4 The present invention provides a technical solution:

[0059] The control method for the wind power hybrid tower adhesive coating system includes the following specific steps:

[0060] S1: Collect the physical parameters of the colloid and the environmental parameters of the wind turbine tower. The physical parameters of the colloid include its composition, component ratio, viscosity, curing time, and rheological properties. The environmental parameters include wall thickness, ambient temperature, and ambient humidity. An optimal mixing ratio model for each component of the colloid is constructed based on a PID control algorithm. By collecting key colloid performance parameters (such as composition and viscosity) and wind turbine tower environmental parameters in real time, a data foundation can be provided for precise control of the mixing ratio. Furthermore, the PID algorithm dynamically adjusts the proportion of colloid components, enabling online optimization of colloid performance.

[0061] In step S1, the logic for constructing the optimal ratio model is as follows:

[0062] S101: Defines the key performance indicators that need to be monitored after the components of the colloid are mixed. Specifically, these are the physical parameters of viscosity, curing time, and rheology. These three parameters can be directly measured and directly reflect the performance of the colloid, therefore they are considered key performance indicators. Real-time monitoring of each performance indicator is performed, and real-time monitoring data for each performance indicator is generated.

[0063] S102: After each stirring is completed, the measurement error between the real-time detection data and the preset reference data is generated;

[0064] S103: Calculate the adjustment amount of the component ratio of different colloidal components based on the PID algorithm, and dynamically adjust the component ratio according to the calculated adjustment amount.

[0065] This section clearly defines the performance indicators of the colloid (i.e., viscosity, curing time, and rheology among physical parameters) as the data basis for PID regulation, ensuring that the regulation model can make comprehensive evaluation based on multi-dimensional physical parameters, making the ratio regulation more comprehensive, ensuring that the colloid ratio is dynamically optimized in a timely manner according to changes in the environment and raw materials, avoiding misjudgment or regulation failure caused by a single indicator, thereby realizing closed-loop control of regulation and improving system stability and response speed.

[0066] In step S102, the measurement error is calculated as follows:

[0067]

[0068] In the formula These represent the measurement error, real-time detection data, and reference data at time t, respectively.

[0069] In step S103, the method for calculating the adjustment amount of the component ratio is as follows:

[0070]

[0071] In the formula K p K i K d These represent the proportional coefficient, integral coefficient, and differential coefficient, respectively. τ represents the adjustment amount of the component proportion at time t, and τ represents the integral variable. mix (t) represents the mixing time of the mixing cycle at time t.

[0072] The integral variable represents the stirring time t from the integration point 0 to the current time. mix (t), accumulating the function over the entire past time interval, serves as the "running variable" of the integration, representing the "current time point under consideration" during integration, avoiding confusion with the upper limit of integration t. In other words, the integration covers the time interval [0, t]. mix The error values ​​within [(t)] are accumulated, thus representing the position of each time point during accumulation.

[0073] In step S103, the calculation method for dynamically adjusting the component ratio is as follows:

[0074]

[0075] In the formula Let represent the component proportions at time t+Δt and time t, respectively, where Δt represents the time step.

[0076] In this embodiment, it is assumed that the working logic of the adhesive coating system is a cyclical working mode of stirring and mixing - coating - ... - stirring and mixing - coating. Then, the number of cycles increases with time, and the two show a positive correlation. The moment here corresponds to the time point at the end of each stirring, which can be regarded as an index of the number of stirrings. Knowing the moment, we also know the corresponding stirring cycle. The time step represents the concept of the time interval between coating time and stirring time at the next stirring. In other words, it is the adjustment of various parameters after each stirring is completed and executed at the beginning of the next stirring.

[0077] Furthermore, in the above formula In vector form, it represents all performance indicators (i.e., viscosity, curing time, rheology), which will be used in subsequent formulas. Similarly, they are all in vector form, representing parameters related to all performance metrics. Reflecting the difference between real-time detection and the target value is the core of feedback control; K pK is used to adjust for the impact of the current error. i K is used to accumulate historical errors, adjust and eliminate setpoint deviations. d The combination of these three elements—predicting error trends and reducing oscillations—makes feedback control both fast-responding and robust, ensuring a smooth and stable adjustment process for colloidal components, avoiding overshoot or oscillations, and facilitating subsequent optimization and adaptation to complex operating conditions.

[0078] S2: Construct an adhesive flow rate model based on the environmental parameters of the wind turbine tower and the process parameters during the adhesive application operation, and control the adhesive flow rate and mixing time during the adhesive application operation based on the adhesive flow rate model and the optimal ratio model, respectively.

[0079] In step S2, the logic for constructing the adhesive flow rate model is as follows:

[0080] The environmental parameters of the wind turbine tower and the process parameters during the adhesive application process are collected. The process parameters include: adhesive application height, adhesive application angle, adhesive application speed, and fullness coefficient.

[0081] A glue flow rate model was constructed based on the environmental parameters of the wind turbine tower and the process parameters during the glue application operation. Its expression is as follows:

[0082] Q = H × (WH × cota) × V × K × 10 -6

[0083] In the formula, Q represents the glue flow rate, H represents the glue application height, W represents the wall thickness, a represents the glue application angle, V represents the glue application speed, and K represents the fullness coefficient, which is generally taken as 0.8 to 1.2.

[0084] As can be seen from the flow model's calculation formula, WH×cota represents the adjustment of the adhesive application cross-sectional width, taking into account both the wall structure and the application angle. The application height and speed are directly related to the volume of adhesive dispensed. The fullness coefficient is an empirical correction factor used to adjust the actual adhesive fullness. Through the flow model, the adhesive application system can automatically adjust the dispensing flow rate according to different working conditions, ensuring coating thickness and uniformity, facilitating parameter-based intelligent flow control, and improving construction accuracy.

[0085] In step S2, the stirring time is calculated as follows:

[0086]

[0087] In the formula, t0 represents the initial stirring time, w1 and w2 both represent calculation weights, and w1 + w2 = 1, T(t) and RH(t) represent the ambient temperature and ambient humidity at time t, respectively. set RH set These represent the reference temperature and reference humidity, respectively.

[0088] The formula for calculating the stirring time shows that it uses the initial stirring time as a baseline, and adds weighted adjustments based on environmental variables and deviations in colloidal performance indicators, forming a linear weighted model. The baseline stirring time is usually determined based on experience in colloidal processes and is the shortest time to ensure basic homogeneity of the colloid; w1|T(t)-T set | represents the temperature deviation term. The viscosity and curing rate of the colloid are sensitive to temperature; temperature changes affect mixing efficiency and reaction rate, therefore, the stirring time also needs to be adjusted appropriately; w2|RH(t)-RH set The value | represents the humidity deviation. Excessive or insufficient humidity can affect mixing and coating performance, necessitating increased mixing time to improve colloid uniformity and performance. Regarding the two sets of calculation weights, the temperature deviation (w1) typically has a larger weight, while the humidity deviation (w2) has a smaller weight. The specific values ​​can be determined based on process experience. This formula intelligently adjusts the mixing time by linearly superimposing environmental deviation information, ensuring colloid mixing uniformity and coating quality during construction, thereby improving system adaptability and automation.

[0089] In this embodiment, the initial stirring time is set to 2 minutes, w1 = 0.6, w2 = 0.4, the reference temperature is 25°C, and the reference humidity is 50%. The viscosity η of the colloid after stirring is used as the standard for whether the stirring time is qualified: that is, the viscosity η of the colloid after stirring is compared with the required viscosity standard η. set The error between them should not exceed 5%, and the required viscosity standard η should be... set Setting it to 500 mPa·s is understandable. All are in vector form, specifically including viscosity, curing time, and rheology at time t, as well as reference viscosity, reference curing time, and reference rheology. Therefore, we have The viscosity data after adjusting the stirring time under different environments are shown in the table below:

[0090] Table 1: Colloidal viscosity under different environments and stirring times

[0091]

[0092]

[0093] From the data in this table and Figure 3 , Figure 4 As can be seen from the variation curve, by introducing a calculation method for stirring time to dynamically adjust the stirring time, the viscosity of the colloid can be kept near the required viscosity standard under different environmental conditions. This ensures that the viscosity of the colloid can be maintained within the standard range even when the environment changes, thereby reducing the impact of environmental changes on process parameters and improving the overall reliability of the coating operation.

[0094] It's understandable that the mixing of industrial colloids involves three main stages. Initially, the components in the mixture are not fully dispersed, and the viscosity may be low or unstable. As mixing time increases, the components gradually mix thoroughly, the colloid structure begins to form, and the viscosity usually gradually increases. After a period of mixing, the colloid structure stabilizes, and the viscosity reaches a relatively constant level. At this point, mixing mainly serves to maintain uniformity, and viscosity changes are minimal. Continued prolonged mixing, especially under high-speed or high-shear conditions, may damage the colloid's network structure or introduce air bubbles, leading to a decrease in viscosity. However, in coating operations, since colloids are used immediately after mixing, prolonged mixing is not possible, nor is it feasible to adjust viscosity through prolonged mixing. Therefore, mixing time and viscosity exhibit a positive correlation with the first two stages. By adjusting the calculation weight in the mixing time formula, the viscosity error can be controlled within the negative range of -5% to 0%. This way, even if the viscosity error is too large (i.e., below -5%), subsequent adjustments can be made by continued mixing, preventing the colloid from being discarded and reducing raw material loss and operating costs.

[0095] S3: Collects 3D point cloud data of the wind turbine tower and the adhesive application area, and generates the optimal path covering the entire adhesive application area based on the A* Star algorithm. Compared with traditional 2D or simplified 3D models, 3D point cloud data modeling has higher accuracy, adapts to complex structures, and can truly reflect the complex geometric structure and surface details of the wind turbine tower. Moreover, the A* Star algorithm is a heuristic search algorithm that can balance speed and optimality, resulting in more intelligent planning.

[0096] In step S3, the logic for generating the optimal path using the A*Star algorithm is as follows:

[0097] Preprocess and mesh the 3D point cloud data of wind turbine towers to construct a state space diagram;

[0098] Based on the division of the adhesive application area and obstacle information, calculate the shortest coverage path from the start point to the end point of the adhesive application area to ensure complete coverage of the adhesive application area and no collisions along the path.

[0099] By introducing the A* algorithm, path planning can cover the entire coating area, avoiding coating omissions caused by missing path planning, directly improving the overall coating quality. This not only reduces on-site operation risks and the workload of manual inspection and adjustment, but also reduces path deviations and anomalies during construction, improving the overall stability of the system.

[0100] S4: Collect the actual path during the glue application operation, and dynamically adjust the glue application speed based on the actual path and the optimal path until the entire glue application area is covered.

[0101] In step S4, the logic for dynamically adjusting the adhesive application speed is as follows:

[0102] When the actual path matches the optimal path well, maintain the glue application speed unchanged;

[0103] When the deviation between the actual path and the optimal path exceeds a preset deviation threshold, the adhesive application speed is reduced, the adhesive application time within the deviation area is extended, and the adhesive thickness is increased. The formula for adjusting the adhesive application speed is as follows:

[0104]

[0105] In the formula V ′ This indicates the adjusted glue application speed, d. err γ represents the deviation distance between the actual path and the optimal path, and γ represents the adjustment coefficient, where γ∈[0.01,0.2].

[0106] In other words, when there is a deviation between the actual path and the optimal path during the glue application process, and the deviation distance is greater than 1 time the deviation threshold but less than or equal to 3 times the deviation threshold, it is considered to be within an acceptable range. The glue application process continues, but the application speed is reduced, the application time in the deviation area is extended, and the total glue output is increased. This allows the glue to fill the gaps caused by the slight path deviation as much as possible through its flowability. The adjustment coefficient is used to control the sensitivity of the glue application speed to path deviation. The larger the value, the faster the speed decays with changes in deviation, meaning it is more "sensitive" to path errors, and the speed decreases faster. The smaller the value, the slower the speed changes with path errors, and the adjustment is smoother. Specifically, based on process experience, a value of 0.01–0.05 indicates low sensitivity, with a speed decrease of approximately 37%–8% when the path deviation is 50mm. This is suitable for scenarios requiring smooth speed changes and avoiding frequent large adjustments. A value of 0.05–0.1 indicates medium sensitivity, with a speed decrease of approximately 37%–14% when the deviation is 20mm. This balances accuracy and response speed and is suitable for routine dynamic adjustments during adhesive application. A value of 0.1–0.2 indicates high sensitivity, with a speed decrease of approximately 37%–14% when the deviation is 10mm. This is more sensitive to path errors and is suitable for scenarios with high error risk requiring rapid compensation. Initially, a value of 0.05 can be selected as the starting point.

[0107] When the deviation between the actual path and the optimal path exceeds three times the preset deviation threshold, the optimal path is regenerated according to the A Star algorithm to complete the painting of the missed areas.

[0108] In this step, by collecting the actual path in real time and combining it with the optimal path to dynamically adjust the adhesive application speed, not only is the impact of path deviation on adhesive application quality during construction resolved, but also an intelligent, closed-loop construction quality assurance mechanism is established, thereby effectively improving the quality, efficiency and intelligence level of construction.

[0109] This embodiment also provides a wind power hybrid tower adhesive application system, which is used to perform the above-described adhesive application method, specifically including:

[0110] The data acquisition module is used to collect physical parameters of the colloid, environmental parameters of the wind turbine tower, and real-time path information of the coating equipment.

[0111] The proportional control module, with a built-in PID algorithm, enables dynamic adjustment of the proportion of colloidal components.

[0112] The process control module enables the calculation of the dispensing flow rate model and the control of the stirring time based on environmental and process parameters;

[0113] The path planning module generates the optimal adhesive application path based on the 3D point cloud data of the wind turbine tower and the A* algorithm;

[0114] The speed adjustment module dynamically adjusts the adhesive application speed and path compensation based on the deviation between the actual path and the optimal path.

[0115] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0116] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A control method for a wind power hybrid tower adhesive coating system, characterized in that, The specific steps include: S1: Collect the physical parameters of the colloid and the environmental parameters of the wind turbine tower, and construct the optimal proportion model of the mixing ratio of each component of the colloid based on the PID control algorithm; S2: Construct a glue dispensing flow rate model based on the environmental parameters of the wind turbine tower and the process parameters during the glue application operation, and control the glue dispensing flow rate and mixing time during the glue application operation based on the glue dispensing flow rate model and the optimal ratio model respectively; S3: Collect 3D point cloud data of the wind turbine tower and the adhesive application area, and generate the optimal path covering the entire adhesive application area based on the A Star algorithm; S4: Collect the actual path during the glue application operation, and dynamically adjust the glue application speed based on the actual path and the optimal path until the entire glue application area is covered.

2. The control method for the wind power hybrid tower adhesive coating system according to claim 1, characterized in that: The physical parameters of the colloid include colloid composition, composition ratio, viscosity, curing time, and rheology; In step S1, the logic for constructing the optimal ratio model is as follows: S101: Define the key performance indicators that need to be monitored after the components of the colloid are mixed, including physical parameters such as viscosity, curing time and rheology. Real-time monitoring of each performance indicator is performed, and real-time monitoring data of each performance indicator is generated. S102: After each stirring is completed, the measurement error between the real-time detection data and the preset reference data is generated; S103: Calculate the adjustment amount of the component ratio of different colloidal components based on the PID algorithm, and dynamically adjust the component ratio according to the calculated adjustment amount.

3. The control method for the wind power hybrid tower adhesive coating system according to claim 2, characterized in that: In step S102, the measurement error is calculated as follows: In the formula These represent the measurement error, real-time detection data, and reference data at time t, respectively. In step S103, the method for calculating the adjustment amount of the component ratio is as follows: In the formula K p K i K d These represent the proportional coefficient, integral coefficient, and differential coefficient, respectively. τ represents the adjustment amount of the component proportion at time t, and τ represents the integral variable. mix (t) represents the mixing time of the mixing cycle at time t; In step S103, the calculation method for dynamically adjusting the component ratio is as follows: In the formula Let represent the component proportions at time t+Δt and time t, respectively, where Δt represents the time step.

4. The control method for the wind power hybrid tower adhesive coating system according to claim 3, characterized in that: In step S2, the logic for constructing the adhesive flow rate model is as follows: The environmental parameters of the wind turbine tower and the process parameters during the adhesive application were collected. The environmental parameters included wall thickness, ambient temperature, and ambient humidity, while the process parameters included adhesive application height, adhesive application angle, adhesive application speed, and saturation coefficient. A glue flow rate model was constructed based on the environmental parameters of the wind turbine tower and the process parameters during the glue application operation. Its expression is as follows: Q=H×(W-H×cota)×V×K×10 -6 In the formula, Q represents the glue flow rate, H represents the glue application height, W represents the wall thickness, a represents the glue application angle, V represents the glue application speed, and K represents the fullness coefficient.

5. The control method for the wind power hybrid tower adhesive coating system according to claim 4, characterized in that: In step S2, the stirring time is calculated as follows: In the formula, t0 represents the initial stirring time, w1 and w2 both represent the calculation weights, and w l +w2=1, T(t) and RH(t) represent the ambient temperature and humidity at time t, respectively. set RH set These represent the reference temperature and reference humidity, respectively.

6. The control method for the wind power hybrid tower adhesive coating system according to claim 5, characterized in that: In step S3, the logic for generating the optimal path based on the A*Star algorithm is as follows: Preprocess and mesh the 3D point cloud data of wind turbine towers to construct a state space diagram; Based on the information of the glued area and obstacles in the state space diagram, calculate the shortest coverage path from the start point to the end point of the glued area to ensure that the glued area is fully covered and the path is collision-free.

7. The control method for the wind power hybrid tower adhesive coating system according to claim 6, characterized in that: In step S4, the logic for dynamically adjusting the adhesive application speed is as follows: When the actual path matches the optimal path well, maintain the glue application speed unchanged; When the deviation between the actual path and the optimal path exceeds a preset deviation threshold, the adhesive application speed is reduced, the adhesive application time within the deviation area is extended, and the adhesive thickness is increased. The formula for adjusting the adhesive application speed is as follows: In the formula V ′ This indicates the adjusted glue application speed, d. err This represents the deviation distance between the actual path and the optimal path, where γ represents the adjustment coefficient, and γ∈[0.01,0.2]. When the deviation between the actual path and the optimal path exceeds three times the preset deviation threshold, the optimal path is regenerated according to the A Star algorithm to complete the painting of the missed areas.

8. A wind power hybrid tower adhesive coating system, characterized in that: The adhesive application system is used to perform the adhesive application method as described in any one of claims 1-7, specifically including: The data acquisition module is used to collect physical parameters of the colloid, environmental parameters of the wind turbine tower, and real-time path information of the coating equipment. The proportional control module, with a built-in PID algorithm, enables dynamic adjustment of the proportion of colloidal components. The process control module enables the calculation of the dispensing flow rate model and the control of the stirring time based on environmental and process parameters; The path planning module generates the optimal adhesive application path based on the 3D point cloud data of the wind turbine tower and the A* algorithm; The speed adjustment module dynamically adjusts the adhesive application speed and path compensation based on the deviation between the actual path and the optimal path.

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