Ultrathin high-pressure aerosol can production control system based on intelligent control integration

Abstract

The invention discloses an ultrathin high-pressure aerosol can production control system based on intelligent control integration, and relates to the technical field of aerosol can production, the ultrathin high-pressure aerosol can production control system comprises a data acquisition module, the data acquisition module is used for acquiring parameter data in the aerosol can production process and image data of the aerosol can surface; the remote control module is used for adjusting parameters in the production process of the aerosol can; the image processing module is used for performing identification according to the image data of the data acquisition module to obtain coating defect information of the aerosol can; optimization of the production process and fault prevention are achieved, the remote control module ensures accurate adjustment of production line parameters, the integrated intelligent control scheme not only improves the production efficiency and the product quality, but also effectively avoids the problems of surface weld joints and internal airtightness of aerosol cans, reduces uncertainty and risks in production, and improves the production efficiency and the product quality. And the consistency and the reliability of the product are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of aerosol can production, and more specifically to an ultra-thin high-pressure aerosol can production control system based on intelligent control integration. Background Art

[0002] The main components of an aerosol can include the can body metal sheet, valve assembly, actuator and propellant gas. The metal sheet, usually made of aluminum or steel, is processed and cut to the appropriate size to form the can body; The sheet metal is fed into a press and formed into a cylindrical shape through a series of punching and forming operations. The formed can can be trimmed to obtain accurate dimensions and smooth edges; The inside of the can may be coated with a protective lining to prevent chemical reactions with the metal; The outside of the can can be printed with brand logos, product information and decorative designs using specialized printing techniques such as lithography or digital printing; Due to the ultra-thin and ultra-pressure characteristics of ultra-thin high-pressure aerosol cans, during the production process, there are higher requirements for the uniformity, coverage and air tightness of the can surface coating; However, in actual use, the integrity of weld patch coating refers to the coverage of the weld patch coating and the adhesion between the coating and the substrate, which is crucial to protect the weld and its surrounding areas from corrosion and environmental damage. Incomplete weld patch coating may cause defects such as pores, inclusions, cracks, or blistering, peeling, pitting and other defects on the coating surface. If these defects are not discovered and handled in time, they may affect the sealing and safety performance of the aerosol can, and may even cause serious accidents such as the explosion of the aerosol can. Aerosol cans need to withstand a certain amount of pressure during use, so their sealing performance has an important impact on their safety performance and service life. If the airtightness is poor, it may cause excessive pressure or leakage inside the aerosol can, thus causing a safety accident. Summary of the invention

[0003] In order to solve the above problems, the present invention provides an ultra-thin high-pressure aerosol can production control system based on intelligent control integration.

[0004] An ultra-thin high-pressure aerosol can production control system based on intelligent control integration, comprising a data acquisition module, wherein the data acquisition module is used to collect parameter data in the aerosol can production process and image data on the surface of the aerosol can; A remote control module, the remote control module is used to adjust parameters in the aerosol can production process; An image processing module, the image processing module is used to identify the image data of the data acquisition module to obtain the coating defect information of the aerosol can; A data analysis module, the data analysis module is used to predict potential fault information in the aerosol can production process based on the parameter data of the data acquisition module and the coating defect information of the image processing module; A remote maintenance module, the remote maintenance module comprising a first maintenance unit, a second maintenance unit and a third maintenance unit, the first maintenance unit being used to analyze the parameter data of the data acquisition module, generate feedback information, and transmit the feedback information to the remote control module to adjust the production parameters of the aerosol can; The second maintenance unit is used to analyze the image data of the data acquisition module, and adjust the acquisition method of the image data through the remote control module according to the analysis result; The third maintenance unit is used to perform corresponding processing according to the fault information of the data analysis module.

[0005] Preferably, the data acquisition module includes a first acquisition unit and a second acquisition unit, wherein the first acquisition unit is used to collect parameter data during the production process of the aerosol can, specifically: Install temperature, pressure and humidity sensors at corresponding locations on the aerosol can production line; Start the temperature sensor to record the temperature data at different production stages; Start the pressure sensor to record the pressure data during the filling and sealing stages; Start the humidity sensor to record humidity data at different production stages; The speed sensors installed on the welding machine and coating equipment can monitor the welding speed and the moving speed of the coating equipment in real time; All the above collected data are uniformly marked as parameter data in the production process.

[0006] The second acquisition unit is used to acquire image data of the surface of the aerosol can.

[0007] Preferably, the specific working steps of the second acquisition unit include: First install and calibrate the industrial camera; Then the industrial camera is started to collect images of the weld and its repair coating on the surface of the aerosol can; The image is then preprocessed, and finally the collected image data is transmitted to the image processing module.

[0008] Preferably, the remote control module comprises a communication unit, an execution unit and a control unit, wherein the communication unit is used to receive an adjustment signal of the remote maintenance module; The control unit is used to generate a control command according to the adjustment signal; The execution unit is used for executing the control command.

[0009] Preferably, the specific working mode of the image processing module is as follows: Acquiring the aerosol can surface image data transmitted by the data acquisition module; Convert each image in the image data into a grayscale image and define the parameters of the gray-level co-occurrence matrix; For each pixel in the image, find its adjacent pixels at the specified distance and angle, and record the grayscale values ​​of these pixel pairs; Extract image texture features from the gray-level co-occurrence matrix, including angular second-order moment, contrast, uniformity, entropy, and correlation; The reference value of the normal weld texture characteristics on the surface of the aerosol can is set in advance; If the angular second-order moment in the image is greater than the reference value, it means that there are no obvious pores or clamp holes on the surface of the aerosol can; If it is less than the reference value, it indicates that there are peeling or pitting on the surface of the aerosol can; If the contrast in the image is greater than the reference value, it means that there are cracks or peeling on the surface of the aerosol can; If it is less than the reference value, it means that the local grayscale of the image changes slowly, indicating that it is normal; If the uniformity in the image is greater than the reference value, it means that the distribution of pixel pairs in the image texture is relatively uniform, indicating normality; If it is less than the reference value, it indicates that the coating on the surface of the aerosol can is bubbling or peeling off; the distribution of pixel pairs in the image texture is uneven; If the entropy in the image is greater than the baseline value, the larger entropy value indicates that the texture complexity in the image is high, indicating that there are pitting or peeling on the coating surface; If it is less than the benchmark value, it is normal; If the correlation in the image is greater than the baseline value: it indicates that there are cracks on the surface of the aerosol can; If it is less than the reference value, it indicates that there are randomly distributed pores or inclusions on the surface of the aerosol can; If it is equal to the reference value, the surface of the aerosol can is normal.

[0010] Preferably, the texture features of the image, including angular second-order moment, contrast, uniformity, entropy and correlation, are specifically acquired as follows: For the angular second moment, according to the formula , calculate and obtain the angular second-order moment of the image extracted from the gray-level co-occurrence matrix ,in is the joint probability of gray levels i and j in the gray-level co-occurrence matrix; For contrast, according to the formula , calculate and obtain the contrast of the image extracted from the gray-level co-occurrence matrix ; For uniformity, according to the formula , calculate and obtain the uniformity H of the image extracted from the gray level co-occurrence matrix; For entropy, according to the formula , calculate and obtain the entropy U of the image extracted from the gray-level co-occurrence matrix; For correlation, according to the formula , calculate and obtain the correlation of the image extracted from the gray level co-occurrence matrix ,in is the mean of the grayscale values, is the standard deviation of the grayscale values.

[0011] Preferably, the specific working method of the data analysis module also includes: Combining the parameter data of the data acquisition module and the texture features of the image into one data set; Standardize the data features in the data set; Split the preprocessed dataset into training and testing sets; Select the random forest model, train the model using the training set data, and adjust the model parameters; The trained model is deployed to the production environment, and the model is used to predict the real-time data. The parameter data of the real-time data acquisition module and the texture features of the image are input, and the risk assessment of potential faults, i.e., fault information, is output.

[0012] Preferably, the specific working mode of the first maintenance unit is as follows: Acquire parameter data of the data acquisition module, and set a threshold value of a normal operating range for each parameter based on historical data; Compare real-time data and thresholds for each parameter to identify out-of-range parameters; Mark all out-of-range parameters as feedback; After receiving the feedback information, the remote control module adjusts the corresponding parameters to within the normal threshold range.

[0013] Preferably, the specific working mode of the second maintenance unit is as follows: Obtaining a feature matching score of the data acquisition module; A threshold value of feature matching score is set in advance. If the image feature matching score of the data acquisition module is lower than the corresponding threshold value, the formula , calculate the adjustment amount of the image acquisition angle ; Adjust the image acquisition angle Transmitted to the remote control module to adjust the angle of the image acquisition device.

[0014] Preferably, the specific working mode of the third maintenance unit is as follows: Receive real-time fault risk assessment results from the data analysis module, including the type and probability of potential faults, assess the urgency and impact of fault information, and determine whether immediate action is needed; According to the probability of failure and the importance of the system, the failure risk is divided into different levels, such as low, medium and high risk; Generate maintenance work orders based on the fault risk level; For example, for a high temperature fault, the work order may include measures such as checking the cooling system and replacing the thermal sensor, with the expected effect of restoring the normal operating temperature of the equipment; Perform specific maintenance operations, such as replacing parts and adjusting parameters, according to the instructions of the maintenance work order.

[0015] Beneficial effects: Through the collaborative work of the data acquisition module, remote control module, image processing module, data analysis module and remote maintenance module, comprehensive monitoring and intelligent management of the aerosol can production process are realized. The system collects production parameters and image data in real time, and uses image processing and data analysis technology to accurately identify coating defects and predict potential faults, thereby optimizing the production process and preventing faults. The remote control module ensures precise adjustment of production line parameters, while the remote maintenance module ensures stable operation of the equipment and timely fault response. This integrated intelligent control solution not only improves production efficiency and product quality, but also effectively avoids problems with surface welds and internal airtightness of aerosol cans, reduces uncertainties and risks in production, and ensures product consistency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the production control system of the present invention. DETAILED DESCRIPTION

[0017] Ultra-thin high-pressure aerosol cans are mainly used for packaging and dispensing various products and are suitable for various fields. The main production process includes the following steps: The main components of an aerosol can include the can body metal sheet, valve assembly, actuator and propellant gas. The metal sheet, usually made of aluminum or steel, is processed and cut to the appropriate size to form the can body; The sheet metal is fed into a press and formed into a cylindrical shape through a series of punching and forming operations. The formed can can be trimmed to obtain accurate dimensions and smooth edges; The inside of the can may be coated with a protective lining to prevent chemical reactions with the metal; The outside of the can can be printed with brand logos, product information and decorative designs using specialized printing techniques such as lithography or digital printing; Due to the ultra-thin and ultra-pressure characteristics of ultra-thin high-pressure aerosol cans, during the production process, there are higher requirements for the uniformity, coverage and air tightness of the can surface coating; However, in actual use, the integrity of weld patch coating refers to the coverage of the weld patch coating and the adhesion between the coating and the substrate, which is crucial to protect the weld and its surrounding areas from corrosion and environmental damage. Incomplete weld patch coating may cause defects such as pores, inclusions, cracks, or blistering, peeling, pitting and other defects on the coating surface. If these defects are not discovered and handled in time, they may affect the sealing and safety performance of the aerosol can, and may even cause serious accidents such as the explosion of the aerosol can. Aerosol cans need to withstand a certain amount of pressure during use, so their sealing performance has an important impact on their safety performance and service life. If the airtightness is poor, it may cause excessive pressure or leakage inside the aerosol can, thus causing safety accidents; like Figure 1 As shown: a production control system for ultra-thin high-pressure aerosol cans based on intelligent control integration, including a data acquisition module, the data acquisition module is used to collect parameter data in the production process of the aerosol cans and image data on the surface of the aerosol cans; it should be noted that in this embodiment, the parameter data such as the temperature, pressure, humidity inside the aerosol cans, etc., also include the welding speed, the moving speed of the coating equipment, the image data is obtained by an industrial camera, specifically the image of the weld on the surface of the aerosol cans and its repair coating. The industrial camera can be of two types, CCD or CMOS, which converts the captured target into an image signal. After obtaining the signal trigger instruction, the industrial camera executes the image acquisition process; A remote control module, which is used to adjust the parameters in the aerosol can production process; it should be noted that, in this embodiment, the control and adjustment of the production line and the adjustment of the image data acquisition device are realized through the remote terminal; An image processing module, wherein the image processing module is used to identify the image data of the data acquisition module to obtain the coating defect information of the aerosol can; specifically, in this embodiment, the image processing module includes steps such as data acquisition, data enhancement, image classification and defect identification, and uses transfer learning to fine-tune the model to improve the detection accuracy and efficiency; A data analysis module, the data analysis module is used to predict potential fault information in the aerosol can production process based on the parameter data of the data acquisition module and the coating defect information of the image processing module; A remote maintenance module, the remote maintenance module includes a first maintenance unit, a second maintenance unit and a third maintenance unit, the first maintenance unit is used to analyze the parameter data of the data acquisition module, generate feedback information, and transmit the feedback information to the remote control module to adjust the production parameters of the aerosol can; it should be noted that, in this embodiment, the purpose of the analysis is to determine whether the parameter data in the aerosol can production process is abnormal, if there is an abnormality, then generate corresponding feedback information, the remote control module is adjusted according to the feedback information; The second maintenance unit is used to analyze the image data of the data acquisition module, and adjust the acquisition method of the image data through the remote control module according to the analysis result; The third maintenance unit is used to perform corresponding processing according to the fault information of the data analysis module.

[0018] It should be noted that the data acquisition module provides the necessary raw data for the remote control module, image processing module and data analysis module; The remote control module makes real-time adjustments based on the analysis results of the data analysis module and the image processing module to optimize the production process; The image processing module and the data analysis module are interdependent, and the visual data provided by the image processing module is used by the data analysis module for further analysis and prediction; The remote maintenance module integrates the results of data acquisition, image processing and data analysis to perform equipment maintenance and troubleshooting to ensure production continuity and equipment performance; Through the close collaboration of these modules, the aerosol can production control system can achieve a high degree of automation and intelligence, improve production efficiency and product quality, and avoid problems with aerosol can surface welds and internal airtightness.

[0019] As an optional embodiment: the data acquisition module includes a first acquisition unit and a second acquisition unit, and the first acquisition unit is used to collect parameter data in the aerosol can production process, specifically: Install temperature, pressure and humidity sensors at corresponding locations on the aerosol can production line; Start the temperature sensor to record the temperature data at different production stages; it can monitor the temperature changes inside the aerosol can in real time at different production stages such as filling and sealing; Start the pressure sensor to record the pressure data during the filling and sealing stages; the pressure changes inside the aerosol can can be monitored in real time; Start the humidity sensor to record the humidity data at different production stages; it should be noted that the humidity level inside the aerosol can can be monitored in real time to ensure that the product is produced in a suitable humidity environment; The speed sensors installed on the welding machine and coating equipment can monitor the welding speed and the moving speed of the coating equipment in real time; All the above collected data are uniformly marked as parameter data in the production process.

[0020] The second acquisition unit is used to acquire image data of the surface of the aerosol can.

[0021] As an optional embodiment: the specific working steps of the second acquisition unit include: First, install and calibrate the industrial camera; it should be noted that in this embodiment, it can be a CCD or CMOS, ensure that its position, angle and focal length are suitable for capturing the image of the weld and its patch coating on the surface of the aerosol can, and adjust the light source and exposure parameters to obtain a clear image; Then the industrial camera is started to collect images of the weld and its repair coating on the surface of the aerosol can; After that, the image is preprocessed, and finally the collected image data is transmitted to the image processing module. It should be noted that the preprocessing includes denoising, contrast enhancement, brightness adjustment and color balance, etc., to improve the accuracy of subsequent image analysis.

[0022] As an optional embodiment: the remote control module includes a communication unit, an execution unit and a control unit, and the communication unit is used to receive the adjustment signal of the remote maintenance module; it should be noted that, in this embodiment, the adjustment signal may include adjustment instructions for parameters in the aerosol can production process, such as temperature, pressure, etc., and may also include adjustment signals for image acquisition equipment; the communication unit is connected to the external network environment through a wireless module to achieve remote transmission and reception of data, and the communication unit may be a part of an RTU (Remote Terminal Unit), which is a remote measurement and control unit device responsible for monitoring and controlling field signals and industrial equipment; The control unit is used to generate control commands according to the adjustment signal; these commands will be used to adjust the equipment parameters on the production line to ensure the stability of the production process and the consistency of the product; The execution unit is used to execute control commands. It should be noted that the execution unit is responsible for executing control commands generated by the control unit, such as adjusting the welding speed, the moving speed of the coating equipment, etc., to ensure that these adjustments can be accurately implemented on the production line. The execution unit may include various motors, valves, sensors, etc., which are directly connected to the equipment on the production line and operate according to the instructions of the control unit.

[0023] For example, on an aerosol can production line, servo motors and frequency converters can adjust speed and position according to the instructions of the control unit to accurately control the welding and coating processes; As part of the execution unit, the industrial camera (CCD or CMOS) adjusts parameters such as exposure and focus according to the instructions of the control unit to obtain high-quality image data.

[0024] As an optional embodiment: the specific working mode of the image processing module is as follows: Acquiring the aerosol can surface image data transmitted by the data acquisition module; Convert each image in the image data into a grayscale image, and define the parameters of the grayscale co-occurrence matrix, including the distance (d) and the angle (θ). In this embodiment, the angle can be 0°, 45°, 90°, and 135°, and the distance is usually greater than or equal to 1 pixel unit; For each pixel in the image, find its neighboring pixels at the specified distance and angle, and record the grayscale values ​​of these pixel pairs; Extract image texture features from the gray-level co-occurrence matrix, including angular second-order moment, contrast, uniformity, entropy, and correlation; The reference value of the normal weld texture characteristics on the surface of the aerosol can is set in advance; If the angular second-order moment in the image is greater than the reference value, it means that there are no obvious pores or pinch holes on the surface of the aerosol can; the image texture is uniform and regular, with little change, and there are no obvious pores or pinch holes on the surface of the aerosol can; If it is less than the reference value, it indicates that there is peeling or pitting on the surface of the aerosol can; there is a change in the image texture; it may be a rough or uneven texture, such as peeling or pitting; If the contrast in the image is greater than the baseline value, it means that there are cracks or peeling on the surface of the aerosol can; the local grayscale of the image changes dramatically, and the texture difference is obvious; in the weld of the aerosol can, high contrast may be associated with cracks or peeling, because these defects will form obvious edges in the image; If it is less than the reference value, it means that the local grayscale of the image changes slowly, which is normal; if the texture difference is not obvious, it may indicate that the coating surface is relatively smooth and lacks the expected texture; If the uniformity in the image is greater than the baseline value, it means that the distribution of pixel pairs in the image texture is relatively uniform, indicating normality; the texture is clear and regular. In the aerosol can weld, high uniformity may indicate that the coating surface is smooth and has no obvious defects; If it is less than the benchmark value, it indicates that the coating on the surface of the aerosol can is bubbling or peeling; the distribution of pixel pairs in the image texture is uneven; the texture is messy and has poor regularity, which may be related to bubbling or peeling of the coating surface, because these defects will destroy the uniformity of the texture; If the entropy in the image is greater than the baseline value, the larger entropy value indicates that the texture complexity in the image is high, indicating the presence of pitting or flaking on the coating surface; in the aerosol can weld, the high entropy value may indicate that there are irregular defects on the coating surface, such as pitting or flaking, which increase the complexity of the texture; If it is less than the benchmark value, it means it is normal; a smaller entropy value means that the texture in the image is simple or uniform and the amount of information is small; If the correlation in the image is greater than the reference value: it indicates that there are cracks on the surface of the aerosol can; it indicates that the grayscale values ​​of the pixel pairs in the image have a strong linear relationship, and the texture has obvious directionality or periodicity. In the weld of the aerosol can, high correlation may indicate that there are regular defects on the coating surface, such as regular cracks; If it is less than the reference value, it indicates that there are randomly distributed pores or inclusions on the surface of the aerosol can; If it is equal to the reference value, the surface of the aerosol can is normal.

[0025] By comparing these texture feature values ​​with the baseline values ​​of normal welds, it is possible to effectively determine whether the aerosol can weld recoating has defects. If these feature values ​​are significantly different from the baseline values ​​of normal welds, it may indicate that the coating has defects.

[0026] As an optional embodiment: the texture features of the image, including angular second-order moment, contrast, uniformity, entropy and correlation, are specifically obtained as follows: For the angular second moment, according to the formula , calculate and obtain the angular second-order moment of the image extracted from the gray-level co-occurrence matrix ,in is the joint probability of gray levels i and j in the gray-level co-occurrence matrix; For contrast, according to the formula , calculate and obtain the contrast of the image extracted from the gray-level co-occurrence matrix ; For uniformity, according to the formula , calculate and obtain the uniformity H of the image extracted from the gray level co-occurrence matrix; For entropy, according to the formula , the entropy U extracted from the gray-level co-occurrence matrix of the image is obtained by calculation; it should be noted that in this embodiment, the logarithm is based on 2, and for the convenience of calculation, the natural logarithm can also be used; For correlation, according to the formula , calculate and obtain the correlation of the image extracted from the gray level co-occurrence matrix ,in is the mean of the grayscale values, is the standard deviation of the grayscale value. It should be noted that, in this embodiment, the mean of the grayscale value is calculated according to the formula , we can get is the edge probability of gray level i; The standard deviation is calculated according to the formula , calculated and obtained; It should be noted that, in this embodiment, the angular second-order moment represents the uniformity of the image texture; Contrast: A high contrast value indicates a large grayscale variation and clear texture in the image; Uniformity: A high uniformity value indicates that the grayscale distribution in the image is uniform; Entropy: A high entropy value indicates that the grayscale distribution in the image is highly random and the texture is complex; Correlation: A high correlation value indicates a strong linear relationship between the grayscale values ​​in the image.

[0027] As an optional embodiment: the specific working method of the data analysis module also includes: Combining the parameter data of the data acquisition module and the texture features of the image into one data set; Standardize the data features in the data set; Split the preprocessed dataset into training and test sets; usually in a ratio of 70:30 or 80:20; Select the random forest model, train the model using the training set data, and adjust the model parameters; The trained model is deployed to the production environment, and the model is used to predict the real-time data. The parameter data of the real-time data acquisition module and the texture features of the image are input, and the risk assessment of potential faults, i.e., fault information, is output.

[0028] As an optional embodiment: the specific working mode of the first maintenance unit is as follows: Obtain parameter data of the data acquisition module, and set a threshold value of a normal operating range for each parameter according to historical data; the threshold value of the normal operating range can be obtained by obtaining a historical average value of the parameter; Compare real-time data and thresholds for each parameter to identify out-of-range parameters; Mark all out-of-range parameters as feedback; After receiving the feedback information, the remote control module adjusts the corresponding parameters to within the normal threshold range.

[0029] As an optional embodiment: the specific working mode of the second maintenance unit is as follows: Obtaining a feature matching score of the data acquisition module; A threshold value of feature matching score is set in advance. If the image feature matching score of the data acquisition module is lower than the corresponding threshold value, the formula , calculate the adjustment amount of the image acquisition angle It should be noted that, in this embodiment, K is the feature difference, which is the difference between the threshold of the feature matching score and the image feature matching score of the data acquisition module, and L is the threshold of the feature matching score; Adjust the image acquisition angle Transmitted to the remote control module to adjust the angle of the image acquisition device.

[0030] As an optional embodiment: the specific working mode of the third maintenance unit is as follows: Receive real-time fault risk assessment results from the data analysis module, including the type and probability of potential faults, assess the urgency and impact of fault information, and determine whether immediate action is needed; According to the failure probability and the importance of the system, the failure risk is divided into different levels, such as low, medium and high risks. It should be noted that in this embodiment, the calculation formula (taking the risk score as an example) is: {risk score} = {failure probability} multiplied by {system importance coefficient}; Generate maintenance work orders based on the fault risk level; maintenance work orders contain detailed fault descriptions, recommended maintenance measures, and expected results, which can be automatically filled in based on preset templates based on fault types and historical maintenance experience; For example, for a high temperature fault, the work order may include measures such as checking the cooling system and replacing the thermal sensor, with the expected effect of restoring the normal operating temperature of the equipment. It should be noted that the maintenance task, responsible person and completion time limit are clearly defined. The maintenance work order contains the fault description, recommended maintenance measures and expected results. According to the requirements of the maintenance work order, the necessary maintenance personnel, tools and spare parts are dispatched; Perform specific maintenance operations, such as replacing parts and adjusting parameters, according to the instructions of the maintenance work order.

[0031] How it works The data acquisition module is used to collect parameter data in the production process of the aerosol can and image data on the surface of the aerosol can; it should be noted that, in this embodiment, the parameter data such as the temperature, pressure, humidity inside the aerosol can, etc., also include the welding speed and the moving speed of the coating equipment. The image data is obtained by an industrial camera, specifically the image of the weld on the surface of the aerosol can and its repair coating. The industrial camera can be of two types, CCD or CMOS, which converts the captured target into an image signal. After obtaining the signal trigger instruction, the industrial camera executes the image acquisition process; The remote control module is used to adjust the parameters in the aerosol can production process; it should be noted that, in this embodiment, the control and adjustment of the production line and the adjustment of the image data acquisition device are realized through the remote terminal; The image processing module is used to identify the image data of the data acquisition module to obtain the coating defect information of the aerosol can; specifically, in this embodiment, the image processing module includes steps such as data acquisition, data enhancement, image classification and defect identification, and uses transfer learning to fine-tune the model to improve the detection accuracy and efficiency; The data analysis module is used to predict potential fault information in the aerosol can production process based on the parameter data of the data acquisition module and the coating defect information of the image processing module; The remote maintenance module includes a first maintenance unit, a second maintenance unit and a third maintenance unit. The first maintenance unit is used to analyze the parameter data of the data acquisition module, generate feedback information, and transmit the feedback information to the remote control module to adjust the production parameters of the aerosol cans. It should be noted that, in this embodiment, the purpose of the analysis is to determine whether the parameter data in the aerosol can production process is abnormal. If there is an abnormality, the corresponding feedback information is generated, and the remote control module is adjusted according to the feedback information. The second maintenance unit is used to analyze the image data of the data acquisition module, and adjust the acquisition method of the image data through the remote control module according to the analysis result; The third maintenance unit is used to perform corresponding processing according to the fault information of the data analysis module; The data acquisition module provides the necessary raw data for the remote control module, image processing module and data analysis module; The remote control module makes real-time adjustments based on the analysis results of the data analysis module and the image processing module to optimize the production process; The image processing module and the data analysis module are interdependent, and the visual data provided by the image processing module is used by the data analysis module for further analysis and prediction; The remote maintenance module integrates the results of data acquisition, image processing and data analysis to perform equipment maintenance and troubleshooting to ensure production continuity and equipment performance; Through the close collaboration of these modules, the aerosol can production control system can achieve a high degree of automation and intelligence, improve production efficiency and product quality, and avoid problems with aerosol can surface welds and internal airtightness.

[0032] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. An ultra-thin high-pressure aerosol can production control system based on intelligent control integration, characterized in that: It includes a data acquisition module, which is used to collect parameter data in the aerosol can production process and image data on the surface of the aerosol can; A remote control module, the remote control module is used to adjust parameters in the aerosol can production process; An image processing module, the image processing module is used to identify the image data of the data acquisition module to obtain the coating defect information of the aerosol can; A data analysis module, the data analysis module is used to predict potential fault information in the aerosol can production process based on the parameter data of the data acquisition module and the coating defect information of the image processing module; A remote maintenance module, the remote maintenance module comprising a first maintenance unit, a second maintenance unit and a third maintenance unit, the first maintenance unit being used to analyze the parameter data of the data acquisition module, generate feedback information, and transmit the feedback information to the remote control module to adjust the production parameters of the aerosol can; The second maintenance unit is used to analyze the image data of the data acquisition module, and adjust the acquisition method of the image data through the remote control module according to the analysis result; The third maintenance unit is used to perform corresponding processing according to the fault information of the data analysis module.

2. According to claim 1, an ultra-thin high-pressure aerosol can production control system based on intelligent control integration is characterized in that: The data acquisition module includes a first acquisition unit and a second acquisition unit. The first acquisition unit is used to collect parameter data during the production process of the aerosol can, specifically: Install temperature, pressure and humidity sensors at corresponding locations on the aerosol can production line; Start the temperature sensor to record the temperature data at different production stages; Start the pressure sensor to record the pressure data during the filling and sealing stages; Start the humidity sensor to record humidity data at different production stages; The speed sensors installed on the welding machine and coating equipment can monitor the welding speed and the moving speed of the coating equipment in real time; All the above collected data are uniformly marked as parameter data in the production process; The second acquisition unit is used to acquire image data of the surface of the aerosol can.

3. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 2 is characterized in that: The specific working steps of the second acquisition unit include: First install and calibrate the industrial camera; Then the industrial camera is started to collect images of the weld and its repair coating on the surface of the aerosol can; The image is then preprocessed, and finally the collected image data is transmitted to the image processing module.

4. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 1, characterized in that: The remote control module includes a communication unit, an execution unit and a control unit, wherein the communication unit is used to receive the adjustment signal of the remote maintenance module; The control unit is used to generate a control command according to the adjustment signal; The execution unit is used for executing the control command.

5. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 1, characterized in that: The specific working method of the image processing module is as follows: Acquiring the aerosol can surface image data transmitted by the data acquisition module; Convert each image in the image data into a grayscale image and define the parameters of the gray-level co-occurrence matrix; For each pixel in the image, find its adjacent pixels at the specified distance and angle, and record the grayscale values ​​of these pixel pairs; Extract image texture features from the gray-level co-occurrence matrix, including angular second moment, contrast, uniformity, entropy, and correlation; The reference value of the normal weld texture characteristics on the surface of the aerosol can is set in advance; If the angular second-order moment in the image is greater than the reference value, it means that there are no obvious pores or clamp holes on the surface of the aerosol can; If it is less than the reference value, it indicates that there are peeling or pitting on the surface of the aerosol can; If the contrast in the image is greater than the reference value, it means that there are cracks or peeling on the surface of the aerosol can; If it is less than the reference value, it means that the local grayscale of the image changes slowly, indicating that it is normal; If the uniformity in the image is greater than the reference value, it means that the distribution of pixel pairs in the image texture is relatively uniform, indicating normality; If it is less than the reference value, it indicates that the coating on the surface of the aerosol can is bubbling or peeling off; the distribution of pixel pairs in the image texture is uneven; If the entropy in the image is greater than the baseline value, the larger entropy value indicates that the texture complexity in the image is high, indicating that there are pitting or peeling on the coating surface; If it is less than the benchmark value, it is normal; If the correlation in the image is greater than the baseline value: it indicates that there are cracks on the surface of the aerosol can; If it is less than the reference value, it indicates that there are randomly distributed pores or inclusions on the surface of the aerosol can; If it is equal to the reference value, the surface of the aerosol can is normal.

6. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 5, characterized in that: The specific method of obtaining the texture features of the image, including angular second-order moment, contrast, uniformity, entropy and correlation, is as follows: For the angular second moment, according to the formula , calculate and obtain the angular second-order moment of the image extracted from the gray-level co-occurrence matrix ,in is the joint probability of gray levels i and j in the gray-level co-occurrence matrix; For contrast, according to the formula , calculate and obtain the contrast of the image extracted from the gray level co-occurrence matrix ; For uniformity, according to the formula , calculate and obtain the uniformity H of the image extracted from the gray level co-occurrence matrix; For entropy, according to the formula , calculate and obtain the entropy U of the image extracted from the gray-level co-occurrence matrix; For correlation, according to the formula , calculate and obtain the correlation of the image extracted from the gray level co-occurrence matrix ,in is the mean of the grayscale values, is the standard deviation of the grayscale values.

7. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 6, characterized in that: The specific working method of the data analysis module also includes: Combining the parameter data of the data acquisition module and the texture features of the image into one data set; Standardize the data features in the data set; Split the preprocessed dataset into training and testing sets; Select the random forest model, train the model using the training set data, and adjust the model parameters; The trained model is deployed to the production environment, and the model is used to predict the real-time data. The parameter data of the real-time data acquisition module and the texture features of the image are input, and the risk assessment of potential faults, i.e., fault information, is output.

8. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 2, characterized in that: The specific working mode of the first maintenance unit is as follows: Acquire parameter data of the data acquisition module, and set a threshold value of a normal operating range for each parameter based on historical data; Compare real-time data and thresholds for each parameter to identify out-of-range parameters; Mark all out-of-range parameters as feedback; After receiving the feedback information, the remote control module adjusts the corresponding parameters to within the normal threshold range.

9. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 3, characterized in that: The specific working mode of the second maintenance unit is as follows: Obtaining a feature matching score of the data acquisition module; A threshold value of feature matching score is set in advance. If the image feature matching score of the data acquisition module is lower than the corresponding threshold value, the formula , calculate the adjustment amount of the image acquisition angle ; Adjust the image acquisition angle Transmitted to the remote control module to adjust the angle of the image acquisition device.

10. The production control system of ultra-thin high-pressure aerosol cans based on intelligent control integration according to claim 7, characterized in that: The specific working mode of the third maintenance unit is as follows: Receive real-time fault risk assessment results from the data analysis module, including the type and probability of potential faults, assess the urgency and impact of fault information, and determine whether immediate action is needed; According to the probability of failure and the importance of the system, the failure risk is divided into different levels, such as low, medium and high risk; Generate maintenance work orders based on the fault risk level; For example, for a high temperature fault, the work order may include measures such as checking the cooling system and replacing the thermal sensor, with the expected effect of restoring the normal operating temperature of the equipment. Perform specific maintenance operations, such as replacing parts and adjusting parameters, according to the instructions of the maintenance work order.

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