Quality monitoring system for anticorrosion spraying of polyurea elastomer in sewage treatment plant pool

Abstract

The present application relates to sewage treatment equipment anticorrosion technical field, disclose a sewage treatment plant pool polyurea elastomer anticorrosion spraying quality monitoring system, the system includes thickness, bonding strength, humidity detection module, evaluation module and central control module.Detection module real-time acquisition thickness, bonding strength and environmental humidity data;Evaluation module calculates thickness deviation index, obtains dynamic strength correction coefficient through strength model and determines quality deviation grade;Central control module adjusts the spraying pressure according to the grade, combines humidity and thickness change rate to correct the lateral moving rate, generates the optimized spraying trajectory parameters.The method comprises the steps of data acquisition, parameter calculation, pressure and rate adjustment and trajectory optimization etc.The present application realizes the real-time monitoring and intelligent control of multiple parameters, improves the polyurea spraying quality stability and construction efficiency, and is suitable for sewage treatment pool anticorrosion engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wastewater treatment equipment corrosion prevention, in particular to a wastewater treatment plant pool polyurea elastomer corrosion prevention spraying quality monitoring system. BACKGROUND

[0002] In the field of wastewater treatment, infrastructure such as pools are long-term eroded by chemical substances, microorganisms and water flow in sewage, which is prone to corrosion damage. Not only does it affect the service life of the equipment, but it can also cause safety hazards such as sewage leakage. Polyurea elastomer has become a commonly used material for pool corrosion prevention in wastewater treatment plants due to its excellent corrosion resistance, wear resistance and impact resistance. However, polyurea spraying construction quality is affected by many factors, such as uneven spraying thickness, insufficient adhesion strength of the coating to the substrate, and changes in environmental humidity. These problems can lead to unstable corrosion prevention effects, and even cause coating peeling and local corrosion.

[0003] Traditional polyurea spraying quality control mainly relies on manual detection and experience adjustment, which has obvious defects. Manual detection cannot achieve real-time monitoring and is difficult to detect quality fluctuations during construction in a timely manner, resulting in high post-repair costs. Experience adjustment lacks scientific quantitative basis, and the setting of spraying parameters (such as spraying pressure and moving speed) often cannot accurately match the actual working conditions, easily causing large coating thickness deviations or insufficient adhesion strength. In addition, the existing technology lacks a dynamic monitoring and intelligent control mechanism for the comprehensive influence of multiple factors (such as environmental humidity, substrate surface state, and curing temperature), making it difficult to ensure the consistency and reliability of spraying quality in complex environments.

[0004] With the development of industrial automation and intelligent detection technology, there is an urgent need for an intelligent system that can monitor key parameters in real time, dynamically adjust the spraying process, and achieve quality closed-loop control. Although there are some studies on coating quality monitoring in existing technologies, most of them monitor a single parameter (such as thickness or humidity), and do not form a comprehensive evaluation system with multiple parameter fusion. Moreover, there is a lack of real-time feedback adjustment mechanism for spraying equipment parameters. For example, some systems can only monitor coating thickness, but cannot dynamically adjust the spraying pressure according to the thickness deviation, and do not consider the influence of environmental humidity on the spraying path and moving speed, resulting in insufficient adaptability and flexibility of the spraying process.

[0005] Furthermore, in assessing bond strength, traditional methods often employ offline testing, failing to acquire real-time bond strength data during spraying and thus hindering construction guidance. Simultaneously, existing technologies for optimizing spray trajectory often rely on fixed path planning without dynamic adjustments based on real-time monitoring data, making it difficult to adapt to the uniformity requirements of complex water tank geometries. Therefore, developing a polyurea spraying quality monitoring system integrating multi-parameter real-time monitoring, intelligent assessment, and dynamic control is of significant practical importance for improving the automation level and quality stability of anti-corrosion construction in wastewater treatment plant water tanks. Summary of the Invention

[0006] The purpose of this invention is to provide a quality monitoring system for polyurea elastomer anti-corrosion spraying in wastewater treatment plant pools, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a quality monitoring system for polyurea elastomer anti-corrosion spraying in wastewater treatment plant pools, the system comprising:

[0008] The thickness detection module is used to collect the spraying thickness of the polyurea elastomer coating in real time.

[0009] The bonding strength testing module is used to measure the interfacial bonding strength between the coating and the water tank substrate;

[0010] Humidity detection module is used to monitor the real-time ambient humidity of the spraying area;

[0011] An evaluation module is electrically connected to the thickness detection module, the adhesion strength detection module, and the humidity detection module. The evaluation module is used to obtain a thickness deviation index based on the deviation value between the spraying thickness and a preset thickness threshold. The evaluation module is also used to substitute the interface adhesion strength into the strength model pre-established by the evaluation module to obtain a dynamic strength correction coefficient, and determine the coating quality deviation level based on the linear relationship between the thickness deviation index and the dynamic strength correction coefficient.

[0012] The central control module is electrically connected to the evaluation module. The central control module is used to adjust the initial value of the spraying pressure of the spraying device according to the coating quality deviation level. The central control module is also used to determine the lateral movement speed of the spraying path based on the relationship between the real-time ambient humidity and the preset humidity range. The central control module is also used to perform a secondary correction on the lateral movement speed according to the thickness deviation index, and generate spraying trajectory optimization parameters based on the corrected lateral movement speed.

[0013] Preferably, the strength model pre-established by the evaluation module includes:

[0014] The evaluation module is further configured to obtain an interface bonding strength data set in historical spraying data, and to eliminate outliers in the data set, wherein the data set comprises substrate surface roughness, curing temperature and corresponding bonding strength;

[0015] The evaluation module is further configured to establish a multivariate linear regression equation according to the interaction between the substrate surface roughness and the curing temperature;

[0016] The evaluation module is further configured to fit a coefficient matrix of the linear regression equation by a least square method, and to calculate a residual sum of squares;

[0017] The evaluation module is further configured to iteratively optimize the coefficient matrix based on the relationship between the residual sum of squares and a preset error threshold, until the residual sum of squares converges within the error threshold.

[0018] Preferably, the adjusting the initial value of the spraying pressure of the spraying device comprises:

[0019] The central control module obtains a grade interval corresponding to the coating quality deviation grade, and determines a spraying pressure correction amount according to the relationship between the grade interval and a first pressure adjustment coefficient and a second pressure adjustment coefficient configured by the central control module:

[0020] When the grade interval belongs to the first deviation grade, the central control module takes the product of a preset reference pressure value and the first pressure adjustment coefficient as the initial value of the spraying pressure;

[0021] When the grade interval belongs to the second deviation grade, the central control module calculates a pressure compensation amount according to the ratio of the dynamic strength correction coefficient to the reference strength, and takes the algebraic sum of the reference pressure value and the pressure compensation amount as the initial value of the spraying pressure.

[0022] Preferably, when the central control module calculates the pressure compensation amount, it comprises:

[0023] Obtaining the relationship between the ratio and preset first and second critical ratios, and selecting a corresponding compensation algorithm:

[0024] When the ratio is less than or equal to the first critical ratio, a logarithmic function is used to perform nonlinear mapping on the ratio to generate a compensation factor;

[0025] When the ratio is greater than the first critical ratio and less than the second critical ratio, a piecewise linear interpolation method is used to generate a compensation factor;

[0026] When the ratio is greater than or equal to the second critical ratio, an exponential function is used to amplify the ratio to generate a compensation factor.

[0027] Preferably, the central control module determines the lateral movement rate, comprising:

[0028] According to the absolute value of the humidity difference between the real-time environmental humidity and the preset standard humidity, combined with the maximum rate limit value of the spraying device, the initial lateral movement rate is calculated by the following formula:

[0029]

[0030] Wherein is the initial lateral movement rate, is the maximum rate limit value, is the absolute value of the humidity difference, is the humidity tolerance threshold.

[0031] Preferably, the central control module performs secondary correction on the lateral movement rate, comprising:

[0032] Obtain the thickness change rate of the current spraying thickness and the previous detection period thickness, and select the rate correction mode according to the comparison result of the thickness change rate and the preset change rate threshold:

[0033] When the thickness change rate exceeds the positive threshold, the speed reduction correction mode is enabled, and the current speed is decreased by a preset step size;

[0034] When the thickness change rate is lower than the negative threshold, the acceleration correction mode is enabled, and the current speed is adjusted by an exponential increase algorithm.

[0035] Preferably, the exponential increase algorithm specifically includes:

[0036] According to the thickness change trend of the continuous detection period, the trend slope is calculated, and the ratio of the trend slope to the preset slope reference value is taken as the exponential base, and the correction rate is iteratively calculated by the formula , wherein is the correction rate, is the current speed, is the base rate coefficient, is the trend influence factor, is the abnormal period count.

[0037] Preferably, the generation of the spraying trajectory optimization parameter includes:

[0038] The corrected lateral movement rate is decomposed into X-axis and Y-axis component speeds, and a path equation under a two-dimensional coordinate system is established according to the geometric characteristics of the pool, and a continuous and smooth spraying trajectory control point sequence is generated by a cubic spline interpolation method.

[0039] Preferably, the implementation of the cubic spline interpolation method includes:

[0040] A cubic polynomial function is constructed in each adjacent control point interval, and boundary conditions are applied to make the second derivative of the whole trajectory continuous, and the coefficient matrix of the polynomial in each interval is obtained by solving a linear equation set.

[0041] Preferably, a method for monitoring the quality of polyurea elastomer anti-corrosion spraying in a sewage treatment plant pool is suitable for the above-mentioned system for monitoring the quality of polyurea elastomer anti-corrosion spraying in a sewage treatment plant pool, and the method comprises:

[0042] Real-time acquisition of spraying thickness, interfacial bonding strength and environmental humidity data of the polyurea elastomer coating;

[0043] Calculate the thickness deviation index and obtain the dynamic strength correction coefficient based on the strength model;

[0044] Determine the initial value of the spraying pressure and the reference parameters of the lateral movement rate according to the coating quality deviation level;

[0045] Combine the environmental humidity data to make a primary adjustment to the movement rate, and make a secondary correction according to the thickness change rate;

[0046] Generate optimized spraying trajectory control instructions and output them to the actuator.

[0047] Compared with the prior art, the beneficial effects of the present application are:

[0048] In terms of quality monitoring, the system integrates a thickness detection module, a bonding strength detection module and a humidity detection module to realize real-time synchronous acquisition of spraying thickness, interfacial bonding strength and environmental humidity. The thickness detection module can feed back coating thickness data in real time, avoiding differences in corrosion resistance caused by uneven thickness; the bonding strength detection module measures the bonding strength of the coating and the substrate online, solving the problem of lag in traditional offline detection and ensuring the reliability of the coating and the pool substrate; the humidity detection module monitors the environmental humidity in real time, providing environmental data support for the adjustment of spraying process parameters and avoiding the adverse effects of abnormal humidity on the curing process of polyurea and the performance of the coating.

[0049] The evaluation module realizes accurate evaluation of the coating quality through scientific algorithm construction. The module calculates the thickness deviation index based on the deviation of the spraying thickness from the preset threshold, constructs a strength model combined with historical data, establishes a dynamic relationship model between the substrate surface roughness, the curing temperature and the bonding strength through multivariate linear regression analysis and least squares fitting, and iteratively optimizes the coefficient matrix through residual sum of squares, so that the model can accurately reflect the change of the bonding strength under actual working conditions, and then obtain the dynamic strength correction coefficient. The coating quality deviation level is determined through the linear relationship between the thickness deviation index and the dynamic strength correction coefficient, realizing multi-dimensional comprehensive evaluation of the coating quality and overcoming the one-sidedness of single parameter evaluation.

[0050] The intelligent control function of the central control module significantly improves the adaptability and stability of the spraying process. According to the deviation level of the coating quality, the central control module can dynamically adjust the initial value of the spraying pressure of the spraying device. For different deviation levels, different pressure adjustment strategies are adopted: when in the first deviation level, the spraying pressure is directly determined by the product of the preset reference pressure and the first pressure adjustment coefficient, quickly responding to the thickness deviation problem; when in the second deviation level, the pressure compensation amount is calculated by combining the ratio of the dynamic strength correction coefficient and the reference strength, and the compensation factor is generated by using nonlinear mapping methods such as logarithmic function, piecewise linear interpolation or exponential function, to realize fine adjustment of the spraying pressure, and ensure that the pressure adjustment matches the bonding strength requirement.

[0051] In terms of spraying path and moving speed control, the system fully considers the dual influence of environmental humidity and thickness change rate. The initial transverse moving speed is calculated according to the difference between the real-time environmental humidity and the preset standard humidity. The greater the humidity difference, the greater the speed adjustment amplitude, effectively avoiding the problem of poor curing of polyurea in high humidity environment. At the same time, by monitoring the current spraying thickness and the thickness change rate of the previous period, the speed correction mode is enabled: when the thickness change rate exceeds the positive threshold, the speed is decreased by a preset step to prevent the coating from being too thick; when the thickness change rate is lower than the negative threshold, the exponential increment algorithm is used to speed up the spraying to improve the construction efficiency. The exponential increment algorithm combines the thickness change trend slope of the continuous detection period to realize the dynamic optimization of the speed through iterative calculation, so that the spraying process is more in line with the actual coating accumulation.

[0052] In terms of spraying trajectory optimization, the system decomposes the corrected transverse moving speed into component speeds in a two-dimensional coordinate system, establishes a path equation combined with the geometric characteristics of the pool, and generates a continuous and smooth spraying trajectory control point sequence by using the cubic spline interpolation method. This method ensures that the trajectory is continuous in the second derivative between adjacent control points, avoids sudden changes in speed during spraying, ensures the uniformity of coating thickness, and is especially suitable for spraying operations of complex-shaped pools, improving the scientificity and automation level of spraying path planning. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The working principle diagram of the sewage treatment plant pool polyurea elastomer corrosion prevention spraying quality monitoring system described in the present application;

[0054] Figure 2 The design diagram of the strength model establishment method;

[0055] Figure 3 The design diagram of the spraying pressure adjustment logic;

[0056] Figure 4 The design diagram of the transverse moving speed adjustment;

[0057] Figure 5A design map generated for spray trajectory optimization parameters. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] Please refer to Figures 1-5 The present application relates to a sewage treatment plant pool polyurea elastomer anti-corrosion spraying quality monitoring system, which comprises a thickness detection module, a bonding strength detection module, a humidity detection module, an evaluation module and a central control module. The modules work together to monitor the polyurea elastomer anti-corrosion spraying quality and optimize the spraying parameters. Specifically, the following steps are included:

[0060] The thickness detection module uses a non-contact laser thickness measurement technology to collect the spraying thickness of the polyurea elastomer coating in real time through a laser sensor. The laser sensor scans the coating surface at a preset sampling frequency, converts the collected distance data into coating thickness values, and transmits them to the evaluation module in real time.

[0061] The bonding strength detection module uses the principle of the pull-out method to measure the interfacial bonding strength of the coating and the pool substrate through a built-in micro pull-out device. During the spraying process, the micro pull-out device automatically selects the detection points according to the set detection period, applies a pulling force perpendicular to the substrate surface to the coating, until the coating and the substrate are separated, records the maximum pulling force value at this time, calculates the interfacial bonding strength according to the relationship between the pulling force and the bonding area, and transmits the data to the evaluation module.

[0062] The humidity detection module uses a high-precision humidity sensor to monitor the environmental humidity of the spraying area in real time. The humidity sensor is arranged near the spraying device, can accurately sense the real-time humidity changes of the spraying area, and transmits the humidity data to the evaluation module and the central control module in real time.

[0063] The evaluation module is electrically connected with the thickness detection module, the adhesive strength detection module and the humidity detection module. After receiving the spraying thickness data transmitted by the thickness detection module, the evaluation module calculates the deviation value of the spraying thickness from the preset thickness threshold value, and obtains the thickness deviation index through a preset deviation calculation formula. At the same time, the evaluation module receives the interfacial adhesive strength data transmitted by the adhesive strength detection module, and substitutes it into the pre-established strength model to obtain a dynamic strength correction coefficient. The establishment process of the strength model is as follows: first, the interfacial adhesive strength data set in the historical spraying data is obtained, which includes the substrate surface roughness, the curing temperature and the corresponding adhesive strength; then, the abnormal values in the data set are removed to ensure the accuracy of the data; then, according to the interaction between the substrate surface roughness and the curing temperature, a multivariate linear regression equation is established; then the least square method is used to fit the coefficient matrix of the linear regression equation, and the residual sum of squares is calculated; finally, based on the relationship between the residual sum of squares and the preset error threshold, the coefficient matrix is iteratively optimized until the residual sum of squares converges within the error threshold. According to the linear relationship between the thickness deviation index and the dynamic strength correction coefficient, the evaluation module determines the coating quality deviation level, which is divided into different levels such as the first deviation level and the second deviation level.

[0064] The central control module is electrically connected with the evaluation module and receives the coating quality deviation level, the thickness deviation index and other data output by the evaluation module. According to the coating quality deviation level, the central control module adjusts the initial value of the spraying pressure of the spraying device. The specific adjustment method is as follows: the central control module obtains the level interval corresponding to the coating quality deviation level, when the level interval belongs to the first deviation level, the central control module takes the product of the preset reference pressure value and the first pressure adjustment coefficient as the initial value of the spraying pressure; when the level interval belongs to the second deviation level, the central control module calculates the pressure compensation amount according to the ratio of the dynamic strength correction coefficient to the reference strength, and the specific calculation process is as follows: the relationship between the ratio and the preset first critical ratio and the second critical ratio is obtained, when the ratio is less than or equal to the first critical ratio, the ratio is nonlinearly mapped by using a logarithmic function to generate a compensation factor; when the ratio is greater than the first critical ratio and less than the second critical ratio, a piecewise linear interpolation method is used to generate the compensation factor; when the ratio is greater than or equal to the second critical ratio, the ratio is amplified by using an exponential function to generate the compensation factor, and then the algebraic sum of the reference pressure value and the pressure compensation amount is taken as the initial value of the spraying pressure.

[0065] At the same time, the central control module determines the lateral movement rate of the spraying path based on the relationship between the real-time environmental humidity and the preset humidity interval. Specifically, according to the humidity difference absolute value between the real-time environmental humidity and the preset standard humidity, combined with the maximum rate limit value of the spraying device, the initial lateral movement rate is calculated by the formula , wherein is the initial lateral movement rate, is the maximum rate limit value, is the absolute value of the humidity difference, is the humidity tolerance threshold. Then, the central control module performs a secondary correction on the lateral movement rate according to the thickness deviation index. The specific process is as follows: obtaining the thickness change rate of the current spraying thickness and the previous detection period thickness, when the thickness change rate exceeds the positive threshold, the speed correction mode is enabled, and the current speed is decreased by a preset step; when the thickness change rate is lower than the negative threshold, the acceleration correction mode is enabled. The acceleration correction mode uses an exponential increment algorithm. According to the thickness change trend of the continuous detection period, the trend slope is calculated, and the ratio of the trend slope to the preset slope reference value is taken as the exponential base. The correction speed is iteratively calculated according to the formula , where is the correction speed, is the base rate coefficient, is the trend influence factor, is the abnormal period count. Finally, the central control module generates spraying trajectory optimization parameters based on the corrected lateral movement rate. Specifically, the corrected lateral movement rate is decomposed into X-axis and Y-axis component speeds, and a path equation in a two-dimensional coordinate system is established according to the geometric characteristics of the water tank. A continuous and smooth spraying trajectory control point sequence is generated by using the cubic spline interpolation method. The implementation process of the cubic spline interpolation method is as follows: a cubic polynomial function is constructed in each adjacent control point interval, and boundary conditions are applied to make the second derivative of the entire trajectory continuous. The coefficient matrix of each interval polynomial is obtained by solving a linear equation system.

[0066] The application will be further described in conjunction with Examples 1 to 5:

[0067] Example 1:

[0068] The specific implementation process of the evaluation module for establishing the strength model involves multiple key steps, which are interrelated and need to be accurately executed to ensure that the model can accurately reflect the relationship between the interfacial bonding strength and various influencing factors. The evaluation module first obtains the interfacial bonding strength data set in the historical spraying data, which covers different spraying parameters and corresponding detection results under different working conditions. Each data sample in the data set contains three key parameters: substrate surface roughness, curing temperature and corresponding bonding strength. The substrate surface roughness is measured by a profilometer, and the arithmetic mean deviation Ra is used as the characterization parameter; the curing temperature is recorded by a temperature sensor arranged in the spraying area in real time; and the bonding strength data is obtained by a tensile test device according to the standard test method.

[0069] To ensure the reliability and effectiveness of the data, the evaluation module pre-processes the obtained raw data set, in which outlier elimination is a key step. The evaluation module uses the boxplot method based on statistical principles to identify outliers. The boxplot method determines the reasonable range of data by calculating the interquartile range (IQR) of the data set. In specific operation, the lower quartile Q1 and the upper quartile Q3 of the data set are calculated, and the interquartile range IQR = Q3 - Q1. Data points less than Q1 - 1.5IQR or greater than Q3 + 1.5IQR are considered outliers. For example, when processing the bonding strength data, it is found that the bonding strength value corresponding to a certain data point is much lower than that of other data points. After judging it to be an outlier by the boxplot method, it is eliminated.

[0070] After completing the outlier elimination, the evaluation module analyzes the interaction between the substrate surface roughness and the curing temperature on the bonding strength. This interaction shows that the influence of the two on the bonding strength is not simply linear superposition, but there is a complex nonlinear relationship. The evaluation module preliminarily observes the relationship between the three through a scatter plot matrix and finds that when the substrate surface roughness is in different intervals, the influence trend of the curing temperature on the bonding strength is different. For example, in the low roughness interval, with the increase of the curing temperature, the bonding strength shows an approximately linear growth trend; while in the high roughness interval, the growth rate of the bonding strength gradually slows down after the curing temperature rises to a certain extent.

[0071] Based on the observation results of this interaction, the evaluation module establishes a multivariate linear regression equation to describe the relationship between the three. The equation form is assumed to be wherein is the bonding strength, is the substrate surface roughness, is the curing temperature, is the coefficient to be fitted. This equation form can capture the interaction effect between the two independent variables, which represents the influence of the interaction on the bonding strength.

[0072] The evaluation module uses the least squares method to fit the coefficient matrix of the linear regression equation. The basic principle of the least squares method is to determine the optimal coefficient by minimizing the sum of squares of residuals between the predicted value and the actual value. In specific implementation, the evaluation module first initializes the parameter value of the coefficient matrix, then calculates the residual between the predicted bonding strength and the actual bonding strength of each data point. After the residual calculation is completed, the sum of squares of all residuals is added to obtain the sum of squares of residuals. The evaluation module continuously adjusts the value of the coefficient matrix through iterative optimization, so that the sum of squares of residuals gradually decreases.

[0073] In the iterative optimization process, the evaluation module updates the coefficient matrix using the gradient descent method. The gradient descent method is a commonly used optimization algorithm that calculates the gradient of the residual sum of squares with respect to the coefficient matrix, then updates the coefficient matrix in the opposite direction of the gradient, so that the residual sum of squares is constantly reduced. The step size of each iteration update is controlled by the learning rate, and a too large learning rate will lead to the algorithm failing to converge, and a too small learning rate will lead to slow convergence. The evaluation module adjusts the learning rate adaptively to improve the optimization efficiency while ensuring the convergence of the algorithm.

[0074] After calculating the residual sum of squares, the evaluation module compares it with the preset error threshold. The preset error threshold is a value set in advance according to the application scenario and accuracy requirements of the model. If the residual sum of squares is greater than the preset error threshold, it means that the fitting effect of the current model is not ideal, and the coefficient matrix needs to be further optimized iteratively. The evaluation module will continue to adjust the value of the coefficient matrix, repeatedly calculate the residual sum of squares and compare it with the preset error threshold, until the residual sum of squares converges within the error threshold.

[0075] During the iterative optimization process, the evaluation module also performs multicollinearity test to ensure that there is no high linear correlation between the independent variables. Multicollinearity will cause the estimation of the coefficient matrix to be unstable, affecting the prediction accuracy of the model. The evaluation module detects multicollinearity by calculating the variance inflation factor (VIF), and when the VIF value of a certain independent variable is greater than 10, it indicates that there is a strong linear correlation between this independent variable and other independent variables, and the independent variables need to be screened or transformed.

[0076] To further improve the accuracy of the model, the evaluation module also performs cross-validation after the residual sum of squares converges within the error threshold. Cross-validation is a commonly used model evaluation method that divides the data set into training set and test set, trains the model using the training set, and then evaluates the performance of the model using the test set. The evaluation module uses the k-fold cross-validation method to divide the data set into k subsets, selects k-1 subsets as the training set each time, and the remaining one subset as the test set, repeats k times to get k model performance evaluation results. Through cross-validation, the evaluation module can better understand the generalization ability of the model and avoid overfitting.

[0077] After confirming the good performance of the model through cross-validation, the evaluation module finally determines the coefficient matrix of the strength model. At this time, the strength model has been able to accurately describe the relationship between the surface roughness of the substrate, the curing temperature and the bonding strength. The evaluation module stores this strength model in the system, so that in actual application, the dynamic strength correction coefficient can be accurately obtained according to the input interface bonding strength data.

[0078] In practical applications, when the evaluation module receives the interfacial adhesion strength data transmitted by the adhesion strength detection module, the substrate surface roughness and the curing temperature are taken as input parameters into the strength model. The model calculates according to the determined coefficient matrix, and outputs the corresponding dynamic strength correction coefficient. This dynamic strength correction coefficient reflects the difference between the interfacial adhesion strength under the current working condition and the adhesion strength under the standard working condition, providing an important basis for the subsequent determination of the coating quality deviation level.

[0079] The evaluation module determines the coating quality deviation level according to the linear relationship between the thickness deviation index and the dynamic strength correction coefficient. The dynamic strength correction coefficient, as the output result of the strength model, works together with the thickness deviation index, enabling the evaluation module to more comprehensively and accurately evaluate the coating quality status. In this way, the strength model plays a key role in the entire wastewater treatment plant pool polyurea elastomer anti-corrosion spraying quality monitoring system, providing strong support for the precise control and optimization adjustment of the system.

[0080] Example 2:

[0081] After receiving the coating quality deviation level output by the evaluation module, the central control module needs to adjust the initial value of the spraying pressure of the spraying device according to this level. This process involves accurate judgment of the coating quality status and intelligent control of the pressure parameter to ensure that the spraying effect meets the expected standard. The central control module first analyzes the coating quality deviation level to determine its corresponding level interval. The system pre-sets multiple level intervals, each corresponding to different coating quality status and adjustment strategies. When the coating quality deviation level is in the first deviation level interval, it indicates that the coating quality deviation is relatively small, and the central control module adopts a more direct pressure adjustment method.

[0082] The central control module multiplies the preset reference pressure value by the first pressure adjustment coefficient to obtain the initial value of the spraying pressure. The preset reference pressure value is a basic pressure value obtained from a large amount of historical spraying data and theoretical calculation, which represents the pressure setting that can achieve good spraying effect under ideal working conditions. The first pressure adjustment coefficient is an optimized proportional coefficient used to fine-tune the reference pressure within the first deviation level interval. The determination of this coefficient takes into account various factors, including coating material properties, substrate surface conditions, and spraying equipment performance. Through this simple multiplication operation, the central control module can quickly respond to small coating quality deviations and adjust the spraying pressure in a timely manner, so that the spraying process can quickly return to the ideal state.

[0083] When the central control module determines that the coating quality deviation level belongs to the second deviation level interval, it means that the coating quality deviation is relatively large, and a more precise pressure adjustment strategy is needed. In this case, the central control module first calculates the ratio of the dynamic strength correction coefficient to the reference strength. The dynamic strength correction coefficient is calculated by the evaluation module based on the strength model, which reflects the change of the current coating and substrate interface bonding strength relative to the standard strength. The reference strength is a pre-set reference value, representing the ideal bonding strength between the coating and the substrate.

[0084] After calculating the ratio, the central control module compares it with the pre-set first critical ratio and second critical ratio to determine which compensation algorithm to use to calculate the pressure compensation. These two critical ratios are threshold values determined by the system based on a large amount of experimental data and engineering experience, used to divide different compensation strategy intervals. When the ratio is less than or equal to the first critical ratio, it means that the difference between the current interface bonding strength and the reference strength is large, and the coating may have the risk of not being firmly bonded. At this time, the central control module uses a logarithmic function to perform nonlinear mapping on the ratio to generate a compensation factor. The logarithmic function has the characteristic that when the input value is small, the output value changes more sensitively, which can provide more significant pressure compensation when the bonding strength difference is large, thereby enhancing the bonding force between the coating and the substrate.

[0085] When the central control module determines that the ratio is greater than the first critical ratio and less than the second critical ratio, it means that the difference between the interface bonding strength and the reference strength is at a medium level. In this case, the central control module uses a piecewise linear interpolation method to generate a compensation factor. The piecewise linear interpolation method divides the compensation interval into multiple small segments, and uses a linear function for interpolation calculation in each small segment. This method can simplify the calculation process while ensuring the accuracy of the compensation, and improve the response speed of the system. Through piecewise linear interpolation, the central control module can use different linear relationships in different intervals to calculate the compensation factor according to the specific position of the ratio, so that the pressure compensation is more in line with the actual needs.

[0086] When the ratio is greater than or equal to the second critical ratio, it means that the difference between the interface bonding strength and the reference strength is small, and the coating has good bonding performance. At this time, the central control module uses an exponential function to amplify the ratio to generate a compensation factor. The exponential function has the characteristic that when the input value is large, the output value will quickly grow, which can further fine-tune the spraying pressure when the bonding strength is high, making the coating thickness more uniform and improving the overall spraying quality. Through exponential amplification, the central control module can respond sensitively to small strength differences and achieve fine adjustment of the spraying pressure.

[0087] After determining the compensation factor, the central control module calculates the pressure compensation amount based on the compensation factor. The calculation method of the pressure compensation amount is different according to different compensation algorithms, but the general idea is to multiply the compensation factor by a basic compensation amount to obtain the final pressure compensation amount. The basic compensation amount is a value set in advance according to the system characteristics and process requirements, which represents the amplitude of pressure adjustment under a unit compensation factor. By combining the compensation factor with the basic compensation amount, the central control module can accurately calculate the pressure adjustment amount required to correct the coating quality deviation.

[0088] After calculating the pressure compensation amount, the central control module performs an algebraic sum operation on the reference pressure value and the pressure compensation amount to obtain the initial value of the spraying pressure. This process ensures that the reference pressure value is not completely deviated while considering the coating quality deviation, thereby ensuring the stability and controllability of the spraying process. The central control module sends the calculated initial value of the spraying pressure to the pressure control system of the spraying device, and the pressure control system adjusts the opening of the pressure regulating valve according to the value, thereby realizing accurate control of the spraying pressure.

[0089] In actual operation, the central control module continuously receives data such as coating quality deviation level and dynamic intensity correction coefficient output by the evaluation module, and adjusts the initial value of the spraying pressure in real time according to these data. This closed-loop control mechanism ensures that the spraying process can be dynamically adjusted according to the real-time changes of the coating quality, improving the adaptability and stability of the system. At the same time, the central control module also records relevant data of each pressure adjustment, including adjustment time, pressure values before and after adjustment, coating quality deviation level, etc., forming a complete historical record. These historical records can not only be used for subsequent process analysis and optimization, but also serve as an important basis for quality traceability.

[0090] In order to ensure the accuracy and reliability of pressure adjustment, the central control module also carries out a series of verification and protection measures. After calculating the initial value of the spraying pressure, the central control module checks whether the value is within the safe working pressure range of the spraying device. If it exceeds the safe range, the central control module will automatically limit the pressure value and issue a warning signal to prompt the operator to intervene. At the same time, the central control module also limits the amplitude of pressure adjustment to avoid unstable spraying process due to excessive adjustment amplitude.

[0091] In the process of adjusting the initial value of the spraying pressure, the central control module also considers the influence of other factors such as environmental temperature and humidity. These factors may affect the flowability and curing performance of the coating material, thereby indirectly affecting the coating quality. The central control module will appropriately modify the pressure adjustment strategy according to the real-time monitored environmental parameters to ensure good spraying effect under different environmental conditions.

[0092] Through this fine pressure adjustment strategy, the central control module can dynamically adjust the initial value of the spraying pressure according to the specific situation of the coating quality deviation, so that the spraying process always remains in the best state. This intelligent control method not only improves the quality and uniformity of the coating, but also reduces manual intervention, reduces labor intensity, and improves production efficiency. In the polyurea elastomer anti-corrosion spraying engineering of the sewage treatment plant pool, this pressure adjustment strategy can effectively guarantee the corrosion resistance and service life of the coating, providing a strong guarantee for the safe operation of the pool.

[0093] Example 3:

[0094] The process of determining the lateral movement rate by the central control module involves environmental humidity sensing, initial rate calculation, and secondary correction based on thickness change, and requires the combination of multi-dimensional data to achieve dynamic optimization of the spraying path. First, the central control module obtains the real-time environmental humidity data of the spraying area through the humidity detection module, and at the same time calls the standard humidity value preset by the system, calculates the absolute value of the humidity difference , which reflects the deviation of the current environmental humidity from the ideal spraying humidity. The preset standard humidity value is determined according to the curing characteristics of the polyurea elastomer material, and is usually set to the median value of the humidity interval where the material curing rate and adhesion performance are best; the calculation formula of the absolute value of the humidity difference is , where is the real-time environmental humidity, is the preset standard humidity.

[0095] After obtaining the absolute value of the humidity difference, the central control module combines the maximum rate limit value of the spraying device and the humidity tolerance threshold , and calculates the initial lateral movement rate through the formula. The formula is:

[0096]

[0097] where is the maximum lateral movement rate of the spraying device under ideal working conditions, which is determined by the mechanical properties of the equipment and the requirements of the spraying process; is the maximum tolerance value of the humidity difference allowed by the system, which is used to measure the influence of environmental humidity on the spraying quality. The physical meaning of this formula is: when the real-time environmental humidity is consistent with the standard humidity , the initial lateral movement rate reaches the maximum value ; as the absolute value of the humidity difference increases, the rate decreases in linear proportion, in order to slow down the spraying speed and adapt to the influence of environmental changes on the coating curing. For example, if , , when , the initial rate is calculated as .

[0098] After the initial transverse movement rate is calculated, the central control module needs to modify it again according to the thickness deviation index to further adapt to the real-time changes of the coating thickness. The core of the modification process is to obtain the thickness change rate of the current spraying thickness and the previous detection period thickness , which is calculated by the historical data of the thickness detection module, and the formula is:

[0099]

[0100] Among them, is the coating thickness of the current detection period, is the coating thickness of the previous detection period, is the time interval of adjacent detection periods. The thickness change rate reflects the growth trend of the coating thickness per unit time, which is a key indicator to determine whether the spraying amount is reasonable.

[0101] The central control module compares the thickness change rate with the preset positive threshold and the negative threshold to trigger different rate modification modes. The preset threshold is set according to the spraying process parameters of the polyurea elastomer and the coating thickness tolerance requirement, which represents the critical value of the thickness growth too fast, which represents the critical value of the thickness growth too slow. When , it means that the spraying amount per unit time is too large, which may cause the coating thickness to exceed the preset threshold or defects such as sagging, so the speed reduction modification mode is enabled. The speed reduction modification mode adopts a linear decreasing strategy, i.e. decreasing the current speed by a preset step , and the formula is:

[0102]

[0103] Among them, is the speed before modification, is the current speed, is the preset speed reduction step (unit: ), which is set according to the spraying accuracy requirement, usually . For example, if the current speed is and the preset step is , then the modified speed is , until the thickness change rate falls to a reasonable range.

[0104] When , it means that the spraying amount per unit time is insufficient, which may cause the coating thickness to not meet the preset requirement, so the acceleration modification mode is enabled. The acceleration modification mode adopts a nonlinear adjustment strategy, which analyzes the continuous trend slope is calculated by linear regression method The trend slope is calculated by linear regression method

[0105] The trend slope is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope

[0106] is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope

[0107] is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope The trend slope

[0108] The trend slope is calculated by linear regression method The trend slope is calculated by linear regression method

[0109] The trend slope is calculated by linear regression method The trend slope

[0110] is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope is calculated by linear regression method The trend slope

[0111] is calculated by linear regression method The trend slope is calculated by linear regression methodIf the modified rate exceeds the range, the system will automatically trigger the protection mechanism to limit the rate within the safe range and issue a warning signal. In addition, the central control module also needs to consider the influence of the pool geometry on the rate, for example, in the corner or curved area of the pool, due to the change of spraying distance and angle, additional compensation needs to be made to the lateral movement rate to avoid the difference in coating thickness caused by uneven speed.

[0112] The secondary modification process of the lateral movement rate forms a closed-loop control with the initial rate calculation, dynamically adjusts the spraying speed by real-time sensing of environmental humidity and coating thickness changes, and realizes adaptive control of the spraying process. This mechanism not only can cope with the influence of environmental humidity fluctuations on coating curing, but also can correct thickness deviation in time to ensure the uniformity and thickness precision of polyurea elastomer coating, providing a reliable speed parameter basis for subsequent spraying trajectory optimization. The whole process does not need manual intervention and is completely completed automatically by the system, improving the intelligent level and construction efficiency of spraying operation, and reducing the quality risk caused by human factors.

[0113] Example 4:

[0114] When the central control module enables the acceleration modification mode, it needs to dynamically adjust the lateral movement rate through an exponential incremental algorithm. This process is based on the thickness change trend detected in consecutive detection periods, and realizes nonlinear optimization of the rate through multi-parameter linkage. The following will detail the implementation process in combination with specific scenarios:

[0115] Suppose that in the spraying operation of a sewage treatment plant pool, the preset thickness of the polyurea elastomer coating is 1.5 mm, and the detection period is set to 1 minute (i.e. the time interval between adjacent detection periods is 1 minute). In the first to fifth minute detection period, the coating thickness data obtained by the central control module are 0.2 mm, 0.35 mm, 0.5 mm, 0.6 mm, and 0.7 mm, respectively. By calculating the thickness change amount of adjacent periods, the thickness changes of the second to fifth minutes are 0.15 mm, 0.15 mm, 0.1 mm, and 0.1 mm, respectively. It can be seen that from the third minute, the thickness change amount shows a gradually decreasing trend, indicating that the coating thickness growth rate slows down.

[0116] The central control module first judges whether the current thickness change rate is lower than the preset negative threshold. Suppose the preset negative threshold is 0.12 mm / min, the thickness change rate of the third minute is (0.5-0.35) / 1=0.15 mm / min, which is higher than the negative threshold; the thickness change rate of the fourth minute is (0.6-0.5) / 1=0.1 mm / min, which is lower than the negative threshold; the thickness change rate of the fifth minute is (0.7-0.6) / 1=0.1 mm / min, which is also lower than the negative threshold. At this time, the abnormal period count From the fourth minute, the count is accumulated to the fifth minute (That is, the thickness change rate is lower than the negative threshold for two consecutive cycles).

[0117] Next, the central control module collects continuous data. The thickness data for each detection cycle (i.e., data from minutes 1 to 5: 0.2, 0.35, 0.5, 0.6, 0.7 mm) corresponds to time points from 1 to 5 minutes. The thickness variation trend of this data is fitted using linear regression, and the slope of the trend is calculated. The core of linear regression is finding a straight line. This minimizes the sum of the squared perpendicular distances from each data point to the line. In this example, the time can be calculated. With thickness The sum of the products is The sum of time is The sum of the thicknesses is The sum of squares of time is According to the linear regression formula, the trend slope mm / min.

[0118] Preset slope baseline value If set to 0.15 mm / min (the expected thickness growth rate under ideal operating conditions), then the ratio of the trend slope to the preset slope baseline value is... This ratio reflects the degree to which the current thickness growth trend lags behind the ideal state; the smaller the value, the slower the growth, and the greater the rate adjustment required.

[0119] The central control module uses the preset base coefficient. and trend influencing factors Calculate the correction rate. Assume... (Control the basic adjustment range). (Amplified trend impact), current rate Let the initial lateral movement rate at the 5th minute be 4.5 m / min, calculated from the initial rate (based on ambient humidity). Substituting this into the exponentially increasing algorithm formula:

[0120] Correction rate

[0121] Right now meters per minute.

[0122] At this point, the central control module needs to verify whether the corrected speed is within the safe speed range of the spraying device (assuming the safe range is 2-6 meters / minute). 5.148 meters / minute is within this range, so no adjustment is needed. The corrected speed will be used as the baseline value for the lateral movement speed at the 6th minute to generate the spraying trajectory parameters.

[0123] In the subsequent detection cycle, if the thickness at the 6th minute is 0.8 mm, the thickness change rate is (0.8-0.7) / 1=0.1 mm / min, which is still lower than the negative threshold, the abnormal cycle count to 3. Recollect the thickness data at the 2nd to 6th minute (0.35, 0.5, 0.6, 0.7, 0.8 mm), and calculate the trend slope. The time points are 2 to 6 minutes, the sum of products is , the sum of times is , the sum of thicknesses is , and the sum of time squares is . The trend slope mm / min, and the ratio .

[0124] The correction rate mm / min. At this time, the rate is close to the upper limit of safety (6 mm / min), and the system triggers the speed limiting mechanism to limit the rate to 6 mm / min to avoid exceeding the equipment limit.

[0125] If the thickness at the 7th minute is 0.95 mm, the thickness change rate is (0.95-0.8) / 1=0.15 mm / min, which is higher than the negative threshold, and the abnormal cycle count is reset to 0, the acceleration correction mode stops, and the rate returns to the initial rate calculated according to the environmental humidity (assuming that the humidity difference decreases at this time, and the initial rate is calculated as 5.8 mm / min).

[0126] Throughout the process, the central control module dynamically adjusts the rate correction amplitude by continuously monitoring the thickness change trend. When the thickness grows slowly and continuously, the exponential growth algorithm uses the cumulative abnormal cycle number and the ratio of the trend slope to achieve nonlinear increase of the rate, avoiding the response lag or over-adjustment problems that may be caused by linear adjustment. At the same time, combined with the device safety threshold and real-time data verification, the rate adjustment ensures that the coating thickness requirement is met and the device performance range is not exceeded. This mechanism enables the spraying operation to adapt to changes in coating accumulation state, maximizes spraying efficiency under the premise of ensuring uniform thickness, and is especially suitable for scenarios where the thickness grows abnormally due to material supply fluctuations, equipment wear, etc. in large-area pool spraying.

[0127] Example 5:

[0128] The process of the central control module generating spraying trajectory optimization parameters and implementing the cubic spline interpolation method needs to be combined with the pool geometric characteristics and the corrected transverse movement rate to achieve fine planning of the spraying path. The following takes a rectangular pool as an example to detail the specific implementation process:

[0129] Suppose that the pool of a sewage treatment plant is rectangular structure, 20 meters long, 10 meters wide, 5 meters deep, and the pool wall is vertical. After completing the correction of the lateral movement rate, the control module needs to decompose the corrected rate into component velocities in the two-dimensional coordinate system and generate a continuous and smooth spraying trajectory. First, taking the lower left corner of the pool bottom as the origin, a two-dimensional rectangular coordinate system is established, in which the X-axis is along the length direction of the pool (20 meters), and the Y-axis is along the width direction of the pool (10 meters). The spraying device moves laterally along the X-axis direction, while lifting layer by layer along the Y-axis direction, to realize full coverage spraying of the pool wall.

[0130] Taking a corrected lateral movement rate as an example, suppose that after correction by the ambient humidity and thickness change rate, the lateral movement rate is 5 meters / minute. Since the spraying device operates on the vertical surface of the pool wall, it needs to control both horizontal movement and vertical lifting. The control module decomposes the rate into X-axis component velocity and Y-axis component velocity . According to the spraying process requirements, the angle between the spraying direction and the X-axis is set to 0 degrees (i.e. horizontal direction), so meters / minute, which is applicable to the horizontal plane spraying stage. After completing a layer of horizontal spraying, it needs to lift a certain height (such as 0.5 meters) along the Y-axis direction for the next layer of spraying, at this time According to the lifting speed requirement, it is set to 0.2 meters / minute, which remains unchanged.

[0131] Next, the control module establishes the path equation according to the geometric characteristics of the pool. For the planar spraying of the rectangular pool wall, the path equation is a piecewise linear function: in the bottom layer spraying, the Y-axis coordinate is fixed at 0, and the X-axis moves linearly from 0 to 20 meters; after completing the bottom layer spraying, the Y-axis coordinate is lifted to 0.5 meters, and the X-axis moves reversely from 20 meters to 0 meters, forming a back-and-forth spraying path. To avoid seams between adjacent coating layers, the spraying path needs to overlap a certain width (such as 10% of the spraying gun coverage width), so the overlap amount parameter needs to be introduced in the path equation to adjust the start and end coordinates of the X-axis.

[0132] When generating the spraying trajectory control point sequence, the control module first determines the control point interval according to the corrected lateral movement rate and the detection period (such as 10 seconds). At a rate of 5 meters / minute, the movement distance per second is about 0.083 meters, and the movement distance per 10-second detection period is 0.83 meters, so a control point is set every 0.83 meters in the X-axis direction. For a 20-meter-long pool wall, the control point sequence for the bottom layer spraying is that the X-axis starts from 0, and 25 control points are set at intervals of 0.83 meters (including the start and end points), and the Y-axis coordinates are all 0. In the second layer spraying, the Y-axis coordinate is 0.5 meters, and the X-axis control point sequence starts from 20 meters, and 25 control points are set at intervals of -0.83 meters in reverse.

[0133] The implementation of cubic spline interpolation requires the construction of a cubic polynomial function between adjacent control points to ensure the continuity of the second derivative of the trajectory. Taking the first two control points of the bottom layer spraying (0, 0) and (0.83, 0) as an example, assuming the interval between adjacent control points is , a cubic polynomial needs to be constructed. This polynomial needs to satisfy the following conditions:

[0134] End point function value: , ;

[0135] End point first derivative: assuming the first derivative (speed) at the starting point is 5 meters / minute (i.e. ), the first derivative at the end point needs to be consistent with the starting point derivative of the next interval, determined by the global boundary condition (e.g. 5 meters / minute, maintaining constant speed);

[0136] End point second derivative: usually set to 0 (natural boundary condition), making the curvature of the trajectory zero at the end point.

[0137] According to the above conditions, the equation group is established to solve the coefficients . Since , we have ; i.e. ; the first derivative , substituting gives ; substituting gives , which simplifies to . The simultaneous equations can be solved to obtain the values of and , thus determining the cubic polynomial of the interval.

[0138] For the entire bottom layer spraying trajectory, 24 adjacent control point intervals (25 control points) need to be constructed with cubic polynomials, and global boundary conditions (such as the second derivative of the starting and ending points being 0) need to be applied. By solving the linear equation group, the coefficient matrix of all intervals is obtained, thus generating a continuous and smooth spraying trajectory. For example, between the third control point (1.66, 0) and the fourth control point (2.49, 0), a polynomial is constructed by the above method to ensure the continuity of the function value, first derivative, and second derivative at the connection point of adjacent intervals, avoiding sudden changes or inflection points in the trajectory.

[0139] ​When spraying to the top of the pool wall (such as Y axis 5 meters), the trajectory transition of the corner area needs to be handled. Taking the right-angle corner of the pool wall and the pool top as an example, the central control module gradually reduces the X-axis component speed while increasing the Y-axis component speed when approaching the corner (such as Y = 4.8 meters), so that the spraying device transitions to the pool top plane along a curved trajectory. At this time, the curved trajectory is generated in the two-dimensional coordinate system by the cubic spline interpolation method, ensuring the continuity of the curvature at the corner, avoiding the abnormal coating thickness or spraying overlap caused by sudden changes in speed.

[0140] In the actual spraying process, the central control module monitors the position feedback data of the spraying device in real time and compares it with the generated trajectory control point sequence. If there is a position deviation (such as trajectory deviation caused by equipment vibration), the position and speed of the nozzle are automatically adjusted through the servo control system to ensure that the spraying path is consistent with the planned trajectory. At the same time, the system records the actual spraying time and thickness data of each control point to form a trajectory execution log for subsequent quality traceability and process optimization.

[0141] For complex geometric shapes of the pool (such as circular or with curved pool walls), the central control module needs to first discretize the pool surface into multiple two-dimensional plane units, establish a coordinate system for each unit and generate a trajectory, and then stitch the unit trajectories into a whole trajectory through coordinate transformation. For example, a circular pool can be divided into multiple fan-shaped units according to the angle, a local coordinate system is established in each unit, and an arc-shaped trajectory is generated by cubic spline interpolation method, and the trajectories of adjacent units are smoothly transitioned through common control points at the joint.

[0142] The application of cubic spline interpolation method ensures the smoothness of the motion while meeting the speed constraint, effectively reducing the impact when the spraying device starts and stops and turns, prolonging the service life of the equipment. At the same time, the continuous trajectory avoids the sudden change in speed that may occur in traditional segmented linear path, ensuring the uniformity of coating thickness, especially in large-area continuous spraying scenarios, significantly improving the construction quality of polyurea elastomer anticorrosive coating. The entire process does not require human intervention, and the trajectory generation and optimization are automatically implemented through algorithms, reflecting the intelligent and automated characteristics of the system, which is suitable for spraying operations of different specifications and shapes of pools in sewage treatment plants.

[0143] It should be noted that in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0144] While embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and variations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wastewater treatment plant tank polyurea elastomer anticorrosion spray quality monitoring system, characterized in that, The application relates to a coating quality real-time monitoring and adjusting system for a pool, which comprises the following parts: a thickness detection module for collecting the spraying thickness of the polyurea elastomer coating in real time; a bonding strength detection module for measuring the interface bonding strength of the coating and the pool substrate; a humidity detection module for monitoring the real-time environmental humidity of the spraying area; an evaluation module electrically connected with the thickness detection module, the bonding strength detection module and the humidity detection module, which is used for obtaining a thickness deviation index according to the deviation value of the spraying thickness from a preset thickness threshold value; the evaluation module is also used for substituting the interface bonding strength into a strength model previously established by the evaluation module to obtain a dynamic strength correction coefficient, and determining a coating quality deviation grade according to the linear relationship between the thickness deviation index and the dynamic strength correction coefficient; a central control module electrically connected with the evaluation module, which is used for adjusting the initial value of the spraying pressure of a spraying device according to the coating quality deviation grade; the central control module is also used for determining the lateral movement rate of the spraying path based on the relationship between the real-time environmental humidity and a preset humidity interval; the central control module is also used for secondarily correcting the lateral movement rate according to the thickness deviation index, and generating spraying trajectory optimization parameters based on the corrected lateral movement rate.

2. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 1, wherein, The strength model previously established by the evaluation module comprises the following parts: The evaluation module is also used for obtaining the interface bonding strength data set in the historical spraying data, and performing outlier processing on the data set, wherein the data set comprises the substrate surface roughness, the curing temperature and the corresponding bonding strength; The evaluation module is also used for establishing a multivariate linear regression equation according to the interaction between the substrate surface roughness and the curing temperature; The evaluation module is also used for fitting the coefficient matrix of the linear regression equation by the least square method, and calculating the residual sum of squares; The evaluation module is also used for iteratively optimizing the coefficient matrix based on the relationship between the residual sum of squares and a preset error threshold value, until the residual sum of squares converges in the error threshold value.

3. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 1, wherein, The adjustment of the initial value of the spraying pressure of the spraying device comprises the following parts: The central control module obtains the grade interval corresponding to the coating quality deviation grade, and determines the pressure correction amount according to the relationship between the grade interval and the first pressure adjustment coefficient and the second pressure adjustment coefficient configured by the central control module: When the grade interval belongs to the first deviation grade, the central control module takes the product of a preset reference pressure value and the first pressure adjustment coefficient as the initial value of the spraying pressure; When the grade interval belongs to the second deviation grade, the central control module calculates the pressure compensation amount according to the ratio of the dynamic strength correction coefficient to the reference strength, and takes the algebraic sum of the reference pressure value and the pressure compensation amount as the initial value of the spraying pressure.

4. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 3, wherein, When the central control module calculates the pressure compensation amount, the following steps are included: Obtain the relationship between the ratio and preset first critical ratio and second critical ratio, and select the corresponding compensation algorithm: When the ratio is less than or equal to the first critical ratio, a logarithmic function is used to perform nonlinear mapping on the ratio to generate a compensation factor; When the ratio is greater than a first critical ratio and less than a second critical ratio, a piecewise linear interpolation method is used to generate the compensation factor; When the ratio is greater than or equal to the second critical ratio, an exponential function is used to amplify the ratio to generate the compensation factor.

5. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 4, wherein, When the central control module determines the lateral movement rate, it includes: According to the absolute value of the humidity difference between the real-time environmental humidity and the preset standard humidity, combined with the maximum rate limit value of the spraying device, the initial lateral movement rate is calculated by the following formula: ; wherein is an initial lateral movement rate, is a maximum rate limit value, is a humidity difference absolute value, is a humidity tolerance threshold value.

6. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 5, wherein, When the central control module performs secondary correction on the lateral movement rate, it includes: Obtain the thickness change rate of the current spraying thickness and the previous detection period thickness, and select the rate correction mode according to the comparison result of the thickness change rate and the preset change rate threshold: When the thickness change rate exceeds the positive threshold, the speed correction mode is enabled, and the current speed is decreased by a preset step; When the thickness change rate is lower than the negative threshold, the acceleration correction mode is enabled, and the current speed is adjusted by an exponential increasing algorithm.

7. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 6, wherein, The exponential increasing algorithm specifically includes: According to the continuous The thickness change trend of each detection cycle is analyzed, the trend slope is calculated, and the ratio of the trend slope to a preset slope benchmark value is used as the exponent. The formula iteratively calculates the correction rate, where To correct the rate, Current rate, Base rate coefficient, As a trend influencing factor, This is for counting abnormal cycles.

8. The wastewater treatment plant basin polyurea elastomer anticorrosive spray coating quality monitoring system of claim 7, wherein, The generation of the spraying trajectory optimization parameter includes: The corrected lateral movement rate is decomposed into X-axis and Y-axis component speeds, and a path equation under a two-dimensional coordinate system is established according to the geometric characteristics of the pool, and a continuous and smooth spraying trajectory control point sequence is generated by a cubic spline interpolation method.

9. The wastewater treatment plant tank polyurea elastomer anticorrosive spray coating quality monitoring system of claim 8, wherein, The implementation of the cubic spline interpolation method includes: A cubic polynomial function is constructed in each adjacent control point interval, and boundary conditions are applied to make the second derivative of the entire trajectory continuous, and the coefficient matrix of each interval polynomial is obtained by solving a linear equation system.

10. A method for monitoring the quality of the polyurea elastomer anticorrosive spray coating of a sewage treatment plant tank, suitable for use in the system for monitoring the quality of the polyurea elastomer anticorrosive spray coating of a sewage treatment plant tank according to any one of claims 1 to 9, characterized in that, It includes: Real-time acquisition of spraying thickness, interfacial bonding strength and environmental humidity data of polyurea elastomer coating; Calculate the thickness deviation index and obtain the dynamic strength correction coefficient based on the strength model; Determine the initial value of the spraying pressure and the reference parameter of the lateral movement rate according to the coating quality deviation level; Combine the environmental humidity data to make a primary adjustment to the movement rate, and perform a secondary correction according to the thickness change rate; Generate optimized spraying trajectory control instructions and output to the actuator.

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