Vamp spraying control method and system for intelligent shoemaking production line

Abstract

The invention relates to the technical field of intelligent spraying control, in particular to a vamp spraying control method and system for an intelligent shoemaking production line, and the method comprises the following steps: collecting spraying speed, spraying quantity, pressure and coating quality information, analyzing the influence of parameters on coating uniformity, coverage range and drying speed according to the current spraying effect, and generating a parameter influence data set. According to the method, the influence of each parameter on the coating quality is calculated, the priorities of the parameters in the control process are sequentially determined, and then historical records and current monitoring data are compared, so that abnormal fluctuation can be identified, potential abnormal conditions and positions of future key objects can be predicted according to the data, and fault modes can be identified. And the predictability and the reaction speed of the system are improved. And in combination with an image processing technology, the coating thickness of the vamp spraying area is further analyzed through image comparison, so that the thin coating area, the thick coating area and the spraying missing area can be more accurately detected, and the spraying process is more stable and efficient.

Description

Technical Field

[0001] The present invention relates to the field of intelligent spraying control technology, and in particular to a method and system for spraying control of shoe uppers on an intelligent shoemaking production line. Background Art

[0002] The field of intelligent spray control technology integrates robotics, sensor technology, computer vision and artificial intelligence algorithms to achieve precise control of the spray process. The intelligent spray system can automatically adjust spray parameters such as flow, pressure and nozzle movement speed and path according to different workpiece shapes and sizes to ensure the uniformity and quality of the coating. It is widely used in industries such as automobiles, aviation, furniture and electronics to improve production efficiency, reduce material waste and improve the working environment.

[0003] Among them, the upper spraying control method of the intelligent shoe production line is an intelligent spraying technology applied to footwear manufacturing. During the upper manufacturing process, the coating or pattern is accurately applied to the upper material through the intelligent control of the spraying system. This method can adjust the spraying parameters such as spraying speed, pressure and color mixing according to the material and design requirements of different uppers, thereby achieving efficient and consistent production results.

[0004] The existing technology mainly adapts to the material and design requirements of different uppers by adjusting parameters such as spraying speed, pressure and color mixing during the upper manufacturing process. However, the limitation of this method is that the adjustment of spraying parameters is mostly preset values, which makes the system susceptible to instantaneous fluctuations or sudden abnormalities in actual production. For example, during the production process, abnormal fluctuations in spraying pressure or sudden changes in speed may cause uneven coating, and inconsistent spraying may also occur, especially when the upper material and design requirements change, the system is difficult to respond in time. In addition, although the existing technology can adjust the spraying parameters through intelligent control, it is difficult to predict potential problems in the future based on historical data analysis, which increases uncertainty and downtime in production, and affects the stability of production efficiency and product quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for controlling the spraying of uppers in an intelligent shoe production line.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for controlling the spraying of the upper of an intelligent shoe production line, comprising the following steps: S1: Collect information on spraying speed, spraying volume, pressure and coating quality. According to the current spraying effect, analyze the influence of parameters on coating uniformity, coverage and drying speed, and generate parameter influence data set: S2: Based on the parameter impact data set, the weight values ​​of the parameters are calculated, and the size of each weight is compared, and the weight values ​​are arranged in sequence to determine the priority in the control process, and a parameter weight table is generated: S3: Based on the parameter weight table, according to the currently monitored spraying pressure, nozzle status, and speed change information, the instantaneous fluctuation situation is compared with the historical records, abnormal fluctuations are identified, and spraying behavior trend analysis results are generated; S4: predicting the occurrence time and location of potential abnormalities in the future based on the spraying behavior trend analysis results, identifying the failure mode of the spraying equipment under specified conditions, and generating abnormal pattern recognition results; S5: The acquisition camera takes an image of the sprayed shoe upper, compares the thickness consistency of the sprayed area, analyzes the thin coating, thick coating and missed spraying areas in the image, and generates the spraying image quality assessment result: S6: Based on the spray image quality assessment results and abnormal pattern recognition results, simulate the influence of various spray parameters on the quality of the upper coating, adjust and update the speed and pressure parameters of the spray equipment according to the simulation results, and generate a spray optimization control result.

[0007] The present invention has improvements in that the parameter influence data set includes a set of standardized values ​​of parameters, an influence index of parameters on spraying uniformity and coverage range, and an influence coefficient of parameters on drying speed; the parameter weight table includes a weighted numerical arrangement of spraying speed, spraying amount and pressure; the spraying behavior trend analysis result includes a pressure change curve, a nozzle switch status diagram and a speed change trend diagram; the abnormal pattern recognition result includes a time warning of potential faults, a position identification, and a classification of spraying equipment fault types; the spraying image quality assessment result includes a coating thickness distribution diagram, a regional uniformity evaluation, the coordinates of the spray leakage area, and the proportion data of the thin coating and thick coating areas; the spraying optimization control result includes an adjusted spraying speed range, an optimal pressure setting value, and a spraying angle configuration.

[0008] The present invention is improved in that the spraying speed, spraying amount, pressure and coating quality information are collected, and according to the current spraying effect, the influence of the parameters on the coating uniformity, coverage and drying speed is analyzed. The specific steps of generating the parameter influence data set are as follows: S101: Collect spray speed, spray volume, pressure and coating quality data, eliminate erroneous readings and outliers, verify data integrity, and generate raw data sets; S102: Based on the original data set, adjust the data to the same scale, eliminate the difference between measurement units, and generate a processed data set; S103: Based on the processed data set, according to the current coating uniformity, coverage and drying speed data, analyze the correlation between each parameter and the spraying effect, calculate the influence of the parameter on the coating characteristics, and generate a parameter influence data set.

[0009] The present invention is improved in that, based on the parameter influence data set, the weight value of the parameter is calculated, and the size of each weight is compared, and the weight values ​​are arranged in sequence to determine the priority in the control process. The specific steps of generating the parameter weight table are as follows: S201: extracting the influence intensity of each parameter from the parameter influence data set, determining the relative importance of each parameter in influencing the spraying quality, and generating an initial parameter effect relationship table; S202: Based on the initial parameter effect relationship table, weight is assigned to each parameter, and the weight is matched with the contribution of the parameter to the spraying effect to generate adjusted parameter weight data; S203: Integrate the adjusted parameter weight data, sort the parameter weight values ​​from high to low, determine the priority of the parameters in the spray control strategy, and generate a parameter weight table.

[0010] The present invention is improved in that, based on the parameter weight table, according to the currently monitored spraying pressure, nozzle state, speed change information, the instantaneous fluctuation situation is compared with the historical record, abnormal fluctuation is identified, and the specific steps of generating the spraying behavior trend analysis result are as follows: S301: monitoring the spraying pressure, nozzle state, and speed parameter change data with high weights in the parameter weight table, and recording the real-time fluctuation of the data to generate a real-time monitoring data set; S302: Based on the real-time monitoring data set, compare the historical data records, identify abnormal fluctuations such as pressure drop, nozzle switch instability or speed mutation, mark the abnormal events, and generate an abnormal fluctuation mark set; S303: Analyze the frequency and pattern of abnormal fluctuations using the abnormal fluctuation mark set, determine the impact of abnormal fluctuations on spraying effects, and generate spraying behavior trend analysis results.

[0011] The present invention is improved in that, according to the spraying behavior trend analysis result, the occurrence time and location of potential anomalies in the future are predicted, the failure mode of the spraying equipment under specified conditions is identified, and the specific steps of generating the abnormal pattern recognition result are as follows: S401: creating a data sequence based on the spraying behavior trend analysis result, marking abnormal data as a reference point for future analysis, and generating an abnormal data sequence; S402: classifying the fluctuation information in the abnormal data sequence, associating each type of fluctuation with the spraying problem it may cause, determining the spraying defects caused by each abnormal type, and generating abnormal fluctuation classification results; S403: Based on the abnormal fluctuation classification result, predict the occurrence time and location of potential abnormalities in the future, identify the failure modes under various spraying conditions, and generate abnormal pattern recognition results.

[0012] The improvement of the present invention is that the acquisition camera takes the sprayed shoe upper image, and by comparing the thickness consistency of the sprayed area, the thin coating, thick coating and missed spraying areas in the image are analyzed, and the specific steps of generating the sprayed image quality evaluation result are as follows: S501: collecting continuous image information of the sprayed shoe upper continuously photographed by the camera to generate an original image data set; S502: grayscale the original image data set, remove background noise in the image, optimize edge details of the sprayed area in the image, and generate a processed image data set; S503: Identify the pixel density and distribution differences in the processed image data set, compare the thickness consistency of the spraying area, identify the thin coating, thick coating and missed spraying areas, and generate a spraying image quality assessment result.

[0013] The present invention is improved in that, based on the spraying image quality evaluation result and the abnormal pattern recognition result, the influence of various spraying parameters on the quality of the upper coating is simulated, and the speed and pressure parameters of the spraying equipment are adjusted and updated according to the simulation results. The specific steps of generating the spraying optimization control result are as follows: S601: Based on the spraying image quality assessment result and the abnormal pattern recognition result, three-dimensional digital modeling is performed to simulate the influence of various spraying parameters, including pressure and speed, on the spraying shoe upper, and a three-dimensional simulation effect data set is generated; S602: Determine the influence of various spraying speed and pressure settings on coating uniformity and coverage effect by evaluating the three-dimensional simulation effect data set, and obtain the optimal parameter configuration by comparing and selecting spraying parameters; S603: Apply the optimal parameter configuration to the spraying equipment, adjust and update the parameters, so that each spraying reaches the preset quality standard, and generate a spraying optimization control result.

[0014] Intelligent shoe production line upper spraying control system, the system includes: The data acquisition and processing module collects information on spraying speed, spraying volume, pressure and coating quality, formats and cleans the collected data, analyzes the impact of parameters on coating uniformity, coverage and drying speed, and generates parameter impact data sets; The parameter analysis and weight configuration module calculates the weight values ​​of the parameters based on the parameter influence data set according to the influence size, arranges the weight data, and generates a parameter weight table; The abnormal monitoring and trend analysis module monitors the real-time fluctuation of key parameters based on the parameter weight table, compares and analyzes the detected fluctuations with historical data, identifies potential abnormal fluctuation patterns, and generates spraying behavior trend analysis results; The fault prediction and pattern recognition module builds a data model based on the spraying behavior trend analysis results to predict the time and location of potential abnormalities in the future, determines the fault mode of the spraying equipment under specified conditions, and generates abnormal pattern recognition results; Based on the abnormal pattern recognition results, the parameter optimization and control module simulates the influence of various spraying parameters on the quality of the upper coating according to the image of the sprayed upper taken by the camera, determines the optimal spraying speed and pressure parameters, adjusts and updates the settings of the spraying equipment, and generates a spraying optimization control result.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by calculating the influence of each parameter on the coating quality, and determining their priority in the control process in turn, and then comparing the historical records with the current monitoring data, not only can abnormal fluctuations be identified, but also the potential abnormal conditions and locations of key objects in the future can be predicted based on these data, and the fault mode can be identified. The predictability and response speed of the system are improved. In combination with image processing technology, the coating thickness of the upper spraying area is further analyzed through image comparison, which can more accurately detect thin coating, thick coating and missed spraying areas, making the spraying process more stable and efficient. Finally, based on these evaluation data, the influence of various spraying parameters on the coating quality is simulated, so that the system can automatically adjust and optimize the spraying parameters, ensure the uniformity and consistency of the upper coating, and improve product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention proposes a flow chart of a method for controlling the spraying of uppers on an intelligent shoe production line; Figure 2 This is a detailed flow chart of step S1 of the present invention; Figure 3 This is a detailed flow chart of step S2 of the present invention; Figure 4 This is a detailed flow chart of step S3 of the present invention; Figure 5 This is a detailed flow chart of step S4 of the present invention; Figure 6 This is a detailed flow chart of step S5 of the present invention; Figure 7 This is a detailed flow chart of step S6 of the present invention; Figure 8 The present invention proposes a module diagram of the upper spraying control system of an intelligent shoe production line. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0018] 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", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0019] See also Figure 1 The present invention provides a technical solution: a method for controlling the spraying of the upper of an intelligent shoe production line, comprising the following steps: S1: Collect information on spraying speed, spraying volume, pressure and coating quality. According to the current spraying effect, analyze the influence of parameters on coating uniformity, coverage and drying speed, and generate parameter influence data set: S2: Based on the parameter impact data set, calculate the parameter weight value, compare the size of each weight, arrange the weight values ​​in sequence to determine the priority in the control process, and generate a parameter weight table: S3: Based on the parameter weight table, according to the currently monitored spraying pressure, nozzle status, and speed change information, the instant fluctuation situation is compared with the historical records, abnormal fluctuations are identified, and the spraying behavior trend analysis results are generated; S4: Based on the spraying behavior trend analysis results, predict the time and location of potential abnormalities in the future, identify the failure mode of the spraying equipment under specified conditions, and generate abnormal pattern recognition results; S5: The acquisition camera takes an image of the sprayed shoe upper, compares the thickness consistency of the sprayed area, analyzes the thin coating, thick coating and missed spraying areas in the image, and generates the spraying image quality assessment result: S6: Based on the spraying image quality assessment results and abnormal pattern recognition results, simulate the influence of various spraying parameters on the quality of the upper coating, adjust and update the speed and pressure parameters of the spraying equipment according to the simulation results, and generate spraying optimization control results.

[0020] The parameter influence data set includes a set of standardized parameter values, an index of parameter influence on spray uniformity and coverage, and a coefficient of parameter influence on drying speed. The parameter weight table includes a numerical arrangement of weights for spray speed, spray volume, and pressure. The spray behavior trend analysis results include a pressure change curve, a nozzle switch status diagram, and a speed change trend diagram. The abnormal pattern recognition results include time warnings of potential faults, location identification, and classification of spray equipment fault types. The spray image quality assessment results include a coating thickness distribution diagram, regional uniformity evaluation, coordinates of missed spray areas, and proportion data of thin and thick coating areas. The spray optimization control results include the adjusted spray speed range, optimal pressure setting value, and spray angle configuration.

[0021] See also Figure 2 , collect spraying speed, spraying amount, pressure and coating quality information, analyze the influence of parameters on coating uniformity, coverage and drying speed according to the current spraying effect, and generate parameter influence data set in the following specific steps: S101: Collect spray speed, spray volume, pressure and coating quality data, eliminate erroneous readings and outliers, verify data integrity, and generate raw data sets; During the collection process, the original collection of spraying speed, spraying volume, pressure and coating quality data is often accompanied by noise and errors. To ensure data quality, data cleaning technology is first applied to eliminate obvious erroneous readings and outliers. This includes using statistical methods to identify and process outliers, such as using box plots to identify data points that are beyond the quartile range and eliminate them. Next, data integrity checks are used to check whether each necessary field has a value or whether the data type of the field conforms to the predefined format, thereby effectively cleaning and verifying the data to ensure the accuracy and reliability of subsequent analysis.

[0022] S102: Based on the original data set, adjust the data to the same scale, eliminate the difference between the measurement units, and generate a processed data set; After obtaining the raw data set that has been preliminarily cleaned, in order to eliminate the scale differences between the spray speed, spray amount, pressure and coating quality data, all data need to be adjusted to the same scale. The Z-score standardization method can be selected, that is, each eigenvalue of the data is subtracted from its mean and divided by the standard deviation, so that the processed data set has zero mean and unit variance. In addition, by converting data between different measurement units, such as converting the unit of speed from m / s to cm / s, ensure that all data are compared and analyzed under the same standard.

[0023] S103: Based on the processed data set, according to the current coating uniformity, coverage and drying speed data, analyze the correlation between each parameter and the spraying effect, calculate the influence of the parameter on the coating characteristics, and generate a parameter influence data set; According to the processed data set, the correlation between each parameter and the spraying effect is analyzed according to the formula: , calculate the influence of each parameter on the coating characteristics. Where, Represents the regression coefficient, indicating the parameter And spray effect The strength and direction of the linear relationship between Representative Observed parameter values, such as spraying speed, Representative Evaluation index values ​​of spraying effect, such as coating uniformity, and All and The average of the observed values. These values ​​are obtained through data collection and then preprocessed to ensure accuracy and consistency.

[0024] Taking the effect of spraying speed on coating uniformity as an example, the spraying speed values ​​collected from the data set are m / s, the corresponding coating uniformity score is .

[0025] First calculate and The mean m / s, . Then substitute each observation into the formula and calculate the difference :

[0026] calculate :

[0027] calculate :

[0028] result:

[0029] result It shows that for every unit increase in spray speed, the coating uniformity score will increase by about The calculated regression coefficient Provides a quantitative indicator to evaluate the linear relationship between parameters and spraying effect. For example, in the example of spraying speed and coating uniformity, The value of 0.092 indicates that the spraying speed is positively correlated with the coating uniformity, that is, as the spraying speed increases, the coating uniformity will also improve. The value can be used to determine which parameters are the key factors affecting the spraying effect. If the value is large, it indicates that it has a significant impact on the spraying effect, and this parameter should be adjusted first to obtain the best spraying quality. If the value is close to zero or very small, it means that it has little effect on the spraying effect, and you can consider focusing your adjustment efforts on more critical parameters. In addition, by comparing the parameter settings in the actual spraying operation with the regression analysis results, possible deviations and deficiencies can be found. For example, if the spraying speed is low in actual operation, but the regression analysis shows that increasing the spraying speed can significantly improve the coating uniformity, it can be recommended to adjust the setting range of the spraying speed in order to achieve a higher quality production effect.

[0030] See also Figure 3 , based on the parameter impact data set, calculate the parameter weight value, compare the size of each weight, and arrange the weight values ​​in sequence to determine the priority in the control process. The specific steps for generating the parameter weight table are as follows: S201: extracting the influence intensity of each parameter from the parameter influence data set, determining the relative importance of each parameter in influencing the spraying quality, and generating an initial parameter effect relationship table; First, the influence intensity of each parameter is extracted. Data analysis technology is needed to quantify the relationship between each parameter and the spraying quality. The information entropy of each parameter is calculated to evaluate its influence on the spraying quality. Information entropy, as an indicator of data variability, can effectively represent the uncertainty and influence of parameters. The influence intensity of each parameter is determined by the size of its variability in the data set. A higher information entropy means that the parameter has a greater impact on the result during the spraying process. Therefore, the relative importance is determined. These steps are implemented through statistical software and data processing algorithms, such as using Python's Pandas library to obtain the standard deviation and coefficient of variation, and listing each parameter and its influence intensity on the spraying quality.

[0031] S202: Based on the initial parameter effect relationship table, weights are assigned to each parameter, the weights are matched with the contribution of the parameters to the spraying effect, and adjusted parameter weight data are generated; For weight distribution, according to the formula , calculate the weight of each parameter, where Representation parameters The weight is used to indicate the relative importance of this parameter in the spraying effect. Representation parameters The information entropy is used to quantify the uncertainty and variability of the parameter during the spraying process. A high information entropy indicates that the change of the parameter during the spraying process has a greater impact on the final effect. is information entropy The natural exponent of is used to amplify the information entropy value and make the weight distribution more sensitive to the high entropy value parameters. Represents all parameters, information entropy The calculation is based on the statistical data analysis of the relationship between each parameter and the spraying quality, and is obtained from historical data according to the discreteness of the influence of each parameter on the spraying quality.

[0032] If there are four parameters, the information entropy values ​​are .

[0033] Calculate the exponential value of each information entropy:

[0034] Compute the sum of these exponential values:

[0035] Calculate the weight of each parameter:

[0036]

[0037]

[0038]

[0039] The weight distribution results show that the second parameter has the highest information entropy and has the greatest impact on the spraying effect, so it should be given priority when formulating the spraying control strategy. This is consistent with the observation that this parameter often changes in historical data and has a high correlation with the spraying quality. In this way, it can ensure that the control strategy of the spraying process is more focused on key parameters, optimizing resource allocation and operating efficiency.

[0040] S203: Integrate the adjusted parameter weight data, sort the parameter weight values ​​from high to low, determine the priority of the parameters in the spraying control strategy, and generate a parameter weight table; After integrating the adjusted parameter weight data, the parameter weight values ​​need to be sorted from high to low. Use data sorting algorithms, such as quick sort or merge sort, to ensure that the parameters are correctly arranged according to their weights. During the sorting process, parameters with high weights are placed at the front of the table, indicating that they have a higher priority in spray control.

[0041] See also Figure 4 , based on the parameter weight table, according to the currently monitored spraying pressure, nozzle status, speed change information, compare the immediate fluctuation situation with the historical records, identify abnormal fluctuations, and generate the specific steps of spraying behavior trend analysis results as follows: S301: monitoring the spraying pressure, nozzle state, and speed parameter change data with high weights in the parameter weight table, and recording the real-time fluctuation of the data to generate a real-time monitoring data set; High-weight parameters such as spray pressure, nozzle status, and speed changes are key monitoring points. The system monitors the changes in these parameters in real time to capture immediate fluctuations in the data. In particular, the monitoring equipment records the real-time readings of each parameter. The system uses a sliding window averaging algorithm to smooth data fluctuations. The sliding window algorithm eliminates errors caused by short-term fluctuations by smoothing the data within a continuous time period. The length of the sliding window is determined based on historical data and is usually set to the number of data within a certain time window (such as 5-second or 10-second readings). The system continuously rolls and calculates new window values, updates the smoothed monitoring data in real time, ensures data accuracy and reliability, and generates a real-time monitoring data set. This data set provides a basis for subsequent analysis, allowing the system to respond more quickly to possible equipment failures or process deviations.

[0042] S302: Based on the real-time monitoring data set, compare the historical data records, identify abnormal fluctuations such as pressure drop, nozzle switch instability or speed mutation, mark the abnormal events, and generate an abnormal fluctuation mark set; By comparing historical data, threshold detection algorithms and autoregressive moving average models are used to identify abnormal fluctuations such as pressure drops, unstable nozzle switches, or sudden speed changes. First, through the threshold detection algorithm, when the spraying pressure or speed exceeds the set normal range (for example, the speed exceeds the range of 3 standard deviations or the pressure drops by more than 10%), the system will mark the abnormal event. Next, the system analyzes the time series trend of the data through the ARMA model. The ARMA model predicts normal parameter changes by combining autoregressive terms (past observations) and moving average terms (prediction error terms). If the detected change value deviates significantly from the predicted value of the ARMA model, the point is identified as an abnormal event. Threshold detection is used to capture instantaneous fluctuations, while the ARMA model is used to capture more complex abnormal fluctuation behaviors in time series.

[0043] S303: using the abnormal fluctuation mark set, analyzing the frequency and pattern of the abnormal fluctuation, determining the impact of the abnormal fluctuation on the spraying effect, and generating a spraying behavior trend analysis result; Cluster analysis and anomaly detection algorithms are used to analyze the frequency and pattern of fluctuations. K-means clustering is used to classify normal working conditions and abnormal fluctuation conditions. The K-means algorithm divides data points into several categories based on the characteristics of fluctuations (such as fluctuation amplitude, duration, etc.) to help identify typical abnormal patterns. At the same time, the anomaly detection algorithm is used to further detect outliers. By randomly selecting features and recursively splitting the data set according to the threshold of the features, outliers will be separated in a shorter path and marked as abnormal points. Through the combination of these two algorithms, the system can accurately identify common abnormal fluctuation patterns and analyze the impact of their frequency on the spraying effect.

[0044] See also Figure 5 ,According to the spraying behavior trend analysis results, the occurrence time and location of potential anomalies in the future are predicted, and the failure mode of the spraying equipment under specified conditions is identified. The specific steps for generating abnormal pattern recognition results are as follows: S401: Based on the spraying behavior trend analysis results, a data sequence is created, abnormal data is marked as a reference point for future analysis, and an abnormal data sequence is generated; By processing all the collected abnormal fluctuation data, we first create a time series data set and arrange these fluctuation events in chronological order so that the order and duration of these events can be quickly determined in subsequent analysis. In addition, by marking and classifying abnormal events, we can better define future benchmarks, which become the basis for identifying similar fluctuation behaviors in the future. The generated abnormal data sequence contains different types of fluctuations and corresponding time information. The system marks the abnormal data as a reference for future analysis to help improve the accuracy of predictions and provide a basis for the handling of anomalies, ensuring that these abnormal events can be responded to quickly in future monitoring.

[0045] S402: classifying the fluctuation information in the abnormal data sequence, associating each type of fluctuation with the spraying problem it may cause, determining the spraying defects caused by each abnormal type, and generating abnormal fluctuation classification results; The classification of fluctuation information in abnormal data sequences is based on the identification of each abnormal type and the correlation analysis of the spraying defects they may cause. The system adopts a decision tree classification algorithm. By training historical data, it learns how the characteristics of each abnormal fluctuation, such as pressure fluctuation and speed mutation, are associated with spraying defects such as uneven coating, missed spraying, overspraying, etc. During the classification process, the system will automatically assign them to preset categories according to different fluctuation amplitudes, durations and frequencies of abnormal occurrence. The system quickly determines the spraying problem corresponding to each type of fluctuation through the branching structure of the decision tree. The generated abnormal fluctuation classification results clearly associate each fluctuation with the spraying defect, thereby providing a reference for subsequent predictions and problem handling.

[0046] S403: Based on the abnormal fluctuation classification results, predict the occurrence time and location of potential abnormalities in the future, identify the failure modes under various spraying conditions, and generate abnormal pattern recognition results; For identification of failure modes under various spraying conditions, the formula is used:

[0047] Calculating future time The probability of abnormal fluctuations ; in, is the time interval between two consecutive time steps, usually determined by the sampling frequency of the monitoring equipment. This probability value estimates future abnormal risks through current parameters and their changing trends, and the value range is between 0 and 1. Represents the current moment The probability of abnormal occurrence under the condition is obtained through the statistics of historical data and current parameter status, usually ranging from 0 to 1. Is an influencing factor The weight coefficient is determined by regression analysis of historical data or machine learning methods. is the current time Next The instantaneous value of each parameter (such as spraying pressure, nozzle status, speed) is obtained by the system monitoring equipment. It is the total number of parameters, indicating the number of all relevant parameters (such as spraying pressure, nozzle status, speed, etc.) monitored in the system, obtained through system monitoring. Indicates The increment of a parameter, that is, from the previous time step to the current time The change in , used to capture short-term fluctuations, is the weight of the incremental term, which is determined by analyzing the correlation between historical abnormal fluctuations and parameter changes, and adjusting the impact of short-term changes in parameters on the probability of abnormal occurrence. represents the rate of change of the parameter, It is The time derivative of a parameter represents the rate of change of the parameter over time, which is calculated by the finite difference method or automatic differentiation method. It is the weight of the rate term, which captures the impact of long-term trend changes in parameters on future anomalies and is determined through historical data analysis.

[0048] The weight coefficient obtained from historical data analysis is: , , , .

[0049] Current time point Spraying pressure , nozzle status , whose increment is and , rate of change and If the current abnormality occurs, the probability .

[0050] Substituting the values ​​into the formula:

[0051]

[0052]

[0053] Since the probability cannot exceed 1, the system corrects it to 1. Indicates the future time The probability of abnormal fluctuation is very high, close to 100%. Through this prediction result, the system can identify the possible fault location in advance, help operators to perform preventive maintenance, and provide an effective early warning mechanism for potential faults in the production process. If the average probability of abnormal fluctuation under similar conditions in the past was 0.8, the predicted value this time indicates that the probability of fluctuation is significantly increased, so more active intervention measures should be taken to perform preventive maintenance and parameter adjustments.

[0054] See also Figure 6 , the acquisition camera takes the sprayed shoe upper image, and by comparing the thickness consistency of the sprayed area, analyzes the thin coating, thick coating and missed spraying areas in the image, and generates the spraying image quality assessment results in the following specific steps: S501: collecting continuous image information of the sprayed shoe upper continuously photographed by the camera to generate an original image data set; The camera continuously shoots the sprayed shoe upper to obtain continuous image information and convert it into a raw image data set. The collected image data includes every step and angle in the spraying process. The image acquisition device in the system uses a high-resolution industrial camera to ensure that every detail is captured. The data acquisition frequency is adjusted according to the spraying speed, usually multiple frames per second. The system stores these images in chronological order and attaches spraying parameters such as pressure, speed and temperature. The image data set includes a complete timestamp and parameter record, which provides complete data for subsequent image processing and analysis, ensuring the traceability of the spraying process.

[0055] S502: grayscale the original image data set, remove background noise in the image, optimize edge details of the sprayed area in the image, and generate a processed image data set; First, the image is grayed out, and the image preprocessing algorithm is used to convert the color image into a grayscale image, retaining the important details of the sprayed area. Then, the Gaussian filtering algorithm is used to remove the background noise and subtle interference in the image. The convolution kernel operation of the algorithm is used to smooth the noise points in the image. Subsequently, the Canny edge detection algorithm is used to detect and optimize the edges of the sprayed area, enhance the edge details of the sprayed area, and ensure that the outline and boundary of the sprayed area can be accurately identified in subsequent analysis. This dataset provides an optimized image basis for subsequent quality assessment and detection.

[0056] S503: Identify the pixel density and distribution differences in the processed image data set, compare the thickness consistency of the spraying area, identify the thin coating, thick coating and missed spraying areas, and generate a spraying image quality assessment result; Identify the differences in pixel density and distribution in the processed image dataset, according to the formula: Calculate the thickness consistency of the sprayed area, where It represents the average value of pixel density difference within the spraying area, which is used to measure the consistency of spraying thickness. The smaller the value, the more uniform the spraying. is the total number of pixels in the spray area, which is automatically determined by the system's image processing and spray area segmentation algorithm. It is The brightness value (grayscale value) of each pixel is extracted through image processing technology. The brightness value represents the relative density of the spraying thickness. The larger the value, the greater the relative thickness. It is the average brightness value of all pixels in the spraying area, which is used to represent the average thickness of the area. The calculation formula is: , the overall thickness of the spraying is estimated by the average brightness value of all pixels. :Indicates the The brightness value of each pixel and the average brightness value of the area The absolute difference is used to indicate the deviation of the pixel relative to the overall thickness.

[0057] Assuming that there are 5 pixels in the spraying area, the brightness values ​​of these pixels obtained through image processing are: Each brightness value Indicates the spray thickness of the corresponding pixel.

[0058] Calculate the average brightness

[0059]

[0060]

[0061] Average brightness value , indicating the standard thickness of the spraying area.

[0062] Using the formula , calculate the difference between each pixel and the mean:

[0063]

[0064] According to the formula , add up these differences and calculate the average:

[0065] Calculated spray thickness difference , indicating that there is a certain difference in the spray thickness in this area, and the difference is large, and there may be thick or thin coating phenomena.

[0066] Thin coating area: pixel value is The number of pixels is significantly lower than the average , indicating that the pixel may correspond to a thin coating area.

[0067] Thick Paint Area: Pixel value is The number of pixels is significantly higher than the average , indicating that the pixel corresponds to the thick coating area.

[0068] Missing area: If the value of some pixels is close to zero (such as or ), it may be identified as a missed area, but there is no missed area in this example.

[0069] See also Figure 7 ,Based on the spraying image quality evaluation results and abnormal pattern recognition results,,the influence of various spraying parameters on the quality of the upper coating is simulated,,and the speed and pressure parameters of the spraying equipment are adjusted and updated according to the simulation results,,and the specific steps for generating the spraying optimization control results are as follows: S601: Based on the spraying image quality assessment results and abnormal pattern recognition results, three-dimensional digital modeling is performed to simulate the effects of various spraying parameters, including pressure and speed, on the spraying shoe upper, and a three-dimensional simulation effect data set is generated; The three-dimensional model of the shoe upper is generated by three-dimensional digital modeling software. The three-dimensional modeling requires the input of the geometric size and shape data of the shoe upper, which is obtained through a laser scanner to obtain a high-precision geometric mesh model. On this basis, the system will simulate the influence of different pressure, speed and other parameters on the shoe upper coating during the spraying process. Specific parameters such as spraying pressure are collected by sensors on the equipment and transmitted to the system for simulation. The spraying speed is determined by the nozzle movement speed, which is also used as an input parameter for the simulation. The system will simulate the spraying thickness, coating uniformity and shoe upper coverage in three-dimensional space based on these parameters, and generate a three-dimensional simulation effect data set through different parameter combinations. The data set records the changes in coating thickness, surface flatness, coating adhesion and coverage distribution under different conditions.

[0070] S602: Determine the effects of various spraying speed and pressure settings on coating uniformity and coverage by evaluating the three-dimensional simulation effect data set, and obtain the optimal parameter configuration by comparing and selecting spraying parameters; First, multiple combinations of spraying speeds and pressures are selected and simulated in turn. The spraying speed is controlled by the moving speed of the nozzle, and the pressure is detected by the sensor of the equipment. A set of simulation data is generated for each combination. These data will record the thickness, uniformity and coverage effect of the coating. The thickness uniformity of the coating can be obtained through statistical methods such as calculating the standard deviation. In addition, the coverage effect can be determined by analyzing the distribution range of the coating and the adhesion of the coating in different areas. The system compares each set of data to determine which combination of spraying speed and pressure performs best in uniformity and coverage, and selects the parameters with the smallest standard deviation and the widest coverage as the optimal parameter configuration.

[0071] S603: Apply the optimal parameter configuration to the spraying equipment, adjust and update the parameters, so that each spraying reaches the preset quality standard, and generate the spraying optimization control result; After uploading the parameters to the control system of the spraying equipment, the spraying equipment will automatically adjust the spraying speed and pressure to meet the preset optimal parameters. The adjusted parameters such as spraying speed will control the movement speed of the nozzle through the stepper motor, and the spraying pressure will be adjusted in real time through the air pressure regulating device. These parameters are continuously monitored to ensure that various indicators in the spraying process remain within the optimal range. If a parameter deviation is detected during the spraying process, such as a slowdown in the spraying speed or excessive pressure fluctuations, the system will immediately make adjustments through the feedback mechanism to ensure that the coating thickness and coverage effect meet the preset quality standards, and finally record the results of the spraying optimization control.

[0072] See also Figure 8 , intelligent shoe production line upper spraying control system, the system includes: The data acquisition and processing module collects information on spraying speed, spraying volume, pressure and coating quality, formats and cleans the collected data, analyzes the impact of parameters on coating uniformity, coverage and drying speed, and generates parameter impact data sets; The parameter analysis and weight configuration module calculates the weight value of the parameter based on the parameter impact data set according to the influence size, arranges the weight data, and generates a parameter weight table; The abnormal monitoring and trend analysis module monitors the real-time fluctuation of key parameters based on the parameter weight table, compares and analyzes the detected fluctuations with historical data, identifies potential abnormal fluctuation patterns, and generates trend analysis results of spraying behavior; The fault prediction and pattern recognition module builds a data model based on the spraying behavior trend analysis results to predict the time and location of potential abnormalities in the future, determine the failure mode of the spraying equipment under specified conditions, and generate abnormal pattern recognition results; Based on the abnormal pattern recognition results, the parameter optimization and control module simulates the influence of various spraying parameters on the quality of the upper coating according to the image of the sprayed upper taken by the camera, determines the optimal spraying speed and pressure parameters, adjusts and updates the settings of the spraying equipment, and generates spraying optimization control results.

[0073] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Intelligent shoe production line upper spraying control method, characterized in that: The following steps are involved: Collect information on spraying speed, spraying volume, pressure and coating quality. According to the current spraying effect, analyze the influence of parameters on coating uniformity, coverage and drying speed, and generate parameter influence data set: Based on the parameter impact data set, the weight values ​​of the parameters are calculated, and the size of each weight is compared. The weight values ​​are arranged in sequence to determine the priority in the control process, and a parameter weight table is generated: Based on the parameter weight table, according to the currently monitored spraying pressure, nozzle status, and speed change information, the instantaneous fluctuation situation is compared with the historical records, abnormal fluctuations are identified, and the spraying behavior trend analysis results are generated; According to the spraying behavior trend analysis results, predict the occurrence time and location of potential anomalies in the future, identify the failure mode of the spraying equipment under specified conditions, and generate an abnormal pattern recognition result; The acquisition camera takes images of the sprayed shoe uppers, compares the thickness consistency of the sprayed areas, analyzes the thin coating, thick coating and missed spraying areas in the image, and generates spraying image quality assessment results: Based on the spray image quality assessment results and abnormal pattern recognition results, the influence of various spray parameters on the quality of the upper coating is simulated, and the speed and pressure parameters of the spray equipment are adjusted and updated according to the simulation results to generate spray optimization control results.

2. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: The parameter influence data set includes a set of standardized parameter values, an index of the influence of the parameters on spraying uniformity and coverage, and an influence coefficient of the parameters on drying speed. The parameter weight table includes a weighted numerical arrangement of spraying speed, spraying amount and pressure. The spraying behavior trend analysis results include a pressure change curve, a nozzle switch status diagram and a speed change trend diagram. The abnormal pattern recognition results include a time warning of potential faults, a location identification, and a classification of spraying equipment fault types. The spraying image quality assessment results include a coating thickness distribution diagram, a regional uniformity evaluation, the coordinates of the missed spraying area, and the proportion data of thin and thick coating areas. The spraying optimization control results include an adjusted spraying speed range, an optimal pressure setting value, and a spraying angle configuration.

3. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: Collect information on spraying speed, spraying volume, pressure and coating quality. According to the current spraying effect, analyze the influence of parameters on coating uniformity, coverage and drying speed. The specific steps to generate parameter influence data set are as follows: Collect spray speed, spray volume, pressure and coating quality data, eliminate false readings and outliers, verify data integrity, and generate raw data sets; Based on the original data set, adjusting the data to the same scale, eliminating the difference between measurement units, and generating a processed data set; Based on the processed data set, according to the current coating uniformity, coverage and drying speed data, the correlation between each parameter and the spraying effect is analyzed, the influence of the parameter on the coating characteristics is calculated, and the parameter influence data set is generated.

4. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: Based on the parameter impact data set, the weight values ​​of the parameters are calculated, and the size of each weight is compared, and the weight values ​​are arranged in sequence to determine the priority in the control process. The specific steps for generating the parameter weight table are as follows: Extracting the influence intensity of each parameter from the parameter influence data set, determining the relative importance of each parameter in influencing the spraying quality, and generating an initial parameter effect relationship table; Based on the initial parameter effect relationship table, weights are assigned to each parameter, and the weights are matched with the contribution of the parameters to the spraying effect to generate adjusted parameter weight data; The adjusted parameter weight data are integrated, the parameter weight values ​​are sorted from high to low, the priority of the parameters in the spray control strategy is determined, and a parameter weight table is generated.

5. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: Based on the parameter weight table, according to the currently monitored spraying pressure, nozzle status, speed change information, the instant fluctuation situation is compared with the historical records, abnormal fluctuations are identified, and the specific steps of generating the spraying behavior trend analysis results are as follows: Monitor the spraying pressure, nozzle state, and speed parameter change data with high weights in the parameter weight table, and record the real-time fluctuation of the data to generate a real-time monitoring data set; Based on the real-time monitoring data set, compare the historical data records, identify abnormal fluctuations such as pressure drop, nozzle switch instability or speed mutation, mark the abnormal events, and generate an abnormal fluctuation mark set; The abnormal fluctuation mark set is used to analyze the frequency and pattern of the abnormal fluctuation, determine the impact of the abnormal fluctuation on the spraying effect, and generate a spraying behavior trend analysis result.

6. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: According to the spraying behavior trend analysis results, the specific steps of predicting the occurrence time and location of potential abnormalities in the future, identifying the failure mode of the spraying equipment under specified conditions, and generating abnormal pattern recognition results are as follows: Based on the spraying behavior trend analysis results, a data sequence is created, abnormal data is marked as a reference point for future analysis, and an abnormal data sequence is generated; Classifying the fluctuation information in the abnormal data sequence, associating each type of fluctuation with the spraying problem it may cause, determining the spraying defects caused by each abnormal type, and generating abnormal fluctuation classification results; Based on the abnormal fluctuation classification results, the occurrence time and location of potential abnormalities in the future are predicted, the failure modes under various spraying conditions are identified, and the abnormal pattern recognition results are generated.

7. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: For identification of failure modes under various spraying conditions, the formula is used: Calculating future time The probability of abnormal fluctuations ; in, is the time interval between two consecutive time steps, Represents the current moment The probability of abnormal occurrence under Is an influencing factor The weight coefficient of is the current time Next The instantaneous value of a parameter, is the total number of parameters, Indicates The increment of the parameter, is the weight of the incremental term, It is The time derivative of a parameter, is the weight of the rate term.

8. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: The acquisition camera takes the sprayed shoe upper image, compares the thickness consistency of the sprayed area, analyzes the thin coating, thick coating and missed spraying areas in the image, and generates the spraying image quality assessment results in the following specific steps: Collect continuous image information of the sprayed shoe upper continuously shot by the camera to generate a raw image data set; grayscale the original image data set, remove background noise in the image, optimize edge details of the sprayed area in the image, and generate a processed image data set; Identify the pixel density and distribution differences in the processed image data set, compare the thickness consistency of the spraying area, identify the thin coating, thick coating and missed spraying areas, and generate a spraying image quality assessment result.

9. The method for controlling the spraying of the upper of the intelligent shoe production line according to claim 1 is characterized in that: Based on the spraying image quality evaluation results and abnormal pattern recognition results, the specific steps of simulating the influence of various spraying parameters on the quality of the upper coating, adjusting and updating the speed and pressure parameters of the spraying equipment according to the simulation results, and generating the spraying optimization control results are as follows: Based on the spraying image quality assessment result and the abnormal pattern recognition result, three-dimensional digital modeling is performed to simulate the effects of various spraying parameters, including pressure and speed, on the spraying shoe upper, and a three-dimensional simulation effect data set is generated; By evaluating the three-dimensional simulation effect data set, determining the effects of various spraying speed and pressure settings on coating uniformity and coverage, and obtaining the optimal parameter configuration by comparing and selecting spraying parameters; The optimal parameter configuration is applied to the spraying equipment to adjust and update the parameters so that each spraying can meet the preset quality standards and generate a spraying optimization control result.

10. Intelligent shoe production line upper spraying control system, characterized in that: According to the intelligent shoe production line upper spraying control method according to any one of claims 1 to 9, the system comprises: The data acquisition and processing module collects information on spraying speed, spraying volume, pressure and coating quality, formats and cleans the collected data, analyzes the impact of parameters on coating uniformity, coverage and drying speed, and generates parameter impact data sets; The parameter analysis and weight configuration module calculates the weight values ​​of the parameters based on the parameter influence data set according to the influence size, arranges the weight data, and generates a parameter weight table; The abnormal monitoring and trend analysis module monitors the real-time fluctuation of key parameters based on the parameter weight table, compares and analyzes the detected fluctuations with historical data, identifies potential abnormal fluctuation patterns, and generates spraying behavior trend analysis results; The fault prediction and pattern recognition module builds a data model based on the spraying behavior trend analysis results to predict the time and location of potential abnormalities in the future, determines the fault mode of the spraying equipment under specified conditions, and generates abnormal pattern recognition results; Based on the abnormal pattern recognition results, the parameter optimization and control module simulates the influence of various spraying parameters on the quality of the upper coating according to the image of the sprayed upper taken by the camera, determines the optimal spraying speed and pressure parameters, adjusts and updates the settings of the spraying equipment, and generates a spraying optimization control result.

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