Pole piece deviation rectification control method of automatic cutting lithium battery winding machine

Abstract

The invention provides a pole piece deviation rectification control method of an automatic cutting lithium battery winding machine, which comprises the following steps of: installing a width sensor on the winding machine, acquiring width data of a current winding pole piece in real time, and transmitting the acquired width data to a deviation rectification control system as an input parameter of a control decision; when the control strategy is adjusted, historical production data are analyzed through a machine learning algorithm, optimal control parameter combinations under different pole piece widths are obtained, and a self-learning parameter optimization model is formed and used for guiding deviation correction control decisions of subsequent batches of products; in the winding process, the offset and the motion state of the pole piece are continuously monitored, filtering processing is conducted on collected data through a Kalman filtering algorithm, interference noise is eliminated, the accuracy of offset detection is improved, and a reliable feedback signal is provided for a deviation correction executing mechanism.

Description

Technical Field

[0001] The present invention relates to the field of information technology, in particular to the field of lithium battery winding machines, and specifically to a pole piece deviation correction control method of an automatic cutting lithium battery winding machine. Background Art

[0002] In the production process of lithium battery winding machines, the fluctuation of pole piece width poses a severe challenge to the correction control. The pole piece widths of different batches vary greatly, resulting in changes in the motion characteristics of the pole pieces during the winding process. Narrow pole pieces are prone to frequent deviations, while wide pole pieces are more likely to wrinkle when wound at high speeds. The fluctuation of width not only affects the control accuracy of the correction actuator, but also brings difficulties to the design of the control algorithm.

[0003] Traditional deviation correction control methods are mostly based on the motion model of fixed-width pole pieces, which cannot adapt to width fluctuations. When the actual pole piece width does not match the preset value, it is difficult for the control system to make timely and effective adjustments, resulting in the pole piece offset exceeding the allowable range. If an overly conservative control strategy is adopted, although pole piece offset can be avoided, the production efficiency of the winder will be significantly reduced. In addition, due to the rapid iteration of lithium battery production processes, the width specifications of new pole pieces change more frequently. Whenever a new pole piece model is introduced, the deviation correction control program needs to be revised, which increases the workload of system maintenance. For some small-batch customized products, it is even necessary to write control codes separately for each batch of pole pieces, which undoubtedly reduces the flexibility and versatility of the winder.

[0004] Therefore, there is an urgent need for an adaptive deviation correction control method that can automatically adjust the control strategy and parameters according to the real-time changes in the pole piece width. This method requires the establishment of a dynamic mapping relationship between the pole piece width and the motion characteristics, and the design of corresponding control algorithms for different width ranges. At the same time, it is also necessary to consider how to maximize the winding efficiency while ensuring the control accuracy. This puts higher requirements on the real-time, stability and robustness of the control system. Summary of the invention

[0005] The present invention provides a pole piece deviation correction control method for an automatic cutting lithium battery winding machine, characterized in that the method comprises:

[0006] S101, installing a width sensor on the winding machine, obtaining the width data of the currently wound pole piece in real time, and transmitting the obtained width data to the deviation correction control system as an input parameter for control decision;

[0007] S102, according to the acquired pole piece width data, access the pre-established pole piece width and motion characteristic mapping table to obtain the theoretical motion parameters of the pole piece under the current width, including the control accuracy threshold of the deviation correction actuator and the upper limit of the winding speed, and the theoretical motion parameters specifically include the expected tension, expected linear speed and expected deviation range of the pole piece during the winding process;

[0008] S103, using an adaptive control algorithm to compare the pole piece width and motion parameters with the actual control accuracy of the current deviation correction actuator. If the actual control accuracy is lower than the theoretical threshold requirement, the control strategy is dynamically adjusted to reduce the winding speed or increase the deviation correction execution force until the accuracy requirement is met;

[0009] S104, while adjusting the control strategy, analyze the historical production data through machine learning algorithms to obtain the optimal control parameter combination under different pole piece widths, and form a self-learning parameter optimization model to guide the correction control decision of subsequent batches of products;

[0010] S105, for small batch customized products, obtains the actual width, material properties and production process parameters of the batch of pole pieces through rapid testing, and performs multi-dimensional matching with the historical data in the parameter optimization model, selects the control strategy under the most similar conditions as the initial strategy, and then performs fine-tuning and optimization to shorten the adaptation time of the control system;

[0011] S106, during the winding process, the offset and movement state of the pole piece are continuously monitored, and the collected data is filtered through the Kalman filter algorithm to remove interference noise, improve the accuracy of offset detection, and provide a reliable feedback signal for the deviation correction actuator;

[0012] S107 adopts an intelligent decision-making algorithm based on fuzzy logic, takes the obtained offset detection result as the main input, and comprehensively considers the influencing factors of the pole piece width and winding speed, dynamically adjusts the control force and response time of the correction actuator, adjusts the displacement of the actuator according to the offset size, and adjusts the response speed of the actuator according to the offset speed. While ensuring the correction accuracy, the winding efficiency is improved to achieve adaptive correction control under width fluctuations.

[0013] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0014] The present invention discloses an adaptive deviation correction control method suitable for a winding machine. The method obtains pole piece width data in real time, combines a pre-established width and motion characteristic mapping table, determines theoretical motion parameters, adopts an adaptive control algorithm, and dynamically adjusts the control strategy to meet the accuracy requirements. At the same time, machine learning is used to analyze historical data to form a self-learning parameter optimization model to guide subsequent batch production. For small batch customized products, the control strategy under similar conditions is quickly matched and fine-tuned. During the winding process, the Kalman filter algorithm is used to improve the accuracy of offset detection, and the intelligent decision-making algorithm based on fuzzy logic is used to dynamically adjust the deviation correction execution by comprehensively considering multiple factors. The present invention realizes precise deviation correction control under the condition of pole piece width fluctuation, improves winding efficiency and product quality, has strong adaptability, and is particularly suitable for flexible production needs of multiple varieties and small batches. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flow chart of a pole piece deviation correction control method of an automatic cutting lithium battery winding machine. DETAILED DESCRIPTION

[0016] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for the convenience of description, only the parts related to the invention are shown in the accompanying drawings.

[0017] Reference Figure 1 In this embodiment, a pole piece deviation correction control method of an automatic cutting lithium battery winding machine may specifically include:

[0018] S101. Install a width sensor on the winding machine to obtain the width data of the currently wound pole piece in real time, and transmit the obtained width data to the deviation correction control system as an input parameter for control decision-making.

[0019] By installing a width sensor on the winding machine, the width data of the winding pole piece is obtained in real time, and the obtained width data is transmitted to the correction control system as an input parameter for the correction control system to make control decisions. According to the winding pole piece width data received by the correction control system, it is judged whether the width of the winding pole piece exceeds the preset width threshold range. If it exceeds the preset width threshold range, the correction control system is triggered to perform correction control. The correction control system determines the correction control parameters for the current winding pole piece width according to the pre-established correspondence between the winding pole piece width and the correction control parameters, and transmits the correction control parameters to the actuator of the winding machine. The actuator of the winding machine adjusts the operating state of the winding machine according to the received correction control parameters, controls the width of the winding pole piece, and maintains the width of the winding pole piece within the preset width threshold range. In the process of correcting the width of the winding pole piece, the width data of the winding pole piece is continuously obtained, and the width data is transmitted to the correction control system in real time to form a closed-loop control. The correction control system adopts PID control algorithm, and dynamically adjusts the parameters of PID control algorithm according to the deviation between the acquired winding pole piece width data and the preset width threshold, so as to realize the precise control of the winding pole piece width. The width sensor installed on the winding machine continuously obtains the width data of the winding pole piece, and transmits the width data to the host computer, analyzes the change trend of the winding pole piece width, predicts the possible abnormal situation of the winding pole piece width, and takes preventive measures in advance.

[0020] Specifically, the width sensor on the winding machine is like the eyes of the production line, monitoring the width of the pole piece in real time. A laser displacement sensor can be used, which emits laser to the surface of the pole piece, calculates the distance based on the reflected light, and thus obtains the width. Assuming that the standard width of the pole piece is set to 100mm, the allowable error range is ±1mm, that is, the width threshold range is 99mm-101mm. The sensor collects width data 10 times per second and immediately transmits it to the correction control system. The correction control system is like the brain of the production line, which receives width data and makes decisions. For example, at a certain moment, the sensor measures a width of 102mm, which exceeds the preset width threshold range. At this time, the correction control system will start the correction program. The corresponding relationship table between width and correction parameters is pre-established in the correction control system. For example, when the width deviation is +2mm, the corresponding correction parameter is to adjust the guide roller angle by 1 degree. This relationship table can be adjusted and optimized according to actual conditions, for example, it can be fitted through experimental data or set based on experience. This can ensure the accuracy and efficiency of correction. The correction parameter is like an instruction to the production line, which tells the actuator how to act. The winding machine's actuator, such as the guide roller motor, will adjust the angle of the guide roller after receiving the correction parameters, thereby changing the running trajectory of the pole piece and ultimately controlling the width of the pole piece.

[0021] In this example, the actuator will adjust the guide roller angle by 1 degree. The entire correction process is a closed-loop control, like a cycle. The width sensor continuously collects data, the correction control system continuously adjusts parameters, and the actuator continuously performs actions, ultimately maintaining the width of the pole piece within the set range. This closed-loop control method can effectively deal with various interference factors and ensure the stability of the pole piece width. The PID control algorithm is the core of the correction control system, which is like a precision regulator. It dynamically adjusts the control parameters according to the width deviation, deviation change rate and deviation accumulation value to achieve precise control. For example, when the width deviation is large, the PID algorithm will increase the control strength and quickly correct the deviation; when the width deviation is small, the PID algorithm will reduce the control strength to avoid over-correction. The host computer is like the command center of the production line. It receives width data and analyzes and predicts it. For example, by analyzing the change trend of the width data, it is possible to predict abnormal conditions in the width of the pole piece, such as continuous increase or decrease in width. The host computer can issue an alarm in advance to remind the operator to take preventive measures, such as adjusting equipment parameters or changing materials, to avoid quality problems. The advantage of doing this is that problems can be discovered in advance, avoiding greater losses and improving production efficiency.

[0022] S102: Based on the acquired pole piece width data, access the pre-established pole piece width and motion characteristic mapping table to obtain the theoretical motion parameters of the pole piece under the current width, including the control accuracy threshold of the deviation correction actuator and the upper limit of the winding speed. The theoretical motion parameters specifically include the expected tension, expected linear speed and expected deviation range of the pole piece during the winding process.

[0023] Get the width data of the current pole piece and use it as the query condition. Access the pole piece width and motion characteristic mapping table pre-established by the system, and query the corresponding theoretical motion parameter record according to the current pole piece width. Extract the control accuracy threshold and winding speed upper limit data of the correction actuator from the query results for subsequent correction control and winding speed control. Extract the expected tension, expected line speed and expected offset range data of the pole piece during the winding process from the query results as a reference for subsequent winding process control. Compare the extracted control accuracy threshold of the correction actuator with the actual control accuracy of the correction actuator. If the actual control accuracy exceeds the threshold range, the correction control strategy adjustment is triggered. Compare the extracted winding speed upper limit with the current winding speed. If the current winding speed exceeds the upper limit, the winding deceleration control is triggered. During the winding process, the pole piece tension, line speed and offset data are collected in real time, and compared with the expected tension, expected line speed and expected offset range respectively. The winding control parameters are dynamically adjusted according to the deviation to ensure a smooth winding process.

[0024] Specifically, the purpose of establishing the pole piece width and motion characteristic mapping table is to associate the pole piece width with its optimal motion parameters during the winding process, so as to achieve precise control. This table can be obtained through a large amount of experimental data and theoretical analysis. For example, for a pole piece with a width of 100 mm, the corresponding optimal tension is 20 Newtons, the optimal linear speed is 5 meters per second, the optimal offset range is plus or minus 0.5 mm, the control accuracy threshold of the deviation correction actuator is 0.1 mm, and the upper limit of the winding speed is 6 meters per second. The determination of these parameters needs to consider multiple factors such as the material properties, thickness, and mechanical properties of the winding machine of the pole piece. The current pole piece width data can be obtained by a width sensor installed on the winding machine. The width sensor measures the width of the pole piece in real time and transmits the data to the control system. For example, the currently measured pole piece width is 98 mm. According to the acquired width data, such as 98 mm, the corresponding theoretical motion parameters are queried in the pole piece width and motion characteristic mapping table. Since there may be no record that corresponds exactly to a width of 98 mm in the mapping table, it can be obtained by interpolation. Assuming that there are records of 95 mm and 100 mm in the table, the theoretical motion parameters corresponding to 98 mm can be calculated through linear interpolation based on the data of these two records.

[0025] Extract the required parameters from the query results. For example, after table lookup or interpolation calculation, the control accuracy threshold of the correction actuator corresponding to a width of 98 mm is 0.09 mm, the upper limit of the winding speed is 5.8 m / s, the expected tension is 19 N, the expected linear speed is 4.8 m / s, and the expected offset range is plus or minus 0.4 mm. Compare the extracted control accuracy threshold with the control accuracy of the actual correction actuator. Assuming the actual control accuracy is 0.11 mm, it exceeds the threshold of 0.09 mm, which means that the current correction control accuracy is insufficient and the correction control strategy needs to be adjusted. For example, the PID parameters of the correction control system can be increased to improve the response speed and accuracy of the control system. Compare the extracted winding speed upper limit with the current winding speed. Assuming the current winding speed is 6 m / s, it exceeds the upper limit of 5.8 m / s. At this time, it is necessary to trigger the winding deceleration control to reduce the winding speed to avoid problems such as wrinkles or breaks in the pole piece due to excessive speed. During the winding process, the tension, linear speed and offset data of the pole piece are collected in real time. For example, the collected tension is 18 Newtons, the line speed is 4.7 meters per second, and the offset is 0.3 millimeters. Compare the collected data with the expected value. For example, the tension deviation is negative 1 Newton, the line speed deviation is negative 0.1 meters per second, and the offset is within the expected range. Dynamically adjust the winding control parameters according to the deviation. For example, since the tension is lower than the expected value, the output of the winding tension control system can be appropriately increased to bring the tension of the pole piece back to the expected range. Since the line speed is lower than the expected value, the speed of the winding motor can be appropriately increased to bring the line speed of the pole piece to the expected value. Through continuous monitoring and adjustment, ensure that the winding process proceeds smoothly and the quality of the final wound product is guaranteed. This can effectively control the tension, speed and position of the pole piece during the winding process, prevent quality defects, and improve the yield rate of the product.

[0026] S103, using an adaptive control algorithm to compare the pole piece width, motion parameters and the actual control accuracy of the current deviation correction actuator. If the actual control accuracy is lower than the theoretical threshold requirement, dynamically adjust the control strategy to reduce the winding speed or increase the deviation correction execution force until the accuracy requirement is met.

[0027] Obtain production data such as pole piece width and motion parameters, and input them into the adaptive control algorithm model. The actual control accuracy data of the deviation correction actuator is collected in real time through sensors, and compared with the preset theoretical threshold. If the actual control accuracy is lower than the theoretical threshold, the adaptive control algorithm is triggered to dynamically adjust the control strategy according to the degree of deviation. Through the machine learning algorithm, combined with historical data and real-time feedback, it is determined whether it is more conducive to improving accuracy to reduce the winding speed or increase the deviation correction execution force. According to the optimization suggestions output by the algorithm, the control system automatically adjusts the speed of the winding motor or the driving force of the deviation correction actuator. Continuously monitor the actual control accuracy of the deviation correction actuator and compare it with the dynamically updated theoretical threshold to form a closed-loop feedback control. When the actual control accuracy continues to meet the accuracy requirements, maintain the current control strategy; if deviation occurs again, repeat steps 3-6 until it is stable and meets the standard.

[0028] S104. While adjusting the control strategy, the historical production data is analyzed through a machine learning algorithm to obtain the optimal control parameter combination under different pole piece widths, and a self-learning parameter optimization model is formed to guide the correction control decision of subsequent batches of products.

[0029] Obtain historical production data, including process parameters and quality data under different pole piece widths, denoise and standardize the data according to the preset data cleaning rules, and obtain a production data set that can be used for analysis. Use a clustering algorithm to analyze the production data set, divide the data into several clusters according to the pole piece width, train the optimization model for each cluster, and obtain the optimal process parameter combination under the cluster. In subsequent production batches, obtain the current pole piece width, determine the cluster to which it belongs, and obtain the optimal process parameter combination from the corresponding optimization model, which is used as the initial parameter for production control. Collect various process parameters and quality data in real time during the production process. If the quality deviation is detected to exceed the preset threshold, the correction control process is triggered. In the correction control process, according to the deviation direction and amplitude, select the optimal parameter combination under similar working conditions from the optimization model of the current cluster, adjust the control strategy, and make the process parameters converge to the optimal combination. The corrected process parameters and quality data are fed back to the optimization model, and the incremental learning algorithm is used to update the model to adapt it to the latest production conditions, so as to achieve self-learning and continuous optimization of the model. The generalization performance of the optimization model is evaluated regularly. When the prediction error of the model on a new production batch exceeds the preset threshold, the clusters are re-divided and the optimization model is trained to adapt to changes in production conditions and ensure the effectiveness of the control strategy.

[0030] Specifically, historical production data is obtained, such as process parameters such as winding tension, winding speed, and correcting force corresponding to pole pieces of different widths (such as 120mm, 125mm, 130mm, etc.), as well as quality data such as thickness uniformity and surface flatness of the final product. These data may come from production databases, MES systems, or other data recording platforms. In order to ensure data quality, data cleaning is required according to preset rules, such as removing outliers and filling missing values. For example, the winding tension range is set between 10-20N, and data outside the range is regarded as an outlier, which needs to be removed or specially processed. At the same time, data of different dimensions are standardized, such as scaling the thickness uniformity value range to between 0-1, for subsequent analysis and modeling. The cleaned data is divided according to the pole piece width through a clustering algorithm, such as the K-Means algorithm. Assume that the data is divided into three categories according to the pole piece width: narrow pole pieces (for example, width less than 123mm), medium pole pieces (for example, width between 123mm-127mm), and wide pole pieces (for example, width greater than 127mm). For each type of pole piece, an optimization model is trained, such as a support vector machine or a neural network model. The input of the model is the process parameters, and the output is the quality index. Through training, the optimal process parameter combination under each cluster is obtained. For example, the optimal process parameter combination for narrow-width pole pieces is: winding tension 12N, winding speed 10m / min, and correction force 5N. The advantage of this is that more accurate control strategies can be formulated for pole pieces of different widths.

[0031] When producing a new batch of pole pieces, first measure the width of the current pole piece, for example, 125mm. According to the width, determine the cluster to which it belongs, for example, it belongs to a medium-width pole piece. Then obtain the optimal process parameter combination from the corresponding optimization model (that is, the optimization model corresponding to the medium-width pole piece), such as winding tension 15N, winding speed 12m / min, and correction force 8N, and apply it as the initial parameter to production control. This can effectively reduce the parameter debugging time and improve production efficiency. During the production process, various process parameters and quality data are collected in real time. For example, data such as winding tension, winding speed, correction force, and thickness uniformity are collected every 1 second. If it is detected that the quality deviation exceeds the preset threshold, for example, the thickness uniformity is less than 95, the correction control process is triggered. This can timely discover and correct deviations in the production process to ensure product quality. In the correction control process, according to the direction and amplitude of the deviation, the optimal parameter combination under similar working conditions is selected from the optimization model of the current cluster. For example, if the thickness uniformity is low and the deviation is large, a parameter combination with a smaller winding tension and a slower winding speed is selected from the optimization model of the current cluster (mid-width pole piece), such as a winding tension of 13N and a winding speed of 11m / min. By adjusting the control strategy, the process parameters converge to the optimal combination, thereby improving product quality. This allows for faster and more accurate deviation correction control.

[0032] The corrected process parameters and quality data are fed back to the optimization model, and the model is updated using an incremental learning algorithm. For example, new data is added to the training set, and the model is retrained to adapt it to the latest production conditions. This allows the model to continuously learn new data and improve the prediction accuracy and adaptability of the model. Regularly evaluate the generalization performance of the optimization model. For example, after every 100 batches of products are produced, the prediction error of the model on the new production batches is evaluated. If the prediction error of the model exceeds the preset threshold, such as more than 5%, the clusters are re-divided and the optimization model is trained to adapt to changes in production conditions. For example, with the improvement of the production process, the original cluster division may no longer be applicable, and the clusters need to be re-divided, and the optimization model needs to be trained for the new clusters. This ensures the effectiveness of the control strategy and continuously improves product quality.

[0033] S105. For small batch customized products, the actual width, material properties and production process parameters of the batch of pole pieces are obtained through rapid testing, and multi-dimensional matching is performed with the historical data in the parameter optimization model. The control strategy under the most similar conditions is selected as the initial strategy, and then fine-tuning and optimization are performed to shorten the adaptation time of the control system.

[0034] The actual width, material properties and production process parameters of the small batch customized pole pieces are obtained, and stored in a database to form structured data; a pre-trained machine learning model is used to predict the optimal initial control strategy of the pole piece according to the structured data; if the confidence of the optimal initial control strategy is lower than a preset threshold, a K nearest neighbor algorithm is used to search the historical database for K samples that are most similar to the current pole piece parameters, and a weighted average is calculated according to the control strategies corresponding to the K samples to obtain a new initial control strategy; the optimal initial control strategy is applied to the production process, and the key indicators in the production process are monitored in real time, and the key indicator data is stored in the database; a Bayesian optimization algorithm is used to fine-tune and optimize the optimal initial control strategy with the key indicators as the optimization target, and the optimized control strategy parameters are obtained; if the difference between the optimized control strategy parameters and the optimal initial control strategy parameters is less than a preset threshold, it is judged that the control system has reached a stable state; the optimized control strategy parameters and the corresponding key indicator data of the production process are stored in the historical database for updating the machine learning model and the sample library of the K nearest neighbor algorithm.

[0035] Specifically, the actual width, material properties and production process parameters of small batch customized pole pieces are obtained and stored in the database to form structured data. For example, for a pole piece order with a width of 100 mm, a graphene material with a specific formula and a coating speed of 50 m / min, its production process parameters such as coating roller pressure and drying temperature will be recorded and stored in the database. These data will be stored in a structured form, such as a relational database, to facilitate subsequent query and analysis. The advantage of this is that historical data can be easily retrieved and analyzed, providing data support for subsequent model training and optimization. A pre-trained machine learning model (such as a support vector machine) is used to predict the optimal initial control strategy of the pole piece based on historical data. The input features of the model include: pole piece width, material properties and production process parameters. The output of the model is the predicted initial control strategy parameters. For example, for a pole piece with a width of 95 mm and a graphene material with the same formula as the above example, the model can predict its optimal coating roller pressure of 2 MPa and drying temperature of 80 degrees Celsius based on historical data. Support vector machines can effectively process high-dimensional data and have good generalization ability. They can predict the optimal initial control strategy for new pole pieces based on historical data. If the confidence of the predicted initial control strategy parameters is lower than the preset threshold, the K nearest neighbor algorithm is used to search the historical database for the K samples most similar to the current pole piece parameters. According to the control strategies corresponding to the K samples, the weighted average is calculated to obtain a new initial control strategy. For example, assuming that the confidence of the initial control strategy predicted by the model is lower than 80%, the K nearest neighbor algorithm can be used to search the historical database for the 5 samples most similar to the current pole piece parameters. Assuming that the coating roller pressures of these 5 samples are 1, 2, 3, 2, and 1 MPa, respectively, the weighted average can be calculated to obtain a new coating roller pressure as a new initial control strategy parameter. The K nearest neighbor algorithm can effectively utilize historical data and improve the reliability of the control strategy, especially when the prediction confidence is low. The obtained initial control strategy is applied to the production process, and key indicators in the production process are monitored in real time, such as temperature, pressure, speed, etc. These key indicator data are stored in the database for subsequent model optimization. For example, the predicted coating roller pressure of 2 MPa and drying temperature of 80 degrees Celsius are applied to actual production, and key indicators such as temperature, pressure, and speed in the production process are monitored in real time. These data will be stored in the database for subsequent model optimization. This can monitor the production process in real time and provide data support for subsequent model optimization. The Bayesian optimization algorithm is used to fine-tune and optimize the initial control strategy. The input of the Bayesian optimization algorithm is the control strategy parameters, and the output is the key indicators of the production process. The optimization goal is to maximize or minimize a key indicator, such as minimizing production time or maximizing product quality.For example, with the goal of minimizing production time, the Bayesian optimization algorithm can fine-tune the coating roller pressure and drying temperature based on the initial control strategy to find the best parameter combination to minimize production time. The Bayesian optimization algorithm can effectively search the parameter space and find the optimal control strategy.

[0036] According to the output of the Bayesian optimization algorithm, the control strategy parameters are adjusted. If the difference between the adjusted control strategy parameters and the initial control strategy parameters is less than the preset threshold, the control system is judged to have reached a stable state. For example, the optimal coating roller pressure found by the Bayesian optimization algorithm is 25 MPa and the drying temperature is 82 degrees Celsius. If the difference between these two parameters and the initial control strategy parameters is less than the preset threshold, such as 5%, the control system is considered to have reached a stable state. This can avoid excessive adjustment of the control system and ensure the stability of the production process. The optimized control strategy parameters and the corresponding key indicator data of the production process are stored in the historical database for updating the sample library of the machine learning model and the K nearest neighbor algorithm to achieve continuous learning and optimization of the model. For example, the optimized coating roller pressure of 25 MPa, the drying temperature of 82 degrees Celsius and the corresponding key indicator data of the production process are stored in the historical database. These data will be used to update the sample library of the support vector machine model and the K nearest neighbor algorithm, so that the model can continuously learn new production conditions and improve prediction accuracy and control effect. This can achieve continuous learning and optimization of the model and make the control strategy more adaptable to changes in the production environment.

[0037] S106. During the winding process, the offset and movement state of the pole piece are continuously monitored, and the collected data is filtered through the Kalman filter algorithm to eliminate interference noise, improve the accuracy of offset detection, and provide a reliable feedback signal for the deviation correction actuator.

[0038] According to the motion state of the pole piece during the winding process, the offset of the pole piece is continuously monitored by the sensor to obtain the original data reflecting the offset state of the pole piece. For the original offset data obtained, the Kalman filter algorithm is used for data filtering processing, and the optimal value of the pole piece offset is estimated by establishing the state equation and the observation equation. In the Kalman filtering process, by setting the appropriate process noise covariance matrix and observation noise covariance matrix, the interference noise is effectively eliminated and the accuracy of the offset estimation is improved. According to the optimal estimate of the pole piece offset obtained by the Kalman filter, it is judged whether the offset exceeds the preset allowable range. If it exceeds the range, the correction actuator is triggered to make adjustments. According to the size and direction of the offset, the correction actuator controls the motion parameters of the winding equipment, such as adjusting the winding speed, tension, etc., to achieve real-time correction of the pole piece position. In the correction process, the offset change of the pole piece is continuously monitored, and the control parameters of the correction actuator are dynamically adjusted by feedback control to ensure that the pole piece offset is always within the allowable range. The pole piece offset data and correction execution parameters in the correction process are recorded to form a data log for subsequent process optimization and equipment maintenance.

[0039] S107, using an intelligent decision-making algorithm based on fuzzy logic, taking the offset detection result obtained in the previous step as the main input, and comprehensively considering multiple influencing factors such as pole piece width and winding speed, dynamically adjusting the control strength and response time of the correction actuator. The specific adjustment method includes: adjusting the displacement of the actuator according to the offset size, adjusting the response speed of the actuator according to the offset speed, while ensuring the correction accuracy, improving the winding efficiency, and realizing adaptive correction control under width fluctuations.

[0040] Sensors are used to collect parameters such as pole piece width and winding speed in real time to obtain the influencing factor data under the current production state. The acquired influencing factor data is preprocessed to remove outliers and smooth data fluctuations to provide high-quality input for subsequent analysis. The preprocessed influencing factor data is input into the intelligent decision-making model based on fuzzy logic, and the importance of each influencing factor is determined through fuzzy reasoning. According to the offset detection results and the importance of the influencing factors, the fuzzy rule library is used to dynamically generate the control strategy of the correction actuator. If the offset is large, a larger actuator displacement control strategy is generated; if the offset speed is fast, a faster actuator response speed control strategy is generated. The generated control strategy is converted into a specific actuator control instruction to drive the correction actuator to make real-time adjustments. The correction process is continuously monitored to obtain the offset changes before and after the correction, evaluate the correction effect, and feed the evaluation results back to the intelligent decision-making model to achieve adaptive optimization.

[0041] Specifically, the sensor collects parameters such as pole piece width and winding speed in real time to provide a data basis for subsequent analysis. For example, a laser displacement sensor is used to measure the pole piece width, and an encoder is used to measure the winding speed. The data of these sensors constitute the influencing factors that describe the current production status. For example, the pole piece width is measured to be 60mm and the winding speed is 10m / min. These data reflect the current production status and are the basis for subsequent analysis and control. After acquiring the data, preprocessing is required to remove outliers and data fluctuations. For example, the collected pole piece width data may have spikes due to instantaneous interference from the sensor, which is an outlier and needs to be eliminated. Simple mean filtering or median filtering methods can be used to smooth the data. For example, the width data collected continuously is 59mm, 61mm, 65mm, 58mm, and 62mm. Among them, 65mm is obviously an outlier and needs to be eliminated. After elimination, mean filtering is performed again to obtain smoothed width data of about 60mm. This can prevent abnormal data from interfering with subsequent analysis and control and improve the stability of the system. The preprocessed data is input into the intelligent decision model based on fuzzy logic. Fuzzy logic is used to process fuzzy concepts, such as "big", "small", "fast", "slow", etc. Fuzzify the influencing factors, such as fuzzifying the pole piece width of 60mm to "normal width" and the winding speed of 10m / min to "medium speed". The fuzzy reasoning process is carried out according to pre-set fuzzy rules. For example, a rule can be: if the pole piece width is "normal width" and the winding speed is "medium speed", the offset impact is "medium".

[0042] Through fuzzy reasoning, the importance of each influencing factor can be determined, for example, the importance of the pole piece width is 8, and the importance of the winding speed is 5. The advantage of this is that it can simulate human experience and knowledge, deal with factors that are difficult to accurately quantify, and make the control strategy more flexible. According to the offset detection results and the importance of the influencing factors, the fuzzy rule library is used to dynamically generate the control strategy of the deviation correction actuator. For example, the offset after Kalman filtering is 1mm, which is fuzzified as "small offset". Combined with the importance of the influencing factors and the preset fuzzy rules, for example: if the offset is "small offset" and the pole piece width influence is "high", the actuator displacement is "small". Finally, a specific actuator control instruction is generated, for example: move the actuator 5mm to the left. This can dynamically adjust the control strategy according to the actual situation to achieve more precise control. Assuming that the offset is large, such as 5mm, it is fuzzified as "large offset", and the offset speed is fast, it is fuzzified as "fast offset". According to the fuzzy rules, for example: if the offset is "large offset" and the offset speed is "fast offset", the actuator displacement is "large" and the actuator response speed is "fast". The generated control strategy may be: quickly move the actuator to the right by 2mm. This strategy can quickly and effectively correct large offsets and ensure product quality. After the control strategy is converted into specific actuator control instructions, the correction actuator is driven to make real-time adjustments. For example, the control instruction "move the actuator to the right by 2mm" will be converted into an electrical signal or other type of control signal to drive the actuator motor to rotate the corresponding angle or distance to achieve precise displacement control. The correction process is continuously monitored. For example, the offset before correction is 5mm, and the offset after correction is 1mm. If the evaluation results show that the correction effect is good, this result will be fed back to the intelligent decision-making model. If the correction effect is not good, the fuzzy rules or other parameters are adjusted to achieve adaptive optimization. This feedback mechanism can continuously improve the control strategy and improve the adaptability and robustness of the system.

[0043] Through fuzzy logic, intelligent decision-making is performed on the pre-processed influencing factor data, the importance of each factor is determined based on fuzzy reasoning, and the deviation change obtained by deviation correction monitoring is combined to evaluate the deviation correction effect and realize adaptive optimization of the decision-making model.

[0044] According to the influencing factor data, the fuzzy clustering algorithm is used to group and classify the data to obtain different categories of influencing factor data sets. For each category of influencing factor data sets, the importance weight values ​​of each influencing factor in the category are determined through fuzzy logic reasoning. The offset change data of each influencing factor output by the correction monitoring module is obtained, and it is weighted with the weight value of the factor importance obtained by fuzzy reasoning to obtain a comprehensive offset change value. According to the comprehensive offset change value, the fuzzy evaluation method is used to judge the current correction effect. If the correction effect does not reach the preset threshold, the adaptive optimization mechanism of the decision model is triggered. The fuzzy reasoning rule base and membership function are dynamically updated through the incremental learning algorithm, and the parameters of the fuzzy logic intelligent decision model are adjusted to achieve adaptive optimization of the decision model. The optimized fuzzy logic intelligent decision model is applied to the influencing factor data processing, and the optimized factor importance weights are output to guide the correction process. The above steps are continuously iterated to continuously improve the correction effect through adaptive optimization of the decision model and correction monitoring feedback, and to achieve accurate evaluation and intelligent decision-making of influencing factors.

[0045] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A pole piece deviation correction control method for an automatic cutting lithium battery winding machine, characterized in that: The method comprises: S101, installing a width sensor on the winding machine to obtain the width data of the currently wound pole piece in real time, and transmitting the obtained width data to the correction control system as an input parameter for control decision; S102: Based on the acquired pole piece width data, access a pre-established mapping table of pole piece width and motion characteristics to obtain theoretical motion parameters of the pole piece at the current width, including a control accuracy threshold of a deviation correction actuator and an upper limit of a winding speed. The theoretical motion parameters specifically include an expected tension, an expected linear speed, and an expected offset range of the pole piece during the winding process. S103: Using an adaptive control algorithm, the pole piece width and motion parameters are compared with the actual control accuracy of the current deviation correction actuator. If the actual control accuracy is less than the theoretical threshold requirement, the control strategy is dynamically adjusted to reduce the winding speed or increase the deviation correction execution force until the accuracy requirement is met. S104: While adjusting the control strategy, historical production data is analyzed using a machine learning algorithm to obtain the optimal control parameter combination for different electrode widths, forming a self-learning parameter optimization model to guide the correction control decision-making for subsequent batches of products; S105, for small-batch customized products, obtains the actual width, material properties, and production process parameters of the pole piece through rapid testing, and performs multi-dimensional matching with the historical data in the parameter optimization model. The control strategy under the most similar conditions is selected as the initial strategy, and then fine-tuned and optimized to shorten the adaptation time of the control system; S106, during the winding process, continuously monitor the offset and motion state of the pole piece, filter the collected data using the Kalman filter algorithm to remove interference noise, improve the accuracy of offset detection, and provide a reliable feedback signal for the correction actuator; S107 adopts an intelligent decision-making algorithm based on fuzzy logic, takes the obtained offset detection results as the main input, and comprehensively considers the influencing factors of pole piece width and winding speed, dynamically adjusts the control force and response time of the correction actuator, adjusts the displacement of the actuator according to the offset size, and adjusts the response speed of the actuator according to the offset speed. While ensuring the correction accuracy, it improves the winding efficiency and realizes adaptive correction control under width fluctuations.

2. The method according to claim 1, characterized in that The step S101 includes: installing a width sensor on the winding machine to obtain width data of the wound pole piece in real time, and transmitting the obtained width data to the correction control system as an input parameter for the correction control system to make control decisions; According to the winding pole piece width data received by the correction control system, it is determined whether the width of the winding pole piece exceeds the preset width threshold range. If it exceeds the preset width threshold range, the correction control system is triggered to perform correction control; The correction control system determines the correction control parameters for the current winding pole piece width according to the pre-established correspondence between the winding pole piece width and the correction control parameters, and transmits the correction control parameters to the winding machine's actuator; The actuator of the winding machine adjusts the operating state of the winding machine according to the received correction control parameters, controls the width of the wound pole piece, and maintains the width of the wound pole piece within a preset width threshold range; During the process of correcting the width of the wound pole piece, the width data of the wound pole piece is continuously acquired and transmitted to the correction control system in real time to form a closed-loop control; The correction control system adopts PID control algorithm. According to the deviation between the acquired winding pole piece width data and the preset width threshold, the parameters of the PID control algorithm are dynamically adjusted to achieve precise control of the winding pole piece width. The width sensor installed on the winding machine continuously obtains the width data of the wound pole piece, and transmits the width data to the host computer to analyze the changing trend of the wound pole piece width, predict the abnormal situation of the wound pole piece width, and take preventive measures in advance.

3. The method according to claim 1, characterized in that The S102 includes: obtaining width data of the current electrode and using it as a query condition; Access the system's pre-established mapping table of pole piece width and motion characteristics, and query the corresponding theoretical motion parameter records based on the current pole piece width; Extract the control accuracy threshold of the deviation correction actuator and the upper limit data of the winding speed from the query results for subsequent deviation correction control and winding speed control; Extract the expected tension, expected linear speed and expected offset range of the pole piece during the winding process from the query results as the basis for subsequent winding process control; The extracted control accuracy threshold of the correction actuator is compared with the actual control accuracy of the correction actuator. If the actual control accuracy exceeds the threshold range, the correction control strategy adjustment is triggered; The extracted winding speed upper limit is compared with the current winding speed. If the current winding speed exceeds the upper limit, the winding deceleration control is triggered; During the winding process, the pole piece tension, line speed and offset data are collected in real time and compared with the expected tension, expected line speed and expected offset range respectively. The winding control parameters are dynamically adjusted according to the deviation to ensure a smooth winding process.

4. The method according to claim 1, wherein The S103 includes: obtaining pole piece width and motion parameter production data, and inputting the data into the adaptive control algorithm model; The actual control accuracy data of the correction actuator is collected in real time through sensors and compared with the preset theoretical threshold value; If the actual control accuracy is less than the theoretical threshold, the adaptive control algorithm is triggered to dynamically adjust the control strategy according to the degree of deviation; Through machine learning algorithms, combined with historical data and real-time feedback, it is determined whether reducing the winding speed or increasing the correction force is more conducive to improving accuracy; Based on the optimization suggestions output by the algorithm, the control system automatically adjusts the speed of the winding motor or the driving force of the deviation correction actuator; Continuously monitor the actual control accuracy of the correction actuator and compare it with the dynamically updated theoretical threshold to form a closed-loop feedback control; When the actual control accuracy continues to meet the accuracy requirements, the current control strategy is maintained.

5. The method according to claim 1, wherein The S104 includes: acquiring historical production data, including process parameters and quality data under different electrode widths, and performing denoising and standardization processing on the data according to preset data cleaning rules to obtain a production data set that can be used for analysis; A clustering algorithm is used to analyze the production data set, dividing the data into several clusters according to the electrode width. An optimization model is trained for each cluster to obtain the optimal process parameter combination under the cluster. In subsequent production batches, the current electrode width is obtained, the cluster to which it belongs is determined, and the optimal process parameter combination is obtained from the corresponding optimization model and applied as the initial parameter to production control; During the production process, various process parameters and quality data are collected in real time. If quality deviations exceeding the preset threshold are detected, the deviation correction control process is triggered. In the deviation correction control process, according to the deviation direction and magnitude, the optimal parameter combination under similar working conditions is selected from the current clustered optimization model, and the control strategy is adjusted to make the process parameters converge to the optimal combination; The corrected process parameters and quality data are fed back to the optimization model, and the model is updated using an incremental learning algorithm to adapt it to the latest production conditions, enabling self-learning and continuous optimization of the model. The generalization performance of the optimization model is evaluated regularly. When the prediction error of the model on a new production batch exceeds the preset threshold, the clusters are re-divided and the optimization model is trained to adapt to changes in production conditions and ensure the effectiveness of the control strategy.

6. The method according to claim 1, characterized in that The S105 includes: obtaining the actual width, material properties and production process parameters of the small batch customized electrode, storing them in a database, and forming structured data; Use pre-trained machine learning models to predict the optimal initial control strategy for the pole piece based on structured data; If the confidence of the optimal initial control strategy is less than the preset threshold, the K nearest neighbor algorithm is used to search the historical database for the K samples most similar to the current pole piece parameters. The weighted average value is calculated based on the control strategies corresponding to the K samples to obtain a new initial control strategy. Apply the optimal initial control strategy to the production process, monitor key indicators in the production process in real time, and store key indicator data in the database; Using the Bayesian optimization algorithm and taking key indicators as optimization targets, the optimal initial control strategy is fine-tuned and optimized to obtain the optimized control strategy parameters. If the difference between the optimized control strategy parameters and the optimal initial control strategy parameters is less than a preset threshold, it is judged that the control system has reached a stable state; The optimized control strategy parameters and the corresponding key indicator data of the production process are stored in the historical database for updating the sample library of the machine learning model and the K-nearest neighbor algorithm.

7. The method according to claim 1, characterized in that The S106 includes: according to the movement state of the pole piece during the winding process, continuously monitoring the offset of the pole piece through a sensor to obtain raw data reflecting the offset state of the pole piece; The Kalman filter algorithm is used to filter the acquired original offset data, and the optimal value of the pole piece offset is estimated by establishing the state equation and observation equation. In the Kalman filtering process, by setting appropriate process noise covariance matrix and observation noise covariance matrix, the interference noise can be effectively eliminated and the accuracy of offset estimation can be improved; Based on the optimal estimated value of the pole piece offset obtained by Kalman filtering, it is determined whether the offset exceeds the preset allowable range. If it exceeds the range, the correction actuator is triggered to make adjustments; The deviation correction actuator adjusts the winding speed and tension according to the size and direction of the offset by controlling the motion parameters of the winding equipment, thereby achieving real-time correction of the pole piece position. During the correction process, the pole piece offset changes are continuously monitored, and the control parameters of the correction actuator are dynamically adjusted through feedback control to ensure that the pole piece offset is always within the allowable range. The pole piece offset data and correction execution parameters during the correction process are recorded to form a data log for subsequent process optimization and equipment maintenance.

8. The method according to claim 1, characterized in that The S107 includes: collecting the pole piece width and winding speed parameters in real time through the sensor to obtain the influencing factor data under the current production state; Preprocess the acquired influencing factor data to remove outliers and smooth data fluctuations to provide high-quality input for subsequent analysis; The pre-processed influencing factor data is input into the intelligent decision-making model based on fuzzy logic, and the importance of each influencing factor is determined through fuzzy reasoning; According to the deviation detection results and the importance of the influencing factors, the control strategy of the deviation correction actuator is dynamically generated using the fuzzy rule base; If the offset is greater than the preset threshold, a control strategy greater than the actuator displacement is generated; If the offset speed is greater than the preset threshold, a control strategy is generated that is greater than the actuator response speed; Convert the generated control strategy into specific actuator control instructions to drive the correction actuator to make real-time adjustments; Continuously monitor the correction process, obtain the offset changes before and after correction, evaluate the correction effect, and feed the evaluation results back to the intelligent decision-making model to achieve adaptive optimization.

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