A dynamic spraying method and system for electromechanical product plates

Abstract

The present invention relates to the field of dynamic spraying technology, specifically to a method and system for dynamic spraying of electromechanical product plates, the method comprising: based on real-time feedback control data, monitoring the position and speed parameters of nozzle positioning, calculating the speed deviation through a PID control algorithm, adjusting the nozzle positioning speed parameters, obtaining a nozzle speed adjustment value, and adjusting the nozzle positioning position in real time based on the nozzle speed adjustment value to obtain a nozzle position correction result. In the present invention, dynamic spraying technology is introduced through real-time feedback control to optimize the nozzle position and speed, directly improve the coating uniformity and quality, utilize spray trajectory simulation and motion strategy optimization to reduce conflict points and lags, improve coating efficiency and coating consistency, multi-parameter optimization to improve energy efficiency, reduce material waste, optimize information transmission to enhance nozzle coordination, improve product corrosion resistance, wear resistance and overall performance, ensure high quality of each spraying, be suitable for fast production lines, and enhance product market competitiveness.

Description

Technical Field

[0001] The present invention relates to the field of dynamic spraying technology, and in particular to a dynamic spraying method and system for electromechanical product plates. Background Art

[0002] The field of dynamic spraying technology is an advanced surface treatment technology that focuses on applying coatings to the surfaces of various materials to improve their functionality or protective properties. The core of this technology is the ability to adjust spraying parameters such as nozzle position, spraying speed and coating thickness in real time according to the movement or changes of the coated object. Dynamic spraying technology is widely used in electromechanical products, automobile manufacturing, aerospace and other fields to meet the high-efficiency coating needs on fast production lines. By using highly automated and precise control systems, dynamic spraying can achieve more uniform coating quality, optimize material utilization efficiency, and reduce environmental pollution.

[0003] Among them, the dynamic spraying method for electromechanical product plates refers to a specific dynamic spraying technology application, which aims to provide efficient and uniform coating treatment for the plate surface of electromechanical products. This method automatically adjusts the parameters of the spraying device by real-time monitoring of the movement and position of the plate to ensure the uniformity and quality of the coating. Its main uses include enhancing the corrosion resistance, wear resistance and aesthetics of electromechanical products, and also helping to improve the overall performance and life of the product. This method is very important in the manufacturing industry, especially in those fields that have high requirements for product appearance and durability.

[0004] While existing dynamic spray coating methods for electromechanical product panels have been widely used in the fields of electromechanical products and high-end manufacturing, they suffer from insufficient response speed and adjustment precision when handling rapidly moving or changing objects, leading to uneven coatings and quality issues. Traditional spraying technology also has significant limitations in nozzle coordination and trajectory optimization, relying on experience rather than precise calculations. This can easily lead to inefficiency and material waste in certain high-precision applications. For example, in the automotive or aerospace industries, even minor coating defects can affect the performance and safety of the final product. In practice, the shortcomings of existing technologies lead to product rework or quality degradation, increasing production costs and environmental impact. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a dynamic spraying method and system for electromechanical product plates.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a dynamic spraying method for electromechanical product plates, comprising the following steps:

[0007] S1: Based on real-time feedback control data, the position and speed parameters of the nozzle positioning are monitored, the speed deviation is calculated through the PID control algorithm, the nozzle positioning speed parameters are adjusted to obtain a nozzle speed adjustment value, and the nozzle positioning position is adjusted in real time based on the nozzle speed adjustment value to obtain a nozzle position correction result;

[0008] S2: Based on the nozzle position correction result, simulate the dynamic motion trajectory of the spraying trajectory on the plate, calculate potential conflict points and hysteresis areas, adjust the spraying trajectory path parameters, and obtain a path adjustment plan. Based on the path adjustment plan, optimize the spraying trajectory motion strategy to obtain an optimized spraying path;

[0009] S3: Based on the optimized spraying path, multi-parameter optimization is performed on the spraying speed, accuracy, and energy efficiency, and an optimal motion parameter combination is calculated to obtain a motion parameter optimization result. Based on the motion parameter optimization result, the operating parameters of the nozzle are adjusted to obtain fine-tuning operating parameters;

[0010] S4: Based on the fine-tuning operating parameters, analyze the information transmission efficiency between the nozzles, adjust the information transmission path, calculate the optimal transmission strategy, and obtain an information transmission optimization plan. Based on the information transmission optimization plan, implement coordinated operations between the nozzles to obtain a plan for controlling the spraying uniformity of the plate surface.

[0011] Preferably, the step of obtaining the nozzle speed adjustment value is specifically as follows:

[0012] S111: Based on the real-time feedback control data, the current nozzle speed and the target nozzle speed in the feedback control data are obtained using the formula:

[0013]

[0014] Calculate the deviation between the current nozzle speed and the target nozzle speed to generate the speed deviation, where represents the speed deviation, Represents the current speed, represents the target speed;

[0015] S112: Based on the speed deviation, apply the PID control formula:

[0016]

[0017] Calculate the adjustment signal and increase the integration time to generate the adjustment signal, where is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the integration time, To adjust the signal;

[0018] S113: Calculate the new nozzle speed using the adjustment signal using the formula:

[0019]

[0020] Generate nozzle speed adjustment value, where Represents the current speed, Represents the adjustment signal, represents the speed deviation, Represents the new nozzle speed.

[0021] Preferably, the steps for obtaining the nozzle position correction result are as follows:

[0022] S121: Using the nozzle speed adjustment value, update the real-time position of the nozzle using the formula:

[0023]

[0024] Generate updated sprinkler head positions, where Represents the original position, Represents the adjusted speed, represents the time increment, represents acceleration, Represents the updated sprinkler position;

[0025] S122: Based on the updated nozzle position, the error between the nozzle position and the target position is calculated using the formula:

[0026]

[0027] Adjust the sensitivity of the error response and calculate the resulting position error, where Represents the updated nozzle position, represents the target position, represents the adjustment parameter, represents the position error,

[0028] S123: Based on the position error and the correction factor, the formula is used:

[0029]

[0030] Optimize the accuracy of the adjustment and generate the nozzle position correction result, where: Represents the updated nozzle position, represents the position error, represents the correction factor, Represents the new position after correction.

[0031] Preferably, the step of obtaining the path adjustment solution is specifically as follows:

[0032] S211: Based on the nozzle position correction result, simulate the dynamic motion trajectory of the spraying trajectory on the plate, using the formula:

[0033]

[0034] Generate spray simulation path, where is the path length, is the moving speed, is the rate constant, is the adjustment coefficient, is the disturbance factor, Simulate paths for spraying;

[0035] S212: Based on the spray simulation path, potential conflict points and hysteresis areas are identified and calculated using the formula:

[0036]

[0037] Generate predicted conflict points and lag segments, where To simulate the trajectory, is the reference trajectory, 、 and To adjust the parameters, for areas of conflict and lag;

[0038] S213: Based on the predicted conflict point and hysteresis segment, adjust the spray trajectory path parameters using the formula:

[0039]

[0040] Generate a path adjustment plan, where To simulate the trajectory, For conflict points and hysteresis areas, is the adjustment coefficient, is the smoothing coefficient, is the stability enhancement factor, Adjust the path plan.

[0041] Preferably, the step of obtaining the optimized spraying path is specifically as follows:

[0042] S221: Based on the path adjustment scheme, the spray trajectory motion strategy is optimized using the formula:

[0043]

[0044] Generate an optimized spray path, where For the path adjustment plan, is the sensitivity adjustment coefficient, is the variability control parameter, is the deviation control parameter, To optimize the spray path,

[0045] S222: Based on the optimized spraying path, the effect of the optimized spraying path is calculated using the formula:

[0046]

[0047] Generate potential optimization effects, where To optimize the spray path, is the path length, and To adjust the parameters, For potential optimization effects;

[0048] S223: Determine spray path parameters based on the potential optimization effect, using the formula:

[0049]

[0050] Adjust the path and generate an optimized spray path, where: To optimize the path, For potential optimization effects, is the discount factor, To optimize the spray path.

[0051] Preferably, the steps of obtaining the motion parameter optimization results are specifically as follows:

[0052] S311: Based on the optimized spray path, referring to speed, accuracy and energy efficiency, the formula is adopted:

[0053]

[0054] Evaluate the overall performance and generate a multi-parameter optimization model, where To optimize the spray path, are speed, accuracy and energy efficiency parameters respectively, For multi-parameter optimization models;

[0055] S312: Optimize the multi-parameter optimization model, apply the Lagrange multiplier method with reference to the constraint conditions, and use the formula:

[0056]

[0057] Generate optimized parameter combinations, where For multi-parameter optimization models, is the Lagrange multiplier, is the optimized parameter combination, are speed, accuracy, and energy efficiency parameters respectively;

[0058] S313: Based on the optimized parameter combination and the dynamic feedback of the spraying device, the formula is adopted:

[0059]

[0060] The parameters are adjusted using the logistic regression model to generate the motion parameter optimization results, where: is the optimized parameter combination, Optimize results for motion parameters.

[0061] Preferably, the step of obtaining the fine-tuning operating parameters is specifically as follows:

[0062] S321: Based on the motion parameter optimization result, the current nozzle operation parameters are pre-evaluated using the formula:

[0063]

[0064] Enhance the adaptability and adjustment accuracy of the model and generate pre-adjusted parameters, where For the motion parameter optimization results, is the adjustment coefficient, is the pre-adjustment parameter;

[0065] S322: Using the pre-adjustment parameters, adjust using a composite function, using the formula:

[0066]

[0067] Balance the response sensitivity and adjustment amplitude to generate the adjusted operating parameters, where is the pre-adjustment parameter, is the adjustment coefficient, is the adjusted operating parameter;

[0068] S323: Based on the adjusted operating parameters, parameter adjustment operations are performed step by step, using the formula:

[0069]

[0070] Introduce nonlinear adjustment factors to optimize parameters and generate fine-tuning operating parameters, where: are the adjusted operating parameters, is the adjustment factor, To fine-tune the operating parameters.

[0071] Preferably, the steps for obtaining the control scheme for the spraying uniformity of the plate surface are specifically as follows:

[0072] S411: Based on the fine-tuning operating parameters, analyze the information transmission efficiency between the printheads using the formula:

[0073]

[0074] The information transmission efficiency is obtained by integrating the parameters of the multi-nozzle through a logical function, where , Indicates the number of nozzles. Indicates the efficiency of information transmission;

[0075] S412: Calculate the optimal information transmission path using the information transmission efficiency, using the formula:

[0076]

[0077] Get the transmission optimization strategy, where is the average efficiency, The efficiency of each nozzle, Represents the optimization result of the transmission strategy;

[0078] S413: According to the , implement coordinated operation between nozzles to optimize the spraying uniformity on the plate surface, using the formula:

[0079]

[0080] Through standardization, the spraying uniformity is optimized and the control scheme of the spraying uniformity of the plate surface is obtained, wherein: The nozzles are The value in Indicates the uniformity control scheme.

[0081] A dynamic spraying system for electromechanical product plates, the dynamic spraying system for electromechanical product plates being used to execute the above-mentioned dynamic spraying method for electromechanical product plates, the system comprising:

[0082] The nozzle speed control module receives the real-time position and speed data of the nozzle, adjusts the nozzle speed parameters according to the data difference, and adopts a dynamic response adjustment mechanism to optimize the nozzle's response to speed deviation to obtain the nozzle speed adjustment value;

[0083] The trajectory adjustment module analyzes the deviation of the dynamic motion trajectory based on the nozzle speed adjustment value, uses trajectory repositioning technology to adjust the motion path of the nozzle, optimizes the trajectory configuration, and obtains a path adjustment plan;

[0084] The parameter optimization module uses the path adjustment scheme to analyze and adjust the spraying speed and accuracy parameters, and reconfigures the energy efficiency and accuracy of the spraying process through the performance evaluation model to obtain the motion parameter optimization results;

[0085] The information transmission efficiency analysis module evaluates and optimizes the information transmission path between the printheads based on the motion parameter optimization results, and uses data stream reorganization technology to optimize the real-time performance and efficiency of information transmission to obtain an information transmission optimization solution;

[0086] The spraying uniformity control module performs nozzle operation coordination based on the information transmission optimization scheme, adjusts the uniformity of the spraying operation, and uses dynamic control technology to generate a spraying uniformity control scheme for the plate surface.

[0087] Compared with the prior art, the advantages and positive effects of the present invention are:

[0088] In the present invention, by introducing real-time feedback control data and precise adjustment process into the dynamic spraying technology, the optimized management of the nozzle position and speed is achieved, thereby directly improving the uniformity and quality of the coating. By simulating the spraying trajectory and optimizing the motion strategy, the conflict points and lag areas are reduced, which not only improves the coating efficiency, but also ensures the consistency and aesthetics of the coating. The implementation of multi-parameter optimization effectively improves the energy efficiency of the spraying process, reduces material waste, and through refined information transmission optimization, the coordination between the nozzles is significantly enhanced, improving the corrosion resistance, wear resistance and overall performance of the product. The control scheme of the spraying uniformity of the plate surface further ensures that each spraying can achieve high-quality results. Especially on a fast production line, this optimization significantly improves the continuity of production and the market competitiveness of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 The present invention proposes an overall flow chart of a dynamic spraying method for electromechanical product plates;

[0090] Figure 2 The present invention proposes a flow chart of the nozzle speed adjustment value of a dynamic spraying method for electromechanical product plates;

[0091] Figure 3 The present invention proposes a flow chart of the nozzle position correction results of a dynamic spraying method for electromechanical product plates;

[0092] Figure 4 The present invention proposes a flow chart of a path adjustment scheme for a dynamic spraying method for electromechanical product plates;

[0093] Figure 5 The present invention proposes a flow chart of an optimized spraying path for a dynamic spraying method for electromechanical product plates;

[0094] Figure 6 A flow chart of the motion parameter optimization results of a dynamic spraying method for electromechanical product plates proposed by the present invention;

[0095] Figure 7 The present invention proposes a flow chart of fine-tuning operating parameters of a dynamic spraying method for electromechanical product plates;

[0096] Figure 8 The present invention proposes a flow chart of a solution for controlling the spraying uniformity of a plate surface in a dynamic spraying method for a plate of an electromechanical product. DETAILED DESCRIPTION

[0097] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0098] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined. Example 1

[0099] See also Figure 1 The present invention provides a technical solution: a dynamic spraying method for electromechanical product plates, comprising the following steps:

[0100] S1: Based on real-time feedback control data, monitor the position and speed parameters of the nozzle positioning, calculate the speed deviation through the PID control algorithm, adjust the nozzle positioning speed parameters, and obtain the nozzle speed adjustment value. Based on the nozzle speed adjustment value, adjust the nozzle positioning position in real time to obtain the nozzle position correction result;

[0101] S2: Based on the nozzle position correction results, simulate the dynamic motion trajectory of the spraying trajectory on the plate, calculate the potential conflict points and hysteresis areas, adjust the spraying trajectory path parameters, and obtain a path adjustment plan. Based on the path adjustment plan, optimize the spraying trajectory motion strategy to obtain the optimized spraying path;

[0102] S3: Based on the optimized spray path, multi-parameter optimization is performed on the spraying speed, accuracy, and energy efficiency. The optimal motion parameter combination is calculated to obtain the motion parameter optimization result. Based on the motion parameter optimization result, the operating parameters of the printhead are adjusted to obtain the fine-tuning operating parameters.

[0103] S4: Based on the fine-tuning of operating parameters, analyze the information transmission efficiency between the nozzles, adjust the information transmission path, calculate the optimal transmission strategy, and obtain the information transmission optimization plan. Based on the information transmission optimization plan, implement coordinated operations between the nozzles to obtain the plate surface spraying uniformity control plan.

[0104] The nozzle speed adjustment value includes the speed increase or decrease, target speed, and actual speed. The nozzle position correction result includes the corrected position, original error, and error correction value. The path adjustment plan includes the new path point, path smoothness, and path response time. The optimized spray path includes the optimized path length, path angle adjustment, and path intersection. The motion parameter optimization results include the optimized speed setting, precision adjustment value, and energy consumption data. The fine-tuning operation parameters include operation speed adjustment, operation accuracy, and stability index. The information transmission optimization plan includes optimized data flow, transmission delay, and signal stability. The plate surface spraying uniformity control plan includes uniformity index, coating thickness, and coating smoothness.

[0105] Specifically, if Figure 2 As shown in FIG, the steps for obtaining the nozzle speed adjustment value are as follows:

[0106] S111: Based on the real-time feedback control data, the current nozzle speed and the target nozzle speed in the feedback control data are obtained using the formula:

[0107]

[0108] Calculate the deviation between the current nozzle speed and the target nozzle speed to generate the speed deviation, where represents the speed deviation, Represents the current speed, represents the target speed;

[0109] S112: Based on the speed deviation, apply the PID control formula:

[0110]

[0111] Calculate the adjustment signal and increase the integration time to generate the adjustment signal, where is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the integration time, To adjust the signal;

[0112] S113: Calculate the new nozzle speed using the adjustment signal using the formula:

[0113]

[0114] Generate nozzle speed adjustment value, where Represents the current speed, Represents the adjustment signal, represents the speed deviation, Represents the new nozzle speed.

[0115] Calculation formula and derivation of speed deviation:

[0116]

[0117] This is a difference calculation used to determine the target speed With current speed The deviation between Indicates speed deviation, assuming the current speed m / s, target speed m / s, the speed deviation is calculated as follows:

[0118]

[0119] This means that an increase of 10 m / s is required to reach the target speed.

[0120] Formula and derivation of adjustment signal:

[0121]

[0122] This formula is a PID (proportional-integral-differential) control formula, the parameters 、 and represent the proportional, integral and differential coefficients respectively, is the integration time, and this formula is used to generate the control signal , based on the speed deviation to adjust the current speed.

[0123] set up seconds, and the speed deviation m / s for 5 seconds, the integral and differential parts can be simplified by assuming they are constants:

[0124] , (Assume that the speed deviation is abrupt, not gradual);

[0125] control signal The calculation is:

[0126]

[0127] This means adjusting the signal to 5.5 m / s to bring the current speed closer to the target speed.

[0128] New nozzle velocity formula and derivation:

[0129]

[0130] This formula is used to calculate the new nozzle speed , by putting the control signal Multiply The square root of the current speed is used to adjust. m / s, control signal , speed deviation Meters / second, then:

[0131]

[0132] The new nozzle speed is 67.38 m / s. The parameters and calculation process ensure that the logic of each step is based on practical and feasible values, and each parameter is within a reasonable range in reality.

[0133] Specifically, if Figure 3 As shown in the figure, the steps for obtaining the nozzle position correction result are as follows:

[0134] S121: Use the nozzle speed adjustment value to update the real-time position of the nozzle using the formula:

[0135]

[0136] Generate updated sprinkler head positions, where Represents the original position, Represents the adjusted speed, represents the time increment, represents acceleration, Represents the updated sprinkler position;

[0137] S122: Based on the updated nozzle position, the error between the nozzle position and the target position is calculated using the formula:

[0138]

[0139] Adjust the sensitivity of the error response and calculate the resulting position error, where Represents the updated nozzle position, represents the target position, represents the adjustment parameter, represents the position error,

[0140] S123: Based on the position error and correction factor, the formula is used:

[0141]

[0142] Optimize the accuracy of the adjustment and generate the nozzle position correction result, where: Represents the updated nozzle position, represents the position error, represents the correction factor, Represents the new position after correction.

[0143] Updated position formula:

[0144]

[0145] : Original position, assuming the initial position is rice;

[0146] : Adjusted speed, assuming the adjusted speed is m / s;

[0147] : Time increment, assuming the time increment is Second;

[0148] : acceleration, assuming the acceleration is m / s;

[0149] Calculate first m / s s=10m;

[0150] Then calculate s m;

[0151] Finally calculate the new position m+10m+2m=12m;

[0152] The sprinkler head moves from its original position to a position of 12 meters.

[0153] The calculation formula of nozzle position error is:

[0154]

[0155] : Current location, m;

[0156] : Target position, assuming the target position is m;

[0157] : Adjustment parameters, assuming ;

[0158] Calculate position difference m-12m|=3m;

[0159] Calculation error m;

[0160] The error decreases, reflecting the closeness to the target position after adjustment.

[0161] The formula for generating the nozzle position correction result is:

[0162]

[0163] : Current location, m;

[0164] : Position error, m;

[0165] : Correction coefficient, assumed to be ;

[0166] Calculation correction m;

[0167] Calculate the new position after correction m m=14.54m

[0168] The nozzle position after correction is close to the target position, which shows the effectiveness of the correction algorithm.

[0169] Specifically, if Figure 4 As shown in FIG, the steps for obtaining the path adjustment plan are as follows:

[0170] S211: Based on the nozzle position correction results, simulate the dynamic motion trajectory of the spraying trajectory on the plate using the formula:

[0171]

[0172] Generate spray simulation path, where is the path length, is the moving speed, is the rate constant, is the adjustment coefficient, is the disturbance factor, Simulate paths for spraying;

[0173] S212: Based on the spray simulation path, identify and calculate potential conflict points and lag areas using the formula:

[0174]

[0175] Generate predicted conflict points and lag segments, where To simulate the trajectory, is the reference trajectory, 、 and To adjust the parameters, for areas of conflict and lag;

[0176] S213: Based on the predicted conflict point and hysteresis segment, adjust the spray trajectory path parameters using the formula:

[0177]

[0178] Generate a path adjustment plan, where To simulate the trajectory, For conflict points and hysteresis areas, is the adjustment coefficient, is the smoothing coefficient, is the stability enhancement factor, Adjust the path plan.

[0179] Formula for spraying simulation path:

[0180]

[0181] : Path length, assumed to be 100 meters;

[0182] : The nozzle moving speed is assumed to be 2 m / s;

[0183] : Spraying speed constant, assumed to be 10 m / s, the constant is used to standardize the speed effect;

[0184] : nonlinear adjustment coefficient, assumed to be 2, used to strengthen the impact of speed on the path;

[0185] : Environmental disturbance factor, assumed to be 5, represents the impact of environmental changes on the trajectory;

[0186] Calculate the ratio of velocity to velocity constant ;

[0187] Adjustment of calculation speed ratio ;

[0188] Calculating the impact of environmental disturbances ;

[0189] Calculate the final simulation trajectory rice;

[0190] The final simulated trajectory length is 136.97 meters, which represents the length of the actual dynamic motion trajectory after considering speed adjustment and environmental factors.

[0191] Formulas for conflict and hysteresis areas:

[0192]

[0193] : Reference trajectory, assumed to be 130 meters;

[0194] : Sensitivity adjustment parameter, assumed to be 1;

[0195] : Response adjustment coefficient, assumed to be 1.5;

[0196] : Adjust the weight, assuming it is 10;

[0197] Calculating the impact of trajectory differences ;

[0198] Calculate the adjusted trajectory difference effect ;

[0199] Calculation weight adjustment ;

[0200] The combined influence of two factors ;

[0201] The calculated values ​​for the conflict and hysteresis areas are , which represents the relative size and impact of potential conflict points and hysteresis areas under a given environment and parameter settings.

[0202] Path adjustment solution formula:

[0203]

[0204] : Adjustment coefficient, assumed to be 0.5;

[0205] : Smoothing coefficient, assumed to be 2;

[0206] : Stability enhancement factor, assumed to be 1;

[0207] Calculating the effect of the adjustment factor ;

[0208] Calculation path adjustment ;

[0209] Computational stability adjustments ;

[0210] Final path adjustment rice;

[0211] Final path adjustment result Meters represent the adjusted length of the spray path determined after a series of calculations.

[0212] Specifically, if Figure 5 As shown in the figure, the steps for obtaining the optimized spray path are as follows:

[0213] S221: Based on the path adjustment scheme, optimize the spray trajectory motion strategy using the formula:

[0214]

[0215] Generate an optimized spray path, where For the path adjustment plan, is the sensitivity adjustment coefficient, is the variability control parameter, is the deviation control parameter, To optimize the spray path,

[0216] S222: Based on the optimized spray path, calculate the effect of the optimized spray path using the formula:

[0217]

[0218] Generate potential optimization effects, where To optimize the spray path, is the path length, and To adjust the parameters, For potential optimization effects;

[0219] S223: Determine the spray path parameters based on the potential optimization effect using the formula:

[0220]

[0221] Adjust the path and generate an optimized spray path, where: To optimize the path, For potential optimization effects, is the discount factor, To optimize the spray path.

[0222] Optimized spray path formula:

[0223]

[0224] : Path adjustment scheme, assumed to be 100 (unit path length);

[0225] : Sensitivity adjustment coefficient, assumed to be 5;

[0226] : Variability control parameter, assumed to be 3;

[0227] : Deviation control parameter, assumed to be 2;

[0228] Calculate the adjusted intensity ratio ;

[0229] Calculate the index part 0.801;

[0230] Calculate the overall adjustment factor ;

[0231] Calculate the final optimized path ;

[0232] Final optimization path is 71.4, which means the adjustment plan from the original path The optimized path length after subtracting the adjustment factor.

[0233] Potential optimization effect formula:

[0234]

[0235] : The optimized spray path is 71.4;

[0236] : Path length, assumed to be 10 (unit length);

[0237] : Adjustment parameter, assumed to be 1;

[0238] : Another adjustment parameter, assumed to be ;

[0239] Since this is an integral calculation, the result can be estimated numerically. Here we use a simplified form to estimate it:

[0240] Calculate path impact factor ;

[0241] Estimating the integral using the mean ;

[0242] integral From 0 to 10, it is a complete sine cycle, and its integral result is 0;

[0243] Potential optimization effects is close to 0 because the integral of the sine function over one complete cycle is zero.

[0244] Step 3: Get the optimized spray path

[0245] formula:

[0246] : The optimized spray path is 71.4;

[0247] : potential optimization effect, which is 0;

[0248] : Discount factor, assumed to be 0.5;

[0249] Calculate the discount ;

[0250] Calculate the final optimized path ;

[0251] Final optimization path Keep it at 71.4 because of potential optimization effect is 0, so no further adjustments are made.

[0252] Specifically, if Figure 6 As shown in FIG, the steps for obtaining the motion parameter optimization results are as follows:

[0253] S311: Based on the optimized spray path, referring to speed, accuracy and energy efficiency, the formula is used:

[0254]

[0255] Evaluate the overall performance and generate a multi-parameter optimization model, where To optimize the spray path, are speed, accuracy and energy efficiency parameters respectively, For multi-parameter optimization models;

[0256] S312: Optimize the multi-parameter optimization model, apply the Lagrange multiplier method to refer to the constraints, and use the formula:

[0257]

[0258] Generate optimized parameter combinations, where For multi-parameter optimization models, is the Lagrange multiplier, is the optimized parameter combination, are speed, accuracy, and energy efficiency parameters respectively;

[0259] S313: Based on the optimized parameter combination and the dynamic feedback of the spraying device, the formula is used:

[0260]

[0261] The parameters are adjusted using the logistic regression model to generate the motion parameter optimization results, where: is the optimized parameter combination, Optimize results for motion parameters.

[0262] Multi-parameter optimization model formula:

[0263]

[0264] : Spraying speed, assuming the value is m / s, depending on equipment capabilities and operating conditions;

[0265] : Spraying accuracy, assuming the value is , this value is between 0 and 1, reflecting the precise coverage of the spray;

[0266] : Energy efficiency, assuming the value is J / s, which represents the energy consumed per second;

[0267] Assuming spray speed m / s, accuracy , energy efficiency J / s.

[0268] Calculate the values:

[0269] ;

[0270] ;

[0271] (using the inverse of the natural logarithm);

[0272] Plugging the values ​​into the formula:

[0273]

[0274] This result Indicates the optimization model score after comprehensively considering speed, accuracy, and energy efficiency.

[0275] Optimized parameter combination formula:

[0276]

[0277] : Lagrange multiplier, assumed to be , used to adjust the impact of constraints;

[0278] : Multi-parameter optimization model,

[0279] : Spraying speed, assuming the value is , depending on equipment capabilities and operating conditions;

[0280] : Spraying accuracy, assuming the value is , this value is between 0-1, reflecting the precise coverage of the spray;

[0281] : Energy efficiency, assuming the value is ,, which represents the energy consumed per second;

[0282] Use Results ,calculate ;

[0283] Substitute into the formula:

[0284]

[0285] This result Represents the optimization model score under given constraints.

[0286] Generate motion parameter optimization result formula:

[0287]

[0288] : Optimized parameter combination, ;

[0289] Exponential inverse operation of ;

[0290] Use Results ,calculate ;

[0291] Substitute into the formula:

[0292]

[0293] This result It represents the final optimized motion parameter score, reflecting the performance of the optimization strategy in actual operation.

[0294] Specifically, if Figure 7 As shown in the figure, the steps for obtaining fine-tuning operating parameters are as follows:

[0295] S321: Based on the motion parameter optimization results, the current nozzle operation parameters are pre-evaluated using the formula:

[0296]

[0297] Enhance the adaptability and adjustment accuracy of the model and generate pre-adjusted parameters, where For the motion parameter optimization results, is the adjustment coefficient, is the pre-adjustment parameter;

[0298] S322: Use pre-adjusted parameters and a composite function for adjustment using the formula:

[0299]

[0300] Balance the response sensitivity and adjustment amplitude to generate the adjusted operating parameters, where is the pre-adjustment parameter, is the adjustment coefficient, is the adjusted operating parameter;

[0301] S323: Based on the adjusted operating parameters, parameter adjustment operations are performed step by step using the formula:

[0302]

[0303] Introduce nonlinear adjustment factors to optimize parameters and generate fine-tuning operating parameters, where: are the adjusted operating parameters, is the adjustment factor, To fine-tune the operating parameters.

[0304] Formula for pre-tuning parameters:

[0305]

[0306] : Motion parameter optimization results;

[0307] : Adjustment coefficient, used to enhance the adaptability and adjustment accuracy of the model;

[0308] Assumptions: (optimization result example value), ;

[0309] calculate :

[0310]

[0311] calculate :

[0312]

[0313] Calculate the ratio:

[0314]

[0315] calculate

[0316]

[0317] Final result:

[0318]

[0319] This result Represents an optimized motion parameter indicator for further adjustment of the strategy.

[0320] Adjusted operating parameter formula:

[0321]

[0322] : Pre-adjusted parameters of the previous step;

[0323] : Added adjustment coefficient;

[0324] Assumptions: (calculated from the previous step), ;

[0325] calculate :

[0326]

[0327] Index part calculation:

[0328]

[0329] Final result:

[0330]

[0331] This result Used to adjust the operating parameters of the printhead to ensure the accuracy and efficiency of the spraying process.

[0332] Formula for fine-tuning operating parameters:

[0333] Original text of the formula:

[0334]

[0335] : Adjusted operating parameters;

[0336] : adjustment factor;

[0337] Assumptions: (calculated from the previous step), , assuming that in operation The minimum value is 0.8 and the maximum value is 1.2;

[0338] Compute the product of a root and a maximum value:

[0339]

[0340] Adjustment factor calculation:

[0341]

[0342] Final result:

[0343]

[0344] This result It represents the final fine-tuning operating parameters, which are used to precisely control the movement of the printhead to ensure spraying quality and efficiency.

[0345] Specifically, if Figure 8 As shown in the figure, the steps for obtaining the control scheme for the spraying uniformity of the plate surface are as follows:

[0346] S411: Based on the fine-tuning of operating parameters, analyze the information transmission efficiency between the printheads using the formula:

[0347]

[0348] The information transmission efficiency is obtained by integrating the parameters of the multi-nozzle through a logical function, where , Indicates the number of nozzles. Indicates the efficiency of information transmission;

[0349] S412: Calculate the optimal information transmission path using the information transmission efficiency using the formula:

[0350]

[0351] Get the transmission optimization strategy, where is the average efficiency, The efficiency of each nozzle, Represents the optimization result of the transmission strategy;

[0352] S413: According to , implement coordinated operation between nozzles to optimize the spraying uniformity on the plate surface, using the formula:

[0353]

[0354] Through standardization, the spraying uniformity is optimized and the control scheme of the spraying uniformity of the plate surface is obtained, wherein: The nozzles are The value in Indicates the uniformity control scheme.

[0355] Information transmission efficiency formula:

[0356]

[0357] : No. The operating parameters of each nozzle are assumed to represent the voltage adjustment value of the nozzle;

[0358] : The number of nozzles;

[0359] : average information transmission efficiency;

[0360] Get the operating parameters of each nozzle :Assume there are three nozzles, the operating parameters are , , ;

[0361] Calculate the information transmission efficiency of each nozzle: Use the logical function Calculate the efficiency of each sprinkler head;

[0362] for ;

[0363] for ;

[0364] for ;

[0365] Calculating average efficiency :

[0366]

[0367] This calculation result Represents the average information transmission efficiency of the three nozzles.

[0368] The optimization result formula of the transmission strategy is:

[0369]

[0370] : No. Information transmission efficiency of each nozzle;

[0371] : Average information transmission efficiency, obtained from the previous step ;

[0372] : The optimization result of the transmission strategy, which represents the total variance.

[0373] Calculate variance: According to The result is to calculate the sum of the squares of the differences between the efficiency of each nozzle and the average efficiency;

[0374]

[0375]

[0376]

[0377] Calculate the total variance:

[0378]

[0379] This calculation result Represents the optimization result of the transmission strategy after minimizing the variance.

[0380] Uniformity control scheme formula:

[0381]

[0382] : No. The nozzles are The value in ;

[0383] : The result of the uniformity control scheme;

[0384] Assumptions 0.9, maximum value ;

[0385] Calculate uniformity:

[0386]

[0387] This calculation result Represents the effect of the uniformity control scheme for spraying on the plate surface.

[0388] A dynamic spraying system for electromechanical product plates, which is used to perform the above-mentioned dynamic spraying method for electromechanical product plates, comprises:

[0389] The nozzle speed control module receives the real-time position and speed data of the nozzle, adjusts the nozzle speed parameters according to the data difference, and adopts a dynamic response adjustment mechanism to optimize the nozzle's response to speed deviation to obtain the nozzle speed adjustment value;

[0390] The trajectory adjustment module analyzes the deviation of the dynamic motion trajectory based on the nozzle speed adjustment value, uses trajectory repositioning technology to adjust the motion path of the nozzle, optimizes the trajectory configuration, and obtains a path adjustment plan;

[0391] The parameter optimization module uses the path adjustment scheme to analyze and adjust the spraying speed and accuracy parameters. Through the performance evaluation model, it reconfigures the energy efficiency and accuracy of the spraying process to obtain the motion parameter optimization results.

[0392] The information transmission efficiency analysis module evaluates and optimizes the information transmission path between printheads based on the motion parameter optimization results. It uses data stream reorganization technology to optimize the real-time performance and efficiency of information transmission and obtain an information transmission optimization solution.

[0393] The spraying uniformity control module is based on the information transmission optimization plan, performs nozzle operation coordination, adjusts the uniformity of the spraying operation, and uses dynamic control technology to generate a spraying uniformity control plan for the plate surface.

[0394] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A dynamic spraying method for electromechanical product plates, characterized in that: The method comprises the following steps: Based on the real-time feedback control data, the position and speed parameters of the nozzle positioning are monitored, the speed deviation is calculated through the PID control algorithm, the nozzle positioning speed parameters are adjusted, and the nozzle speed adjustment value is obtained. Based on the nozzle speed adjustment value, the nozzle positioning position is adjusted in real time to obtain the nozzle position correction result; Based on the nozzle position correction result, the dynamic motion trajectory of the spraying trajectory on the plate is simulated, potential conflict points and hysteresis areas are calculated, the spraying trajectory path parameters are adjusted, and a path adjustment scheme is obtained. Based on the path adjustment scheme, the spraying trajectory motion strategy is optimized to obtain an optimized spraying path; Based on the optimized spraying path, multi-parameter optimization is performed on the spraying speed, accuracy and energy efficiency, and the optimal motion parameter combination is calculated to obtain the motion parameter optimization result. Based on the motion parameter optimization result, the operating parameters of the nozzle are adjusted to obtain the fine-tuning operating parameters; Based on the fine-tuning operating parameters, the information transmission efficiency between the nozzles is analyzed, the information transmission path is adjusted, the optimal transmission strategy is calculated, and an information transmission optimization plan is obtained. Based on the information transmission optimization plan, coordinated operations between the nozzles are implemented to obtain a plan for controlling the spraying uniformity of the plate surface.

2. The dynamic spraying method for electromechanical product plates according to claim 1 is characterized in that: The steps for obtaining the nozzle speed adjustment value are specifically as follows: Based on the real-time feedback control data, the current nozzle speed and target nozzle speed in the feedback control data are obtained using the formula: ； Calculate the deviation between the current nozzle speed and the target nozzle speed to generate the speed deviation, where: represents the speed deviation, Represents the current speed, represents the target speed; Based on the speed deviation, the PID control formula is applied: ； Calculate the adjustment signal and increase the integration time to generate the adjustment signal, where is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the speed deviation, is the integration time, To adjust the signal; Using the adjustment signal, the new nozzle speed is calculated using the formula: ； Generate a nozzle speed adjustment value, where Represents the current speed, Represents the adjustment signal, represents the speed deviation, Represents the new nozzle speed.

3. The dynamic spraying method for electromechanical product plates according to claim 2 is characterized in that: The steps for obtaining the nozzle position correction result are specifically as follows: The nozzle speed adjustment value is used to update the real-time position of the nozzle, using the formula: ； Generate an updated sprinkler position, where represents the original position, represents the adjusted speed, represents the time increment, represents acceleration, Represents the updated sprinkler position; Based on the updated nozzle position, the error between the nozzle position and the target position is calculated using the formula: ； Adjust the sensitivity of the error response and calculate the resulting position error, where Represents the updated nozzle position, represents the target location, represents the adjustment parameter, represents the position error, According to the position error, combined with the correction factor, the formula is adopted: ； Optimize the accuracy of adjustment and generate the nozzle position correction result, where: Represents the updated nozzle position, represents the position error, represents the correction factor, Represents the new position after correction.

4. The dynamic spraying method for electromechanical product plates according to claim 3 is characterized in that: The steps for obtaining the path adjustment solution are specifically as follows: Based on the nozzle position correction result, the dynamic motion trajectory of the spraying trajectory on the plate is simulated using the formula: ； Generate a spray simulation path, where is the path length, is the moving speed, is the velocity constant, is the adjustment coefficient, is the disturbance factor, Simulate paths for spraying; Based on the spray simulation path, potential conflict points and hysteresis areas are identified and calculated using the formula: ； Generate predicted conflict points and lag segments, where To simulate the trajectory, is the reference trajectory, , and To adjust the parameters, for areas of conflict and lag; Based on the predicted conflict point and hysteresis segment, the spraying trajectory path parameters are adjusted using the formula: ； Generate a path adjustment plan, where To simulate the trajectory, For conflict points and hysteresis areas, is the adjustment factor, is the smoothing coefficient, is the stability enhancement factor, Adjust the scheme for the path.

5. The dynamic spraying method for electromechanical product plates according to claim 4 is characterized in that: The steps for obtaining the optimized spraying path are specifically as follows: Based on the path adjustment scheme, the spray trajectory motion strategy is optimized using the formula: ； Generate an optimized spray path, where For the path adjustment scheme, is the sensitivity adjustment factor, is the variability control parameter, is the deviation control parameter, To optimize the spray path, Based on the optimized spraying path, the effect of the optimized spraying path is calculated using the formula: ； Generate potential optimization effects, where To optimize the spray path, is the path length, and To adjust the parameters, For potential optimization effects; According to the potential optimization effect, the spray path parameters are determined using the formula: ； Adjust the path and generate an optimized spray path, where: To optimize the path, For potential optimization effects, is the discount factor, To optimize the spray path.

6. The method for dynamic spraying of electromechanical product plates according to claim 5, characterized in that: The steps for obtaining the motion parameter optimization result are specifically as follows: Based on the optimized spray path, referring to speed, accuracy and energy efficiency, the formula is adopted: ； Evaluate the overall performance and generate a multi-parameter optimization model, where To optimize the spray path, are speed, accuracy and energy efficiency parameters respectively, For multi-parameter optimization models; The multi-parameter optimization model is optimized by applying the Lagrange multiplier method with reference to the constraints, using the formula: ； Generate an optimized parameter combination, where For multi-parameter optimization models, is the Lagrange multiplier, is the optimized parameter combination, They are speed, accuracy and energy efficiency parameters respectively; According to the optimized parameter combination, combined with the dynamic feedback of the spraying device, the formula is adopted: ； The parameters are adjusted using the logistic regression model to generate motion parameter optimization results, where: is the optimized parameter combination, Optimize results for motion parameters.

7. The method for dynamic spraying of electromechanical product plates according to claim 6, characterized in that: The steps for obtaining the fine-tuning operating parameters are specifically as follows: Based on the motion parameter optimization results, the current nozzle operation parameters are pre-evaluated using the formula: ； Enhance the adaptability and adjustment accuracy of the model and generate pre-adjusted parameters, where For the motion parameter optimization results, is the adjustment coefficient, is the pre-adjustment parameter; Using the pre-adjusted parameters, a composite function is used for adjustment, using the formula: ； Balance the response sensitivity and adjustment range to generate adjusted operating parameters, where: is the pre-adjustment parameter, is the adjustment coefficient, is the adjusted operating parameter; Based on the adjusted operating parameters, the parameter adjustment operation is performed step by step, using the formula: ； Introduce nonlinear adjustment factors to optimize parameters and generate fine-tuning operating parameters, where: are the adjusted operating parameters, is the adjustment factor, To fine tune the operating parameters.

8. The method for dynamic spraying of electromechanical product plates according to claim 7, characterized in that: The steps for obtaining the control scheme for the spraying uniformity of the plate surface are specifically as follows: Based on the fine-tuning operating parameters, the information transmission efficiency between the nozzles is analyzed using the formula: ； The information transmission efficiency is obtained by integrating the parameters of the multi-nozzle through a logical function, where , Indicates the number of nozzles. Indicates the efficiency of information transmission; Using the information transmission efficiency, the optimal information transmission path is calculated using the formula: ； Get the transmission optimization strategy, where is the average efficiency, The efficiency of each nozzle, Represents the optimization result of the transmission strategy; According to the , implement coordinated operation between nozzles, optimize the uniformity of spraying on the plate surface, and use the formula: ； Through standardization, the spraying uniformity is optimized and the control scheme of the spraying uniformity on the plate surface is obtained, among which: The nozzles are The value in Represents a uniformity control scheme.

9. A dynamic spraying system for electromechanical product plates, characterized in that: According to the method for dynamic spraying of electromechanical product plates according to any one of claims 1 to 8, the system comprises: The nozzle speed control module receives the real-time position and speed data of the nozzle, adjusts the nozzle speed parameters according to the data difference, adopts a dynamic response adjustment mechanism, optimizes the nozzle's response to the speed deviation, and obtains the nozzle speed adjustment value; The trajectory adjustment module analyzes the deviation of the dynamic motion trajectory based on the nozzle speed adjustment value, uses trajectory repositioning technology to adjust the motion path of the nozzle, optimizes the trajectory configuration, and obtains a path adjustment plan; The parameter optimization module uses the path adjustment scheme to analyze and adjust the spraying speed and precision parameters, and reconfigures the energy efficiency and precision in the spraying process through the performance evaluation model to obtain the motion parameter optimization result; The information transmission efficiency analysis module evaluates and optimizes the information transmission path between the nozzles according to the motion parameter optimization result, and adopts data stream reorganization technology to optimize the real-time performance and efficiency of information transmission to obtain an information transmission optimization plan; The spraying uniformity control module performs nozzle operation coordination based on the information transmission optimization scheme, adjusts the uniformity of the spraying operation, and uses dynamic control technology to generate a spraying uniformity control scheme for the plate surface.

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