Coating circulating system based on packaging carton printing and control method thereof

Abstract

The invention relates to the technical field of packaging carton printing, in particular to a coating circulating system based on packaging carton printing and a control method thereof.The control method comprises the following steps that firstly, viscosity data Nt of the coating circulating system in the printing process and influence factor data at the moment t of viscosity data collection are collected, the influence factor data comprises a transportation temperature Tt, a solvent volatilization rate st and coating compositions pt, ft and ct, and pt, ft and ct are respectively pigment concentration, filler concentration and solvent concentration; and 2, acquiring viscosity data Nt. According to the method, the viscosity state of the coating can be effectively monitored by systematically acquiring, processing and analyzing the data and predicting and judging the viscosity by utilizing a ridge regression model, so that the coating is ensured to always meet the production requirements in the processing process, and meanwhile, the coating can be replaced in the optimal state, so that the production efficiency is improved, and the production cost is reduced. And the problems of replacement waste and influence on printing quality can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of packaging carton printing, in particular to a coating circulation system based on packaging carton printing and a control method thereof. Background Art

[0002] The paint circulation system for packaging carton printing is a device used to optimize paint usage and improve printing quality. It ensures that the paint remains uniform and stable during the printing process by circulating the paint, preventing pigment sedimentation and paint waste. The system usually includes a paint storage tank, a conveying pipeline, a pressure regulating device, and a recovery module. It can effectively improve paint utilization, reduce production costs, and minimize environmental impact. It is widely used in the packaging carton printing industry.

[0003] In the field of packaging carton printing, the paint circulation system is a key equipment to ensure printing quality and production efficiency;

[0004] However, the paint in the existing paint circulation system is prone to sedimentation during the circulation process, resulting in separation of pigments and fillers, affecting the uniformity of the paint and printing quality. In actual use, if the paint is replaced in advance, it will cause waste of resources, and delayed replacement of the paint will affect the printing quality. Therefore, to address the above problems, a paint circulation system based on packaging carton printing and its control method are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a paint circulation system based on packaging paper box printing and a control method thereof, so as to solve the problem that the paint in some paint circulation systems is prone to sedimentation during the circulation process, resulting in separation of pigment and filler, affecting the uniformity of the paint and the printing quality. In actual use, if the paint is replaced in advance, it will cause waste of resources, and delaying the replacement of the paint will affect the quality of printing.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A coating circulation system and control method based on packaging carton printing include the following steps:

[0008] Step 1: Collect viscosity data N of the coating circulation system during the printing process t , and the influencing factor data at time t of viscosity data collection, the influencing factor data including the transport temperature T t , solvent volatility s t 、Coating composition t 、f t and c t , where p t 、f t and c tare pigment concentration, filler concentration, and solvent concentration, respectively;

[0009] Step 2: Obtained viscosity data N t , and pre-processing the data of various influencing factors at time t when viscosity data is collected;

[0010] Step 3: Use the ridge regression model to obtain the viscosity data N t The viscosity and the influencing factors are fitted, and the mathematical expression between the viscosity and the influencing factors is determined according to the fitting result, which is specifically:

[0011] N(t)=β0+β1T(t)+β2s(t)+β3p(t)+β4f(t)+β5c(t)+ε

[0012] Where N(t) is the viscosity observation value, which represents the viscosity of the coating collected at time t; β0 is the intercept term, which represents the viscosity value when all influencing factors are zero; β1, β2, β3, β4, and β5 are the temperature coefficient, solvent volatility coefficient, pigment concentration coefficient, filler concentration coefficient, and solvent concentration coefficient, respectively, representing the degree of influence of temperature T(t), solvent volatility s(t), pigment concentration p(t), filler concentration f(t), and solvent concentration c(t) on viscosity; ε is the error term, which represents the random error or noise that cannot be explained by the model;

[0013] Step 4: Input the real-time collected data into the ridge regression model to predict viscosity. The viscosity prediction formula is:

[0014]

[0015] Where, is the predicted value of viscosity;

[0016] Step 5: Use the viscosity prediction results of the regression model to determine whether the coating meets the processing requirements;

[0017] The judgment principles are:

[0018] when It is determined that the coating does not meet the processing requirements and needs to be replaced;

[0019] when It is determined that the coating meets the processing requirements and does not need to be replaced.

[0020] As a further optimized content of the present invention, wherein: the method further comprises:

[0021] Step 6: Record the judgment results in the system log and feed them back to the paint circulation system to optimize subsequent viscosity prediction and control strategies;

[0022] Step 7: When the predicted viscosity approaches the threshold, the early warning mechanism is triggered to remind the operator to pay attention to the coating status.

[0023] As a further optimization of the present invention, the β1, β2, β3, β4 and β5 are solved by the following formula:

[0024]

[0025] Where, β j is the model parameter vector, including β1, β2, β3, β4 and β5, N(t i ) is the actual viscosity of the i-th observation, is the predicted viscosity of the i-th observation, and λ is the regularization parameter used to control the complexity of the model and prevent overfitting.

[0026] As a further optimized content of the present invention, wherein: the threshold N n Methods for determining include:

[0027] Set the upper viscosity limit based on production needs and equipment limitations;

[0028] Analyze historical viscosity data and calculate mean and standard deviation;

[0029] Verify the performance of coatings at different viscosities through experiments;

[0030] Dynamically adjust thresholds based on real-time monitoring data and production feedback.

[0031] As a further optimization of the present invention, the pretreatment of step 2 includes:

[0032] Data cleaning: remove outliers and missing values ​​from collected data;

[0033] Data normalization: Normalize all data to the range of [0,1]. The normalization formula is:

[0034]

[0035] Where, X represents the viscosity data N(t) or the data of various influencing factors;

[0036] Feature extraction: Extract temperature change rate △T(t), solvent evaporation rate △s(t), pigment concentration change rate △p(t)p(t), filler concentration change rate △f(t) and solvent concentration change rate △c(t). The calculation formulas are:

[0037]

[0038] As further optimized content of the present invention, including:

[0039] Data acquisition module: used to collect viscosity data of the paint circulation system during the printing process t And data of various influencing factors, including transportation temperature T t , solvent volatility s t , pigment concentration p t , filler concentration f t and solvent concentration c t ;

[0040] Data preprocessing module: used to preprocess the collected data, including data cleaning, normalization and feature extraction;

[0041] Model training module: used to fit viscosity data and influencing factor data using the ridge regression model to determine the mathematical expression between viscosity and various influencing factors;

[0042] Viscosity prediction module: used to input real-time collected data into the ridge regression model to predict the viscosity of the coating;

[0043] Determination module: used to determine whether the coating meets the processing requirements based on the predicted viscosity and the set threshold;

[0044] Early warning module: used to trigger an early warning when the predicted viscosity approaches the threshold, prompting the operator to pay attention to the coating status;

[0045] Feedback optimization module: used to record the judgment results in the system log and feed them back to the paint circulation system to optimize subsequent viscosity prediction and control strategies.

[0046] As a further optimized content of the present invention, it also includes:

[0047] User interface module: used to display real-time viscosity data, prediction results and judgment results, and provide an interactive interface between operators and the system;

[0048] Alarm module: used to trigger an alarm when the predicted viscosity exceeds the threshold, prompting the operator to replace the paint in time;

[0049] Historical data storage module: used to store historical viscosity data, influencing factor data and judgment results, and support data query and analysis.

[0050] As a further optimized content of the present invention, it also includes:

[0051] Mobile application module: supports Android and iOS platforms, used to remotely monitor the operating status of the paint circulation system, receive early warning and alarm information, and upload operation feedback;

[0052] Cloud service platform: used for remote storage and analysis of data, providing remote monitoring and management functions, and supporting centralized management of multiple devices;

[0053] Automatic control module: Automatically adjusts parameters in the paint circulation system based on the judgment results, such as adding solvent and adjusting temperature to keep the viscosity within the target range.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. In the present invention, by systematically collecting, processing and analyzing data, and using the ridge regression model to predict and determine viscosity, the viscosity state of the coating can be effectively monitored to ensure that the coating always meets production requirements during the processing. At the same time, the coating can be replaced in the optimal state, which can avoid replacement waste and problems that affect printing quality.

[0056] 2. In the present invention, the threshold is dynamically adjusted based on real-time monitoring data and production feedback. The threshold is determined by comprehensively considering production needs, equipment limitations, historical data and experimental results, and the threshold is dynamically adjusted based on real-time data. This ensures the rationality and adaptability of the threshold, helps to more accurately determine whether the coating meets processing requirements, avoids production problems caused by unreasonable thresholds, and improves production efficiency and product quality.

[0057] 3. In the present invention, through modular design, the system can efficiently complete tasks such as data collection, processing, prediction and judgment, thereby improving the overall performance and reliability of the system. The modular design also facilitates system maintenance and upgrades, thereby reducing the operating costs of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a control method for a coating circulation system based on packaging carton printing according to the present invention;

[0059] Figure 2 This is a system block diagram of the coating circulation system based on packaging carton printing of the present invention. DETAILED DESCRIPTION

[0060] See also Figure 1-2 The present invention provides a technical solution: a coating circulation system based on packaging carton printing and a control method thereof, comprising the following steps:

[0061] Step 1: Collect viscosity data N of the coating circulation system during the printing process t , and the influencing factor data at time t of viscosity data collection, including the transport temperature T t , solvent volatility s t 、Coating composition t 、f t and c t , where p t 、f t and ct are pigment concentration, filler concentration, and solvent concentration, respectively;

[0062] Step 2: Obtained viscosity data N t , and pre-process the data of various influencing factors at time t when viscosity data is collected;

[0063] Step 3: Use the ridge regression model to obtain the viscosity data N t The data of various influencing factors are fitted, and the mathematical expression between viscosity and the data of various influencing factors is determined according to the fitting results, which is specifically:

[0064] N(t)=β0+β1T(t)+β2s(t)+β3p(t)+β4f(t)+β5c(t)+ε

[0065] Where N(t) is the viscosity observation value, which represents the viscosity of the coating collected at time t; β0 is the intercept term, which represents the viscosity value when all influencing factors are zero; β1, β2, β3, β4, and β5 are the temperature coefficient, solvent volatility coefficient, pigment concentration coefficient, filler concentration coefficient, and solvent concentration coefficient, respectively, representing the degree of influence of temperature T(t), solvent volatility s(t), pigment concentration p(t), filler concentration f(t), and solvent concentration c(t) on viscosity; ε is the error term, which represents the random error or noise that cannot be explained by the model;

[0066] Step 4: Input the real-time collected data into the ridge regression model to predict viscosity. The viscosity prediction formula is:

[0067]

[0068] Where, is the predicted value of viscosity;

[0069] Step 5: Use the viscosity prediction results of the regression model to determine whether the coating meets the processing requirements;

[0070] The judgment principles are:

[0071] when It is determined that the coating does not meet the processing requirements and needs to be replaced;

[0072] when It is determined that the coating meets the processing requirements and does not need to be replaced. Through systematic steps to collect, process and analyze data, and use the ridge regression model to predict viscosity and make judgments, the viscosity state of the coating can be effectively monitored to ensure that the coating always meets production requirements during the processing process, thereby improving production efficiency and product quality.

[0073] As a technical solution for further implementing this solution, the method also includes:

[0074] Step 6: Record the judgment results in the system log and feed them back to the paint circulation system to optimize subsequent viscosity prediction and control strategies;

[0075] Step 7: When the predicted viscosity approaches the threshold, the early warning mechanism is triggered to remind the operator to pay attention to the paint status. By recording the judgment results and feeding them back to the system, the paint circulation system can be dynamically optimized, improving the system's adaptability and stability. At the same time, the early warning mechanism can remind the operator to pay attention to the paint status in advance, avoiding production problems caused by abnormal viscosity, and further improving production efficiency and product quality.

[0076] As a technical solution for further implementation of this scheme, β1, β2, β3, β4 and β5 are solved by the following formula:

[0077]

[0078] Where, β j is the model parameter vector, including β1, β2, β3, β4 and β5, N(t i ) is the actual viscosity of the i-th observation, is the predicted viscosity of the i-th observation value, and λ is the regularization parameter, which is used to control the complexity of the model and prevent overfitting. It can effectively prevent overfitting, improve the generalization ability and prediction accuracy of the model, help to more accurately predict the viscosity of the coating, ensure the reliability of the judgment results, and thus better meet production needs;

[0079] As a technical solution for further implementation of this solution, the threshold N n Methods for determining include:

[0080] Set the upper viscosity limit based on production needs and equipment limitations;

[0081] Analyze historical viscosity data and calculate mean and standard deviation;

[0082] Verify the performance of coatings at different viscosities through experiments;

[0083] Dynamically adjust thresholds based on real-time monitoring data and production feedback. By comprehensively considering production needs, equipment limitations, historical data, and experimental results to determine thresholds, and dynamically adjusting thresholds based on real-time data, it can ensure the rationality and adaptability of thresholds, help to more accurately determine whether the coating meets processing requirements, avoid production problems caused by unreasonable thresholds, and improve production efficiency and product quality.

[0084] As a technical solution for further implementation of this solution, the pretreatment of step 2 includes:

[0085] Data cleaning: remove outliers and missing values ​​from collected data;

[0086] Data normalization: Normalize all data to the range of [0,1]. The normalization formula is:

[0087]

[0088] Where, X represents the viscosity data N(t) or the data of various influencing factors;

[0089] Feature extraction: Extract temperature change rate △T(t), solvent evaporation rate △s(t), pigment concentration change rate △p(t)p(t), filler concentration change rate △f(t) and solvent concentration change rate △c(t). The calculation formulas are:

[0090] Through data cleaning, normalization and feature extraction, the quality and consistency of data can be effectively improved, providing more reliable data support for subsequent model training and viscosity prediction. This helps to improve the prediction accuracy of the model and ensure the accuracy of the judgment results, thereby better meeting production needs.

[0091] The technical solutions for further implementation of this plan include:

[0092] Data acquisition module: used to collect viscosity data of the paint circulation system during the printing process t And data of various influencing factors, including transportation temperature T t , solvent volatility s t , pigment concentration p t , filler concentration f t and solvent concentration c t ;

[0093] Data preprocessing module: used to preprocess the collected data, including data cleaning, normalization and feature extraction;

[0094] Model training module: used to fit viscosity data and influencing factor data using the ridge regression model to determine the mathematical expression between viscosity and various influencing factors;

[0095] Viscosity prediction module: used to input real-time collected data into the ridge regression model to predict the viscosity of the coating;

[0096] Determination module: used to determine whether the coating meets the processing requirements based on the predicted viscosity and the set threshold;

[0097] Early warning module: used to trigger an early warning when the predicted viscosity approaches the threshold, prompting the operator to pay attention to the coating status;

[0098] Feedback Optimization Module: This module records the determination results in the system log and feeds them back to the paint circulation system to optimize subsequent viscosity prediction and control strategies. Through modular design, the system can efficiently complete tasks such as data acquisition, processing, prediction, and determination, improving the overall performance and reliability of the system. The modular design also facilitates system maintenance and upgrades, reducing system operating costs.

[0099] As a further technical solution for the implementation of this plan, it also includes:

[0100] User interface module: used to display real-time viscosity data, prediction results and judgment results, and provide an interactive interface between operators and the system;

[0101] Alarm module: used to trigger an alarm when the predicted viscosity exceeds the threshold, prompting the operator to replace the paint in time;

[0102] Historical data storage module: used to store historical viscosity data, influencing factor data and judgment results, and supports data query and analysis. The addition of the user interface module, alarm module and historical data storage module can provide operators with more intuitive monitoring and management tools to timely discover and handle problems. At the same time, the historical data storage module supports data query and analysis, providing data support for production optimization;

[0103] As a further technical solution for the implementation of this plan, it also includes:

[0104] Mobile application module: supports Android and iOS platforms, used to remotely monitor the operating status of the paint circulation system, receive early warning and alarm information, and upload operation feedback;

[0105] Cloud service platform: used for remote storage and analysis of data, providing remote monitoring and management functions, and supporting centralized management of multiple devices;

[0106] Automatic control module: Automatically adjusts parameters in the paint circulation system based on the judgment results, such as adding solvents and adjusting temperature to keep the viscosity within the target range. The addition of mobile application modules, cloud service platforms and automatic control modules can achieve remote monitoring and management of the paint circulation system and support centralized management of multiple devices. The automatic control module can automatically adjust system parameters based on the judgment results, realize intelligent control, and further improve production efficiency and product quality.

[0107] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.

Claims

1. A control method for a coating circulation system for packaging carton printing, characterized in that: The following steps are involved: Step 1: Collect viscosity data N of the coating circulation system during the printing process t , and the influencing factor data at time t of viscosity data collection, the influencing factor data including the transport temperature T t , solvent volatility s t 、Coating composition t 、f t and c t , where p t 、f t and c t are pigment concentration, filler concentration, and solvent concentration, respectively; Step 2: Obtained viscosity data N t , and pre-processing the data of various influencing factors at time t when viscosity data is collected; Step 3: Use the ridge regression model to obtain the viscosity data N t The viscosity and the influencing factors are fitted, and the mathematical expression between the viscosity and the influencing factors is determined according to the fitting result, which is specifically: N(t)=β0+β1T(t)+β2s(t)+β3p(t)+β4f(t)+β5c(t)+ε Where N(t) is the viscosity observation value, which represents the viscosity of the coating collected at time t; β0 is the intercept term, which represents the viscosity value when all influencing factors are zero; β1, β2, β3, β4, and β5 are the temperature coefficient, solvent volatility coefficient, pigment concentration coefficient, filler concentration coefficient, and solvent concentration coefficient, respectively, representing the degree of influence of temperature T(t), solvent volatility s(t), pigment concentration p(t), filler concentration f(t), and solvent concentration c(t) on viscosity; ε is the error term, which represents the random error or noise that cannot be explained by the model; Step 4: Input the real-time collected data into the ridge regression model to predict viscosity. The viscosity prediction formula is: Where, is the predicted value of viscosity; Step 5: Use the viscosity prediction results of the regression model to determine whether the coating meets the processing requirements; The judgment principles are: when It is determined that the coating does not meet the processing requirements and needs to be replaced; when It is determined that the coating meets the processing requirements and does not need to be replaced.

2. The control method of the coating circulation system based on packaging carton printing according to claim 1 is characterized in that: The method further comprises: Step 6: Record the judgment results in the system log and feed them back to the paint circulation system to optimize subsequent viscosity prediction and control strategies; Step 7: When the predicted viscosity approaches the threshold, the early warning mechanism is triggered to remind the operator to pay attention to the coating status.

3. The control method of the coating circulation system based on packaging carton printing according to claim 1 is characterized in that: The β1, β2, β3, β4 and β5 are solved by the following formula: Where, β j is the model parameter vector, including β1, β2, β3, β4 and β5, N(t i ) is the actual viscosity of the i-th observation, is the predicted viscosity of the i-th observation, and λ is the regularization parameter used to control the complexity of the model and prevent overfitting.

4. The control method of the coating circulation system based on packaging carton printing according to claim 1 is characterized in that: The threshold N n Methods for determining include: Set the upper viscosity limit based on production needs and equipment limitations; Analyze historical viscosity data and calculate mean and standard deviation; Verify the performance of coatings at different viscosities through experiments; Dynamically adjust thresholds based on real-time monitoring data and production feedback.

5. The control method of the coating circulation system based on packaging carton printing according to claim 1 is characterized in that: The pretreatment of step 2 includes: Data cleaning: remove outliers and missing values ​​from collected data; Data normalization: Normalize all data to the range of [0,1]. The normalization formula is: Where, X represents the viscosity data N(t) or the data of various influencing factors; Feature extraction: Extract temperature change rate △T(t), solvent evaporation rate △s(t), pigment concentration change rate △p(t)p(t), filler concentration change rate △f(t) and solvent concentration change rate △c(t). The calculation formulas are: ...。 6. The coating circulation system for packaging carton printing according to any one of claims 1 to 5, characterized in that: include: Data acquisition module: used to collect viscosity data of the paint circulation system during the printing process t And data of various influencing factors, including transportation temperature T t , solvent volatility s t , pigment concentration p t , filler concentration f t and solvent concentration c t ; Data preprocessing module: used to preprocess the collected data, including data cleaning, normalization and feature extraction; Model training module: used to fit viscosity data and influencing factor data using the ridge regression model to determine the mathematical expression between viscosity and various influencing factors; Viscosity prediction module: used to input real-time collected data into the ridge regression model to predict the viscosity of the coating; Determination module: used to determine whether the coating meets the processing requirements based on the predicted viscosity and the set threshold; Early warning module: used to trigger an early warning when the predicted viscosity approaches the threshold, prompting the operator to pay attention to the coating status; Feedback optimization module: used to record the judgment results in the system log and feed them back to the paint circulation system to optimize subsequent viscosity prediction and control strategies.

7. The coating circulation system based on packaging carton printing according to claim 6 is characterized in that: Also includes: User interface module: used to display real-time viscosity data, prediction results and judgment results, and provide an interactive interface between operators and the system; Alarm module: used to trigger an alarm when the predicted viscosity exceeds the threshold, prompting the operator to replace the paint in time; Historical data storage module: used to store historical viscosity data, influencing factor data and judgment results, and support data query and analysis.

8. The coating circulation system based on packaging carton printing according to claim 6 is characterized in that: Also includes: Mobile application module: supports Android and iOS platforms, used to remotely monitor the operating status of the paint circulation system, receive early warning and alarm information, and upload operation feedback; Cloud service platform: used for remote storage and analysis of data, providing remote monitoring and management functions, and supporting centralized management of multiple devices; Automatic control module: Automatically adjusts parameters in the paint circulation system based on the judgment results, such as adding solvent and adjusting temperature to keep the viscosity within the target range.

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