High-precision battery coating operation method and system based on air cylinder servo assembly

Abstract

The invention discloses a high-precision battery film coating operation method and system based on an air cylinder servo assembly, and relates to the technical field of film coating. The method comprises the steps that when a target battery is conveyed to a preset film wrapping station, film wrapping requirement parameters of the target battery are received; optimizing coating control parameters based on the coating demand parameters to generate target coating control parameters; the target film wrapping control parameters are input into a servo air cylinder control platform of an air cylinder servo assembly to be controlled, and film material attaching state image data are collected in real time through a visual detection device; inputting the film material fitting state image data into a pre-trained film material fitting detection model for analysis, and outputting a fitting anomaly detection result; and performing feedback adjustment on the target envelope control parameters based on the lamination anomaly detection result. The technical problems of unstable film pasting quality and low efficiency in the battery film coating process in the prior art are solved, and the technical effects of improving the film coating quality stability and the production efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of film coating, and particularly to a high-precision battery film coating operation method and system based on a cylinder servo assembly. Background Art

[0002] Existing battery film coating processes are widely used in the production of various batteries. Their main purpose is to provide an additional protective layer for the batteries to enhance the mechanical strength of the batteries, prevent the invasion of the external environment, and improve the appearance consistency of the batteries. However, in traditional battery film coating technologies, the stability of the film coating quality is usually affected by various factors, such as improper control of the film material tension, deviation of the film coating position, and insufficient precision of the mechanical structure of the film coating equipment. These factors will cause quality defects such as wrinkles, bubbles, and film material displacement during the film coating process, affecting the overall performance and appearance of the batteries. In addition, traditional film coating processes often rely on manual monitoring and adjustment of film coating parameters, resulting in low production efficiency and difficulty in meeting the requirements of high precision and consistency in large-scale production. Although some equipment is already equipped with automatic control, due to the lack of high-precision real-time detection means, it is still difficult to detect film coating abnormalities in a timely manner and make adjustments, which further limits the improvement of the film coating quality. Summary of the Invention

[0003] This application provides a high-precision battery film coating operation method and system based on a cylinder servo assembly, which solves the technical problems of unstable film coating quality and low efficiency in the process of battery film coating in the prior art.

[0004] In view of the above problems, this application provides a high-precision battery film coating operation method and system based on a cylinder servo assembly.

[0005] In the first aspect of this application, a high-precision battery film coating operation method based on a cylinder servo assembly is provided. The method includes:

[0006] When the target battery is conveyed to a preset film coating station, receiving the film coating requirement parameters of the target battery, where the preset film coating station performs film feeding and film coating control through a cylinder servo assembly; optimizing the film coating control parameters based on the film coating requirement parameters to generate target film coating control parameters; inputting the target film coating control parameters into the servo cylinder control platform of the cylinder servo assembly for control, and real-time collecting image data of the film material fitting state through a vision detection device; inputting the film material fitting state image data into a pre-trained film material fitting detection model for analysis, and outputting a fitting abnormality detection result; and performing feedback adjustment on the target film coating control parameters based on the fitting abnormality detection result.

[0007] In the second aspect of this application, a high-precision battery film coating operation system based on a cylinder servo assembly is provided. The system includes:

[0008] Data receiving module: When the target battery is conveyed to a preset film wrapping station, it receives the film wrapping requirement parameters of the target battery. Among them, the preset film wrapping station controls film feeding and film wrapping through a cylinder servo assembly; Optimization module: Optimize the film wrapping control parameters based on the film wrapping requirement parameters to generate target film wrapping control parameters; Control module: Input the target film wrapping control parameters into the servo cylinder control platform of the cylinder servo assembly for control, and collect the image data of the film material fitting state in real time through a vision detection device; Analysis module: Input the image data of the film material fitting state into a pre-trained film material fitting detection model for analysis, and output a fitting abnormality detection result; Feedback adjustment module: Perform feedback adjustment on the target film wrapping control parameters based on the fitting abnormality detection result.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] When the target battery is conveyed to a preset film wrapping station, it receives the film wrapping requirement parameters of the target battery. Among them, the preset film wrapping station controls film feeding and film wrapping through a cylinder servo assembly. Then, optimize the film wrapping control parameters based on the film wrapping requirement parameters to generate target film wrapping control parameters. Next, input the target film wrapping control parameters into the servo cylinder control platform of the cylinder servo assembly for control, and collect the image data of the film material fitting state in real time through a vision detection device. Further, input the image data of the film material fitting state into a pre-trained film material fitting detection model for analysis, and output a fitting abnormality detection result. Finally, perform feedback adjustment on the target film wrapping control parameters based on the fitting abnormality detection result. It solves the technical problems of unstable film sticking quality and low efficiency in the battery film wrapping process in the prior art, and achieves the technical effect of improving the stability of film wrapping quality and production efficiency. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is a schematic flowchart of a high-precision battery film wrapping operation method based on a cylinder servo assembly provided by an embodiment of this application;

[0013] Figure 2 It is a schematic structural diagram of a high-precision battery film wrapping operation system based on a cylinder servo assembly provided by an embodiment of this application.

[0014] Explanation of the reference numerals: data receiving module 11 , optimization module 12 , control module 13 , analysis module 14 , feedback adjustment module 15 . DETAILED DESCRIPTION

[0015] The present application solves the technical problems of unstable film pasting quality and low efficiency in the battery film pasting process in the prior art by providing a high-precision battery film pasting operation method and system based on a cylinder servo assembly.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a high-precision battery coating operation method based on a cylinder servo assembly, wherein the method includes:

[0019] When the target battery is delivered to a preset film coating station, the film coating requirement parameters of the target battery are received, wherein the preset film coating station performs film feeding and film coating control through a cylinder servo assembly.

[0020] When the target battery is transferred to the preset coating station through the transmission device, the identification system in the station (such as RFID identifier or barcode scanner) is first started to identify the target battery to confirm the specific information of the battery and extract the relevant coating requirement parameters. The coating requirement parameters include but are not limited to the size specifications (length, width, thickness) of the target battery, the type of film material required for coating (such as material, thickness, flexibility), film accuracy requirements (such as position tolerance range), and operating speed and film tension parameters.

[0021] The preset film wrapping station performs the conveying and film wrapping operations of the film material through an integrated cylinder servo component. First, the servo control system adjusts the initial state of the film feeding mechanism according to the received film wrapping requirement parameters, including the initial position of the film material, tension control, and conveying speed, to ensure that the film material moves to the predetermined position on the battery surface along the target path. Subsequently, the servo cylinder component performs the film fitting operation according to the set film sticking path and pressure parameters. The entire process is supported by the real-time feedback mechanism of the servo control platform, and the displacement, speed, and pressure of the cylinder are dynamically adjusted through the position sensor and the force control system to ensure that the film material always maintains a stable fit during the film sticking process, avoiding quality problems such as wrinkles, offsets, or bubbles.

[0022] Furthermore, the preset film wrapping station includes a U-shaped wrapping station and a loop wrapping station; among them, both the U-shaped wrapping station and the loop wrapping station are transported in an equidistant transportation mode, and the U-shaped wrapping station is before the loop wrapping station.

[0023] In the U-shaped wrapping station, the film wrapping process adopts the U-shaped film sticking method, that is, the film material starts to fit from one side of the battery, sequentially covers the bottom surface and the other side surface of the battery, and completes the basic three-sided wrapping; this station precisely controls the conveying position, tension, and film sticking force of the film material through the cylinder servo component to ensure that the film material is flat and wrinkle-free during the wrapping process. The U-shaped wrapping station is mainly responsible for realizing the preliminary fixation of the battery wrapping and providing a basis for the subsequent more complex loop wrapping process.

[0024] The loop wrapping station is responsible for performing a more delicate wrapping operation on the battery. The film material starts from the top of the battery and wraps the uncovered parts of the battery (such as the top and two side surfaces) in a loop path to form a complete 360-degree wrapping effect; this station also operates using the cylinder servo component and a high-precision control platform. By adjusting the position and tension of the film material multiple times, the final fitting of the battery is completed to ensure that the film material is tightly fitted on all contact surfaces.

[0025] Whether it is the U-shaped wrapping station or the loop wrapping station, the target battery is transported in an equidistant transportation mode. Through the precise control of the conveyor belt or the robotic arm, the battery can move to each station at fixed time intervals and distances, avoiding the chaos of the film wrapping rhythm caused by position or time errors. This transportation mode also facilitates the unified control of the overall operation rhythm and improves the operation efficiency of the production line.

[0026] Optimize the film wrapping control parameters based on the film wrapping requirement parameters to generate the target film wrapping control parameters.

[0027] Based on the received film wrapping requirement parameters, the system optimizes the film wrapping control parameters to generate target film wrapping control parameters that meet the current film wrapping requirements. For example, the optimization may involve adjusting the film material tension, film sticking speed, or pressure parameters to ensure that the operation meets the specific film wrapping requirements of the battery.

[0028] Furthermore, the optimization of the film wrapping control parameters is carried out based on the film wrapping requirement parameters to generate target film wrapping control parameters, including:

[0029] Establishing a film wrapping optimization space; performing digital twin modeling on the preset film wrapping station to establish a twin film wrapping station; randomly selecting the first film wrapping control parameter in the film wrapping optimization space and inputting it into the twin film wrapping station for simulation to generate a first film wrapping simulation result; evaluating the film wrapping quality based on the first film wrapping simulation result to generate a first quality index; if the first quality index meets the preset quality constraint conditions, taking the first film wrapping control parameter as the target film wrapping control parameter.

[0030] Specifically, an optimization space for the film coating control parameters is established according to the film coating requirement parameters. The film coating optimization space includes the conveying speed of the film material, the tension range, the pressure range of the cylinder servo assembly, the displacement range, and the adjustable range of the film application path parameters. The upper and lower limits and constraint conditions of each control parameter are clarified to ensure that all parameter combinations during the optimization process meet the actual operation capabilities and production requirements. Next, a digital twin model of the preset film coating station is built. By collecting the mechanical structure parameters, motion characteristics, control logic, and process flow of the station equipment, a twin film coating station is constructed. The twin film coating station can accurately simulate the execution process of the actual film coating process in a virtual environment, including the conveying of the film material, the actions of the cylinders, and the changes in the film application state. Subsequently, a set of initial film coating control parameters (the first film coating control parameters), including the film tension, cylinder pressure, film material conveying speed, and film application path parameters, are randomly selected from the film coating optimization space and input into the twin film coating station for simulation operations. During the simulation process, the system records the dynamic fitting state of the film material during film application, the contact situation between the film material and the battery surface, and the overall operation time, generating the first film coating simulation result. Based on the first film coating simulation result, the film coating quality is analyzed and scored through a built-in quality evaluation model. The quality evaluation indicators include film application accuracy (deviation amount), film application integrity (whether the coverage is uniform without wrinkles or bubbles), resource utilization rate (the usage effect of the tension and pressure parameters), and operation efficiency (film coating completion time). After the analysis, the first quality indicator is generated and compared with the preset quality constraint conditions. If the first quality indicator meets the preset quality constraint conditions (such as the film application accuracy deviation is less than the allowable range, the film material is completely fitted without obvious defects, and the efficiency reaches the production standard), the first film coating control parameters are determined as the target film coating control parameters. Otherwise, the system will continue to select new film coating control parameters in the film coating optimization space and repeat the above simulation and quality evaluation process until the control parameters that meet the preset quality constraint conditions are found. Finally, the generated target film coating control parameters will be used as the execution basis for the actual film coating process, and the actual operation is carried out through the control platform of the cylinder servo assembly to ensure that the battery film coating process meets the requirements of high precision, high efficiency, and high quality.

[0031] Furthermore, establishing the film coating optimization space includes:

[0032] Obtaining the set of types of battery film coating control parameters; obtaining the same-type film coating stations of the preset film coating station; connecting the control platforms of the same-type film coating stations with the set of types of battery film coating control parameters and the film coating requirement parameters as constraints, and collecting several battery film coating process control records; constructing the film coating optimization space with the several battery film coating process control records.

[0033] Specifically, analyze the encapsulation requirement parameters of the target battery, and clarify the set of types of battery encapsulation control parameters required for optimization, including film material conveying speed, film material tension, cylinder pressure, cylinder displacement range, and film application path parameters, etc. Then, determine the same-type encapsulation workstations of the preset encapsulation workstations, and identify the encapsulation workstations with the same structure and control logic through the equipment files and production line layout. The historical operation data of these workstations can provide rich reference bases for expanding the diversity of the optimization search space. Subsequently, with the set of types of battery encapsulation control parameters and the encapsulation requirement parameters as the constraint conditions, connect the control platforms of the same-type encapsulation workstations through the Manufacturing Execution System (MES), and retrieve the historical encapsulation process control records during their operation; collect several historical encapsulation process control records from the same-type encapsulation workstations, and each record includes specific encapsulation input parameters (such as film material tension, cylinder pressure, film application speed, path, etc.) and the corresponding output results (such as film application accuracy, encapsulation integrity, operation efficiency, etc.); at the same time, clean and verify the collected data, remove outliers and invalid data to ensure the accuracy and integrity of the data. Finally, based on these screened historical process control records, construct an encapsulation optimization search space, which is presented in the form of a multi-dimensional parameter matrix, with each dimension corresponding to a type of control parameter, and each data point in the matrix representing a set of historical encapsulation process records.

[0034] Furthermore, based on the first encapsulation simulation result, conduct an encapsulation quality evaluation to generate a first quality index, including:

[0035] Construct an encapsulation quality evaluation index, where the encapsulation quality evaluation index includes the fitting tightness between the film material and the battery surface, the smoothness of the encapsulation surface, the encapsulation integrity, the encapsulation sealing performance, and the consistency of the encapsulation thickness; extract the quality evaluation index values corresponding to the encapsulation quality evaluation index according to the first encapsulation simulation result to generate the first quality index.

[0036] Specifically, according to the quality requirements of the coating process, a comprehensive coating quality evaluation index is constructed. The coating quality evaluation index includes the fitting tightness between the film material and the battery surface (measuring the tightness of the contact between the film material and the battery surface to avoid voids or bubbles), the smoothness of the coating surface (evaluating the flatness of the coating surface to avoid wrinkles or corrugations), the integrity of the coating (checking whether the coating completely covers the target area of the battery to avoid exposed or uncovered areas), the sealing performance of the coating (evaluating the sealing effect of the coating to ensure that the battery is protected from the external environment), and the thickness consistency of the coating (detecting whether the thickness of the coating on the battery surface is uniform to avoid uneven thickness affecting the battery performance or appearance); extract the actual values of each quality evaluation index based on the first coating simulation results: use image processing algorithms to analyze the fitting state of the film material and calculate the fitting tightness; analyze the height difference of the coating surface simulation image to extract the smoothness data; check the actual area covered by the film material and the target area in the simulation image to calculate the coating integrity; simulate the airtightness test and calculate the coating sealing performance through the leakage data; extract the thickness distribution of the film material in the simulation data and calculate the standard deviation to evaluate the thickness consistency; normalize the above five index values so that their ranges are all between 0 and 1, and assign weights according to the process requirements; multiply the normalized index values by the weights and accumulate them to generate the total score of the first quality index.

[0037] Furthermore, it also includes:

[0038] If the first quality index does not meet the preset quality constraint conditions, mark the first coating control parameters with taboos;

[0039] Continue to randomly select second coating control parameters without taboo marks in the coating optimization space and input them into the twin coating station for simulation to generate second coating simulation results;

[0040] Conduct coating quality evaluation based on the second coating simulation results to generate a second quality index;

[0041] If the second quality index meets the preset quality constraint conditions, use the second coating control parameters as the target coating control parameters.

[0042] When the system evaluates the first quality index and finds that it does not meet the preset quality constraint conditions (for example, the score is lower than 90 points), the current first encapsulation control parameters are marked as taboo in the encapsulation optimization space. The operation of the taboo marking is as follows: In the parameter matrix of the optimization space, the index of this set of encapsulation control parameters is marked as disabled, specifically by setting a boolean value (such as the disabled mark being True) or adding a taboo weight (such as setting the weight to an extremely high value to reduce the selection probability) to ensure that this parameter combination will not be selected repeatedly in the subsequent optimization process. Subsequently, the system filters the set of encapsulation control parameters that have not been marked as taboo from the encapsulation optimization space and randomly selects a new set of parameters as the second encapsulation control parameters (for example, randomly selects a different combination of film tension, cylinder pressure, and film application path parameters again). Then, the second encapsulation control parameters are input into the twin encapsulation station for simulation. The twin encapsulation station performs simulation operations according to the new parameter combination, including the conveyance of the film material, the execution of the film application path, and pressure regulation, etc., and generates the second encapsulation simulation result. The second encapsulation simulation result outputs key data through the simulation model, such as the tightness of the film material fit, surface smoothness, encapsulation integrity, sealing performance, and thickness consistency, etc. Based on the second encapsulation simulation result, the result is analyzed according to the quality evaluation process, the value of each encapsulation quality evaluation index is extracted, and the total score of the second quality index is calculated by combining the preset weights. If the second quality index meets the preset quality constraint conditions (for example, the total score ≥ 90 points), then the second encapsulation control parameters are determined as the target encapsulation control parameters and recorded in the control system for subsequent operations. If the second quality index still does not meet the preset standard, the second encapsulation control parameters are marked as taboo, and the above process is repeated. Continuously select the encapsulation control parameters that have not been marked as taboo in the encapsulation optimization space, and perform simulation, quality evaluation, and taboo marking operations in sequence until the target encapsulation control parameters that meet the quality requirements are found.

[0043] Input the target encapsulation control parameters into the servo cylinder control platform of the cylinder servo assembly for control, and real-time collect image data of the film material fitting state through a vision detection device.

[0044] Through the system interface, input the generated target encapsulation control parameters (such as film material conveyance speed, tension, cylinder pressure, displacement range, and film application path, etc.) into the servo cylinder control platform. After the servo cylinder control platform analyzes the parameters, it adjusts the servo cylinder according to the set value. The cylinder servo assembly starts to work under the command of the control platform, and through its high-precision servo control function, ensures that the film material fits evenly and tightly on the battery surface, avoiding phenomena such as film material slack, wrinkles, or being too tight. During the encapsulation process, through the vision detection device (such as an industrial camera or a multispectral camera) installed on the station, real-time collect image data of the film material fitting state.

[0045] Input the image data of the film laminating state into a pre-trained film laminating detection model for analysis, and output the detection result of laminating abnormality.

[0046] Preprocess the image data of the film laminating state collected by the vision detection device to ensure that it meets the input requirements of the pre-trained detection model; input the preprocessed image data into the pre-trained film laminating detection model, which is based on machine learning or deep learning algorithms and has learned the characteristics of the film laminating state from a large amount of training data, including lamination tightness characteristics, surface smoothness characteristics, and edge coverage characteristics; the detection model analyzes the input image data, extracts the key characteristics of the laminating state, and calculates relevant indicators, such as the lamination deviation value (the distance between the actual lamination position of the film and the target position), the surface smoothness score (calculating the smoothness score based on the surface height difference distribution), the coverage rate (the ratio of the detected film coverage area to the target area), and the defect position annotation (locating the positions of wrinkles, bubbles, or unlaminated edges in the image); according to the feature data extracted by the model analysis, output the detection result of laminating abnormality.

[0047] Furthermore, input the image data of the film laminating state into a pre-trained film laminating detection model for analysis, and output the detection result of laminating abnormality, including:

[0048] Collect samples of abnormal lamination images; based on the convolutional neural network, train the film laminating detection model with the abnormal lamination image samples until convergence; input the image data of the film laminating state into the converged film laminating detection model for analysis, and output the detection result of laminating abnormality.

[0049] In the actual film coating process, first, image samples of various film sticking states are collected through a visual detection device, including normal sticking samples and abnormal sticking samples. The abnormal samples need to cover common defect types, such as edge uncovered, film material wrinkles, and bubbles or voids. At the same time, the collected image samples are labeled to clarify the abnormal type, location, and severity. Subsequently, all samples are subjected to standardized preprocessing, including adjusting the resolution, denoising, and normalization, to unify the sample data into a standard format acceptable to the model. On this basis, a film material sticking detection model is constructed based on a convolutional neural network (CNN). The model includes multiple convolutional layers for extracting edge features, local sticking features, and texture features, a pooling layer for dimensionality reduction to retain key information, and a fully connected layer for outputting classification results. The preprocessed samples are divided into a training set, a validation set, and a test set. The model is trained using a cross-entropy loss function and an Adam optimizer, and the parameters are iteratively optimized by inputting the training data batch by batch until the accuracy and loss value on the validation set reach the convergence standard (such as an accuracy of more than 95%). Finally, the converged model is saved for real-time detection. In the actual operation of film coating, the image data of the film material sticking state collected in real time is input into the trained detection model after the same preprocessing. After analyzing the image features, the model outputs the detection results, including the abnormal type (such as wrinkles or bubbles), the abnormal location (such as marked by a rectangular box), and the abnormal score (based on the area and severity of the region).

[0050] Based on the fitting abnormality detection result, feedback adjustment is performed on the target film coating control parameter.

[0051] When the film material sticking detection model outputs a fitting abnormality detection result, the system first analyzes the detection result, including key information such as the abnormal type, abnormal location, and abnormal score. For example, the abnormal type may be film material wrinkles, edge offset, or bubbles. The abnormal location marks the specific area through coordinates, and the abnormal score quantifies the severity of the abnormality. After the analysis is completed, the system generates corresponding control adjustment strategies according to the abnormal type and location, in combination with the preset adjustment rules of the film sticking process. Specifically, if wrinkles are detected in the film sticking area, the system will improve the film material sticking tightness by increasing or decreasing the cylinder pressure, and at the same time adjust the film material tension parameter to relieve the wrinkles; if the film material edge offset is detected, the system will modify the displacement parameter or film sticking path of the cylinder to recalibrate the film material position; if bubbles are detected, the local pressure is increased or the film sticking time is extended to make the film material fit more tightly to the battery surface. The system will determine the adjustment amplitude according to the level of the abnormal score. The higher the score, the greater the adjustment amplitude, ensuring that the abnormal state can be quickly corrected.

[0052] In summary, the embodiments of the present application have at least the following technical effects:

[0053] When the target battery is conveyed to the preset film wrapping station, the film wrapping requirement parameters of the target battery are received. Among them, the preset film wrapping station controls film feeding and film wrapping through a cylinder servo assembly. Then, based on the film wrapping requirement parameters, optimization of the film wrapping control parameters is performed to generate target film wrapping control parameters. Next, the target film wrapping control parameters are input into the servo cylinder control platform of the cylinder servo assembly for control, and image data of the film material fitting state is collected in real time through a vision detection device. Further, the image data of the film material fitting state is input into a pre-trained film material fitting detection model for analysis, and a fitting abnormality detection result is output. Finally, based on the fitting abnormality detection result, feedback adjustment is performed on the target film wrapping control parameters. This solves the technical problems of unstable film sticking quality and low efficiency in the battery film wrapping process in the prior art, and achieves the technical effects of improving the stability of film wrapping quality and production efficiency.

[0054] Embodiment 2, based on the same inventive concept as the high-precision battery film wrapping operation method based on a cylinder servo assembly in the foregoing embodiment, as Figure 2 shown, the present application provides a high-precision battery film wrapping operation system based on a cylinder servo assembly. Among them, the system includes:

[0055] A data receiving module 11: When the target battery is conveyed to the preset film wrapping station, it receives the film wrapping requirement parameters of the target battery. Among them, the preset film wrapping station controls film feeding and film wrapping through a cylinder servo assembly; an optimization module 12: Based on the film wrapping requirement parameters, optimization of the film wrapping control parameters is performed to generate target film wrapping control parameters; a control module 13: Inputs the target film wrapping control parameters into the servo cylinder control platform of the cylinder servo assembly for control, and collects image data of the film material fitting state in real time through a vision detection device; an analysis module 14: Inputs the image data of the film material fitting state into a pre-trained film material fitting detection model for analysis, and outputs a fitting abnormality detection result; a feedback adjustment module 15: Based on the fitting abnormality detection result, feedback adjustment is performed on the target film wrapping control parameters.

[0056] Furthermore, the data receiving module 11 is used to execute the following method:

[0057] The preset film wrapping station includes a U-shaped wrapping station and a loop wrapping station; among them, both the U-shaped wrapping station and the loop wrapping station are transported in an equidistant transportation mode, and the U-shaped wrapping station is before the loop wrapping station.

[0058] Furthermore, the optimization module 12 is used to execute the following method:

[0059] Establish an envelope optimization space; perform digital twin modeling on the preset envelope station to establish a twin envelope station; randomly select the first envelope control parameter in the envelope optimization space and input it into the twin envelope station for simulation to generate the first envelope simulation result; perform envelope quality evaluation based on the first envelope simulation result to generate the first quality index; if the first quality index meets the preset quality constraint conditions, use the first envelope control parameter as the target envelope control parameter.

[0060] Further, the optimization module 12 is used to execute the following method:

[0061] Obtain the set of battery envelope control parameter types; obtain the same-type envelope stations of the preset envelope station; connect the control platforms of the same-type envelope stations with the set of battery envelope control parameter types and the envelope requirement parameters as constraints, and collect several battery envelope process control records; use the several battery envelope process control records to form the envelope optimization space.

[0062] Further, the optimization module 12 is used to execute the following method:

[0063] Construct an envelope quality evaluation index, where the envelope quality evaluation index includes the fitting tightness between the film material and the battery surface, the smoothness of the envelope surface, the integrity of the envelope, the sealing of the envelope, and the consistency of the envelope thickness; extract the quality evaluation index values corresponding to the envelope quality evaluation index according to the first envelope simulation result to generate the first quality index.

[0064] Further, the optimization module 12 is used to execute the following method:

[0065] If the first quality index does not meet the preset quality constraint conditions, mark the first envelope control parameter as taboo; continue to randomly select the second envelope control parameter without taboo in the envelope optimization space and input it into the twin envelope station for simulation to generate the second envelope simulation result; perform envelope quality evaluation based on the second envelope simulation result to generate the second quality index; if the second quality index meets the preset quality constraint conditions, use the second envelope control parameter as the target envelope control parameter.

[0066] Further, the analysis module 14 is used to execute the following method:

[0067] Collect fitting abnormal image samples; based on the convolutional neural network, train the film material fitting detection model with the fitting abnormal image samples until convergence; input the film material fitting state image data into the converged film material fitting detection model for analysis and output the fitting abnormal detection result.

[0068] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. The processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0070] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A high-precision battery coating operation method based on a cylinder servo assembly, characterized in that: The method comprises: When the target battery is delivered to a preset film coating station, film coating requirement parameters of the target battery are received, wherein the preset film coating station performs film delivery and film coating control through a cylinder servo assembly; Optimizing the envelope control parameters based on the envelope demand parameters to generate target envelope control parameters; The target film coating control parameters are input into the servo cylinder control platform of the cylinder servo assembly for control, and the film material lamination state image data is collected in real time through the visual detection device; Inputting the film material bonding state image data into a pre-trained film material bonding detection model for analysis, and outputting a bonding abnormality detection result; Feedback adjustment is performed on the target envelope control parameter based on the abnormal fitting detection result.

2. The high-precision battery encapsulation operation method based on the cylinder servo assembly according to claim 1, characterized in that: The preset film wrapping stations include a U-shaped film wrapping station and a return-shaped film wrapping station; Wherein, the U-shaped wrapping station and the return-shaped wrapping station are both transported in an equidistant transport mode, and the U-shaped wrapping station is before the return-shaped wrapping station.

3. The high-precision battery encapsulation operation method based on the cylinder servo assembly according to claim 1, characterized in that: Optimizing the envelope control parameters based on the envelope requirement parameters to generate target envelope control parameters includes: Establishing envelope optimization space; Performing digital twin modeling on the preset film coating station to establish a twin film coating station; Randomly selecting a first enveloping film control parameter in the enveloping film optimization space, and inputting the first enveloping film control parameter into the twin enveloping film station for simulation to generate a first enveloping film simulation result; Performing an envelope quality evaluation based on the first envelope simulation result to generate a first quality index; If the first quality indicator meets the preset quality constraint condition, the first envelope control parameter is used as the target envelope control parameter.

4. The high-precision battery encapsulation operation method based on the cylinder servo assembly according to claim 3 is characterized in that: Establishing envelope optimization space, including: Get the battery envelope control parameter type set; Acquire a same-type encapsulation station as the preset encapsulation station; Taking the battery coating control parameter type set and the coating requirement parameters as constraints, connecting to the control platform of the same type of coating station, and collecting a number of battery coating process control records; The coating optimization space is constructed based on the plurality of battery coating process control records.

5. The high-precision battery encapsulation operation method based on the cylinder servo assembly according to claim 3, characterized in that: Performing an envelope quality evaluation based on the first envelope simulation result to generate a first quality index includes: Constructing an evaluation index for the quality of the coating, wherein the evaluation index for the quality of the coating includes the tightness of the coating material and the battery surface, the smoothness of the coating surface, the integrity of the coating, the sealing of the coating, and the consistency of the coating thickness; The quality evaluation index value corresponding to the envelope quality evaluation index is extracted according to the first envelope simulation result to generate the first quality index.

6. The high-precision battery encapsulation operation method based on the cylinder servo assembly according to claim 5, characterized in that: Also includes: If the first quality indicator does not meet the preset quality constraint condition, a taboo mark is performed on the first envelope control parameter; In the envelope optimization space, the second envelope control parameter without a taboo mark is continuously randomly selected, and the parameter is input into the twin envelope station for simulation to generate a second envelope simulation result; Performing envelope quality evaluation based on the second envelope simulation result to generate a second quality index; If the second quality indicator meets the preset quality constraint condition, the second envelope control parameter is used as the target envelope control parameter.

7. The high-precision battery encapsulation operation method based on the cylinder servo assembly according to claim 1, characterized in that: The film material bonding state image data is input into a pre-trained film material bonding detection model for analysis, and a bonding abnormality detection result is output, including: Collect and fit abnormal image samples; Based on a convolutional neural network, the film material bonding detection model is trained with the bonding abnormality image samples until convergence; The film material bonding state image data is input into a converged film material bonding detection model for analysis, and the bonding abnormality detection result is output.

8. High-precision battery coating operation system based on cylinder servo assembly, characterized in that: The system is used to implement the high-precision battery encapsulation operation method based on the cylinder servo assembly according to any one of claims 1 to 7, and comprises: Data receiving module: when the target battery is delivered to the preset coating station, receiving the coating requirement parameters of the target battery, wherein the preset coating station performs film delivery and coating control through the cylinder servo assembly; Optimization module: optimizes the envelope control parameters based on the envelope demand parameters to generate target envelope control parameters; Control module: input the target film coating control parameters into the servo cylinder control platform of the cylinder servo assembly for control, and collect the film material lamination state image data in real time through the visual detection device; Analysis module: inputs the film material bonding state image data into a pre-trained film material bonding detection model for analysis, and outputs bonding abnormality detection results; Feedback adjustment module: feedback adjustment of the target envelope control parameters based on the abnormal fitting detection result.

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