Intelligent preparation and coating method of lithium battery electrode slurry

Abstract

The present invention discloses an intelligent preparation and coating method for lithium battery electrode slurry in the field of information technology, comprising: obtaining the particle size distribution, specific surface area and chemical composition property parameters of the raw materials of the lithium battery electrode slurry, establishing a quantitative relationship model between the slurry component ratio and the rheological properties and stability through an autonomous learning algorithm, and adjusting the component ratio using a dynamic optimization algorithm to obtain a slurry formula that meets the target properties; performing feature extraction and classification on the constructed data set, using a decision tree algorithm to train and generate optimization rules and judgment logic for the slurry preparation process parameters, forming a preparation process knowledge base, and realizing autonomous optimization of the process parameters; for coating quality issues, tracing and analyzing the slurry preparation process parameters, and realizing coordinated optimization of the slurry formula, preparation process and coating parameters through a human-computer interaction interface, thereby improving production efficiency and yield while ensuring electrode performance.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for intelligently preparing and coating lithium battery electrode slurry. Background Art

[0002] In the production process of lithium batteries, the preparation and coating of electrode slurry is a key process that directly affects the performance and consistency of the battery. However, the formulation and preparation process of electrode slurry are very complex, involving the selection, proportioning, mixing and dispersion of multiple raw materials. Among them, parameters such as the type, morphology, particle size distribution, and surface characteristics of components such as active materials, conductive agents, and binders will affect the rheological properties, stability, and coating quality of the slurry. At the same time, during the preparation of the slurry, process parameters such as the type of mixing equipment, rotation speed, time, and temperature also need to be precisely controlled, otherwise it will lead to problems such as uneven slurry, agglomeration, or sedimentation.

[0003] During the slurry coating process, the prepared slurry needs to be evenly coated on the current collector foil to form an electrode layer with uniform thickness and surface density. However, since the rheological properties and viscosity of the slurry will change with factors such as time, temperature, and shear rate, problems such as uneven coating thickness, surface defects, peeling, and cracking may occur during the coating process. In addition, factors such as the model, parameters, and operating status of the coating equipment can also affect the coating quality, such as the shape, gap, pressure, coating speed, tension, and drying conditions of the coating head. Even slight changes in these factors can lead to differences and instability in electrode performance.

[0004] Therefore, how to achieve precise control and optimization of electrode slurry preparation and coating processes in a complex production environment is a technical challenge that needs to be solved urgently. This requires in-depth research on the rheological properties and coating mechanism of the slurry, the establishment of scientific formulation design and process optimization methods, and the introduction of advanced technologies such as online monitoring and intelligent control to achieve real-time feedback and closed-loop optimization of the production process. Ultimately, this will ensure high quality and consistency in electrode preparation and improve the performance and yield of lithium batteries. Summary of the Invention

[0005] The present invention provides a method for intelligent preparation and coating of lithium battery electrode slurry, which comprises the following steps:

[0006] S1. Obtain the particle size distribution, specific surface area, and chemical composition property parameters of the raw materials for lithium battery electrode slurry. Use an autonomous learning algorithm to establish a quantitative relationship model between the slurry component ratio and rheological properties and stability. Use a dynamic optimization algorithm to adjust the component ratio to obtain a slurry formula that meets the target properties.

[0007] S2. On the automated slurry preparation production line, according to the optimized slurry formula, different mixing equipment types, rotation speeds, time and temperature process parameters are set to prepare multiple groups of slurry samples. Laser scattering, conductivity, and sedimentation rate measuring instruments are used to test the slurry uniformity, agglomeration, and sedimentation rate indicators, and to construct a data set between the slurry preparation process and quality attributes;

[0008] S3. Extract and classify the constructed data set, use the decision tree algorithm to train and generate optimization rules and judgment logic for slurry preparation process parameters, form a preparation process knowledge base, and realize autonomous optimization of process parameters;

[0009] S4. Based on the target slurry performance requirements, the preparation process knowledge base automatically recommends the corresponding equipment combination and process parameters. Through digital twin technology, a virtual model of the slurry preparation process is constructed to monitor equipment operating status, material flow, and temperature distribution in real time. Equipment failures and slurry quality fluctuations are predicted, and production line layout and material distribution are dynamically adjusted to ensure the stability and consistency of slurry preparation.

[0010] S5. After the slurry is prepared, it is transported to the electrode coating process through a pipeline. During the coating process, sensors collect slurry temperature, viscosity, and shear rate parameters in real time. Combined with the slurry rheological model, the dynamic changes in slurry properties during the coating process are predicted. An adaptive algorithm based on fuzzy control is used to adjust the coating speed, coating pressure, and drying temperature in real time to ensure the uniformity of the coating quality.

[0011] S6. The machine vision system performs online defect detection on the electrode surface after coating, obtains coating thickness distribution maps and defect distribution maps, and judges the coating quality based on preset thresholds. If unqualified products are detected, the defect information is fed back to the preparation process knowledge base. The slurry formula and preparation rules are updated through the online learning algorithm to achieve dynamic optimization of the knowledge base;

[0012] S7. To address coating quality issues, trace and analyze slurry preparation process parameters, and achieve collaborative optimization of slurry formula, preparation process, and coating parameters through a human-computer interaction interface, thereby improving production efficiency and yield while ensuring electrode performance;

[0013] S8. Adopt a networked collaborative manufacturing model to connect the raw material supply, slurry preparation, coating production and battery assembly links, achieve end-to-end data flow and logistics integration, and build a digital twin model of the entire electrode manufacturing process, including real-time mapping of equipment operating status, material flow, energy consumption and quality indicators, to achieve real-time monitoring of the process flow, predictive maintenance and intelligent scheduling, to ensure the consistency, stability and efficiency of electrode manufacturing.

[0014] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0015] The present invention discloses a method for intelligent preparation and coating of lithium battery electrode slurry. The method establishes a quantitative relationship model between the slurry component ratio and performance through an autonomous learning algorithm, and adopts a dynamic optimization algorithm to adjust the formula. On an automated production line, process parameters are set according to the optimized formula to prepare slurry samples, and a preparation process and quality attribute data set is constructed. The decision tree algorithm is used to train and generate process parameter optimization rules to form a preparation process knowledge base. During the coating process, the slurry rheology model is combined to predict performance changes, and an adaptive algorithm is used to adjust the process parameters in real time. The machine vision system performs online defect detection and feedback information to update the knowledge base. Collaborative optimization of slurry formula, preparation process and coating parameters is achieved through human-computer interaction. A full-process digital twin model is constructed to achieve real-time monitoring and intelligent scheduling of the process flow, ensuring the consistency, stability and efficiency of electrode manufacturing. DETAILED DESCRIPTION

[0016] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification in conjunction with the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this specification.

[0017] The intelligent preparation and coating method of lithium battery electrode slurry in this embodiment may specifically include:

[0018] S1. Obtain the particle size distribution, specific surface area, and chemical composition property parameters of the raw materials for lithium battery electrode slurry. Establish a quantitative relationship model between the slurry component ratio and rheological properties and stability through an autonomous learning algorithm. Use a dynamic optimization algorithm to adjust the component ratio to obtain a slurry formula that meets the target properties.

[0019] Obtain the particle size distribution, specific surface area and chemical composition attribute parameters of the raw materials of the lithium battery electrode slurry; based on the attribute parameters, use an autonomous learning algorithm to establish a quantitative relationship model between the slurry component ratio and the rheological properties and stability; based on the quantitative relationship model, use a dynamic optimization algorithm to adjust the slurry component ratio to obtain a candidate formula that meets the target rheological properties and stability.

[0020] Specifically, the preparation of lithium battery electrode slurry is a complex process that requires precise control of multiple parameters. First, it is crucial to obtain the property parameters of the raw materials. For example, for the positive electrode material lithium cobalt oxide, its particle size distribution can be measured to be in the range of 1-10μm, and the specific surface area is about 0.5-2m 2 / g, the main chemical component is LiCoO2. Conductive agents such as acetylene black may have a particle size of 20-50nm and a specific surface area of ​​up to 50-100m2 / g. These parameters directly affect the performance of the slurry. Establishing a quantitative relationship model between the slurry component ratio and performance is a key step. A neural network algorithm can be used, with the input being the ratio percentage of each component and the output being rheological indicators (such as viscosity) and stability indicators (such as sedimentation rate). For example, a three-layer neural network with 10 hidden layer neurons is set, and the model is trained using the backpropagation algorithm. If the model prediction error exceeds a preset threshold (such as 5%), the network structure needs to be adjusted or training data needs to be added to re-model the model. Once the quantitative relationship model is established, it can be used to optimize the formula. A genetic algorithm is used for dynamic optimization, with a population size of 100 and a number of iterations of 1000. New candidate formulas are generated through crossover and mutation operations. The evaluation function can comprehensively consider multiple indicators such as rheology and stability, such as setting a target viscosity value of 2000mPa·s and a sedimentation rate of less than 1% / h. Ultimately, the formula with the highest comprehensive score is screened out, such as a mass ratio of 92:4:4 for the positive electrode material, conductive agent, and binder. The advantage of this method is that it can quickly and systematically optimize the slurry formula, reduce manual trial and error, and improve efficiency. By precisely controlling slurry properties, electrode coating quality can be improved, thereby enhancing battery capacity and cycle performance. However, this approach also faces challenges, such as the need for extensive experimental data to support model training and potentially limited generalization capabilities. Future efforts could consider introducing techniques such as transfer learning to enhance the model's applicability and accuracy.

[0021] S2. Based on the optimized slurry formulation, multiple slurry samples were prepared on an automated slurry preparation line using different mixing equipment types, rotation speeds, time, and temperature process parameters. Laser scattering, conductivity, and sedimentation rate measurement instruments were used to measure slurry uniformity, agglomeration, and sedimentation rate, establishing a data set linking the slurry preparation process to quality attributes.

[0022] S2 also includes: obtaining process data of the slurry sample, the process data of the slurry sample including operating parameters, duration and temperature information of the mixing equipment; recording the formula information of the slurry sample; detecting the particle size distribution data of the slurry sample to determine the degree of agglomeration of the slurry sample; detecting the conductivity of the slurry sample to determine the conductive properties of the electrolyte solution in the slurry sample; establishing a temporary database based on the process data and formula information of the slurry sample as well as the conductivity and particle size distribution data; detecting the sedimentation velocity of the slurry sample, and determining the sedimentation ratio relationship of the slurry sample according to the change in sedimentation velocity; merging the sedimentation ratio relationship with the associated degree of agglomeration, conductive performance data and multiple sets of collected slurry sample process data and formula information to obtain a full slurry database; establishing at least one machine learning model based on the full slurry database ; If the machine learning model is a linear regression model, determine whether there is a linear relationship between the independent variable and the dependent variable in the training set; if there is a linear relationship between the dependent variable and the independent variable in the training set and the determination coefficient is greater than a preset threshold, the linear regression model judgment result is valid; If the machine learning model is a decision tree model or a random forest model, determine whether there is a nonlinear relationship between the independent variable and the dependent variable in the training set. If the relationship between the dependent variable and the independent variable cannot be described by a straight line and the determination coefficient is less than and the mean square error is greater than a preset threshold, then the decision tree model or random forest model result is invalid; According to the conductive performance and the corresponding operating parameters of the mixing equipment, the slurry sample particle size distribution data and the corresponding formula, the effectively trained machine learning model is iteratively traversed and calculated to obtain the optimal process parameter combination and optimized slurry formula data.

[0023] Specifically, in the production process of lithium battery electrode slurry, collecting process data for multiple slurry samples is a critical first step. For example, operating parameters of the mixing equipment include stirring speed, stirring time, and temperature. Suppose a slurry sample is stirred in the mixing equipment at a speed of 500 rpm, a stirring time of 30 minutes, and a temperature controlled at 25°C. This data is recorded simultaneously with the recipe information number of the slurry sample (e.g., A001) to ensure the accuracy of subsequent analysis. Next, the slurry sample numbered A001 is tested using a laser scattering instrument to obtain its particle size distribution data. Suppose the test results show that the average particle size of this sample is 10 microns, with a particle size distribution range of 5-15 microns. By analyzing this data, the degree of agglomeration of the sample can be assessed. A wide particle size distribution indicates severe agglomeration; conversely, a low degree of agglomeration indicates low agglomeration. The conductivity of the A001 slurry sample, after the agglomeration analysis, is then tested using a conductivity meter. Assume that the conductivity of the sample is measured to be 1.2S / m, which indicates that the electrolyte solution has good conductivity in the slurry. Associate this data with the previous process data and formula information to establish a temporary database for subsequent comprehensive analysis. Use a sedimentation instrument to detect the solid phase particle sedimentation rate of the A001 slurry sample. Assume that the test results show that the sedimentation rate of the sample within 1 hour is 0.5mm / h, and the sedimentation ratio is calculated to be 5%. This sedimentation ratio relationship is combined with the degree of agglomeration, conductive performance data, process data and formula information to form a full slurry database. Based on the full slurry database, a machine learning algorithm including a linear regression model, a decision tree model, and a random forest model is established. Assume that when training the linear regression model, the R square threshold is set to 0.8 and the mean square error MSE threshold is set to 0.05. If the training results show that there is a linear trend between the dependent variable Y (such as conductive performance) and the independent variable X (such as stirring speed, particle size distribution, etc.), and the R square is 0.85, which is greater than the set threshold, then the model training result is judged to be valid. On the contrary, if the R square is only 0.6 and the MSE is 0.1, which is greater than the preset threshold, the model is judged to be invalid. For the three established effective machine learning models, iterative traversal calculations of different equipment process combinations are performed. Suppose that in a certain iteration, the process combination scheme with a stirring speed of 600 rpm, a stirring time of 40 minutes, and a temperature of 30°C, the conductivity and sedimentation ratio predicted by the model are better than other schemes. After many iterations, it was finally determined that this process parameter combination was the optimal, and the slurry formula was optimized accordingly. Through this multi-dimensional data analysis and method application, not only can the various performance indicators of the slurry sample be accurately grasped, but the production process and formula can also be effectively optimized to improve the quality and stability of the lithium battery electrode slurry. For example, the optimized slurry formula improved the conductivity by 10% while reducing the sedimentation ratio by 15%, significantly improving the comprehensive performance of the slurry.This systematic analysis and optimization process provides solid technical support for lithium battery production and ensures continuous improvement in product quality.

[0024] S3. Extract and classify the constructed data set, use the decision tree algorithm to train and generate the optimization rules and judgment logic of the slurry preparation process parameters, form a preparation process knowledge base, and realize the autonomous optimization of the process parameters.

[0025] Incomplete information and unreasonable data in the data set are removed to obtain a training set with high slurry quality; the slurry preparation process parameters of the high-quality slurry data group in the training set are used to extract multi-dimensional features; if the influencing factor value is higher than the set value, it is set as the target factor, and a rule base is established based on the characteristic value and the target factor, and a judgment table is constructed by judging different feature combinations.

[0026] Specifically, for the constructed dataset, all data entries are first extracted as the original dataset. Assume the original dataset contains 1000 records, each of which includes process parameters such as mixing equipment type, speed, time, and temperature, as well as corresponding slurry quality indicators such as slurry uniformity, agglomeration, and sedimentation rate. To determine the relationship between slurry quality and process parameters, the impact of different mixing equipment types on slurry uniformity can be analyzed. For example, equipment A produces slurry with a uniformity of 90% at a speed of 1000 rpm, a time of 30 minutes, and a temperature of 50°C, while equipment B achieves only 70% uniformity under the same conditions. This indicates that equipment type significantly affects slurry quality. During the preprocessing phase, incomplete data is removed. For example, records with missing temperature information are discarded. Additionally, unreasonable data is identified and removed. For example, records showing a negative speed value are clearly erroneous. After preprocessing, 800 high-quality data entries remain as the training set. When extracting multidimensional features, features such as mixing equipment type, speed, time, and temperature can be extracted from the training set. For example, under the conditions of a speed of 1000 rpm, a time of 30 minutes, and a temperature of 50°C, machine A achieved slurry uniformity of 90%, a degree of agglomeration of 5%, and a sedimentation rate of 2%. Analysis revealed that slurry uniformity was higher under the combination of high speed and moderate temperature. When determining the quantitative relationship between slurry quality and the corresponding characteristic values ​​of different dimensions, it was observed that slurries with high uniformity were more likely to be found under conditions of high speed and moderate temperature. By calculating the information gain value, it was found that speed had the highest impact on slurry uniformity. If the impact factor was greater than 0.5, it was set as the target factor. A rule base was established based on the characteristic values ​​and target factors, such as "high speed (>900 rpm) and moderate temperature (40-60°C) → high uniformity." A judgment table was constructed by judging different feature combinations. For example, machine A at a speed of 1000 rpm and a temperature of 50°C was judged to have high uniformity. When constructing the decision tree model, the judgment condition was set as "speed >900 rpm and temperature between 40-60°C." If these conditions were met, the optimization process would proceed to the next step. The optimization direction can be to increase the speed or fine-tune the temperature to generate a new parameter group of different dimensions, such as a speed of 1100 rpm and a temperature of 55°C. After obtaining the new parameter group, it is trained with the original parameter group in the dataset through the model. Assuming that the slurry uniformity of the new parameter group is improved to 95% during training, which is significantly higher than the original parameter group, this parameter group is determined to be the optimal process parameter group. When building the knowledge base, the optimal process parameter group is stored, such as "Equipment A, speed 1100 rpm, temperature 55°C → uniformity 95%." By inputting new process parameters to be optimized, such as equipment A at a speed of 1000 rpm and a temperature of 50°C, comparison and optimization are carried out based on the optimal process parameters. If it is determined that the new input parameters meet the optimization direction requirements, fine-tuning is performed, such as increasing the speed to 1100 rpm and adjusting the temperature to 55°C. After optimization, the optimized value with uniformity increased to 95% is obtained.The implementation of this method can effectively improve the accuracy and efficiency of the slurry preparation process. Through a data-driven approach, it ensures that each optimization step is based on evidence, ultimately achieving maximum improvement in slurry quality. Through multi-dimensional feature analysis and information gain calculation, key factors affecting slurry quality can be accurately identified, allowing targeted process parameter optimization to improve production efficiency and product quality. The construction of a knowledge base provides data support for further process optimization, allowing new input parameters to quickly receive optimization suggestions and achieve continuous improvement.

[0027] S4. Based on the target slurry performance requirements, the preparation process knowledge base automatically recommends the appropriate equipment combination and process parameters. Using digital twin technology, a virtual model of the slurry preparation process is constructed to monitor equipment operating status, material flow, and temperature distribution in real time. This allows for the prediction of equipment failures and slurry quality fluctuations, and allows for dynamic adjustments to production line layout and material distribution to ensure the stability and consistency of slurry preparation.

[0028] S4 also includes the following steps: obtaining the slurry performance index requirements, matching similar slurry cases from the index library; recommending equipment groups and initial values ​​of process parameters based on the case results; applying information to create a production plan; synchronously creating a corresponding digital twin model through the production plan, the model integrating equipment parameters, material properties, and process parameters; obtaining a virtual production space based on the twin model calculation results; obtaining the equipment operation data collected by sensors in the actual production line, integrating the equipment status information, and comparing it with the virtual model operation information. If the deviation exceeds the preset threshold range, it is determined that the equipment operation is abnormal; collecting space temperature data and material flow speed, updating the virtual model temperature field and material flow distribution, Establish an association relationship based on historical production data. If the fluctuation exceeds the preset threshold, an early warning of slurry quality risks will be issued; based on the early warning information, obtain the early warning type, and combine it with the knowledge base diagnosis. If it is determined that the equipment is abnormal, generate an equipment maintenance plan; if it is determined to be due to material and environmental factors, generate raw material ratio or environmental parameter adjustment information; according to the fault prediction results, adjust the production plan arrangement, reconstruct it based on the virtual model, obtain a new reconstructed production line layout plan, update the equipment deployment plan, and apply the new plan to adjust the equipment operation plan; according to the quality prediction results, obtain the material ratio plan, generate the raw material distribution task instruction, transmit it to the automatic distribution system through the execution mechanism, and perform real-time material distribution according to the task.

[0029] Specifically, matching slurry performance indicators is a key starting point for developing a production plan. For example, a new lithium battery slurry may require a viscosity between 3000-5000 mPa·s and a solids content between 60-65%. By matching the indicator library, a similar lithium iron phosphate slurry example may be found that uses a planetary mixer and three-roll mill, with an initial mixing speed of 800 rpm and a grinding gap of 50 μm. This information can be directly used to create a preliminary production plan. The construction of a digital twin model enables the simulation of a virtual production space. Taking slurry preparation as an example, the model can be fed with geometric parameters and material properties of equipment such as the mixing drum and piping system. Process parameters such as mixing speed and temperature are also incorporated into the model. Using methods such as computational fluid dynamics, the flow state and temperature distribution of the slurry in the equipment can be simulated, creating a complete virtual production space. In actual production, equipment operating data can be collected using torque sensors and temperature sensors installed on the mixing drum. For example, if the actual torque of the mixing drum is 250 N·m, while the virtual model predicts 200 N·m, and the deviation exceeds a set threshold of 20%, it can be determined that there may be an equipment anomaly, such as a worn agitator blade. Real-time updates of the temperature field and material flow distribution are crucial for ensuring slurry quality. For example, an infrared thermal imager may capture a mixing drum wall temperature of 35°C, while historical data indicates a normal temperature range of 30 ± 2°C. This abnormal fluctuation may indicate abnormal slurry viscosity, which in turn affects the quality of the final product. Promptly identifying and alerting such risks can significantly improve production efficiency and product qualification rates. The system generates corresponding solutions for different types of warning information. If the diagnosis indicates an equipment anomaly, such as a worn agitator bearing, the system generates a detailed repair plan with bearing replacement steps, required tools, and estimated time. If the cause is material factors, such as raw material batch variations, it may recommend adjusting the dispersant dosage. Reconstructing the virtual model can optimize the production line layout. For example, analysis reveals that the current layout requires excessively long material transport distances, resulting in increased energy consumption and slurry temperature fluctuations. A refactored solution might recommend shortening the distance between the mixing tank and coating machine by 20% and adding an intermediate storage tank to stabilize the slurry temperature, thereby improving production efficiency and product quality consistency. Quality prediction results directly impact raw material distribution. If the prediction indicates that the current slurry is insufficiently conductive, the system might generate a 2% increase in conductive agent. This task instruction is transmitted to the automated distribution system via an actuator, triggering the appropriate material conveyor to precisely add the additional conductive agent to the next batch of raw materials, enabling real-time optimization and adjustment of the production process.

[0030] After S5 is prepared, the slurry is piped to the electrode coating process. During the coating process, sensors collect real-time slurry temperature, viscosity, and shear rate parameters. Combined with the slurry rheology model, the dynamic changes in slurry properties during the coating process are predicted. A fuzzy-based adaptive algorithm is used to adjust the coating speed, coating pressure, and drying temperature in real time to ensure uniform coating quality.

[0031] The slurry temperature set T, viscosity set V and shear rate set S in the pipeline are collected and obtained, and according to the set T, the set V and the set S, they are transmitted to a pre-established slurry rheology model database to obtain a slurry rheology curve data set K; the data set K is used as a training data set for the fuzzy control algorithm to obtain a control model C1, and a preliminary prediction is made based on the control model C1 to obtain a first coating speed initial value SV; a new rheology model C2 is determined based on the initial value SV, the coating temperature initial value and the coating pressure initial value, and a coating quality data set QC_D is collected. If there are data points in the data set QC_D that exceed the preset threshold value TQ, it is judged that there is an unreasonable area in the parameters this time, and the parameters are recalculated to obtain an optimized coating production process parameter set NEW_P.

[0032] Specifically, in the slurry preparation process, temperature, viscosity and shear rate are key parameters that directly affect the coating quality. First, the slurry temperature data is collected through the temperature sensor in the pipeline to obtain a temperature value set T. For example, the sensor may record a series of temperature values ​​such as 30°C, 32°C, and 34°C. These data reflect the temperature changes of the slurry at different time points. Then, another sensor obtains the slurry viscosity and shear rate data to form a viscosity set V and a shear rate set S. Assume that the viscosity data is 2000mPa·s, 2200mPa·s, and 2400mPa·s, and the shear rates are 100s-1, 120s-1, and 2400mPa·s, respectively.

[0033] 140s-1. These data reflect the rheological properties of the slurry under different process conditions. These data are transferred to the pre-established slurry rheological model database to obtain the slurry rheological curve data set K under specific temperature, viscosity, and shear rate. For example, the database may show that under the conditions of 30°C, 2000mPa·s, and 100s-1, the rheological curve of the slurry exhibits specific nonlinear characteristics. The data set K is used as the training data for the fuzzy control algorithm to obtain the control model C1. The coater makes a preliminary prediction of the coating speed based on C1 to obtain the first coating speed initial value SV. Assuming that SV is 10m / min, but the preset speed range is 8m / min to 12m / min, if SV exceeds this range, it is necessary to adjust the adaptive algorithm, recalculate the coating pressure parameters, and obtain the corrected coating speed initial value VS. For example, after adjustment, VS is 9m / min, which is in line with the preset range. If VS is still not within the set range, activate the drying temperature correction function of the fuzzy control algorithm to calculate the optimal temperature range. Assuming the optimal temperature range is 50°C to 60°C, this range is transmitted to the coating machine's temperature controller to obtain the optimal initial target coating temperature (BT), such as 55°C. Combining VS, BT, and the modified initial coating pressure, a new rheological model (C2) is determined. For example, C2 might indicate that the slurry's rheological properties are more stable at a coating speed of 9 m / min, a drying temperature of 55°C, and a specific pressure. Preset sensors collect coating quality data (QC) under these conditions, generating a dataset (QC_D). Assume a data point indicates a coating thickness of 100 μm, while the threshold value (TQ) is set between 90 μm and 110 μm. If the data point falls outside this range, the production process parameters are considered unreasonable and require recalculation and optimization. For example, the dispersant dosage or stirring time can be adjusted until the data point meets the standard, resulting in the optimized coating production process parameter set (NEW_P). NEW_P is stored in the process database and prioritized for use when the same operating conditions are required. For example, if the same type of lithium battery slurry is produced again, the system automatically calls NEW_P to ensure the stability and consistency of production parameters. If the various parameters of NEW_P are stable during the production process, without obvious data jitter and drift, the coating thickness is recorded as the target variable for subsequent optimization. For example, if the coating thickness is stable at 100μm, it indicates that the current parameter combination is ideal and can be used as a benchmark for subsequent optimization. The implementation of this method ensures precise control of the coating process and improves product quality and production efficiency. Through real-time data acquisition and model optimization, it can quickly respond to process fluctuations, reduce scrap rates, and improve the flexibility and adaptability of the production line. Ultimately, a closed-loop intelligent control system was formed to achieve efficient coordination of slurry preparation and coating processes.

[0034] S6: A machine vision system performs online defect detection on the electrode surface after coating, obtaining coating thickness and defect distribution maps, and judging coating quality based on preset thresholds. If a defective product is detected, the defect information is fed back to the preparation process knowledge base, and the slurry formula and preparation rules are updated through an online learning algorithm, achieving dynamic optimization of the knowledge base.

[0035] Obtain image information of the electrode surface after coating, the image information being obtained through machine vision technology, and determine a coating thickness distribution map and a defect distribution map; if the coating thickness exceeds a preset coating thickness threshold, or the defect area exceeds a preset defect area threshold, the coating quality of the corresponding electrode is determined to be unqualified; based on the defect information of the unqualified electrode, the defect information including the defect location, defect area and defect type, determine the basis for updating the preparation process knowledge base.

[0036] Specifically, machine vision technology plays a key role in electrode coating quality inspection. Using high-resolution cameras and image processing algorithms, the microscopic topography of the coating surface can be captured in real time. For example, a line scan camera can be used to scan across the electrode width to obtain a coating thickness distribution map. By setting an appropriate grayscale threshold, the coating can be distinguished from the substrate, allowing the coating thickness to be calculated. For a 100mm wide electrode, a data point can be collected every 0.1mm, resulting in 1,000 thickness values, which can be plotted as a thickness distribution curve. Defect detection also relies on image analysis technology. Common defect types include bubbles, cracks, and impurities. For example, bubble detection can be performed using an edge detection algorithm to identify circular contours and, combined with an area threshold, determine whether a bubble defect is present. Bubbles with a diameter greater than 0.5mm are considered defective. For cracks, morphological processing methods can be used to extract slender structural features. The extraction and feedback of defect information are crucial for optimizing the manufacturing process. For example, if bubble defects are primarily distributed at the electrode edge, this may be due to uneven coating pressure. This can be addressed by adjusting the pressure distribution of the coating roller. For example, if crack defects occur frequently during the drying stage, this may be due to excessive temperature gradients. Consider reducing the drying rate or adopting a multi-stage drying process. The application of online learning algorithms makes process optimization a dynamic, iterative process. Incremental learning algorithms can continuously absorb new sample information without discarding existing knowledge. For example, for a defect classification model based on a decision tree, new decision nodes can be incrementally added without retraining the entire model. Transfer learning allows knowledge from existing domains to be transferred to new, related domains. For example, experience in coating cylindrical batteries can be applied to prismatic batteries to accelerate process optimization for new products. Data mining techniques play a key role in analyzing defect causes. Association rule mining can uncover potential relationships between defects and formulations and process parameters. For example, it may be found that the probability of bubble defects increases significantly when the slurry viscosity exceeds 5000 mPa·s and the coating speed exceeds 10 m / min. This finding can guide process parameter optimization, such as reducing the coating speed or adjusting the slurry formulation to reduce viscosity. Dynamic updates to the knowledge base ensure continuous improvement of the production process. For example, if a new additive is discovered to effectively inhibit crack formation, this finding can be added to the recipe database. Simultaneously, the corresponding preparation rules are updated, such as adding 1% of this additive to the slurry recipe. This closed-loop optimization mechanism continuously improves product quality and reduces defective product rates, thereby increasing production efficiency and economic benefits.

[0037] S7. For coating quality issues, trace and analyze the slurry preparation process parameters. Through the human-computer interaction interface, the slurry formula, preparation process, and coating parameters are optimized to improve production efficiency and yield while ensuring electrode performance.

[0038] S7 also includes: obtaining coating quality data, the coating quality data including coating thickness and uniformity parameters; judging whether the coating quality data meets a preset threshold, and if not, triggering a traceability analysis process; obtaining process parameter data during slurry preparation, the process parameter data including raw material ratio, mixing time and temperature; using a data mining algorithm to analyze the correlation between the process parameter data and the coating quality data to obtain key influencing factors; adjusting the slurry formula and preparation process parameters through a human-computer interaction interface according to the key influencing factors; using an expert knowledge base and a machine learning algorithm to optimize the slurry formula and preparation process parameters to obtain an improved slurry preparation solution; obtaining process parameters during the coating process, the process parameters including coating speed, degree and pressure; determine the optimal coating parameter range through data analysis; integrate the optimization functions of slurry formula, preparation process and coating parameters in the human-computer interaction interface; adopt a multi-objective optimization algorithm to maximize production efficiency and yield while ensuring electrode performance, and obtain an optimized production plan; obtain performance data of the coated electrode, the performance data including capacity and rate performance; determine whether the performance data meets the preset performance requirements, and if not, return to the slurry formula and preparation process parameter optimization step; apply the verified production plan to batch production, and monitor the coating quality and electrode performance data in real time; use statistical process control methods to determine whether the production process is under control. If an abnormality occurs, trigger an early warning, automatically adjust the process parameters or return to the traceability analysis step.

[0039] S8. Adopt a networked collaborative manufacturing model to connect raw material supply, slurry preparation, coating production, and battery assembly, achieving end-to-end data flow and logistics integration. Build a digital twin model of the entire electrode manufacturing process, including real-time mapping of equipment operating status, material flow, energy consumption, and quality indicators. This enables real-time process monitoring, predictive maintenance, and intelligent scheduling to ensure consistency, stability, and efficiency in electrode manufacturing.

[0040] S8 also includes the following steps: obtaining the interface data of the raw material supplier's production information system, collecting the quality inspection information of each batch, and obtaining the preliminary screening results of each batch of raw materials through the pre-established regression model between the quality parameters of each batch and the electrochemical properties of each batch of materials; according to the purity and batch information in the preliminary screening result data of the raw materials, a nonlinear programming algorithm is used in combination with the inventory status to calculate, and a production material delivery order that meets the production target is obtained, and synchronized to the raw material three-dimensional warehouse production information system to execute the automatic delivery of raw materials; according to the raw material information feedback from the three-dimensional warehouse production information system, the flow nodes and production rhythm requirements of the production link are generated by the particle swarm algorithm in combination with the real-time data of the digital twin model to obtain the basis for issuing production instructions and control timing to each production equipment; a random forest algorithm classification model is established using the training data in the pre-established process defect information database, and the electrode samples produced by the coating machine are quality sampled and automatically image recognized to obtain the type of defects on each sampled sample, and then the defect type is obtained. Through outlier analysis, a set of process parameter adjustment strategies is obtained; the real-time status of the twin equipment is obtained through various types of sensor networks deployed on the equipment controller and mechanical motion components, and the long short-term memory network classification algorithm is used to identify the changes in the status. According to the historical maintenance plan statistics of each status, a data list of active maintenance signals and execution strategies for different statuses is obtained; the process parameter data of each manufacturing process segment is obtained, and according to the final output battery performance parameters of each batch, a reverse iterative learning model based on a deep neural network is constructed to obtain a data list of the optimal process parameter combination dynamically generated and sent to each production unit according to different quality goals; for the difference between the health status and capacity planning of the equipment of each production unit and the actual production capacity requirements, if the difference exceeds the preset capacity utilization threshold, a hybrid algorithm that integrates a preset number of types of optimization algorithms is used to determine the collaborative operation plan between equipment, and according to the pre-established cross-equipment processing task collaboration rules, the identifier of the optimal plan is obtained, and the operation plan is sent to the production execution information system.

[0041] Specifically, the system obtains data from the raw material supplier's production information system. For example, it collects quality inspection data for each batch of materials from Supplier A's system, including fields such as purity and impurity content. Using a pre-established regression model, it analyzes the relationship between these quality parameters and the material's electrochemical performance. Assuming a batch of materials has a purity of 99.5% and an impurity content of 0.05%, and the model predicts excellent electrochemical performance, the batch passes preliminary screening. Based on this preliminary screening result and inventory status, a nonlinear programming algorithm is used to automatically calculate the material mix. Assuming three materials of varying purity are currently in inventory, the algorithm calculates the optimal mix ratio based on production targets and inventory levels, and generates a production material delivery order. For example, to produce 1,000 kg of slurry, the algorithm recommends using 500 kg of 99.5% purity material, 300 kg of 99.7% purity material, and 200 kg of 99.8% purity material, ensuring that the mix meets quality requirements while maximizing inventory utilization. The warehouse's production information system automatically dispatches raw materials based on the delivery order and, using real-time data from the digital twin model, employs a particle swarm optimization algorithm to generate a slurry preparation task plan. Assuming the digital twin model displays the current equipment status and production pace, the algorithm optimizes task allocation to ensure efficient operation of each device. For example, if device A is suited for high-speed mixing and device B for low-speed stirring, the algorithm will rationally schedule the work of devices A and B based on the task requirements. Leveraging a pre-established database of process defect information, a random forest classification model is developed to perform quality sampling and automatic image recognition on electrode samples produced by the coating machine. For example, image recognition reveals that 5% of a batch of electrode samples contain scratches. Outlier analysis identifies the key process parameters causing these scratches, such as excessive coating speed, and adjusts these parameters to reduce the defect rate. The sensor network captures the real-time status of the twin devices, and a long-short-term memory network classification algorithm identifies state changes. For example, if the vibration sensor data of device C shows abnormal fluctuations, the algorithm identifies bearing wear. Based on historical maintenance plans, the system automatically issues a maintenance signal to replace the bearing, preventing equipment failure. Process parameter data from each manufacturing process segment is collected and combined with the final battery performance parameters to construct a deep neural network reverse iterative learning model. Assuming that the capacity of a batch of batteries is lower than expected, the model analysis finds that the main reason is the excessively high viscosity of the slurry. The optimal process parameter combination is dynamically generated, such as reducing the stirring speed and increasing the temperature, to ensure that the performance of subsequent batches of batteries meets the standards. In view of the health status of the equipment and capacity planning of each production unit, if the difference between the actual capacity and the requirement exceeds the preset threshold, a hybrid algorithm is used to determine the collaborative operation plan between equipment. For example, if the current capacity utilization rate is only 80%, which is lower than the preset 90% threshold, the algorithm will optimize the task allocation between equipment, such as transferring some tasks from busy equipment D to idle equipment E to ensure that the overall capacity is increased to the target level. Through the pre-established collaborative rules for processing tasks across equipment, the identifier of the optimal solution is obtained, and the operation plan is sent to the production execution information system.For example, the rule base contains a collaborative rule that states, "Joint processing of machines F and G is most efficient." Based on this rule, the algorithm generates an optimal solution, ensuring efficient and coordinated operation between machines. The implementation of these steps and technical themes not only improves production efficiency and product quality, but also enables optimal resource allocation and preventive maintenance of equipment, ultimately enhancing the overall intelligence and market competitiveness of manufacturing operations.

[0042] The above embodiments are intended to illustrate the technical solutions of the present invention and are not intended to limit the present invention. The present invention is described in detail with reference to the preferred embodiments only. It should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or equivalents should be included within the scope of the claims of the present invention.

Claims

1. Intelligent preparation and coating method of lithium battery electrode slurry, characterized in that: The method comprises the following steps: S1. Obtain the particle size distribution, specific surface area, and chemical composition property parameters of the raw materials for lithium battery electrode slurry. Use an autonomous learning algorithm to establish a quantitative relationship model between the slurry component ratio and rheological properties and stability. Use a dynamic optimization algorithm to adjust the component ratio to obtain a slurry formula that meets the target properties. S2. On the automated slurry preparation production line, according to the optimized slurry formula, different mixing equipment types, rotation speeds, time and temperature process parameters are set to prepare multiple groups of slurry samples. Laser scattering, conductivity, and sedimentation rate measuring instruments are used to test the slurry uniformity, agglomeration, and sedimentation rate indicators, and to construct a data set between the slurry preparation process and quality attributes; S3. Extract and classify the constructed data set, use the decision tree algorithm to train and generate optimization rules and judgment logic for slurry preparation process parameters, form a preparation process knowledge base, and realize autonomous optimization of process parameters; S4. Based on the target slurry performance requirements, the preparation process knowledge base automatically recommends the corresponding equipment combination and process parameters. Through digital twin technology, a virtual model of the slurry preparation process is constructed to monitor equipment operating status, material flow, and temperature distribution in real time. Equipment failures and slurry quality fluctuations are predicted, and production line layout and material distribution are dynamically adjusted to ensure the stability and consistency of slurry preparation. S5. After the slurry is prepared, it is transported to the electrode coating process through a pipeline. During the coating process, sensors collect slurry temperature, viscosity, and shear rate parameters in real time. Combined with the slurry rheological model, the dynamic changes in slurry properties during the coating process are predicted. An adaptive algorithm based on fuzzy control is used to adjust the coating speed, coating pressure, and drying temperature in real time to ensure the uniformity of the coating quality. S6. The machine vision system performs online defect detection on the electrode surface after coating, obtains coating thickness distribution maps and defect distribution maps, and judges the coating quality based on preset thresholds. If unqualified products are detected, the defect information is fed back to the preparation process knowledge base. The slurry formula and preparation rules are updated through the online learning algorithm to achieve dynamic optimization of the knowledge base; S7. To address coating quality issues, trace and analyze slurry preparation process parameters, and achieve collaborative optimization of slurry formula, preparation process, and coating parameters through a human-computer interaction interface, thereby improving production efficiency and yield while ensuring electrode performance; S8. Adopt a networked collaborative manufacturing model to connect the raw material supply, slurry preparation, coating production and battery assembly links, achieve end-to-end data flow and logistics integration, and build a digital twin model of the entire electrode manufacturing process, including real-time mapping of equipment operating status, material flow, energy consumption and quality indicators, to achieve real-time monitoring of the process flow, predictive maintenance and intelligent scheduling, to ensure the consistency, stability and efficiency of electrode manufacturing.

2. The method for intelligent preparation and coating of lithium battery electrode slurry according to claim 1, characterized in that: The S2 further includes: Acquiring process data of the slurry sample, wherein the process data of the slurry sample includes operating parameters, duration, and temperature information of the mixing equipment; Recording the formula information of the slurry sample; detecting particle size distribution data of the slurry sample to determine the degree of agglomeration of the slurry sample; detecting the electrical conductivity of the slurry sample to determine the conductive properties of the electrolyte solution in the slurry sample; establishing a temporary database based on process data and formulation information as well as conductivity and particle size distribution data of the slurry sample; detecting the sedimentation velocity of the slurry sample, and determining the sedimentation ratio relationship of the slurry sample according to the change of the sedimentation velocity; According to the sedimentation ratio relationship, the associated agglomeration degree, conductive performance data, and the collected process data and formula information of multiple groups of slurry samples are combined to obtain a full slurry database; Establishing at least one machine learning model based on the full slurry database; If the machine learning model is a linear regression model, determine whether there is a linear relationship between the independent variable and the dependent variable in the training set; If there is a linear relationship between the dependent variable and the independent variable in the training set and the determination coefficient is greater than the preset threshold, the linear regression model judgment result is valid; If the machine learning model is a decision tree model or a random forest model, determine whether there is a nonlinear relationship between the independent variable and the dependent variable in the training set. If the relationship between the dependent variable and the independent variable cannot be described by a straight line and the determination coefficient is less than and the mean square error is greater than a pre-established threshold, then the decision tree model or random forest model result is determined to be invalid; According to the conductive properties and the corresponding operating parameters of the mixing equipment, the particle size distribution data of the slurry sample and the corresponding formula, the effectively trained machine learning model is iteratively traversed and calculated to obtain the optimal process parameter combination and optimized slurry formula data.

3. The method for intelligent preparation and coating of lithium battery electrode slurry according to claim 1, characterized in that: Described S4 also comprises the following steps: Obtain slurry performance index requirements and match similar slurry cases from the index library; Based on the case results, recommend equipment groups and initial values ​​for process parameters; Apply information to create production plans; Through the production plan, a corresponding digital twin model is simultaneously created, wherein the model integrates equipment parameters, material properties and process parameters; According to the twin model calculation results, a virtual production space is obtained; Obtain equipment operation data collected by sensors in the actual production line, integrate the equipment status information, and compare it with the virtual model operation information. If the deviation exceeds the preset threshold range, it is determined that the equipment operation is abnormal; Collect spatial temperature data and material flow speed, update the virtual model temperature field and material flow distribution, establish a correlation based on historical production data, and issue an early warning of slurry quality risks if fluctuations exceed a preset threshold; For the warning information, obtain the warning type, combine it with the knowledge base diagnosis, and if it is determined that the equipment is abnormal, generate an equipment maintenance plan; if it is determined to be material and environmental factors, generate raw material ratio or environmental parameter adjustment information; Adjust the production plan according to the fault prediction results, reconstruct the virtual model to obtain a new reconstructed production line layout plan, update the equipment deployment plan, and adjust the equipment operation plan using the new plan; According to the quality prediction results, a material ratio plan is obtained, and a raw material distribution task instruction is generated. The instruction is transmitted to the automatic distribution system through the execution mechanism, and the material is distributed in real time according to the task.

4. The method for intelligent preparation and coating of lithium battery electrode slurry according to claim 1, characterized in that: The S5 includes: Acquire a slurry temperature set T, a viscosity set V, and a shear rate set S in the pipeline, and transmit the data to a pre-established slurry rheology model database based on the data set T, the data set V, and the data set S to obtain a slurry rheology curve dataset K; The data set K is used as a training data set for the fuzzy control algorithm to obtain a control model C1, and a first coating speed initial value SV is obtained based on a preliminary prediction of the control model C1; A new rheological model C2 is determined based on the initial value SV, the initial value of the coating temperature, and the initial value of the coating pressure, and the coating quality data set QC_D is collected. If there are data points in the data set QC_D that exceed the preset threshold value TQ, it is determined that there is an unreasonable area in the parameters, and the parameters are recalculated to obtain the optimized coating production process parameter set NEW_P.

5. The method for intelligent preparation and coating of lithium battery electrode slurry according to any one of claims 1 to 4, characterized in that: The S7 includes: Acquiring coating quality data, wherein the coating quality data includes coating thickness and uniformity parameters; Determine whether the coating quality data meets a preset threshold. If not, trigger the traceability analysis process; Acquiring process parameter data during the slurry preparation process, wherein the process parameter data includes raw material ratio, mixing time and temperature; Using a data mining algorithm to analyze the correlation between the process parameter data and the coating quality data to obtain key influencing factors; According to the key influencing factors, adjust the slurry formula and preparation process parameters through the human-computer interaction interface; Optimizing the slurry formulation and preparation process parameters using an expert knowledge base and a machine learning algorithm to obtain an improved slurry preparation solution; Acquiring process parameters during the coating process, wherein the process parameters include coating speed and pressure; Determine the optimal coating parameter range through data analysis; Integrate optimization functions for slurry formulation, preparation process and coating parameters into the human-computer interaction interface; Using a multi-objective optimization algorithm, we maximize production efficiency and yield while ensuring electrode performance, and obtain an optimized production plan. Obtaining performance data of the coated electrode, the performance data including capacity and rate performance; Determine whether the performance data meets the preset performance requirements. If not, return to the slurry formulation and preparation process parameter optimization step; Apply the verified production plan to mass production and monitor coating quality and electrode performance data in real time; Statistical process control methods are used to determine whether the production process is under control. If an abnormality occurs, an early warning is triggered, and process parameters are automatically adjusted or the traceability analysis step is returned.

6. The method for intelligent preparation and coating of lithium battery electrode slurry according to any one of claims 1 to 4, characterized in that: Described S8 also comprises the following steps: Obtain interface data from the raw material supplier's production information system, collect quality inspection information for each batch, and obtain preliminary screening results for each batch of raw materials through a pre-established regression model between the quality parameters of each batch and the electrochemical properties of each batch of materials; Based on the purity and batch information in the preliminary screening results of the raw materials, a nonlinear programming algorithm is used in combination with the inventory status to calculate and obtain a production material delivery order that meets the production target, and the order is synchronized to the raw material warehouse production information system to automatically issue the raw materials. Based on the raw material information feedback from the stereoscopic warehouse production information system, the production process flow nodes and production rhythm requirements, combined with the real-time data of the digital twin model, the particle swarm algorithm is used to generate the slurry preparation task plan information table, which provides the basis for issuing production instructions and controlling the timing of each production equipment; A random forest algorithm classification model is established using training data from a pre-established process defect information database. Quality sampling and automatic image recognition are performed on electrode samples produced by the coating machine to obtain the defect types on each sample. Outlier analysis is then used to obtain a set of process parameter adjustment strategies. The real-time status of the twin devices is obtained through various types of sensor networks deployed on the device controllers and mechanical motion components. The long-short-term memory network classification algorithm is used to identify state changes. Based on the historical maintenance plan statistics for each state, a data list of active maintenance signals and execution strategies for different states is obtained. Obtain process parameter data for each manufacturing process segment, and build a reverse iterative learning model based on a deep neural network based on the final output battery performance parameters of each batch. This will dynamically generate and send a list of optimal process parameter combinations to each production unit based on different quality targets. For the difference between the health status and capacity planning of the equipment of each production unit and the actual production capacity requirements, if the difference exceeds the preset capacity utilization threshold, a hybrid algorithm that integrates a preset number of types of optimization algorithms is used to determine the collaborative operation plan between equipment. According to the pre-established cross-equipment processing task collaboration rules, the identifier of the optimal plan is obtained, and the operation plan is sent to the production execution information system.