Battery rubberizing, plate folding and rubber winding method, equipment and system

Abstract

The invention relates to a battery rubberizing, folding plate and glue winding method, device and system, the folding process of a battery film is monitored in real time through a camera, and the folding angle and position of the film are detected through a feature point detection algorithm; a visual sensor is used for monitoring the glue coating condition in the glue winding process, and image segmentation and deep learning technologies are adopted for judging the uniformity and thickness of a glue layer; a thermal imaging technology is used for monitoring temperature distribution in the glue curing process, and a high-resolution camera is used for checking the surface of the cured glue to identify bubbles and crack defects; according to the battery rubberizing, plate folding and glue winding method, equipment and system, the key technical problems of inaccurate plate folding, non-uniform glue winding, curing defects and the like are solved, and the automation level, the production efficiency and the product quality in the battery production process can be effectively improved.

Description

Technical Field

[0001] The present invention specifically relates to a battery adhesive laminating, folding and wrapping method, equipment and system. Background Art

[0002] With the continuous development of battery technology, especially in the production process of lithium batteries, soft-pack batteries and other high-performance batteries, the requirements for the process accuracy of each component inside the battery are getting higher and higher. The battery's gluing, folding, winding and curing processes, as core manufacturing links, directly affect the performance, reliability and service life of the battery. Especially in the gluing and curing process, factors such as the uniformity of the glue, thickness control and curing effect determine the key performance of the battery, such as the sealing, battery capacity, and cycle stability. Therefore, it is very important to carry out high-precision control of these processes during the battery production process.

[0003] At present, the folding accuracy and glue winding process in battery production usually use traditional mechanical equipment and manual operation. However, with the improvement of battery performance requirements, traditional production processes and quality control methods have gradually exposed some problems, mainly including the following aspects:

[0004] The folding of the battery membrane is one of the key steps in battery production. The folding accuracy of the membrane directly affects the contact quality between the battery membrane and other components inside the battery. Due to the accuracy limitations of traditional mechanical folding equipment and operational errors during the folding process, the folding angle, position and alignment accuracy of the battery membrane are difficult to control accurately. This will cause poor contact of the battery membrane in subsequent processes, which in turn affects the performance and quality of the battery.

[0005] In the battery winding process, the uniformity and thickness of glue coating are key factors affecting battery quality. Traditional glue winding equipment often cannot monitor the uniformity and thickness of glue coating in real time, and the glue layer may be too thick or too thin, which in turn affects the overall performance of the battery. Uneven glue coating may lead to incomplete curing of the glue, and even defects such as bubbles and cracks during use, affecting the stability and safety of the battery.

[0006] In the battery production process, the curing process of the glue is also crucial. Incomplete curing or curing defects (such as bubbles, cracks, etc.) will directly affect the sealing performance and mechanical strength of the battery, and thus affect the service life and safety of the battery. The traditional curing process usually relies on manual operation or simple temperature control equipment, which makes it difficult to achieve accurate real-time monitoring and feedback. The occurrence of curing defects usually leads to rework and quality fluctuations, increasing production costs and reducing production efficiency. Summary of the invention

[0007] In order to overcome the shortcomings and disadvantages of the prior art, the primary purpose of the present invention is to provide a battery glue laminating, folding and winding method, equipment and system. This battery glue laminating, folding and winding method, equipment and system solves key technical problems such as inaccurate plate folding, uneven winding and curing defects by integrating advanced image processing technology, visual monitoring and deep learning algorithms, as well as automated feedback control mechanisms, and can effectively improve the automation level, production efficiency and product quality of the battery production process.

[0008] The primary purpose of the present invention is achieved through the following technical solutions:

[0009] A battery adhesive folding plate adhesive wrapping method, comprising the following steps:

[0010] The camera monitors the folding process of the battery film in real time, and uses a feature point detection algorithm to detect the folding angle and position of the film. If the folded plate is unqualified, the folding accuracy of the mechanical equipment is adjusted according to the image processing results. If the folded plate is qualified, the glue winding process is entered;

[0011] Use visual sensors to monitor the glue coating during the glue winding process, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer. If the glue winding is unqualified, adjust the glue winding speed and glue coating equipment in real time according to the test results. If the glue winding is qualified, enter the curing process;

[0012] Thermal imaging technology is used to monitor the temperature distribution during the glue curing process, and a high-resolution camera is used to check the surface of the cured glue to identify bubbles and cracks. If the curing is unqualified, the curing equipment is automatically adjusted and rework is performed. If the curing is qualified, the material is discharged.

[0013] Preferably, the method of detecting the folding angle and position of the film using a feature point detection algorithm is:

[0014] Use industrial cameras to monitor the battery membrane folding process in real time, ensuring that the camera can obtain full-view images of the membrane;

[0015] Preprocess the captured original image, including noise removal, contrast enhancement, and edge enhancement;

[0016] The reference images of the membrane before and after folding are used as templates and compared with the images taken in real time;

[0017] By comparing the similarity between the template and the real-time image through a sliding window, the position of the folded part is found and the folded area of ​​the membrane is determined;

[0018] The degree of match between the template and the real-time image is calculated. If the match exceeds a preset threshold, the folding angle and position are considered to meet the requirements.

[0019] As a preferred method, the method of using image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer is:

[0020] Use industrial cameras to capture real-time images during the glue winding process;

[0021] Using image segmentation technology, the glue-coated area in the image is separated from the background, and the boundary between the glue-coated area and the background is identified through the threshold segmentation algorithm;

[0022] After image segmentation, the features of the glue coating layer are extracted through the convolutional neural network (CNN) to evaluate the uniformity of the glue layer.

[0023] The network input is the glue area image after image segmentation;

[0024] The network output is the glue layer uniformity score, which is a score between 0 and 1. The closer to 1, the more uniform it is, and the closer to 0, the less uniform it is.

[0025] According to the brightness of the glue layer in the image, the brightness of the coating area is analyzed to estimate the thickness of the glue;

[0026] Compare the uniformity and thickness detection results output by the deep learning model with the preset coating standards to determine whether the coating quality is qualified.

[0027] Preferably, the method of inspecting the surface of the cured glue by a high-resolution camera to identify air bubbles and cracks is as follows:

[0028] During the curing process of the glue, an infrared thermal imager is used to monitor the temperature distribution on the glue surface in real time;

[0029] After curing, an industrial camera is used to capture images of the glue surface;

[0030] The uniformity of glue curing is judged by thermal imaging, and surface defects after curing are confirmed by visual inspection.

[0031] Preferably, the method for confirming the surface defects after curing by visual inspection is:

[0032] The image is processed using threshold segmentation to extract possible defect areas on the glue surface;

[0033] By image segmentation, surface protrusions or circular transparent areas are identified from the image and marked as bubble areas;

[0034] The thin and long linear defect areas are identified from the image by Canny edge and marked as crack areas.

[0035] Another technical problem to be solved by the present invention is to provide a battery glue folding and glue winding equipment, including:

[0036] The workbench includes a first rotating processing table for driving the battery to be transported to each processing station, and a plurality of battery processing clamping claws arranged on the first rotating processing table, and a second rotating processing table for driving the battery to be transported to a recycling channel and a finished product channel, and a plurality of battery recycling clamping claws arranged on the second rotating processing table;

[0037] A plate folding device, used for folding the battery on the battery processing clamping claw;

[0038] A glue wrapping device, used for wrapping glue around the battery on the battery processing clamping claw;

[0039] A curing device, used for curing the battery on the battery processing clamping claw;

[0040] A folding plate detection device, which is used to monitor the folding process of the battery film in real time through a camera and detect the folding angle and position of the film using a plate matching or feature point detection algorithm;

[0041] The glue winding detection device is used to monitor the glue coating during the glue winding process through visual sensors, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer;

[0042] The curing monitoring device is used to monitor the temperature distribution during the glue curing process using thermal imaging technology, and to inspect the surface of the cured glue with a high-resolution camera to identify air bubbles and crack defects.

[0043] Another technical problem to be solved by the present invention is to provide a battery glue folding and glue winding system, comprising:

[0044] The folding plate detection module is used to monitor the folding process of the battery film in real time through a camera, and use plate matching or feature point detection algorithms to detect the folding angle and position of the film. If the folding plate is unqualified, the folding accuracy of the mechanical equipment is adjusted according to the detection results;

[0045] The glue winding detection module is used to monitor the glue coating during the glue winding process through visual sensors, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer. If the glue winding is unqualified, the glue winding speed and glue coating equipment are adjusted in real time according to the detection results;

[0046] The curing monitoring module is used to monitor the temperature distribution during the curing process of the glue using thermal imaging technology, and to check the surface of the cured glue with a high-resolution camera to identify defects such as bubbles and cracks. If the curing is unsatisfactory, the curing equipment is automatically adjusted and rework is performed;

[0047] The automatic feedback control system is used to adjust the parameters of the corresponding process equipment in real time according to the detection results of each module, including the glue coating amount, glue coating speed, folding accuracy, glue winding speed, glue coating amount, and the temperature and time of the curing equipment.

[0048] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a battery adhesive folding and wrapping method as described above is implemented.

[0049] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, a battery adhesive folding and wrapping method as described in any of the above is implemented.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] This method uses a camera to monitor the folding process in real time, uses plate matching or feature point detection algorithms to detect the angle and position of the folded plate, and adjusts the folding accuracy of the mechanical equipment in real time when the folded plate is unqualified, thereby effectively improving the folding accuracy and avoiding production defects caused by folding problems; this method uses a visual sensor to monitor the glue winding process, combines image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer, and if unqualified is found, the glue winding speed can be automatically adjusted to ensure the quality of the glue winding process.

[0052] This method uses thermal imaging technology to monitor the temperature distribution during the glue curing process, and uses a high-resolution camera to check the surface of the cured glue to promptly identify defects such as bubbles and cracks. If the curing is found to be unqualified, the curing equipment is automatically adjusted and rework is performed to ensure the curing quality of each battery. This method uses image processing, deep learning, thermal imaging and other technologies to achieve real-time monitoring and feedback of each process, and can automatically adjust equipment parameters (such as folding accuracy, glue coating amount, curing temperature, etc.), thereby improving production efficiency and ensuring consistency of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a battery glue-applying, folding and wrapping method according to the present invention;

[0054] Figure 2 This is a structural diagram of a battery glue-sticking, folding and glue-winding device according to the present invention. DETAILED DESCRIPTION

[0055] The specific implementation modes of the present invention are further described in detail to make the technical solutions of the present invention easier to understand and grasp.

[0056] Example

[0057] See also Figure 1 As shown, a battery adhesive folding plate adhesive wrapping method includes the following steps:

[0058] The camera monitors the folding process of the battery film in real time, and uses the feature point detection algorithm to detect the folding angle and position of the film. If the folded plate is unqualified, the folding accuracy of the mechanical equipment is adjusted according to the image processing results. If the folded plate is qualified, the glue winding process is entered;

[0059] Use visual sensors to monitor the glue coating during the glue winding process, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer. If the glue winding is unqualified, adjust the glue winding speed and glue coating equipment in real time according to the test results. If the glue winding is qualified, enter the curing process;

[0060] Thermal imaging technology is used to monitor the temperature distribution during the glue curing process, and a high-resolution camera is used to check the surface of the cured glue to identify bubbles and cracks. If the curing is unqualified, the curing equipment is automatically adjusted and rework is performed. If the curing is qualified, the material is discharged.

[0061] This method greatly reduces the need for manual intervention and adjustment by real-time monitoring and automatically adjusting the parameters of production equipment (such as glue coating amount, plate folding accuracy, glue winding speed, etc.) according to feedback data. Automated control can reduce human operating errors and response time, improve production continuity and efficiency; relying on efficient automated detection and adjustment systems, it reduces the need for manual intervention and reduces the cost of manual operation and detection. At the same time, the intelligent adjustment of equipment can ensure the efficient operation of the production line and shorten the production cycle.

[0062] Through real-time data collection and monitoring of each process (such as image data, temperature distribution, glue coating amount, etc.), the entire production process has strong traceability. The production data of each batch can be recorded to facilitate subsequent quality review and fault diagnosis; thermal imaging technology is used to accurately monitor the curing process to ensure the temperature uniformity and quality of the glue curing process, and to detect defects such as bubbles and cracks in time to avoid unstable product performance caused by incomplete curing. This process not only optimizes the quality of the product, but also improves the safety and durability of the battery.

[0063] The method of detecting the folding angle and position of the film using the feature point detection algorithm is as follows:

[0064] Use industrial cameras to monitor the battery membrane folding process in real time, ensuring that the camera can obtain full-view images of the membrane;

[0065] Preprocess the captured original image, including noise removal, contrast enhancement, and edge enhancement;

[0066] The reference images of the membrane before and after folding are used as templates and compared with the images taken in real time;

[0067] By comparing the similarity between the template and the real-time image through a sliding window, the position of the folded part is found and the folded area of ​​the membrane is determined;

[0068] The degree of match between the template and the real-time image is calculated. If the match exceeds a preset threshold, the folding angle and position are considered to meet the requirements.

[0069] By comparing the reference images of the film before and after folding, the feature point detection algorithm can accurately identify the position and angle of the folding area. The sliding window technology can compare the similarity between the template and the real-time image pixel by pixel to ensure high-precision detection of the folding position and angle. This method can accurately determine whether the film is folded as expected, thereby reducing human errors and ensuring product quality; the real-time monitoring of the camera can quickly capture any unqualified conditions in the folding process, and the algorithm-based feedback can quickly guide equipment adjustments to ensure folding accuracy and quality. Compared with traditional manual inspection, the automated system can respond in a shorter time and improve production efficiency.

[0070] Manual inspection is easily affected by fatigue, visual errors or environmental factors in large-scale production, which may lead to omissions or errors in inspection. The use of image processing and feature point detection algorithms can automatically perform the inspection process, greatly reducing human intervention and ensuring that each product undergoes strict and standardized inspection; the algorithm provides a unified standard to ensure that each inspection is carried out according to the same rules, eliminating deviations and inconsistencies in manual judgment. This makes the inspection results of folding angles and positions more reliable and avoids quality fluctuations caused by different manual judgment standards.

[0071] By monitoring the folding process in real time through image processing, problems can be discovered and adjusted in a timely manner, avoiding the accumulation and rework of unqualified products. This allows the production line to maintain efficient operation and reduce waste and downtime caused by errors; this automated detection method reduces dependence on manual operation, reduces the need for manual labor in the production process, and can optimize human resource allocation, thereby reducing overall production costs. In addition, reducing rework caused by errors due to manual operation also helps save material and time costs.

[0072] The method of using image segmentation and deep learning technology to judge the uniformity and thickness of the glue layer is:

[0073] Use industrial cameras to capture real-time images during the glue winding process;

[0074] Using image segmentation technology, the glue-coated area in the image is separated from the background, and the boundary between the glue-coated area and the background is identified through the threshold segmentation algorithm;

[0075] After image segmentation, the features of the glue coating layer are extracted through the convolutional neural network (CNN) to evaluate the uniformity of the glue layer.

[0076] The network input is the glue area image after image segmentation;

[0077] The network output is the glue layer uniformity score, which is a score between 0 and 1. The closer to 1, the more uniform it is, and the closer to 0, the less uniform it is.

[0078] According to the brightness of the glue layer in the image, the brightness of the coating area is analyzed to estimate the thickness of the glue;

[0079] Compare the uniformity and thickness detection results output by the deep learning model with the preset coating standards to determine whether the coating quality is qualified.

[0080] Image segmentation technology can accurately identify the glue coating area, ensuring that every part of the coating is carefully analyzed. Combined with a convolutional neural network (CNN), this method can extract subtle features from the image, evaluate the uniformity of the glue layer, and accurately estimate the thickness of the glue through image brightness analysis. This high-precision detection method can effectively identify any unevenness or substandard thickness that may exist during the coating process, ensuring that the coating quality meets the expected standards; automated detection greatly reduces the subjectivity and operational errors in manual detection, and can ensure that each test meets uniform standards. This means that the evaluation of coating quality during the production process is more objective and consistent, reducing errors caused by manual judgment.

[0081] This method can provide a quantitative quality assessment for each batch of glue layers applied through the uniformity score output by the deep learning model. This standardized quality inspection process can effectively guarantee the consistency of the coating process, ensuring that each product meets the set quality standards, thereby improving the product qualification rate; through accurate thickness estimation, it can avoid quality problems caused by too thick or too thin glue layers, thereby reducing material waste and defective products caused by uneven glue coating. By adjusting the glue coating process in a timely manner, glue materials can be saved, production costs can be reduced, and rework of unqualified products can be avoided.

[0082] The method of using a high-resolution camera to inspect the surface of the cured glue to identify air bubbles and cracks is as follows:

[0083] During the curing process of the glue, an infrared thermal imager is used to monitor the temperature distribution on the glue surface in real time;

[0084] After curing, an industrial camera is used to capture images of the glue surface;

[0085] The uniformity of glue curing is judged by thermal imaging, and surface defects after curing are confirmed by visual inspection.

[0086] Infrared thermal imagers can monitor the temperature distribution on the surface of the glue in real time, thus reflecting the uniformity of the glue curing. Temperature differences during the curing process usually mean that the glue is cured unevenly, which may lead to defects such as bubbles and cracks. Thermal imaging images can be used to detect uneven curing areas early, so that adjustment measures can be taken to avoid problems. For example, if certain areas cure too quickly or too slowly, surface stress concentration may occur, forming bubbles or cracks; by real-time monitoring during the curing process, potential problems in the curing process can be identified and resolved in a timely manner, avoiding complex repairs of defects after curing is completed, and reducing overall production costs.

[0087] After the glue is cured, using a high-resolution industrial camera to photograph the cured surface can obtain extremely detailed image data. This image can be used to detect tiny bubbles, cracks or other surface defects, which, if not discovered in time, may affect the quality and performance of the final product; combining thermal imaging data with visual inspection images can achieve more accurate defect location. Thermal imaging helps reveal areas of uneven curing, while industrial cameras provide highly detailed surface images that help determine the specific type of defects (such as bubbles, cracks, etc.), thereby providing accurate information for subsequent quality control and repair.

[0088] By detecting surface defects in time after curing, the accumulation of defective products during the production process can be avoided. If defective products can be quickly identified and removed or repaired, the rework rate can be greatly reduced and production efficiency can be improved; timely detection of defects during and after the curing process of the glue can reduce the outflow of defective products and avoid waste of resources and time. Through precise detection and control, it can ensure that the glue material is fully utilized and the scrap rate in the production process is minimized.

[0089] The method for confirming surface defects after curing by visual inspection is:

[0090] The image is processed using threshold segmentation to extract possible defect areas on the glue surface;

[0091] By image segmentation, surface protrusions or circular transparent areas are identified from the image and marked as bubble areas;

[0092] The thin and long linear defect areas are identified from the image by Canny edge and marked as crack areas.

[0093] Through threshold segmentation, image segmentation and Canny edge detection, the surface defects of glue can be automatically identified, reducing manual intervention. This not only improves the detection efficiency, but also ensures the accuracy and consistency of detection; threshold segmentation can extract high-contrast areas in the image to accurately locate possible defective areas. Image segmentation methods can be further refined to identify surface ridges or round transparent areas and clearly identify bubble areas; while Canny edge detection is specifically used to detect elongated cracks, ensuring that various types of defects (such as bubbles and cracks) are clearly located and distinguished.

[0094] This method can not only identify circular transparent bubble areas, but also accurately identify thin linear defects such as cracks through Canny edge detection. In view of the different types of defects that may appear on the glue curing surface, the combination of these two technologies makes the detection method more flexible and can fully capture surface defects; for defects that may form under different types of glue materials or different curing conditions (such as bubbles, cracks, etc.), by adjusting the threshold and segmentation parameters, the solution can flexibly adapt to changing detection needs.

[0095] Through image segmentation and edge detection, defect areas can be quickly extracted from a large amount of image data in a short time. The automation and efficiency of defect recognition can significantly speed up the quality inspection speed of the production line, thereby improving overall production efficiency; this detection method can timely and accurately identify surface defects, ensuring that unqualified products can be quickly eliminated to prevent unqualified products from flowing into the next production link or end users. This helps to maintain product consistency and high quality, and reduce rework and resource waste caused by defects.

[0096] See also Figure 2 The battery adhesive folding and adhesive winding equipment comprises:

[0097] The workbench includes a first rotating processing table 1 for driving the battery to be transported to each processing station, and a plurality of battery processing clamping claws 2 arranged on the first rotating processing table 1, and a second rotating processing table 5 for driving the battery to be transported to a recycling channel 3 and a finished product channel 4, and a plurality of battery recycling clamping claws 6 arranged on the second rotating processing table 5;

[0098] A folding device 7, used for folding the battery on the battery processing clamping claw;

[0099] The glue wrapping device 8 is used to wrap glue around the battery on the battery processing clamping claw;

[0100] A curing device 9, used for curing the battery on the battery processing clamping claw;

[0101] A folding plate detection device 10 is used to monitor the folding process of the battery membrane in real time through a camera, and to detect the folding angle and position of the membrane using a plate matching or feature point detection algorithm;

[0102] The glue winding detection device 11 is used to monitor the glue coating during the glue winding process through a visual sensor, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer;

[0103] The curing monitoring device 12 is used to monitor the temperature distribution during the curing process of the glue using thermal imaging technology, and to check the surface of the cured glue using a high-resolution camera to identify air bubbles and crack defects.

[0104] A battery glue folding and glue winding system, comprising:

[0105] The folding plate detection module is used to monitor the folding process of the battery film in real time through a camera, and use plate matching or feature point detection algorithms to detect the folding angle and position of the film. If the folding plate is unqualified, the folding accuracy of the mechanical equipment is adjusted according to the detection results;

[0106] The glue winding detection module is used to monitor the glue coating during the glue winding process through visual sensors, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer. If the glue winding is unqualified, the glue winding speed and glue coating equipment are adjusted in real time according to the detection results;

[0107] The curing monitoring module is used to monitor the temperature distribution during the curing process of the glue using thermal imaging technology, and to check the surface of the cured glue with a high-resolution camera to identify defects such as bubbles and cracks. If the curing is unsatisfactory, the curing equipment is automatically adjusted and rework is performed;

[0108] The automatic feedback control system is used to adjust the parameters of the corresponding process equipment in real time according to the detection results of each module, including the glue coating amount, glue coating speed, folding accuracy, glue winding speed, glue coating amount, and the temperature and time of the curing equipment.

[0109] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a battery adhesive folding and wrapping method as described above is implemented.

[0110] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a battery adhesive folding and wrapping method as described above is implemented.

[0111] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0112] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0113] Of course, the above are only typical examples of the present invention. In addition to these, the present invention may also have many other specific implementations. Any technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention. Of course, the above are only typical examples of the present invention. In addition to these, the present invention may also have many other specific implementations. Any technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention.

Claims

1. A battery adhesive folding and wrapping method, characterized in that: The following steps are involved: The camera monitors the folding process of the battery film in real time, and uses a feature point detection algorithm to detect the folding angle and position of the film. If the folded plate is unqualified, the folding accuracy of the mechanical equipment is adjusted according to the image processing results. If the folded plate is qualified, the glue winding process is entered; Use visual sensors to monitor the glue coating during the glue winding process, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer. If the glue winding is unqualified, adjust the glue winding speed and glue coating equipment in real time according to the test results. If the glue winding is qualified, enter the curing process; Thermal imaging technology is used to monitor the temperature distribution during the glue curing process, and a high-resolution camera is used to check the surface of the cured glue to identify bubbles and cracks. If the curing is unqualified, the curing equipment is automatically adjusted and rework is performed. If the curing is qualified, the material is discharged.

2. The battery adhesive folding and wrapping method according to claim 1, characterized in that: The method of detecting the folding angle and position of the film using the feature point detection algorithm is as follows: Use industrial cameras to monitor the battery membrane folding process in real time, ensuring that the camera can obtain full-view images of the membrane; Preprocess the captured original image, including noise removal, contrast enhancement, and edge enhancement; The reference images of the membrane before and after folding are used as templates and compared with the images taken in real time; By comparing the similarity between the template and the real-time image through a sliding window, the position of the folded part is found and the folded area of ​​the membrane is determined; The degree of match between the template and the real-time image is calculated. If the match exceeds a preset threshold, the folding angle and position are considered to meet the requirements.

3. The battery adhesive folding and wrapping method according to claim 1, characterized in that: The method of using image segmentation and deep learning technology to judge the uniformity and thickness of the glue layer is: Use industrial cameras to capture real-time images during the glue winding process; Using image segmentation technology, the glue-coated area in the image is separated from the background, and the boundary between the glue-coated area and the background is identified through the threshold segmentation algorithm; After image segmentation, the features of the glue coating layer are extracted through the convolutional neural network (CNN) to evaluate the uniformity of the glue layer. The network input is the glue area image after image segmentation; The network output is the glue layer uniformity score, which is a score between 0 and 1. The closer to 1, the more uniform it is, and the closer to 0, the less uniform it is. According to the brightness of the glue layer in the image, the brightness of the coating area is analyzed to estimate the thickness of the glue; Compare the uniformity and thickness detection results output by the deep learning model with the preset coating standards to determine whether the coating quality is qualified.

4. The battery adhesive folding and wrapping method according to claim 1, characterized in that: The method of using a high-resolution camera to inspect the surface of the cured glue to identify air bubbles and cracks is as follows: During the curing process of the glue, an infrared thermal imager is used to monitor the temperature distribution on the glue surface in real time; After curing, an industrial camera is used to capture images of the glue surface; The uniformity of glue curing is judged by thermal imaging, and surface defects after curing are confirmed by visual inspection.

5. The battery adhesive folding and wrapping method according to claim 4, characterized in that: The method for confirming surface defects after curing by visual inspection is: The image is processed using threshold segmentation to extract possible defect areas on the glue surface; By image segmentation, surface protrusions or circular transparent areas are identified from the image and marked as bubble areas; The thin and long linear defect areas are identified from the image by Canny edge and marked as crack areas.

6. A battery glue folding and glue winding equipment, characterized in that: Included are: The workbench includes a first rotating processing table for driving the battery to be transported to each processing station, and a plurality of battery processing clamping claws arranged on the first rotating processing table, and a second rotating processing table for driving the battery to be transported to a recycling channel and a finished product channel, and a plurality of battery recycling clamping claws arranged on the second rotating processing table; A gluing device, used for gluing the battery on the battery processing clamping claw; A plate folding device, used for folding the battery on the battery processing clamping claw; A glue wrapping device, used for wrapping glue around the battery on the battery processing clamping claw; A curing device, used for curing the battery on the battery processing clamping claw; The glue coating detection device is used to capture images of the glue coating area on the battery surface through an industrial camera and detect the uniformity, thickness and coating defects of the glue coating through an image processing algorithm; A folding plate detection device, which is used to monitor the folding process of the battery film in real time through a camera and detect the folding angle and position of the film using a plate matching or feature point detection algorithm; The glue winding detection device is used to monitor the glue coating during the glue winding process through visual sensors, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer; The curing monitoring device is used to monitor the temperature distribution during the glue curing process using thermal imaging technology, and to inspect the surface of the cured glue with a high-resolution camera to identify air bubbles and crack defects.

7. A battery glue folding and wrapping system, characterized in that: Included are: The glue coating detection module is used to capture images of the glue coating area on the battery surface through an industrial camera, and detect the uniformity, thickness and coating defects of the glue coating through an image processing algorithm. If the glue coating is unqualified, the coating amount and speed of the glue coating equipment are adjusted in real time according to the detection results; The folding plate detection module is used to monitor the folding process of the battery film in real time through a camera, and use plate matching or feature point detection algorithms to detect the folding angle and position of the film. If the folding plate is unqualified, the folding accuracy of the mechanical equipment is adjusted according to the detection results; The glue winding detection module is used to monitor the glue coating during the glue winding process through visual sensors, and use image segmentation and deep learning technology to determine the uniformity and thickness of the glue layer. If the glue winding is unqualified, the glue winding speed and glue coating equipment are adjusted in real time according to the detection results; The curing monitoring module is used to monitor the temperature distribution during the curing process of the glue using thermal imaging technology, and to check the surface of the cured glue with a high-resolution camera to identify defects such as bubbles and cracks. If the curing is unsatisfactory, the curing equipment is automatically adjusted and rework is performed; The automatic feedback control system is used to adjust the parameters of the corresponding process equipment in real time according to the detection results of each module, including the glue coating amount, glue coating speed, folding accuracy, glue winding speed, glue coating amount, and the temperature and time of the curing equipment.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a battery adhesive folding and adhesive wrapping method as described in any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a battery adhesive folding and wrapping method as described in any one of claims 1-5 is implemented.

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