Moisture grain control system for PVB (polyvinyl butyral) film

Abstract

The invention provides a PVB film moisture grain control system. The system is composed of four parts: a sensing layer is responsible for collecting film images, humidity and temperature data; the network transmission layer uploads the data to an analysis layer; the analysis layer generates a control strategy based on the data and sends the control strategy to the execution layer; and the execution layer adjusts PVB film production parameters according to the strategy. According to the invention, the key parameters such as humidity, temperature and moisture grain characteristics in the PVB film production process can be monitored in real time, the correlation between the parameters is analyzed, and the change trend of the moisture grain is predicted. When abnormal data is detected, the system can quickly respond and adaptively generate and execute a corresponding control strategy to adjust production parameters, so that the production of moisture lines is effectively avoided, and the product quality and production efficiency of PVB films are improved.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control, in particular to a PVB film moisture grain control system. Background Art

[0002] In the manufacturing process of PVB (polyvinyl butyral) film, the control of moisture lines is one of the key factors to ensure product quality. The traditional PVB film moisture line control system mainly relies on manual operation and simple automation devices to control the formation of moisture lines by monitoring and adjusting environmental conditions (such as temperature and humidity) during the production process. However, with the development of science and technology and the continuous advancement of industrial automation, traditional control systems have been unable to meet the needs of modern manufacturing for efficient, precise and intelligent production. Specifically, the existing PVB film moisture line control system has obvious deficiencies in intelligence. First, the system lacks advanced sensors and monitoring technology, and cannot obtain the moisture content and grain state of PVB film in the production process in real time and accurately. This makes it difficult for the control system to respond to changes in the production process in a timely manner, thereby affecting the quality and production efficiency of the product. Secondly, the existing control system is relatively simple in control algorithms and strategies, and lacks adaptability and intelligence. When facing PVB films of different specifications, different raw material ratios or different production conditions, the system often requires a lot of manual adjustment and debugging, which not only increases production costs, but also reduces production efficiency and flexibility.

[0003] In addition, with the continuous development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent production has become an important trend in the manufacturing industry. Traditional PVB film moisture and grain control systems have been unable to adapt to the development of this trend due to their lack of intelligent upgrade capabilities. Summary of the invention

[0004] The present invention aims to at least solve the technical problem of low production efficiency in the prior art, and in particular innovatively proposes a PVB film moisture texture control system.

[0005] In order to achieve the above-mentioned object of the present invention, the present invention provides a PVB film moisture texture control system, the system comprising:

[0006] The perception layer is used to collect film surface image data, humidity data and temperature data;

[0007] A network transmission layer, connected to the perception layer, for uploading the film surface image data, humidity data and temperature data to the analysis layer;

[0008] An analysis layer, connected to the network transmission layer, for adaptively generating a control strategy based on the film surface image data, humidity data and temperature data, and sending the control strategy to the execution layer;

[0009] The execution layer is connected to the analysis layer and is used to adjust the production parameters of the PVB film according to the control strategy.

[0010] As an optional embodiment of the present invention, optionally, the analysis layer includes:

[0011] A data preprocessing module, used for preprocessing the film surface image data, humidity data and temperature data;

[0012] A feature extraction module, connected to the data preprocessing module, for extracting moisture grain features based on the film surface image data;

[0013] A correlation analysis module, connected to the data preprocessing module, for analyzing the correlation between the moisture pattern characteristics and the humidity data and the temperature data using a correlation analysis algorithm to obtain the correlation data;

[0014] An abnormality detection module, connected to the feature extraction module and the correlation analysis module, for detecting abnormal data in the production process of the PVB film according to the moisture pattern characteristics and correlation data;

[0015] A control strategy generation module is connected to the abnormality detection module and is used to adaptively generate a control strategy based on the abnormal data and the optimization algorithm.

[0016] As an optional embodiment of the present invention, optionally, the expression of the correlation analysis algorithm is:

[0017]

[0018] Among them, R(X,Y,Z) represents the correlation coefficient between humidity data X, temperature data Y and moisture texture feature Z, n represents the total number of data points, and w i represents the weight coefficient of the i-th data point, X i represents the humidity data of the i-th data point, represents the mean of humidity data X, σ X Indicates the standard deviation of humidity data X, Y i represents the temperature data of the i-th data point, represents the mean of temperature data, σ Y Represents the standard deviation of the temperature data.

[0019] As an optional embodiment of the present invention, optionally, the abnormality detection module includes:

[0020] A water grain prediction unit, used to predict the water grain change trend according to the water grain characteristics;

[0021] The abnormality detection unit is used to detect abnormal data in the production process of the PVB film according to the moisture pattern change trend and correlation data.

[0022] As an optional embodiment of the present invention, optionally, the water pattern prediction unit predicts the water pattern change trend using the expression:

[0023]

[0024] Among them, Z t+k represents the moisture texture feature vector at time t+k, t represents time, k represents the prediction step, W represents the weight matrix, M represents the size of the time window, α m represents the weight coefficient of the mth historical data point, φ(,,) represents the nonlinear function, and Z t-m represents the moisture texture feature vector of the tmth historical data point, X t-m Represents the humidity data of the tmth historical data point, Y t-m The temperature data of the tmth historical data point, β represents the weight coefficient of the trend feature, Trend(·) represents the trend function, Z t represents the moisture texture feature vector of the t-th historical data point, Z t-h Represents the moisture texture feature vector of the th historical data point.

[0025] As an optional embodiment of the present invention, optionally, the abnormality detection unit detects abnormal data in the production process of the PVB film according to the moisture pattern change trend and correlation data, including:

[0026] S1. Setting the threshold of the change rate of moisture texture characteristics and the threshold of the correlation coefficient;

[0027] S2. Calculating the moisture texture change rate based on the predicted moisture texture change trend;

[0028] S3, determining whether the correlation coefficient is lower than the correlation coefficient threshold, if so, executing step S4; if not, determining whether the moisture pattern characteristic change rate exceeds the moisture pattern characteristic change rate threshold;

[0029] When the moisture pattern feature change rate exceeds the moisture pattern feature change rate threshold, it is determined to be abnormal data;

[0030] S4. When the correlation coefficient is lower than the correlation coefficient threshold, the corresponding humidity data or temperature data is marked as abnormal data;

[0031] S5. When the moisture pattern feature change rate exceeds the moisture pattern feature change rate threshold, the corresponding moisture pattern feature is marked as abnormal data;

[0032] S6. Feedback the marked abnormal data to a control strategy generation module, and the strategy generation module generates corresponding control strategies according to different abnormal data and issues an alarm.

[0033] As an optional embodiment of the present invention, optionally, the expression for calculating the rate of change of the moisture texture characteristic is:

[0034]

[0035] Among them, η represents the rate of change of moisture texture characteristics, F (t′+Δt′) represents the moisture pattern feature vector at time t′+Δt′, F (t′) represents the moisture pattern feature vector at time t′, and P·P represents the norm of the vector.

[0036] As an optional embodiment of the present invention, optionally, the control strategy generation module adaptively generates the control strategy including:

[0037] A1. Identify abnormal data types, and analyze the causes of abnormal data according to the identified abnormal data types;

[0038] A2. Select an optimization algorithm based on the abnormal data type and cause, and generate an optimization strategy using the optimization algorithm;

[0039] A3. Verify the optimization strategy to determine whether the optimization strategy is feasible. If so, use the optimization strategy to adjust the production parameters of the PVB film;

[0040] If not, return to step A1 to re-identify the abnormal data type and analyze the cause until a feasible optimization strategy is generated.

[0041] As an optional embodiment of the present invention, optionally, in step A2, selecting an optimization algorithm based on the abnormal data type and cause includes:

[0042] A201. If the abnormal data type is abnormal humidity data or abnormal temperature data, a genetic algorithm is selected as the optimization algorithm;

[0043] A202. If the abnormal data type is abnormal moisture texture characteristics, a texture matching algorithm based on image processing is selected as the optimization algorithm.

[0044] As an optional embodiment of the present invention, optionally, the execution layer includes:

[0045] A dehumidification and humidification module, used to adjust the humidity of the PVB film production environment according to the control strategy;

[0046] A heating and cooling module, used to adjust the temperature of the PVB film production environment according to the control strategy;

[0047] Extruder control module, used to adjust the speed and extrusion pressure of the extruder;

[0048] A curing oven control module, used to adjust the heating power and temperature distribution of the curing oven;

[0049] An alarm module, used to send out an alarm signal when the abnormality detection module detects abnormal data;

[0050] The data recording and storage module is used to record and store the film surface image data, humidity data, temperature data, moisture grain characteristics, correlation data, abnormal data and control strategy.

[0051] Beneficial effects of the present invention: The present invention can automatically adjust the control strategy according to changes in the production environment, thereby achieving precise control of the moisture lines of the PVB film. By integrating advanced sensors, monitoring technology and intelligent algorithms, the present invention can monitor key parameters in the production process of PVB film in real time, such as humidity, temperature and moisture line characteristics, and then analyze the correlation between these parameters and predict the change trend of moisture lines. When abnormal data is detected, the system can respond quickly, adaptively generate and execute corresponding control strategies to adjust production parameters, thereby effectively avoiding the generation of moisture lines and improving the product quality and production efficiency of PVB film.

[0052] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0054] Figure 1 It is a structural schematic diagram of the PVB film moisture grain control system of the present invention. DETAILED DESCRIPTION

[0055] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0056] like Figure 1 As shown, a PVB film moisture texture control system, the system includes:

[0057] The perception layer is used to collect film surface image data, humidity data and temperature data;

[0058] It should be noted that the perception layer of this embodiment includes but is not limited to a high-definition camera, a plurality of humidity sensors and a temperature sensor. The high-definition camera is used to capture images of the surface of the PVB film to extract moisture grain characteristics; the humidity sensor is used to monitor humidity data in the production environment in real time; and the temperature sensor is used to monitor temperature data. These sensors and data acquisition devices together constitute the perception layer, which provides a basis for subsequent data analysis and control strategy generation.

[0059] A network transmission layer, connected to the perception layer, for uploading the film surface image data, humidity data and temperature data to the analysis layer;

[0060] It should be noted that in this embodiment, the network transmission layer adopts an efficient and stable data transmission protocol to ensure that the data collected by the perception layer can be transmitted to the analysis layer in real time and accurately. The transmission layer supports multiple communication methods, such as Ethernet, Wi-Fi, 4G / 5G, etc., to meet the data transmission requirements in different scenarios. At the same time, the network transmission layer also has data encryption and verification functions to ensure the security and reliability of data transmission.

[0061] An analysis layer, connected to the network transmission layer, for adaptively generating a control strategy based on the film surface image data, humidity data and temperature data, and sending the control strategy to the execution layer;

[0062] It should be noted that, in this embodiment, the analysis layer includes multiple submodules such as a data preprocessing module, a feature extraction module, a correlation analysis module, an anomaly detection module, and a control strategy generation module. These submodules work together to conduct in-depth analysis and processing of the data collected by the perception layer to generate accurate control strategies. The data preprocessing module is responsible for cleaning, denoising and formatting the original data to ensure the accuracy and consistency of the data. The feature extraction module extracts the moisture grain features from the surface image of the PVB film captured by the high-definition camera based on image processing technology. The moisture grain features include key information such as the shape, size, and distribution density of the moisture grains. These features are crucial for subsequent analysis and control. The correlation analysis module uses advanced algorithms to analyze the correlation between the moisture grain features and the humidity and temperature data, revealing the inherent connection between them. The anomaly detection module detects abnormal data in the production process based on these correlation data and preset thresholds, and finds problems in a timely manner. Finally, the control strategy generation module adaptively generates control strategies based on abnormal data and optimization algorithms, and sends them to the execution layer for execution through the network transmission layer.

[0063] The execution layer is connected to the analysis layer and is used to adjust the production parameters of the PVB film according to the control strategy.

[0064] It should be noted that the execution layer of this embodiment includes a dehumidification and humidification module, a heating and cooling module, an extruder control module, a curing furnace control module, an alarm module, and a data recording and storage module. These components work together to adjust the production environment of the PVB film in real time according to the control strategy generated by the analysis layer to ensure the stability and efficiency of the production process. Among them, the dehumidification and humidification module is used to adjust the humidity of the production environment to meet the needs of different PVB film production; the heating and cooling module is responsible for controlling the temperature of the production environment to ensure that the film is produced under the optimal temperature conditions; the extruder control module and the curing furnace control module are respectively used to adjust the speed of the extruder, the extrusion pressure, and the heating power and temperature distribution of the curing furnace, so as to accurately control the extrusion and curing process of the film; the alarm module promptly issues an alarm when abnormal data is detected to remind the operator to take corresponding measures; the data recording and storage module is used to record and store various types of data in the production process, providing a basis for subsequent data analysis and system optimization.

[0065] The specific principle of the PVB film moisture grain control system of this embodiment is as follows: the system captures the image of the PVB film surface through the high-definition camera of the perception layer, and uses the humidity sensor and the temperature sensor to monitor the humidity and temperature of the production environment in real time. These data are uploaded to the analysis layer in real time through the network transmission layer. In the analysis layer, the data preprocessing module first cleans and formats the original data to ensure the accuracy and consistency of the data. Subsequently, the feature extraction module uses image processing technology to extract moisture grain features from the image, and these features include key information such as the shape, size, and distribution density of the moisture grain. Next, the correlation analysis module uses advanced algorithms to analyze the correlation between these moisture grain features and humidity and temperature data to reveal the intrinsic connection between them. On this basis, the abnormal detection module detects abnormal data in the production process according to the preset threshold and correlation data. Once an abnormality is detected, the control strategy generation module immediately generates a control strategy adaptively based on the abnormal data and the optimization algorithm. These control strategies are sent to the execution layer through the network, and the various modules of the execution layer adjust the production parameters of the PVB film in real time according to the control strategy, such as humidity, temperature, speed and extrusion pressure of the extruder, heating power and temperature distribution of the curing furnace, etc. The entire system forms a closed-loop feedback control mechanism that can automatically adjust the control strategy according to changes in the production environment, thereby achieving precise control of the moisture texture of the PVB film and improving production efficiency.

[0066] As an optional embodiment of the present invention, optionally, the analysis layer includes:

[0067] A data preprocessing module, used for preprocessing the film surface image data, humidity data and temperature data;

[0068] It should be noted that the preprocessing steps include but are not limited to data cleaning, denoising, format conversion, and outlier processing to ensure the accuracy and reliability of subsequent analysis. Data cleaning aims to remove redundancy and erroneous information in the data, denoising is used to reduce random fluctuations and noise interference in the data, and format conversion ensures that all data has a unified format and unit for subsequent processing and analysis. Outlier processing corrects or removes extreme or unreasonable values ​​in the data to avoid adverse effects on the overall analysis results. Through these preprocessing steps, the quality and availability of the data can be significantly improved, laying a solid foundation for subsequent steps such as feature extraction, correlation analysis, and anomaly detection.

[0069] A feature extraction module, connected to the data preprocessing module, for extracting moisture grain features based on the film surface image data;

[0070] It should be noted that the feature extraction module uses image processing algorithms to extract key features such as the shape, size, direction, and distribution density of moisture lines from the surface image of the PVB film captured by a high-definition camera. These features can fully reflect the moisture line conditions on the film surface and provide an important basis for subsequent correlation analysis and anomaly detection. During the feature extraction process, the feature extraction module will fully consider factors such as image lighting conditions and noise interference, and use adaptive algorithm parameters and strategies to ensure the accuracy and robustness of feature extraction.

[0071] A correlation analysis module, connected to the data preprocessing module, for analyzing the correlation between the moisture pattern characteristics and the humidity data and the temperature data using a correlation analysis algorithm to obtain the correlation data;

[0072] It should be noted that the correlation analysis module uses advanced correlation algorithms to conduct in-depth analysis of the correlation between moisture texture features and humidity and temperature data. These algorithms can quantify the linear or monotonic relationship between different parameters, thereby revealing the intrinsic connection between them. Through correlation analysis, the system can more accurately understand the interaction between moisture texture features and production environment parameters, providing strong support for subsequent anomaly detection and control strategy generation.

[0073] An abnormality detection module, connected to the feature extraction module and the correlation analysis module, for detecting abnormal data in the production process of the PVB film according to the moisture pattern characteristics and correlation data;

[0074] It should be noted that the anomaly detection module can accurately identify abnormal conditions in the production process by setting reasonable thresholds and algorithms, such as changes in moisture pattern characteristics beyond the normal range, abnormal fluctuations in humidity or temperature data, etc. Once abnormal data is detected, the anomaly detection module will immediately trigger the alarm mechanism to alert the operator and send the abnormal data to the control strategy generation module for further processing. The control strategy generation module will automatically select and optimize the control strategy based on the type and characteristics of the abnormal data, and send it to the execution layer through the network for execution, thereby achieving real-time regulation and precise control of the PVB film production process. This intelligent anomaly detection and processing mechanism can significantly improve the production quality and efficiency of PVB film, reduce the production of defective products, reduce production costs, and bring greater economic benefits to the enterprise.

[0075] A control strategy generation module is connected to the abnormality detection module and is used to adaptively generate a control strategy based on the abnormal data and the optimization algorithm.

[0076] It should be noted that the control strategy generation module automatically generates a control strategy that adapts to the current production environment based on the abnormal data provided by the abnormal detection module and the preset optimization algorithm. The module has built-in multiple optimization algorithms, such as genetic algorithms, texture matching algorithms based on image processing, etc., which can select the most appropriate algorithm for processing different types of abnormal data. After generating the control strategy, the control strategy generation module will send the strategy to the execution layer through the network, and the various modules of the execution layer will adjust the production parameters of the PVB film, such as humidity, temperature, extruder speed and extrusion pressure, according to the strategy to achieve precise control of the moisture texture. This intelligent control strategy generation mechanism enables the PVB film moisture texture control system to automatically adjust the control strategy according to changes in the production environment, thereby ensuring the stability and efficiency of the production process.

[0077] As an optional embodiment of the present invention, optionally, the expression of the correlation analysis algorithm is:

[0078]

[0079] Among them, R(X,Y,Z) represents the correlation coefficient between humidity data X, temperature data Y and moisture texture feature Z;

[0080] n represents the total number of data points;

[0081] w i Represents the weight coefficient of the i-th data point;

[0082] X i Represents the humidity data of the i-th data point;

[0083] Represents the mean value of humidity data X;

[0084] σ X Indicates the standard deviation of humidity data X;

[0085] Y i Represents the temperature data of the i-th data point;

[0086] represents the mean of temperature data;

[0087] σ Y Represents the standard deviation of the temperature data.

[0088] It should be noted that the expression of the above correlation analysis algorithm comprehensively considers the interaction between humidity data, temperature data and moisture grain characteristics, and quantifies the intrinsic connection between them by calculating the correlation coefficient. In practical applications, the algorithm can dynamically analyze the correlation between moisture grain characteristics and humidity and temperature data based on real-time collected data, providing accurate data support for abnormal detection and control strategy generation. In addition, the algorithm also considers the weight coefficient of the data point, so that the analysis results are more in line with the actual situation and improves the accuracy and reliability of the system. By adopting this advanced correlation analysis algorithm, the PVB film moisture grain control system can more accurately control parameters such as humidity and temperature in the production process, thereby achieving precise control of moisture grains and improving the product quality and production efficiency of PVB film.

[0089] As an optional embodiment of the present invention, optionally, the abnormality detection module includes:

[0090] A water grain prediction unit, used to predict the water grain change trend according to the water grain characteristics;

[0091] It should be noted that the water grain prediction unit of this embodiment is a prediction model obtained through training of a machine learning algorithm. The model can learn and predict the changing trend of water grains in the future based on historical water grain feature data. By comparing the prediction results with the water grain features captured in real time, the system can promptly detect abnormal changes in water grains, such as significant changes in grain shape, size or distribution density, thereby triggering an abnormality detection mechanism. This predictive capability enables the system to take preventive measures before anomalies occur, further improving the production quality and efficiency of PVB film. The machine learning algorithms used by the water grain prediction unit include but are not limited to support vector machines, neural networks, random forests, etc. These algorithms can automatically adjust model parameters according to the characteristics and needs of the data to improve the accuracy and robustness of the prediction.

[0092] The abnormality detection unit is used to detect abnormal data in the production process of the PVB film according to the moisture pattern change trend and correlation data.

[0093] It should be noted that the anomaly detection unit can accurately identify abnormal situations in the production process by comprehensively analyzing the moisture pattern change trend provided by the water pattern prediction unit and the correlation data provided by the correlation analysis module. The unit adopts advanced anomaly detection algorithms, such as statistical-based anomaly detection, machine learning-based anomaly detection, etc., which can automatically set reasonable thresholds and detection rules to monitor and analyze production data in real time. Once abnormal data is detected, such as changes in moisture pattern characteristics exceeding the preset range, abnormal fluctuations in humidity or temperature data, etc., the anomaly detection unit will immediately trigger the alarm mechanism and send the abnormal data to the control strategy generation module for further processing. This data-driven anomaly detection mechanism can achieve comprehensive monitoring and precise control of the PVB film production process, effectively avoid the production of defective products, and improve production efficiency and product quality.

[0094] As an optional embodiment of the present invention, optionally, the water pattern prediction unit predicts the water pattern change trend using the expression:

[0095]

[0096] Among them, Z t+k represents the moisture texture feature vector at time t+k;

[0097] t represents time;

[0098] k represents the prediction step length;

[0099] W represents the weight matrix;

[0100] M represents the size of the time window;

[0101] α m Represents the weight coefficient of the mth historical data point;

[0102] φ(,,) represents a nonlinear function;

[0103] Z t-m Represents the moisture texture feature vector of the tmth historical data point;

[0104] X t-m Represents the humidity data of the tmth historical data point;

[0105] Y t-m Temperature data of the tmth historical data point;

[0106] β represents the weight coefficient of trend characteristics;

[0107] Trend(·) represents the trend function;

[0108] Zt Represents the moisture texture feature vector of the t-th historical data point;

[0109] Z t-h Represents the moisture texture feature vector of the th historical data point.

[0110] It should be noted that the prediction expression of the water grain prediction unit comprehensively considers multiple factors such as time, weights of historical data points, nonlinear functions, and trend characteristics, and obtains the moisture grain feature vector at future time points by calculation. In practical applications, the prediction expression can dynamically predict the changing trend of moisture grains based on historical moisture grain feature data, humidity data, and temperature data. By comparing the prediction results with the moisture grain characteristics captured in real time, the system can promptly detect abnormal changes in moisture grains, thereby triggering anomaly detection mechanisms. In addition, the prediction expression also takes into account the size of the time window and the weight matrix, so that the prediction results are more in line with the actual situation, and the accuracy and robustness of the prediction are improved. By adopting this advanced prediction expression, the PVB film moisture grain control system can more accurately predict the changing trend of moisture grains, provide strong data support for anomaly detection and control strategy generation, and further improve the production quality and efficiency of PVB film.

[0111] As an optional embodiment of the present invention, optionally, the abnormality detection unit detects abnormal data in the production process of the PVB film according to the moisture pattern change trend and correlation data, including:

[0112] S1. Setting the threshold of the change rate of moisture texture characteristics and the threshold of the correlation coefficient;

[0113] It should be noted that in step S1, the system will set reasonable moisture texture feature change rate thresholds and correlation coefficient thresholds based on historical data and industry experience. These thresholds are used to determine whether the moisture texture feature and correlation data in the current production process are within a normal range.

[0114] S2. Calculating the moisture texture change rate based on the predicted moisture texture change trend;

[0115] It should be noted that in step S2, the system will use the water pattern change trend predicted by the water pattern prediction unit to calculate the change rate of the current water pattern characteristics. This step quantifies the change speed and trend of the water pattern by comparing the predicted water pattern characteristics with the actually captured characteristics, providing a basis for subsequent abnormal judgment.

[0116] S3, determining whether the correlation coefficient is lower than the correlation coefficient threshold, if so, executing step S4; if not, determining whether the moisture pattern characteristic change rate exceeds the moisture pattern characteristic change rate threshold;

[0117] When the moisture pattern feature change rate exceeds the moisture pattern feature change rate threshold, it is determined to be abnormal data;

[0118] It should be noted that in step S3, the system will comprehensively judge the two indicators of correlation coefficient and moisture grain feature change rate. If the correlation coefficient is lower than the preset correlation coefficient threshold, it indicates that the correlation between the humidity, temperature and other parameters in the current production environment and the moisture grain feature is weakened, and there may be abnormal conditions, so step S4 is executed for further analysis. If the correlation coefficient is normal, continue to judge whether the moisture grain feature change rate exceeds the preset moisture grain feature change rate threshold. If the moisture grain feature change rate is abnormal, such as a sharp increase or decrease, beyond the normal fluctuation range, it is also determined as abnormal data. This step can accurately identify potential problems in the production process through quantitative analysis, and provide an important basis for subsequent control strategy adjustments. After being determined as abnormal data, the system will immediately trigger the alarm mechanism and send the abnormal data to the control strategy generation module so that timely measures can be taken to intervene and adjust to ensure the stability and efficiency of the PVB film production process.

[0119] S4. When the correlation coefficient is lower than the correlation coefficient threshold, the corresponding humidity data or temperature data is marked as abnormal data;

[0120] It should be noted that in step S4, the correlation coefficient includes the temperature correlation coefficient and the humidity correlation coefficient, and the correlation coefficient threshold also includes the temperature correlation coefficient threshold and the humidity correlation coefficient threshold; when the temperature correlation coefficient is lower than the temperature correlation coefficient threshold or the humidity correlation coefficient is lower than the humidity correlation coefficient threshold, the system will mark the corresponding humidity data or temperature data as abnormal data. The implementation of this step helps the system to quickly locate the source of the problem, whether it is the abnormal humidity parameter or the abnormal temperature parameter that causes the change in the moisture grain characteristics. By marking the abnormal data, the system can provide more specific and targeted guidance for subsequent control strategy adjustments. For example, if the humidity data is marked as abnormal, the control strategy generation module will adjust the operating parameters of the humidification or dehumidification equipment to restore the humidity to the normal range; if the temperature data is marked as abnormal, the set value of the heating or cooling system will be adjusted to ensure that the temperature is stable within the range of production requirements. This data-driven exception handling mechanism can achieve refined management and precise control of the PVB film production process, effectively improving production efficiency and product quality.

[0121] S5. When the moisture pattern feature change rate exceeds the moisture pattern feature change rate threshold, the corresponding moisture pattern feature is marked as abnormal data;

[0122] It should be noted that the implementation of step S5 helps the system to fully understand and grasp the abnormal conditions in the production process, and provide detailed data support for the subsequent adjustment of the control strategy. For example, if the moisture pattern characteristics are distorted or unevenly distributed, it means that the speed or extrusion pressure of the extruder needs to be adjusted; if the pattern size is abnormal, it may be related to the set value of the humidity or temperature parameter. By marking the abnormal data and recording its characteristics in detail, the system can guide the operator to adjust the production parameters more accurately, thereby achieving precise control of the moisture pattern and improving the product quality and production efficiency of the PVB film. In addition, the system will also store the marked abnormal data and related information in the database for subsequent in-depth research in data analysis, trend prediction and quality control, providing strong support for the continuous improvement and optimization of PVB film production.

[0123] S6. Feedback the marked abnormal data to a control strategy generation module, and the strategy generation module generates corresponding control strategies according to different abnormal data and issues an alarm.

[0124] It should be noted that in step S6, the system will feed back the marked abnormal data and related information to the control strategy generation module in real time. The module will intelligently generate corresponding control strategies according to different abnormal data types and characteristics. For example, for abnormal humidity data, the control strategy may adjust the operating parameters of the humidification or dehumidification equipment; for abnormal temperature data, the set value of the heating or cooling system will be adjusted; and for abnormal moisture pattern characteristics, the speed of the extruder, extrusion pressure and other parameters will be adjusted. This intelligent control strategy generation mechanism based on abnormal data can achieve rapid response and precise control of the PVB film production process, effectively avoid the production of defective products, and improve production efficiency and product quality. At the same time, the system will also trigger the alarm mechanism to promptly notify the operator to pay attention to abnormal situations and take corresponding measures to intervene and adjust. This comprehensive abnormality handling and control strategy generation mechanism ensures the stability and efficiency of the PVB film moisture pattern control system, and provides strong support for the continuous improvement and optimization of PVB film production.

[0125] As an optional embodiment of the present invention, optionally, the expression for calculating the rate of change of the moisture texture characteristic is:

[0126]

[0127] Among them, η represents the rate of change of moisture texture characteristics;

[0128] F (t′+Δt′) represents the moisture pattern feature vector at time t′+Δt′;

[0129] F (t′)represents the moisture texture feature vector at time t′;

[0130] P·P represents the norm of the vector.

[0131] As an optional embodiment of the present invention, optionally, the control strategy generation module adaptively generates the control strategy including:

[0132] A1. Identify abnormal data types, and analyze the causes of abnormal data according to the identified abnormal data types;

[0133] It should be noted that in step A1, the control strategy generation module will intelligently identify the type of abnormal data, such as abnormal humidity data, abnormal temperature data, or abnormal moisture pattern characteristics. Subsequently, the module will conduct an in-depth analysis of the causes of the abnormal data based on the type of abnormal data identified. The implementation of this step helps the system to accurately locate the source of the problem and provide an important basis for subsequent control strategy adjustments. For example, if the humidity data is identified to be abnormal, the system will analyze whether it is caused by improper setting of the operating parameters of the humidification or dehumidification equipment, or excessive fluctuations in the ambient humidity. If the temperature data is identified to be abnormal, the operating status and set values ​​of the heating or cooling system will be checked to see if they are reasonable. If the moisture pattern characteristics are identified to be abnormal, the influence of process parameters such as the speed and extrusion pressure of the extruder will be considered. By deeply analyzing the causes of the abnormal data, the system can provide more accurate and targeted guidance for subsequent control strategy adjustments, thereby achieving refined management and precise control of the PVB film production process.

[0134] A2. Select an optimization algorithm based on the abnormal data type and cause, and generate an optimization strategy using the optimization algorithm;

[0135] It should be noted that in step A2, the control strategy generation module will intelligently select a suitable optimization algorithm based on the identified abnormal data type and cause. These optimization algorithms include but are not limited to genetic algorithms, particle swarm optimization, etc., which can automatically adjust model parameters and strategy rules according to different abnormal situations and production requirements to generate the optimal control strategy. For example, for abnormal humidity data, the system may choose to use genetic algorithms to optimize the operating parameters of humidification or dehumidification equipment, and search for the best humidity control strategy by simulating natural selection and genetic mechanisms. For abnormal temperature data, the particle swarm algorithm will be used to quickly converge to the optimal temperature setting value by simulating the behavior of bird flocks foraging. If the moisture pattern feature is identified to be abnormal, the system will use the neural network optimization algorithm to learn and predict the best process parameter adjustment plan by training the neural network model. This intelligent selection optimization algorithm mechanism based on abnormal data type and cause can achieve rapid response and precise control of the PVB film production process, and further improve production efficiency and product quality. Through the generated optimization strategy, the system can guide operators to adjust production parameters more accurately, thereby achieving precise control of moisture patterns and ensuring the production quality and stability of PVB films. At the same time, these optimization strategies will also be stored in the database for subsequent in-depth research in data analysis, trend prediction, quality control, etc., providing strong support for the continuous improvement and optimization of PVB film production.

[0136] A3. Verify the optimization strategy to determine whether the optimization strategy is feasible. If so, use the optimization strategy to adjust the production parameters of the PVB film;

[0137] If not, return to step A1 to re-identify the abnormal data type and analyze the cause until a feasible optimization strategy is generated.

[0138] It should be noted that in step A3, the system will strictly verify the generated optimization strategy to ensure its feasibility and effectiveness in actual production. The implementation of this step is crucial to ensure the stability and efficiency of PVB film production. The system will comprehensively test and evaluate the optimization strategy by simulating the production environment, comparing historical data, and other methods. If the verification results show that the optimization strategy can significantly improve production efficiency and product quality, and will not have adverse effects on production equipment, the system will consider the optimization strategy feasible and apply it to the actual PVB film production process. This data-driven optimization strategy generation and verification mechanism can ensure the stability and efficiency of the PVB film moisture texture control system, and provide strong support for the continuous improvement and optimization of PVB film production. However, if the verification results show that the optimization strategy is not feasible or the effect is not good, the system will return to step A1, re-identify the abnormal data type and analyze the cause, so as to generate a more accurate and effective optimization strategy. This iterative optimization strategy generation and verification process can continuously improve the control accuracy and response speed of the system, and further improve the production quality and efficiency of PVB film.

[0139] As an optional embodiment of the present invention, optionally, in step A2, selecting an optimization algorithm based on the abnormal data type and cause includes:

[0140] A201. If the abnormal data type is abnormal humidity data or abnormal temperature data, a genetic algorithm is selected as the optimization algorithm;

[0141] It should be noted that in step A201, the system will intelligently select the genetic algorithm as the optimization algorithm according to the type of abnormal data, that is, the humidity data or temperature data is abnormal. As a search algorithm that simulates natural selection and genetic mechanisms, the genetic algorithm has the advantages of strong global search capabilities and the fitness function does not need to be continuously differentiable. It is very suitable for dealing with such complex nonlinear optimization problems. Through the genetic algorithm, the system can automatically adjust the operating parameters of the humidification or dehumidification equipment, heating or cooling system to search for the best humidity and temperature control strategy, thereby realizing the refined management and precise control of the PVB film production process. This mechanism of intelligently selecting the optimization algorithm based on the type of abnormal data can further improve the control accuracy and response speed of the system and ensure the production quality and efficiency of the PVB film.

[0142] A202. If the abnormal data type is abnormal moisture texture characteristics, a texture matching algorithm based on image processing is selected as the optimization algorithm.

[0143] It should be noted that in step A202, the system will intelligently select the texture matching algorithm based on image processing as the optimization algorithm based on the characteristics of the abnormal data, that is, the abnormal moisture texture characteristics. This algorithm can quickly identify the type and degree of texture abnormality by comparing and analyzing the moisture texture characteristics in the actual production process with the information in the standard texture feature library. Through the texture matching algorithm, the system can accurately locate the source of the problem in the production process, such as the influence of process parameters such as the speed of the extruder and the extrusion pressure, so as to provide more accurate and targeted guidance for subsequent control strategy adjustments. This mechanism of intelligently selecting the optimization algorithm based on the abnormal data type not only improves the control accuracy and response speed of the system, but also further ensures the production quality and efficiency of the PVB film. These optimization strategies will also be stored in the database for subsequent in-depth research on data analysis, trend prediction and quality control, providing strong support for the continuous improvement and optimization of PVB film production.

[0144] As an optional embodiment of the present invention, optionally, the execution layer includes:

[0145] A dehumidification and humidification module, used to adjust the humidity of the PVB film production environment according to the control strategy;

[0146] A heating and cooling module, used to adjust the temperature of the PVB film production environment according to the control strategy;

[0147] Extruder control module, used to adjust the speed and extrusion pressure of the extruder;

[0148] A curing oven control module, used to adjust the heating power and temperature distribution of the curing oven;

[0149] An alarm module, used to send out an alarm signal when the abnormality detection module detects abnormal data;

[0150] The data recording and storage module is used to record and store the film surface image data, humidity data, temperature data, moisture grain characteristics, correlation data, abnormal data and control strategy.

[0151] It should be noted that the dehumidification and humidification module, heating and cooling module, extruder control module, curing furnace control module, alarm module and data recording and storage module together constitute the execution layer of the PVB film moisture texture control system, which work together to ensure the stability and efficiency of the PVB film production process. The dehumidification and humidification module effectively avoids the influence of humidity fluctuation on moisture texture by accurately controlling the humidity of the production environment, thereby ensuring the stability of film quality. The heating and cooling module is responsible for adjusting the temperature of the production environment to ensure that it fluctuates within an appropriate range to reduce the adverse effects of abnormal temperature on moisture texture characteristics. The extruder control module can accurately control the extrusion process of the film by adjusting the speed and extrusion pressure of the extruder, thereby achieving precise control of moisture texture. The curing furnace control module ensures the uniformity and stability of the film during the curing process by adjusting the heating power and temperature distribution of the curing furnace. The alarm module promptly issues an alarm signal when abnormal data is detected, reminding the operator to pay attention and take corresponding measures to intervene and adjust, thereby effectively avoiding the production of defective products. The data recording and storage module is responsible for recording and storing various production data and control strategies, providing strong data support for subsequent in-depth research in data analysis, trend prediction and quality control. These modules together constitute the core part of the PVB film moisture and grain control system. Through intelligent anomaly detection, control strategy generation and precise control of the execution layer, it realizes comprehensive monitoring and precise management of the PVB film production process, effectively improving production efficiency and product quality.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. The PVB film moisture texture control system is characterized by: The system comprises: The perception layer is used to collect film surface image data, humidity data and temperature data; A network transmission layer, connected to the perception layer, for uploading the film surface image data, humidity data and temperature data to the analysis layer; An analysis layer, connected to the network transmission layer, for adaptively generating a control strategy based on the film surface image data, humidity data and temperature data, and sending the control strategy to the execution layer; The execution layer is connected to the analysis layer and is used to adjust the production parameters of the PVB film according to the control strategy.

2. The PVB film moisture texture control system according to claim 1, characterized in that: The analysis layer includes: A data preprocessing module, used for preprocessing the film surface image data, humidity data and temperature data; A feature extraction module, connected to the data preprocessing module, for extracting moisture grain features based on the film surface image data; A correlation analysis module, connected to the data preprocessing module, for analyzing the correlation between the moisture pattern characteristics and the humidity data and the temperature data using a correlation analysis algorithm to obtain the correlation data; An abnormality detection module, connected to the feature extraction module and the correlation analysis module, for detecting abnormal data in the production process of the PVB film according to the moisture pattern characteristics and correlation data; A control strategy generation module is connected to the abnormality detection module and is used to adaptively generate a control strategy based on the abnormal data and the optimization algorithm.

3. The PVB film moisture texture control system according to claim 2, characterized in that: The expression of the correlation analysis algorithm is: Among them, R(X,Y,Z) represents the correlation coefficient between humidity data X, temperature data Y and moisture texture feature Z, n represents the total number of data points, and w i represents the weight coefficient of the i-th data point, X i represents the humidity data of the i-th data point, represents the mean of humidity data X, σ X Indicates the standard deviation of humidity data X, Y i represents the temperature data of the i-th data point, represents the mean of temperature data, σ Y Represents the standard deviation of the temperature data.

4. The PVB film moisture texture control system according to claim 2, characterized in that: The anomaly detection module comprises: A water grain prediction unit, used to predict the water grain change trend according to the water grain characteristics; The abnormality detection unit is used to detect abnormal data in the production process of the PVB film according to the moisture pattern change trend and correlation data.

5. The PVB film moisture texture control system according to claim 4, characterized in that: The water pattern prediction unit predicts the water pattern change trend as follows: Among them, Z t+k represents the moisture texture feature vector at time t+k, t represents time, k represents the prediction step, W represents the weight matrix, M represents the size of the time window, α m represents the weight coefficient of the mth historical data point, φ(,,) represents the nonlinear function, and Z t-m represents the moisture texture feature vector of the tmth historical data point, X t-m Represents the humidity data of the tmth historical data point, Y t-m The temperature data of the tmth historical data point, β represents the weight coefficient of the trend feature, Trend(·) represents the trend function, Z t represents the moisture texture feature vector of the t-th historical data point, Z t-h Represents the moisture texture feature vector of the th historical data point.

6. The PVB film moisture texture control system according to claim 4, characterized in that: The abnormality detection unit detects abnormal data in the production process of the PVB film according to the moisture pattern change trend and correlation data, including: S1. Setting the threshold of the change rate of moisture texture characteristics and the threshold of the correlation coefficient; S2. Calculating the moisture texture change rate based on the predicted moisture texture change trend; S3, determining whether the correlation coefficient is lower than the correlation coefficient threshold, if so, executing step S4; if not, determining whether the moisture pattern characteristic change rate exceeds the moisture pattern characteristic change rate threshold; When the moisture pattern feature change rate exceeds the moisture pattern feature change rate threshold, it is determined to be abnormal data; S4. When the correlation coefficient is lower than the correlation coefficient threshold, the corresponding humidity data or temperature data is marked as abnormal data; S5. When the moisture pattern feature change rate exceeds the moisture pattern feature change rate threshold, the corresponding moisture pattern feature is marked as abnormal data; S6. Feedback the marked abnormal data to a control strategy generation module, and the strategy generation module generates corresponding control strategies according to different abnormal data and issues an alarm.

7. The PVB film moisture texture control system according to claim 6, characterized in that: The expression for calculating the change rate of the moisture texture characteristics is: Among them, η represents the rate of change of moisture texture characteristics, F (t′+Δt′) represents the moisture pattern feature vector at time t′+Δt′, F (t′) represents the moisture pattern feature vector at time t′, and P·P represents the norm of the vector.

8. The PVB film moisture texture control system according to claim 2, characterized in that: The control strategy generation module adaptively generates the control strategy including: A1. Identify abnormal data types, and analyze the causes of abnormal data according to the identified abnormal data types; A2. Select an optimization algorithm based on the abnormal data type and cause, and generate an optimization strategy using the optimization algorithm; A3. Verify the optimization strategy to determine whether the optimization strategy is feasible. If so, use the optimization strategy to adjust the production parameters of the PVB film; If not, return to step A1 to re-identify the abnormal data type and analyze the cause until a feasible optimization strategy is generated.

9. The PVB film moisture texture control system according to claim 8, characterized in that: In step A2, selecting an optimization algorithm based on the abnormal data type and cause includes: A201. If the abnormal data type is abnormal humidity data or abnormal temperature data, a genetic algorithm is selected as the optimization algorithm; A202. If the abnormal data type is abnormal moisture texture characteristics, a texture matching algorithm based on image processing is selected as the optimization algorithm.

10. The PVB film moisture texture control system according to claim 2, characterized in that: The execution layer includes: A dehumidification and humidification module, used to adjust the humidity of the PVB film production environment according to the control strategy; A heating and cooling module, used to adjust the temperature of the PVB film production environment according to the control strategy; Extruder control module, used to adjust the speed and extrusion pressure of the extruder; A curing oven control module, used to adjust the heating power and temperature distribution of the curing oven; An alarm module, used to send out an alarm signal when the abnormality detection module detects abnormal data; The data recording and storage module is used to record and store the film surface image data, humidity data, temperature data, moisture grain characteristics, correlation data, abnormal data and control strategy.

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