Battery cell pole cleaning and positioning method, laser cleaning method, and electronic device

Abstract

The application relates to the technical field of laser cleaning, in particular to a battery cell pole cleaning positioning method, a laser cleaning method and electronic equipment, wherein the method comprises the following steps: obtaining a two-dimensional image of a battery cell pole; determining a two-dimensional boundary box view of the battery cell pole according to the two-dimensional image of the battery cell pole; extracting a point cloud in a boundary box of the two-dimensional boundary view of the battery cell pole, generating and segmenting a battery cell pole surface point cloud from an inner region of the point cloud; and positioning a cleaning position of the battery cell pole according to the battery cell pole surface point cloud. Therefore, the problems that the positioning is inaccurate in the related art, the cleaning efficiency is low, the cleaning is not comprehensive, and the cleaning quality is poor are solved.

Description

Technical Field

[0001] This application relates to the field of laser cleaning technology, and in particular to a method for cleaning and positioning battery cell terminals, a laser cleaning method, and an electronic device. Background Technology

[0002] Currently, most battery cell connection processes use laser welding. Since the terminals at the cell connection points are made of copper, they are prone to oxidation. When the terminals are oxidized, the resistance increases, which consumes a certain amount of electrical energy and generates voltage, resulting in poor battery consistency, internal "dissipation" within the battery, and reduced battery performance. Therefore, the surface of the battery cell terminals should be deoxidized before laser welding.

[0003] Among related technologies, cleaning methods mainly include manual cleaning, mechanical cleaning, chemical reagent cleaning, water jet cleaning, and ultrasonic cleaning. Among these, manual cleaning is inefficient and prone to incomplete cleaning; mechanical cleaning methods are inefficient, and the high-density dust generated during the cleaning process disperses into the air, causing great harm to the health of construction workers. Furthermore, during sandblasting, the continuous impact of steel shot on the substrate causes surface deformation and reduces battery performance; chemical reagent cleaning corrodes the substrate, and the waste liquid generated during cleaning, if not properly treated and recycled, causes great environmental pollution; water jet cleaning and ultrasonic cleaning are more environmentally friendly than the first two methods and cause relatively less damage to the substrate, but they are expensive and have low cleaning efficiency. Summary of the Invention

[0004] This application provides a cell electrode cleaning and positioning method, a laser cleaning method, and an electronic device to solve the problems of low cleaning efficiency and poor cleaning quality caused by inaccurate positioning during cleaning in related technologies.

[0005] The first aspect of this application provides a method for cleaning and locating battery cell terminals, comprising the following steps: acquiring a two-dimensional image of the battery cell terminal; determining a two-dimensional bounding box view of the battery cell terminal based on the two-dimensional image of the battery cell terminal; extracting point clouds within the bounding box of the two-dimensional bounding box view of the battery cell terminal; generating and segmenting a point cloud on the surface of the battery cell terminal from the region within the point cloud within the bounding box; and locating the cleaning position of the battery cell terminal based on the point cloud on the surface of the battery cell terminal.

[0006] Optionally, generating and segmenting the point cloud of the battery cell electrode surface from the point cloud region within the bounding box includes: using at least one target region within the point cloud region within the bounding box as an initial seed region; searching for point clouds within the target range with the initial seed region as the center; and fitting the point cloud within the target range to generate the point cloud of the battery cell electrode surface.

[0007] Optionally, the search for point clouds within the target range includes: determining a search threshold based on the target range; if the normal vector of the point cloud is less than or equal to the search threshold, then marking the point cloud as a point cloud within the target range; if the normal vector of the point cloud is greater than the search threshold, then marking the point cloud and outliers within the target range based on the standard deviation of the point cloud within the target range.

[0008] Optionally, the step of marking the point cloud and outliers within the target range based on the standard deviation within the region includes: calculating the ratio of the standard deviation within the region to the standard deviation reference value; if the ratio is greater than a marking threshold, then marking the point cloud as an outlier and removing the outlier; otherwise, marking the point cloud as a point cloud within the target range.

[0009] Optionally, locating the cleaning position of the battery cell electrode based on the point cloud on the surface of the battery cell electrode includes: establishing a camera coordinate system; offsetting the quadrant of the point cloud on the surface of the battery cell electrode to the target quadrant of the camera coordinate system; calculating the Euclidean distance between the point cloud on the surface of each battery cell electrode and the origin of the camera coordinate system; and determining the cleaning position of the battery cell electrode based on the Euclidean distance.

[0010] A second aspect of this application provides a laser cleaning method for battery cell terminals, comprising the following steps: obtaining the cleaning position of the battery cell terminal determined by the battery cell terminal cleaning positioning method as described in the above embodiments; determining the target focusing position of the laser based on the cleaning position of the battery cell terminal; adjusting the positions of the lens and galvanometer in the laser according to the target focusing position; and performing laser cleaning on the battery cell terminal based on the adjusted laser.

[0011] Optionally, adjusting the positions of the lens and galvanometer in the laser according to the target focusing position includes: determining a first target position of the lens and a second target position of the galvanometer according to the target focusing position; and controlling the lens to reach the first target position and the galvanometer to reach the second target position, respectively.

[0012] Optionally, determining the target focusing position of the laser based on the cleaning position of the cell electrode includes: identifying the boundary line of the cleaning position; and determining multiple target focusing positions based on the boundary line and the cleaning spacing.

[0013] Optionally, the laser cleaning of the cell electrode based on the adjusted laser includes: focusing the laser sequentially onto the target focusing position in a preset order.

[0014] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the cell electrode cleaning and positioning method as described in the above embodiments, or the cell electrode laser cleaning method.

[0015] Therefore, this application has at least the following beneficial effects:

[0016] (1) The embodiments of this application combine two-dimensional image target detection technology with three-dimensional point cloud data processing strategy. Two-dimensional image target detection technology extracts the point cloud of the battery cell electrode, and three-dimensional point cloud data processing strategy segments the point cloud data, which can accurately identify the location of the battery cell electrode cleaning point.

[0017] (2) In the embodiments of this application, the position of the segmented point cloud data in the coordinate system is not fixed. By offsetting the data points in the three-dimensional coordinate system, the laser cleaning positioning points can be accurately indexed.

[0018] (3) In this embodiment of the application, the laser is used to automatically zoom and clean the cell electrode cleaning points, making the cleaning more comprehensive and efficient, thereby achieving the best laser cleaning effect.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart of a cell electrode cleaning and positioning method provided according to an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of the point cloud within the target detection and prediction box of the battery cell electrode according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the region growth principle of the fusion attention mechanism provided in the embodiments of this application;

[0024] Figure 4 This is a point cloud coordinate quadrant offset map provided according to an embodiment of this application;

[0025] Figure 5 This is a flowchart of a laser cleaning method for battery cell terminals according to an embodiment of this application;

[0026] Figure 6 This is a schematic diagram of the galvanometer marking principle provided according to an embodiment of this application;

[0027] Figure 7 This is a diagram showing the relationship between focal length and laser position according to an embodiment of this application;

[0028] Figure 8 This is a schematic diagram illustrating the principle of laser cleaning of the battery cell electrode surface according to an embodiment of this application.

[0029] Figure 9 This is a diagram showing the relationship between the world coordinate system and the galvanometer coordinate system according to an embodiment of this application.

[0030] Figure 10 This is a diagram showing the cell electrode cleaning points according to an embodiment of this application;

[0031] Figure 11 This is a schematic diagram of galvanometer marking according to an embodiment of this application;

[0032] Figure 12 This is a schematic connection diagram of the overall process according to the embodiments of this application;

[0033] Figure 13 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0034] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0035] With the rapid development of new energy vehicles, power batteries have become the most core component and power source in new energy vehicles. Power batteries are mainly divided into battery packs, modules, and cells. The battery pack consists of multiple cell modules, and each module consists of several to a dozen cells. The current and signal transmission between modules, between cells, and within the battery management system that manages the cells requires various connections and monitoring of current and temperature. The battery also requires high-voltage connectors for external power transmission. Therefore, the reliability of cell and module connections, high and low voltage interfaces, and current and temperature monitoring is crucial.

[0036] Currently, most battery cell connection processes use laser welding. However, because the terminals at the cell connection points are made of copper, they are prone to oxidation. When the terminals oxidize, the increased resistance consumes electrical energy and generates voltage, leading to poor battery consistency, internal friction, and reduced battery performance. Therefore, an oxidation removal process should be performed on the surface of the cell terminals before laser welding.

[0037] In recent years, with the continuous development of laser technology, laser technology has been applied to the cleaning field and its effects have been found to be significantly superior. Compared with traditional cleaning methods, the advantages of laser cleaning are mainly: (1) the cleaning parameters are adjustable, which can achieve precise cleaning and a wide cleaning range; (2) the cleaning causes little damage to the substrate, ensuring that the substrate performance remains unchanged after cleaning; (3) the "green" cleaning method causes less environmental pollution; and (4) the cleaning efficiency is high, the cost is low, and it is easier to automate.

[0038] However, laser cleaning equipment is difficult to automate in production lines. Its basic working method is to place the material in a pre-set cleaning station and position and align it by red light preview. The positioning process requires manual adjustment multiple times, which is cumbersome and inaccurate, seriously affecting cleaning efficiency and quality, and requiring a large investment of human resources.

[0039] Therefore, this application proposes a method for cleaning and positioning battery cell terminals, a laser cleaning method, and an electronic device. Firstly, this application uses a three-dimensional automated laser cleaning system for battery cell terminals to implement the above method. The system includes a galvanometer laser marking module and an integrated vision module. A camera is used to acquire images of the cleaning surface, and image processing technology is used to obtain cleaning point information. This cleaning point information is then fed back to the PLC control module and the galvanometer laser marking module via upper computer software before cleaning is performed. The specific method is as follows:

[0040] Specifically, Figure 1 This is a schematic flowchart of a battery cell electrode cleaning and positioning method provided in an embodiment of this application.

[0041] like Figure 1 As shown, the cell electrode cleaning and positioning method includes the following steps:

[0042] In step S101, a two-dimensional image of the cell electrode is obtained.

[0043] It is understood that the embodiments of this application can obtain a two-dimensional image of the battery cell terminal to facilitate the subsequent determination of a two-dimensional boundary frame view of the battery cell terminal based on the two-dimensional image of the battery cell terminal.

[0044] It should be noted that this application can control the servo motor to move the camera to a fixed shooting point by sending a shooting command from the host computer to take pictures of the battery cell electrode, thereby obtaining a two-dimensional image of the battery cell electrode.

[0045] Specifically, the battery cell module is placed in a laser marking stage fixture, and multiple images are taken using a structured light camera to form a dataset of battery cell electrode images.

[0046] In step S102, a two-dimensional boundary frame view of the battery cell electrode is determined based on the two-dimensional image of the battery cell electrode.

[0047] It is understood that, in the embodiments of this application, a two-dimensional bounding box view of the battery cell electrode can be determined based on the two-dimensional image of the battery cell electrode, so as to facilitate the subsequent extraction of the point cloud within the bounding box of the two-dimensional bounding view of the battery cell electrode.

[0048] Specifically, such as Figure 2 As shown, the battery cells in the dataset images are bounded and labeled, and then input into the YOLOv5x network model based on the PyTorch framework for offline classification training; the image to be predicted is input into the trained YOLOv5x model for inference prediction; the model outputs the bounding box position and category information of the detected target; the bounding boxes output by the model are post-processed, and non-maximum suppression is used to remove overlapping bounding boxes, and the results are filtered according to the confidence threshold to obtain the final target detection result, i.e., the two-dimensional bounding box view of the battery cell terminal.

[0049] In step S103, the point cloud within the bounding box of the two-dimensional boundary view of the battery cell electrode is extracted, and the point cloud on the surface of the battery cell electrode is generated and segmented from the region within the point cloud within the bounding box.

[0050] It is understood that the embodiments of this application can extract the point cloud within the bounding box of the two-dimensional boundary view of the battery cell electrode, and generate and segment the point cloud on the surface of the battery cell electrode from the region within the point cloud within the bounding box, so as to facilitate the subsequent location of the cleaning position of the battery cell electrode based on the point cloud on the surface of the battery cell electrode.

[0051] It should be noted that after obtaining a 2D image with target bounding boxes using the target recognition algorithm, the point cloud within the bounding boxes is extracted. The point cloud within the bounding boxes includes the point cloud of the cell electrode surface and a small portion of the point cloud of the cell cover plate. An improved region growing algorithm is used to generate the point cloud of the cell electrode surface within the point cloud within the bounding boxes.

[0052] In this embodiment of the application, generating and segmenting the point cloud of the battery cell electrode surface from the point cloud region within the bounding box includes: taking at least one target region within the point cloud region within the bounding box as an initial seed region; searching for point clouds within the target range with the initial seed region as the center; and fitting the point cloud within the target range to generate the point cloud of the battery cell electrode surface.

[0053] The target range can be the point cloud of the target bounding box, without specific limitations.

[0054] It is understood that the embodiments of this application use the initial seed region as the center to search for point clouds within the target range, and fit the point clouds within the target range to generate point clouds on the surface of the battery cell electrode, thereby avoiding the phenomena of under-segmentation and over-segmentation.

[0055] It should be noted that, as Figure 3As shown, the initial seed region can be the neighborhood of the central seed point. Once the central initial seed region is determined, the region growing algorithm will start from this region and gradually add neighboring points to the growing region according to certain rules or conditions. In the context of FLANN (Fast Library for Approximate Nearest Neighbors), neighborhood search is usually used to find neighboring points that meet specific conditions, such as points that meet a normal threshold.

[0056] Specifically, the region growing algorithm starts growing from the initial seed region at the center, and performs a FLANN neighborhood search within a large radius to find 3D point clouds that meet the normal threshold. The least squares algorithm is used to perform plane fitting to obtain the normal vector, and the matching region points are marked as visited to prevent duplicate processing. Since the tolerance for outliers varies depending on the set size of the normal threshold during the large radius search, undersegmentation and oversegmentation may occur.

[0057] In this embodiment of the application, searching for point clouds within a target range includes: determining a search threshold based on the target range; if the normal vector of the point cloud is less than or equal to the search threshold, then marking the point cloud as a point cloud within the target range; if the normal vector of the point cloud is greater than the search threshold, then marking the point cloud and outliers within the target range based on the standard deviation of the point cloud within the target range.

[0058] The search threshold can be set with the center seed point as the center, and the boundary length of the normal vector of the center seed point can be set according to actual needs without specific limitations.

[0059] It is understood that, in the embodiments of this application, point clouds and outliers within the target range can be marked according to the normal vector of the point cloud, so as to make the edges of the point cloud data smoother by discarding outliers later.

[0060] It should be noted that when the region growth in the algorithm tends to the edge, the attention mechanism is triggered when a region exceeds the threshold. The growth criterion is changed to perform region growth and point cloud segmentation based on the ratio of small neighborhood and Euclidean distance standard deviation.

[0061] In this embodiment of the application, marking point clouds and outliers within a target range based on the standard deviation within the region includes: calculating the ratio of the standard deviation within the region to the standard deviation reference value; if the ratio is greater than the marking threshold, then the point cloud is marked as an outlier and the outlier is removed; otherwise, the point cloud is marked as a point cloud within the target range.

[0062] The labeling threshold can be set according to actual needs, without specific limitations.

[0063] It is understood that, in the embodiments of this application, outliers and point clouds within the target range can be determined based on the ratio of the standard deviation to the standard deviation reference value, so as to remove outliers and make the edges of the point cloud data smoother by discarding outliers in the future.

[0064] It should be noted that during large-radius search, the tolerance for outliers varies depending on the normal threshold setting, potentially leading to undersegmentation or oversegmentation. Therefore, to address this, the algorithm grows the region towards the edge, triggering an attention mechanism when a region exceeds the threshold.

[0065] The attention mechanism is based on the ratio of the standard deviation of the distance between two points in a small neighborhood, and a prior value of standard deviation σ is set. If the ratio of the standard deviation within the region to the prior value of standard deviation is less than a threshold, it indicates that the attribute differences between the point clouds are small, and the points in the region are marked as ingroup points. A statistical filtering algorithm is used to remove outliers from the segmented point cloud data. A bilateral filtering algorithm is used to smooth the segmented point cloud data and preserve edge information.

[0066] Specifically, a statistical filtering algorithm is used to remove outliers from the segmented point cloud data; a bilateral filtering algorithm is used to smooth the segmented point cloud data while preserving edge information.

[0067] In step S104, the cleaning position of the battery cell terminal is located based on the point cloud on the surface of the battery cell terminal.

[0068] It is understood that the embodiments of this application can locate the cleaning position of the battery cell terminal based on the point cloud on the surface of the battery cell terminal. This combines two-dimensional image target detection technology with three-dimensional point cloud data processing strategy. The two-dimensional image target detection technology extracts the point cloud of the battery cell terminal, and the three-dimensional point cloud data processing strategy segments the point cloud data, which can accurately identify the location of the cleaning point of the battery cell terminal.

[0069] In this embodiment of the application, the cleaning position of the battery cell electrode is located based on the point cloud on the surface of the battery cell electrode, including: establishing a camera coordinate system; offsetting the quadrant of the point cloud on the surface of the battery cell electrode to the target quadrant of the camera coordinate system; calculating the Euclidean distance between the point cloud on the surface of each battery cell electrode and the origin of the camera coordinate system; and determining the cleaning position of the battery cell electrode based on the Euclidean distance.

[0070] It is understood that, in this embodiment of the application, a camera coordinate system is established, and the quadrant of the point cloud on the surface of the battery cell electrode is offset to the target quadrant of the camera coordinate system; the Euclidean distance between the point cloud on the surface of each battery cell electrode and the origin of the camera coordinate system is calculated; the cleaning position of the battery cell electrode is determined according to the Euclidean distance, and the cleaning point can be better indexed by the quadrant offset.

[0071] It should be noted that, since the cleaning point needs to be located by Euclidean distance from the origin, and considering the randomness of the polar point cloud's position in the camera coordinate system, quadrant offset can be used to better index the cleaning point.

[0072] Specifically, such as Figure 4 As shown, the filtered point cloud data is quadrant-biased in a three-dimensional coordinate system. For example, by biasing the initial point cloud data into different quadrants in the three-dimensional coordinate system, the Euclidean distance from the point cloud data center coordinates to the zero point of the coordinate system is calculated. The cube root of this Euclidean distance is then performed to obtain the weights ω of the same dimensions. The point cloud data is moved to (ω,ω,ω) with the center coordinates as the moving center. The Euclidean distance from the zero point to the point cloud data is calculated, and the nearest point is selected as the positioning point. Based on the bias in different quadrants, the top left point, bottom left point, top right point, and bottom right point can be obtained.

[0073] According to the battery cell electrode cleaning and positioning method proposed in this application, a two-dimensional image of the battery cell electrode is obtained. A two-dimensional bounding box view of the battery cell electrode is determined based on the two-dimensional image. Point clouds within the bounding box of the two-dimensional bounding box view of the battery cell electrode are extracted. Point clouds on the surface of the battery cell electrode are generated and segmented from the region within the point cloud within the bounding box. The cleaning position of the battery cell electrode is located based on the point cloud on the surface of the battery cell electrode. This method combines two-dimensional image target detection technology with three-dimensional point cloud data processing strategy. The two-dimensional image target detection technology extracts the point cloud of the battery cell electrode, and the three-dimensional point cloud data processing strategy segments the point cloud data, which can accurately identify the positioning of the cleaning point of the battery cell electrode.

[0074] The following will Figures 2 to 4 The method for cleaning and positioning battery cell terminals is described in detail with reference to specific embodiments. The specific steps are as follows:

[0075] Step 1: Place the battery cell module on a laser marking stage fixture and take multiple images using a structured light camera to create a dataset of battery cell electrode images. Identify and mark the battery cells in the dataset images and input them into a YOLOv5x network model based on the PyTorch framework for offline classification training. Then, input the image to be predicted into the trained YOLOv5x model for inference and prediction. The model will output the bounding box location and category information of the detected targets. Post-process the bounding boxes output by the model, using non-maximum suppression to remove overlapping bounding boxes, and then filtering them according to a confidence threshold to obtain the final target detection results.

[0076] Step 2: After obtaining a 2D image with target bounding boxes using the target recognition algorithm, extract the point cloud within the bounding boxes. The point cloud within the bounding boxes includes the point cloud on the surface of the battery cell electrode and a small portion of the point cloud on the battery cell cover plate. Starting from the initial seed region at the center, perform a FLANN neighborhood search with a large radius to find 3D point clouds that meet the normal threshold. Use the least squares algorithm to perform plane fitting to obtain the normal vector. Mark the matching region points as visited to prevent duplicate processing.

[0077] Because large-radius search has varying tolerances for outliers depending on the normal threshold setting, undersegmentation and oversegmentation may occur. Therefore, to address this, the algorithm grows regions towards the edges, triggering an attention mechanism when regions exceed the threshold.

[0078] The attention mechanism is based on the ratio of the standard deviation of the distance between two points in a small neighborhood, with a set prior value σ for the standard deviation. If the ratio of the standard deviation within a region to the prior value is less than a threshold, it indicates that the attribute differences between the point clouds are small, and the points in the region are marked as ingroup points. A statistical filtering algorithm is used to remove outliers from the segmented point cloud data. A bilateral filtering algorithm is used to smooth the segmented point cloud data and preserve edge information.

[0079] Step 3: The filtered point cloud data is quadrant-biased in a 3D coordinate system. For example, the initial point cloud data is biased to the first coordinate system in the 3D coordinate system. The Euclidean distance from the point cloud data center coordinates to the zero point of the coordinate system is calculated. The cube root of the Euclidean distance is performed to obtain the weights ω of the three dimensions. The point cloud data is moved to (ω,ω,ω) with the center coordinates as the moving center. The Euclidean distance from the zero point to the point cloud data is calculated, and the nearest point is selected as the positioning point. According to the bias of different quadrants, the top left point, bottom left point, top right point, and bottom right point can be obtained.

[0080] In summary, this application utilizes a structured light camera for 3D positioning of battery cell electrode post laser cleaning, aiming to achieve accurate positioning and automatic zoom cleaning with the galvanometer laser, thus optimizing the laser cleaning effect. The positioning of the battery cell electrode post cleaning points combines 2D image target detection technology with 3D point cloud data processing strategies. YOLOv5 is selected as the core technology for target detection to extract the battery cell electrode post point cloud. For point cloud segmentation, a region growing algorithm is adopted and improved. Since the segmented point cloud data has non-fixed positions in the coordinate system, quadrant offsetting of the data points in the 3D coordinate system accurately indexes the laser cleaning positioning points. Because the point cloud plane has rounded corners, the laser cleaning positioning points are effectively indexed by calculating the point with the minimum Euclidean distance between the origin of the coordinate system and the data points after weighted averaging of the point cloud data centroids.

[0081] Next, with reference to the accompanying drawings, a laser cleaning method for battery cell terminals according to an embodiment of this application is described.

[0082] Figure 5 This is a flowchart of a laser cleaning method for battery cell terminals according to an embodiment of this application.

[0083] like Figure 5 As shown, the laser cleaning method for the battery cell terminals includes the following steps:

[0084] In step S201, the cleaning position of the battery cell terminal determined by the battery cell terminal cleaning and positioning method of the above embodiment is obtained.

[0085] It is understood that the embodiments of this application can obtain the cleaning position of the battery cell electrode to facilitate the subsequent determination of the laser target focusing position based on the cleaning position of the battery cell electrode.

[0086] It should be noted that in order to achieve higher precision in laser cleaning of the battery cell terminals, the galvanometer lens needs to be zoomed in real time to ensure that every part of the terminal is cleaned from the focal point.

[0087] In step S202, the target focusing position of the laser is determined based on the cleaning position of the cell electrode post.

[0088] It is understood that the embodiments of this application can determine the target focusing position of the laser based on the cleaning position of the cell electrode post, so as to adjust the position of the lens and galvanometer in the laser according to the target focusing position.

[0089] In this embodiment of the application, determining the target focusing position of the laser based on the cleaning position of the cell electrode includes: identifying the boundary line of the cleaning position; and determining multiple target focusing positions based on the boundary line and the cleaning spacing.

[0090] It is understood that the embodiments of this application can identify the boundary line of the cleaning position; determine multiple target focusing positions based on the boundary line and the cleaning interval, so as to facilitate subsequent laser cleaning of the cell electrode based on the target focal length position, ensuring more comprehensive cleaning.

[0091] It should be noted that since the battery cell terminals are three-dimensional, the target focal length position changes constantly during full cleaning. Therefore, multiple target focal positions can be determined based on the boundary line and cleaning interval.

[0092] In step S203, the positions of the lens and galvanometer in the laser are adjusted according to the target focusing position, and the cell electrode is laser-cleaned based on the adjusted laser.

[0093] It is understood that, according to the embodiments of this application, the positions of the lens and galvanometer in the laser can be adjusted according to the target focusing position, and the cell electrode post can be laser cleaned based on the adjusted laser, so that the laser emitted by the laser is more precisely focused, thereby improving the cleaning efficiency and accuracy.

[0094] Specifically, such as Figure 6 and Figure 7 As shown in the embodiment of this application, the laser includes a concave lens, a convex lens, and a galvanometer. The concave lens is used to enlarge the diameter of the laser beam. The laser beam generated by the laser may be too thin to cover the entire area of ​​the target surface, so a concave lens is needed to diffuse the laser beam to give it a larger diameter. The convex lens is used to focus the enlarged laser beam onto the target surface. The function of the convex lens is to refocus the diffused laser beam so that it can form a smaller focal point on the target surface, thereby improving cleaning efficiency and accuracy. The galvanometer is a vibrating or rotating mirror used to change the direction of the laser beam. By controlling the vibration or rotation of the galvanometer, the laser beam can be precisely positioned and scanned in space, so that the laser beam can cover different areas of the target surface. Therefore, the position of the lens and galvanometer in the laser affects the cleaning efficiency.

[0095] In this embodiment of the application, adjusting the positions of the lens and galvanometer in the laser according to the target focusing position includes: determining a first target position of the lens and a second target position of the galvanometer according to the target focusing position; and controlling the lens to reach the first target position and the galvanometer to reach the second target position, respectively.

[0096] The positions of the first and second targets can be adjusted in real time according to the target focusing position, without any specific limitations.

[0097] It is understood that, in the embodiments of this application, the first target position of the lens and the second target position of the galvanometer can be determined according to the target focusing position; the lens is controlled to reach the first target position and the galvanometer to reach the second target position respectively, so that the laser beam can accurately position the target focusing position and improve the cleaning efficiency.

[0098] In this embodiment of the application, laser cleaning of the battery cell electrode based on the adjusted laser includes: focusing the laser sequentially to the target focusing position in a preset order.

[0099] The preset order can be either vertical or horizontal, without any specific restrictions.

[0100] It is understood that the embodiments of this application can focus on the target focusing position in a preset order to make the cleaning more comprehensive and the effect more efficient.

[0101] It should be noted that the galvanometer laser cleaning of the battery cell electrode surface is actually achieved by cleaning through a combination of countless straight lines, ultimately cleaning the surface. Therefore, the host computer sends the start and end positions of the straight lines to the marking control card, and the galvanometer laser marking system performs cleaning based on the received positions.

[0102] Specifically, such as Figure 8 As shown, the steps for determining the first and last positions are as follows:

[0103] 1) The host computer obtains the upper left point P1(x1, y1, z1), upper right point P2(x2, y2, z2), and lower right point P through the vision module. n (x n y n , z n )coordinate.

[0104] 2) By solving the equations of points P1, P2, and P3 simultaneously, we obtain the equation of the polar plane in the world coordinate system:

[0105]

[0106] The upper left point P1 of the battery cell electrode is the initial point of the straight line in laser cleaning, that is, the origin of the galvanometer coordinate system, and also the coordinate of the PLC moving point; P2 is the final point of the straight line.

[0107] 3) Since the galvanometer coordinate system is different from the world coordinate system, coordinate transformation is required. The parallelism tolerance between the 3D galvanometer head and the cleaning platform is 0.01mm. Here, we consider the laser emitted from the galvanometer origin to be perpendicular to the cleaning platform, that is, we do not consider the Z-direction coordinate transformation, and transform it into a two-dimensional coordinate system.

[0108] 4) such as Figure 9 As shown, the relationship between the galvanometer coordinate system and the world coordinate system is obtained through calculation:

[0109]

[0110] m is the absolute value of x1 at point P1, and n is the absolute value of y1 at point P1.

[0111] The coordinates of point P2 in the galvanometer coordinate system can be obtained from the above relationship.

[0112] 5) Along Moving DMM to get like Figure 10 As shown

[0113] in Perpendicular to Therefore, the coordinates of point P are (x0, y0, z0), and the coordinates of P3 and P4 are calculated as follows:

[0114]

[0115] 6) such as Figure 11 As shown, because the battery cell terminals are 3D marked, due to the special nature of 3D optical paths, the focal length is not uniform throughout the same plane. That is, for a straight line, the Z-value of the voice coil may change from the starting point to the ending point. Therefore, line segment P1P2 is subdivided into multiple segments, each 1mm in length. Given the coordinates of P1 and P2, P... 1i The coordinates are P1 along the line Move the coordinates by 1mm, that is

[0116]

[0117] According to the laser cleaning method for battery cell terminals proposed in the embodiments of this application, the cleaning position of the battery cell terminals is obtained, the target focusing position of the laser is determined based on the cleaning position of the battery cell terminals, the position of the lens and galvanometer in the laser can be adjusted according to the target focusing position, and the battery cell terminals are laser cleaned based on the adjusted laser, so that the laser emitted by the laser is more precisely focused, thereby improving the cleaning efficiency and accuracy.

[0118] The following will Figures 5 to 11 The laser cleaning method for battery cell terminals is described in detail with reference to specific embodiments. The specific steps are as follows:

[0119] Step 1: To achieve higher precision in laser cleaning of the battery cell terminals, the galvanometer lens needs to be continuously zoomed to ensure that every part of the terminal is cleaned from the focal point. Since there is no focusing lens in 3D marking, it uses a concave lens driven by the Z-axis to change the focusing length of the optical path. It should be noted that this Z-axis does not refer to the z-value in the world coordinate system, but rather the Z-value of the voice coil in the galvanometer system.

[0120] With the convex lens in a fixed position, the focal length of the laser focusing changes when the concave lens moves left or right along the z-axis. In optical paths 1 and 2, the focal length is controlled by the Z-value of the voice coil, while the position is controlled by x and y. In other words, x and y determine the marking position, and the Z-value of the voice coil controls focusing. When x and y are specified, the Z-value of the voice coil must also be determined; otherwise, the focal length will be incorrect, and focusing will fail. Therefore, a reasonable mathematical model needs to be set up so that the pole pieces at different positions can achieve adaptive zoom cleaning: where H is the reference plane focal length, referring to the height from the bottom of the 3D square head to the reference position of the marking object. Based on the 3D spatial coordinates, we can calculate:

[0121]

[0122]

[0123] ...

[0124]

[0125] Step 2: The three-axis scanning head includes a standard XY dual-axis scanning head plus a Z-axis motorized focuser. The voice coil Z-value can be understood as the position of the voice coil motor in the galvanometer. Changing the voice coil Z-value is equivalent to changing the voice coil travel. To determine the voice coil Z-value, the following method is used:

[0126] 1) First, determine a set of focal points (x, y). Using (x, y) as the center, use a galvanometer to control the laser to mark circles of different radii. At this point, the focal point is located with a focal length of H, which corresponds to the coordinates (x, y, H) in the world coordinate system. The Z-value of the voice coil is set to zero by default.

[0127] 2) Set a small radius for the circle and assign a certain voice coil Z value for marking. Observe the effect of laser marking under a microscope. If the effect matches the effect when focused, record the (x, y, Z) at this time; if not, experiment with different voice coil Z values ​​multiple times until focused, and record all (x, y, Z) values ​​that can be focused.

[0128] 3) In order to accurately obtain the relationship between the voice coil Z value and the focal length, steps 1 and 2 are repeated multiple times to obtain more reference data.

[0129] 4) Based on the obtained series of (x, y, Z) and focal length f, the relationship between Z and f can be obtained by the least squares method.

[0130] Among them, according to the least squares criterion, we have:

[0131]

[0132] This represents the measurement data error.

[0133] By differentiating the above equation using the method for finding extrema, we can obtain estimates of a and b:

[0134]

[0135]

[0136] The relationship between focal length f and voice coil Z value is generated based on the actual test data in Table 1 below. Table 1 below shows the test data between focal length f and voice coil Z value.

[0137] Table 1 Test Data

[0138] f(mm) 686.28 706.28 726.28 746.28 776.28 806.28 836.28 866.28 Z(mm) -3.38 -2.92 -2.48 -2.08 -1.56 -1 -0.5 0

[0139] By substituting the formula for estimating a and b into Table 1 above, we can obtain:

[0140]

[0141] Then Z = -14.402 + 0.0164f.

[0142] Step 3: Galvanometer laser cleaning of the battery cell electrode surface actually involves cleaning with a combination of countless straight lines until a surface is formed. The host computer sends the start and end positions of the straight lines to the marking control card, and the galvanometer laser marking system performs the cleaning based on the received positions.

[0143] The steps to determine the first and last positions are as follows:

[0144] 1) The host computer obtains the upper left point P1(x1, y1, z1), upper right point P2(x2, y2, z2), and lower right point P through the vision module. n (x n y n , z n )coordinate.

[0145] 2) By solving the equations of points P1, P2, and P3 simultaneously, we obtain the equation of the polar plane in the world coordinate system:

[0146]

[0147] The upper left point P1 of the battery cell electrode is the initial point of the straight line in laser cleaning, that is, the origin of the galvanometer coordinate system, and also the coordinate of the PLC moving point; P2 is the final point of the straight line.

[0148] 3) Since the galvanometer coordinate system is different from the world coordinate system, coordinate transformation is required. The parallelism tolerance between the 3D galvanometer head and the cleaning platform is 0.01mm. Here, we consider the laser emitted from the galvanometer origin to be perpendicular to the cleaning platform, that is, we do not consider the Z-direction coordinate transformation, and transform it into a two-dimensional coordinate system.

[0149] 4) such as Figure 9 As shown, the relationship between the galvanometer coordinate system and the world coordinate system is obtained through calculation:

[0150]

[0151] m is the absolute value of x1 at point P1, and n is the absolute value of y1 at point P1.

[0152] The coordinates of point P2 in the galvanometer coordinate system can be obtained from the above relationship.

[0153] 5) Along Moving DMM to get like Figure 10 As shown

[0154] in Perpendicular to Therefore, the coordinates of point P are (x0, y0, z0), and the coordinates of P3 and P4 are calculated as follows:

[0155]

[0156] 6) such as Figure 11 As shown, because the battery cell terminals are 3D marked, due to the special nature of 3D optical paths, the focal length is not uniform throughout the same plane. That is, for a straight line, the Z-value of the voice coil may change from the starting point to the ending point. Therefore, line segment P1P2 is subdivided into multiple segments, each 1mm in length. Given the coordinates of P1 and P2, P... 1i The coordinates are P1 along the line Move the coordinates by 1mm, that is

[0157]

[0158] Specifically, such as Figure 12 As shown, to implement the above-mentioned cell electrode cleaning and positioning method and cell electrode laser cleaning method, this application uses a three-dimensional automated laser cleaning system for cell electrodes to perform the corresponding operations, specifically:

[0159] The battery cell electrode three-dimensional automated laser cleaning system includes host computer software, PLC control module, vision module, galvanometer laser 3D marking module, and monitoring module.

[0160] The host computer software is the core of the automated laser cleaning system. This software communicates with the PLC control module, vision module, galvanometer laser 3D marking module, and monitoring module, integrating the functions of different modules and designing the linkage logic between modules and the responses required in the operation process, thereby realizing system automation. The galvanometer laser 3D marking module, which integrates the vision module, uses a camera to collect images of the surface to be cleaned and uses image processing technology to obtain the cleaning point information. The cleaning point information is then fed back to the PLC control module and the galvanometer laser 3D marking module through the host computer software for cleaning.

[0161] The host computer software and the PLC module establish a connection through Socket communication and read and write data through the Modbus TCP protocol; the host computer software and the galvanometer laser marking module establish a communication through the TCP / IP protocol to control the galvanometer motion control card; the host computer software and the vision module establish a communication through the TCP / IP protocol to receive the cell electrode cleaning point information.

[0162] Specifically: The PLC control module uses a ladder diagram to write the automation program and downloads it to the PLC controller. The automation program is divided into two parts. One part involves the PLC starting a loop at the zero point, waiting to receive a photo-taking command from the host computer. After receiving the command, it controls the servo motor to move the camera to a fixed photo-taking point to capture the battery cell electrode. After each photo is taken, there is a response between the vision module and the host computer. When the host computer receives a signal indicating that a photo has been taken, it sends a signal to the PLC module to initiate the next photo. This loop continues until all photos are taken, at which point the PLC controls the servo motor to return to the zero point. The other part involves the PLC starting a loop at the zero point, waiting to receive a cleaning command from the host computer. After receiving the command, it controls the servo motor to move the galvanometer laser to a cleaning point for laser cleaning. It then waits to receive the next cleaning point command, and this loop continues until all cleaning is completed, at which point the PLC controls the servo motor to return to the zero point.

[0163] Four servo drives control four servo motors, including an X-axis motor, a slave axis motor, a Y-axis motor, and a Z-axis motor. Each servo motor is connected to an absolute encoder to provide feedback on the position of the camera and galvanometer and the torque during movement. To prevent cumulative errors during equipment movement, grating limit zero points are set at both ends of the X, Y, and Z axes. Each shooting and cleaning stroke starts and ends from the limit zero point. The laser cleaning time required for each pole piece is related to the marking speed. Different parameters need to be set according to different pole piece sizes to obtain the corresponding cleaning time. The host computer sets a delay to send the next cleaning point command to the PLC according to different time settings.

[0164] The vision module includes a depth camera and its associated industrial control computer. Upon power-up, the vision industrial control computer automatically establishes communication with the host computer via TCP / IP protocol. Simultaneously, the vision processing algorithm starts automatically, waiting for the host computer to send a photo-taking command. After receiving the command, it runs the vision processing program, which includes camera calibration, point cloud filtering, visual localization methods, point cloud segmentation algorithms, and point selection for cleaning. Camera calibration utilizes an A3 asymmetric circular calibration plate. This type of calibration plate has a known center position and a known radius, providing more information to estimate camera distortion parameters and more accurate camera internal parameters, making it more effective and accurate in handling camera distortion. Because measuring camera lens distortion and other parameters can cause eccentricity errors in the circular calibration plate, leading to inaccuracies in the calibration results, the calibration plate is placed at different angles and positions on the laser marking stage for scaling. Each calibration result is tested individually, and different weights are assigned to the test results. The calibration results from different angles and positions are then weighted and averaged. The weighted average value is used as the final fusion result, representing the camera's internal and external parameters.

[0165] The galvanometer laser 3D marking module enables communication between the galvanometer motion control card and the host computer through the TCP / IP protocol, and conducts marking data interaction with the galvanometer motion control card using the XY2-100 data communication protocol. Laser marking involves laser delay. Setting appropriate laser-on delay parameters can eliminate the "match head" phenomenon that appears at the beginning of marking. However, if the laser-on delay parameter is set too large, it will cause the phenomenon of missing strokes at the starting segment; setting appropriate laser-off delay parameters can eliminate the unclosed phenomenon that appears at the end of marking. However, if the laser-off delay is set too large, it will cause the "match head" phenomenon at the end segment; setting appropriate turning delay parameters can eliminate the rounded corner phenomenon that appears when marking a right angle. However, if the corner delay is set too large, it will increase the marking time and there will be a double marking phenomenon at the corner. Therefore, it is necessary to conduct marking tests on fixed-length line segments multiple times, record the length of each line segment, and set reasonable delay parameters.

[0166] The host computer software is the central control of automated laser cleaning, with a visual operation interface. It can simultaneously achieve the linkage between the PLC module, the galvanometer laser 3D marking module, and the vision module, and can also implement the functions of a single module. Among them, the host computer software establishes a connection with the PLC module through Socket communication and reads and writes data through the Modbus Tcp protocol; the host computer software establishes communication with the galvanometer laser marking module using the TCP / IP protocol to control the galvanometer motion control card; the host computer software establishes communication with the vision module using the TCP / IP protocol to receive the cleaning position information of the battery cell pole.

[0167] Among them, the system operation includes the following steps:

[0168] 1: The vision module is powered on. After power-on, the vision processing program is automatically started, waiting to receive the photo-taking instruction from the host computer;

[0169] 2: The PLC control module is started, waiting for the host computer to send a movement instruction;

[0170] 3: The servo control system is started;

[0171] 4: The galvanometer controller and the laser are started;

[0172] 5: The host computer system is started, reads the addresses of the PLC control module and the galvanometer laser marking module and conducts communication connection. After the connection is successful, it waits for the PLC module to send a module ready instruction;

[0173] 6. The battery cell module is fed to the workstation fixture through a safety light curtain door. The safety light curtain uses a set of infrared beams to form a protective light barrier. When the battery cell module passes through the safety light curtain door, the light barrier is blocked, and the photoelectric protection device sends a signal. When the battery cell module is fed to the workstation fixture, the light barrier is unblocked, and the signal is restored. When the battery cell module feeding device exits the cleaning station, the light barrier is blocked, and the photoelectric protection device sends a signal. After completely exiting, the light barrier is unblocked, and the signal is restored. After receiving two restoration signals, the PLC sends a module ready command to the host computer.

[0174] 7: After receiving the module ready instruction, the host computer sends the fixed point data for taking pictures to the PLC;

[0175] 8: The PLC receives data, controls the servo motor to move to the designated position, and sends a response to the host computer upon reaching the designated position.

[0176] 9: The host computer receives the bit signal and sends a photo-taking command to the vision module;

[0177] 10: After receiving the instruction, the vision module controls the camera to take a picture and processes the acquired image in real time. After taking the picture, it sends a picture taking end signal to the host computer.

[0178] 11: Upon receiving the image capture end signal, the host computer repeats the above image capture steps, with the number of captures set according to the module size, until all battery cell terminal images are acquired. The vision module then sends the obtained battery cell terminal cleaning point data to the host computer. This cleaning point data includes the coordinates of the upper left, upper right, and lower right points of the battery cell terminal. The upper left point coordinates are the origin of the galvanometer coordinate system, which is also the data point sent to the PLC, and represent the initial point of the straight line during galvanometer cleaning. The upper right point coordinates represent the final point of the straight line during galvanometer cleaning. The straight line during galvanometer cleaning consists of 1mm line segments from the initial point to the final point.

[0179] 12: After the photo is taken, the host computer responds to the PLC, and the PLC returns to zero.

[0180] 13: After receiving the zero-return signal from the PLC, the host computer sends the cell electrode cleaning point data to the PLC.

[0181] 14: The PLC receives data, controls the servo motor to move to the designated position, and sends a response to the host computer upon reaching the designated position.

[0182] 15: The host computer receives the position signal and sends a cleaning signal to the galvanometer laser 3D marking module;

[0183] 16: The galvanometer laser 3D marking module performs marking and cleaning according to the received marking graphic data. The cleaning time for each cycle is determined based on the size of the battery cell electrode and the settings for different marking speeds, laser power, laser pulse widths, and other parameters.

[0184] 17: Repeat the above cleaning steps until all battery cell terminals are cleaned, then return the PLC to zero and reset the system.

[0185] It should be noted that, except for the battery cell module loading, the above system operates entirely automatically. Once all the hardware is started, staff only need to activate the system in the host computer software. The entire process requires minimal manual operation, conforming to modern manufacturing processes while improving work efficiency.

[0186] In summary, the embodiments of this application employ a target detection algorithm to quickly and accurately detect poles and extract pole point clouds, achieving lightweight point cloud processing; a region growing point cloud segmentation algorithm with a fusion attention mechanism is used to effectively segment the point cloud on the upper surface of the poles; the segmented point cloud data has no fixed position in the coordinate system, and quadrant offsetting of the data points in the three-dimensional coordinate system can accurately index the laser cleaning positioning points; since the point cloud plane has rounded chamfers, the laser cleaning positioning points can be effectively indexed by calculating the point with the minimum Euclidean distance between the origin of the coordinate system and the data points after weighted averaging of the centroids of the point cloud data; and automatic zoom cleaning of the galvanometer is achieved based on the coordinate data of the vision module.

[0187] Figure 13 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0188] The memory 1301, the processor 1302, and the computer program stored on the memory 1301 and executable on the processor 1302.

[0189] When the processor 1302 executes the program, it implements the cell electrode cleaning and positioning method provided in the above embodiments, or the cell electrode laser cleaning method.

[0190] Furthermore, electronic devices also include:

[0191] Communication interface 1303 is used for communication between memory 1301 and processor 1302.

[0192] The memory 1301 is used to store computer programs that can run on the processor 1302.

[0193] The memory 1301 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0194] If the memory 1301, processor 1302, and communication interface 1303 are implemented independently, then the communication interface 1303, memory 1301, and processor 1302 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 13 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0195] Optionally, in a specific implementation, if the memory 1301, processor 1302, and communication interface 1303 are integrated on a single chip, then the memory 1301, processor 1302, and communication interface 1303 can communicate with each other through an internal interface.

[0196] The processor 1302 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0197] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0198] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0199] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0200] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0201] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0202] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for cleaning and positioning battery cell terminals, characterized in that, The method comprises the following steps: acquiring a two-dimensional image of the cell pole; determining a two-dimensional bounding box view of the cell pole according to the two-dimensional image of the cell pole; extracting a point cloud within a bounding box of the two-dimensional bounding box view of the cell pole, generating and segmenting a cell pole surface point cloud from an inner region of the point cloud within the bounding box, wherein the generating and segmenting the cell pole surface point cloud from the inner region of the point cloud within the bounding box comprises: taking at least one target region of the inner region of the point cloud within the bounding box as an initial seed region; searching for a point cloud within a target range with the initial seed region as the center; fitting the point cloud within the target range to generate the cell pole surface point cloud, wherein the searching for the point cloud within the target range comprises: determining a search threshold according to the target range; if a normal vector of the point cloud is less than or equal to the search threshold, marking the point cloud as the point cloud within the target range; if the normal vector of the point cloud is greater than the search threshold, calculating an intra-region standard deviation according to the point cloud within the target range, and marking the point cloud within the target range and outliers according to the intra-region standard deviation, wherein the marking the point cloud within the target range and the outliers according to the intra-region standard deviation comprises: calculating a ratio of the intra-region standard deviation and a standard deviation reference value; if the ratio is greater than a marking threshold, marking the point cloud as an outlier and removing the outlier; otherwise, marking the point cloud as the point cloud within the target range; positioning a cleaning position of the cell pole according to the cell pole surface point cloud, wherein the positioning the cleaning position of the cell pole according to the cell pole surface point cloud comprises: establishing a camera coordinate system; offsetting the cell pole surface point cloud to a target quadrant of the camera coordinate system; calculating a Euclidean distance between each cell pole surface point cloud and an origin of the camera coordinate system; and determining the cleaning position of the cell pole according to the Euclidean distance.

2. A method of laser cleaning of an electrode post of a battery cell, characterized in that, The method comprises the following steps: acquiring a cleaning position of a cell pole determined by the cell pole cleaning positioning method of claim 1; determining a target focusing position of a laser based on the cleaning position of the cell pole; adjusting positions of a lens and a galvanometer mirror in the laser according to the target focusing position, and performing laser cleaning on the cell pole based on the adjusted laser.

3. The laser cleaning method of an electrode post of a battery cell according to claim 2, wherein, The adjusting the positions of the lens and the galvanometer mirror in the laser according to the target focusing position comprises: determining a first target position of the lens and a second target position of the galvanometer mirror according to the target focusing position; respectively controlling the lens to reach the first target position and the galvanometer mirror to reach the second target position.

4. The laser cleaning method of an electrode post of a battery cell according to claim 2, wherein, The determining the target focusing position of the laser based on the cleaning position of the cell pole comprises: identifying a boundary line of the cleaning position; determining a plurality of target focusing positions according to the boundary line and a cleaning interval.

5. The method of claim 2, wherein the laser cleaning is performed by a laser beam having a wavelength of 1,064 nm. The performing laser cleaning on the cell pole based on the adjusted laser comprises: focusing on the target focusing positions in a preset order one by one.

6. An electronic device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of claim 1, or the method of any one of claims 2-5.

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