Electrode testing methods and apparatus, terminal equipment and storage media

Abstract

This application provides an electrode detection method and apparatus, a terminal device, and a storage medium. The electrode detection method includes: acquiring an initial image of the electrode; performing a color space transformation on the filtered initial image to obtain an intermediate image, wherein the color space of the intermediate image is an HSV color space, and the HSV color space includes the HSV values ​​corresponding to each pixel of the intermediate image; performing binary processing on the intermediate image based on the HSV values ​​of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the electrode corresponding to an abnormal parent type, wherein the preset HSV threshold ranges corresponding to different abnormal parent types are different, and each abnormal parent type includes at least one abnormal subtype; performing rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region; and comparing the at least one rectangular region with preset parameter values ​​to determine the abnormal subtype of the at least one abnormal region.

Description

Technical Field

[0001] This application relates to the field of energy storage battery technology, specifically to an electrode detection method and apparatus, terminal equipment, and storage medium. Background Technology

[0002] With the promotion of new energy sources, new energy batteries are being used more and more widely. However, many defects exist in the battery electrodes produced. Since the quality of the electrodes determines the battery's performance and lifespan, it is necessary to inspect the electrodes to eliminate those with serious abnormalities. Typically, electrode anomaly detection involves photographing the electrodes to identify the location and cause of the anomaly. However, issues such as lighting and resolution during photography can easily lead to misjudgments of abnormal areas. Therefore, how to accurately and efficiently detect electrode anomalies has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method and apparatus for electrode detection, a terminal device and a storage medium, which at least solves the problem of low accuracy in determining abnormal areas of electrodes during electrode detection.

[0004] Firstly, this application provides an electrode detection method. The electrode detection method includes: acquiring an initial image of the electrode; performing a color space transformation on the filtered initial image to obtain an intermediate image, wherein the color space of the intermediate image is an HSV color space, and the HSV color space includes the HSV values ​​corresponding to each pixel of the intermediate image; performing binary processing on the intermediate image according to the HSV values ​​of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the electrode corresponding to an abnormal parent type, wherein the preset HSV threshold ranges corresponding to different abnormal types are different, and each abnormal parent type includes at least one abnormal subtype; performing rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region; and comparing the at least one rectangular region with preset parameter values ​​to determine the abnormal subtype of the at least one abnormal region.

[0005] In one possible implementation, the step of performing binary processing on the intermediate image based on the HSV value of the intermediate image and the preset HSV threshold range to determine at least one abnormal region in the pole piece corresponding to an abnormal parent type includes: determining an abnormal pixel set based on the HSV value corresponding to each pixel of the intermediate image and the preset HSV threshold range, wherein one abnormal parent type corresponds to one abnormal pixel set, and the abnormal pixel set contains multiple pixels belonging to the same abnormal parent type; performing binary processing on the intermediate image based on the abnormal pixel set to obtain a binary image, wherein one abnormal pixel set corresponds to one binary image; and determining the at least one abnormal region corresponding to the abnormal parent type based on the binary image and the abnormal pixel set.

[0006] As can be seen, there are multiple abnormal parent types for the electrode, each corresponding to a preset HSV threshold range. For each abnormal parent type, one or more abnormal regions corresponding to that parent type are identified. For example, when identifying one or more abnormal regions for a particular abnormal parent type, the HSV values ​​of each pixel in the intermediate image can be compared one by one with the preset HSV threshold range. Pixels whose HSV values ​​fall within the preset HSV threshold range are categorized into the abnormal pixel set of that abnormal parent type. Then, the intermediate image is binarized based on each abnormal pixel in the abnormal pixel set to obtain a binary image. At least one abnormal region is determined based on the binary image and the abnormal pixel set. By converting the initial image into an intermediate image in the HSV color space, abnormal pixels can be accurately and efficiently identified based on the HSV values ​​of each pixel, thereby improving the accuracy and efficiency of abnormal region identification.

[0007] In one possible implementation, the preset HSV threshold range includes a first threshold range, a second threshold range, and a third threshold range. The HSV color space includes an H channel, an S channel, and a V channel. The first threshold range corresponds to the H channel, the second threshold range corresponds to the S channel, and the third threshold range corresponds to the V channel. Determining the abnormal pixel set based on the HSV values ​​corresponding to each pixel in the intermediate image and the preset HSV threshold range includes: acquiring pixels in the intermediate image whose H channel values ​​are within the first threshold range, whose S channel values ​​are within the second threshold range, and whose V channel values ​​are within the third threshold range, to constitute the abnormal pixel set.

[0008] As can be seen, the HSV color space of the intermediate image is split into H, S, and V channels. The H channel corresponds to the first threshold range within the preset HSV threshold range, the S channel to the second threshold range, and the V channel to the third threshold range. For each pixel in the intermediate image, it is determined whether the H, S, and V channel values ​​are within their respective threshold ranges. For example, the S channel value at pixel (0,0) meets the second threshold range, the V channel value meets the third threshold range, while the H channel value is outside the first threshold range. Therefore, pixel (0,0) is not an abnormal pixel. Determining whether a pixel is an abnormal pixel by using different single channels of the HSV color space results in higher detection precision and accuracy, reducing the possibility of false positives in abnormal areas.

[0009] In one possible implementation, the pixel values ​​in the binary image include a first value and a second value, wherein the first value and the second value are different. The step of performing binary processing on the intermediate image according to the abnormal pixel set to obtain a binary image includes: setting the pixel values ​​of all pixels in the intermediate image that belong to the abnormal pixel set to the first value; and setting the pixel values ​​of all pixels in the intermediate image that do not belong to the abnormal pixel set to the second value, so as to obtain the binary image.

[0010] As can be seen, after determining the set of abnormal pixels corresponding to a certain parent type of anomaly, the pixel values ​​of all pixels in the set of abnormal pixels (i.e., abnormal pixels) are set to the first value, while the pixel values ​​of pixels that do not belong to the set of abnormal pixels are set to the second value. The first value and the second value are different. For example, the first value is "0" and the second value is "255". In the binary image, the abnormal area is represented as black and the non-abnormal area is represented as white. In this way, the abnormal area and the non-abnormal area can be intuitively distinguished through the binary image, which effectively improves the efficiency of determining the abnormal area.

[0011] In one possible implementation, determining the at least one abnormal region corresponding to the abnormal parent type based on the binary image and the abnormal pixel set includes: performing edge recognition on the binary image based on each pixel in the abnormal pixel set to obtain the at least one abnormal region.

[0012] As can be seen, in the binary image, abnormal and non-abnormal regions are represented by different colors. Further edge recognition is used to detect the contour of abnormal regions to facilitate the determination of the area size of abnormal regions. Then, the total area of ​​abnormal regions corresponding to a certain abnormal parent type in the initial image of the electrode is counted to improve the accuracy of abnormal region recognition.

[0013] In one possible implementation, after performing binary processing on the intermediate image based on the HSV value of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the pole piece corresponding to the abnormal parent type, the method further includes: calculating the moments of the at least one abnormal region; determining the centroid and area of ​​the at least one abnormal region based on the moments of the at least one abnormal region; and determining the number of the at least one abnormal region based on the centroid of the at least one abnormal region.

[0014] As can be seen, after identifying at least one anomalous region, the moments of each anomalous region are calculated to obtain its centroid and area. For example, the centroid of the anomalous region is obtained by calculating its zeroth and first moments. After obtaining the centroids, each anomalous region's centroid can be labeled onto the initial image, allowing for intuitive data analysis to determine the anomaly of the electrode. When two anomalous regions are close together, their centroids can be used to quickly distinguish them, avoiding misjudgments of the number of anomalous regions. Furthermore, the distribution of anomalous regions can be analyzed using their areas.

[0015] In one possible implementation, performing rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region includes: performing rectangular template matching on the abnormal region based on the area of ​​the abnormal region and the set of abnormal pixels to determine the rectangular region corresponding to the abnormal region.

[0016] As can be seen, rectangular template matching is performed based on the location and area of ​​the abnormal region corresponding to the abnormal pixel set to determine the rectangular area of ​​the abnormal region, so as to determine the abnormal subtype of the abnormal region based on the rectangular area.

[0017] In one possible implementation, the electrode includes a negative electrode. When the electrode is the negative electrode, the preset parameter value includes a first parameter value, which is the average difference between the width of the preset negative electrode and the width of the preset positive electrode. The step of comparing the at least one rectangular region with the preset parameter value to determine the abnormal subtype of the at least one abnormal region includes: if the width of all at least one rectangular region is greater than the first parameter value, then the abnormal subtype of the at least one abnormal region is determined to be insufficient lithium intercalation in the negative electrode; if the width of all at least one rectangular region is less than the first parameter value, then the abnormal subtype of the at least one abnormal region is determined to be insufficient lithium intercalation in the negative electrode; if at least one rectangular region has a width greater than the first parameter value and at least one rectangular region has a width less than the first parameter value, then the abnormal subtype of the at least one abnormal region is determined to be misalignment of the negative electrode.

[0018] It can be seen that when the electrode is a negative electrode, the preset parameter value can be the first parameter value, which can be the design reference value for standard positive and negative electrodes. That is, the preset negative electrode is a standard negative electrode, and the preset positive electrode is a standard positive electrode. The widths of the determined rectangular areas are compared with the first parameter value. If the width of at least one rectangular area is greater than the first parameter value, it indicates that the negative electrode is too wide and the positive electrode is too narrow. This indicates that the battery capacity is low, and during battery charging, the distance for lithium ions to move from the positive electrode to the negative electrode is long and the resistance is high. Lithium ions cannot properly insert into the negative electrode due to the long distance, resulting in insufficient lithium insertion in the negative electrode. In this case, the abnormal subtype of at least one abnormal region is determined to be insufficient lithium insertion in the negative electrode. If the width of at least one rectangular area is greater than the first parameter value, it indicates that the negative electrode is too narrow, meaning that the lithium ion insertion space in the negative electrode is insufficient. During battery charging, the lithium ion insertion space in the negative electrode is insufficient, and lithium plating occurs due to the insufficient space for lithium ions to extract from the positive electrode. In this case, the abnormal subtype of at least one abnormal region is determined to be insufficient lithium insertion in the negative electrode. Since misalignment of the negative electrode sheet can affect the movement path of lithium ions and lead to abnormal lithium ion embedding on the negative electrode sheet, if there are multiple rectangular regions, and at least one rectangular region has a width greater than the first parameter value, and at least one rectangular region has a width less than the first parameter value, it indicates that the negative electrode sheet is misaligned. In this case, the anomaly subtype of at least one abnormal region is determined as a negative electrode sheet misalignment anomaly. In summary, the anomaly subtype of the abnormal region is further determined based on the first parameter value of the negative electrode sheet, which facilitates the adjustment of the negative electrode sheet according to the anomaly subtype.

[0019] In one possible implementation, the preset parameter value further includes a second parameter value, which is a misalignment threshold for the electrode sheet to become misaligned due to gravity sinking; when the electrode sheet is a negative electrode sheet, the step of comparing the at least one rectangular area with the preset parameter value to determine the abnormal subtype of the at least one abnormal area includes: obtaining the difference between the width of the rectangular area and the first parameter as a first difference; if the first difference is greater than the second parameter value, then the abnormal subtype of the at least one abnormal area is determined to be the misalignment of the negative electrode sheet.

[0020] It can be seen that the second parameter value can be the maximum misalignment threshold caused by the electrode settling due to gravity. Here, the electrode is a negative electrode. When the electrode is a negative electrode, the difference between the rectangular area and the first parameter value is obtained as the first difference, representing the misalignment size of the negative electrode. If the first difference is greater than the second parameter value, it indicates that the misalignment size of the negative electrode exceeds the maximum misalignment threshold caused by gravity settling, indicating that the negative electrode is misaligned. Therefore, it is determined that at least one abnormal region's abnormal subtype is negative electrode misalignment. In summary, based on the first difference and the second parameter value of the negative electrode, it is possible to accurately determine whether the negative electrode exhibits a negative electrode misalignment anomaly.

[0021] Secondly, this application also provides an electrode detection device. The electrode detection device includes an acquisition unit, a filtering unit, a transformation unit, a first determination unit, a second determination unit, and a third determination unit. The acquisition unit is used to acquire an initial image of the electrode. The transformation unit is used to transform the filtered initial image using a color space transformation to obtain an intermediate image. The color space of the intermediate image is an HSV color space, and the HSV color space includes the HSV values ​​corresponding to each pixel of the intermediate image. The first determination unit is used to perform binary processing on the intermediate image based on the HSV values ​​and a preset HSV range to determine abnormal regions in the electrode corresponding to abnormal parent types. The preset HSV threshold ranges corresponding to different abnormal parent types are different, and each abnormal parent type includes at least one abnormal subtype. The second determination unit is used to perform rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region. The third determination unit is used to compare the at least one rectangular region with preset parameter values ​​to determine the abnormal subtype of the at least one abnormal region.

[0022] Thirdly, embodiments of this application also provide a terminal device. The terminal device includes a processor and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the electrode detection method as described in the first aspect.

[0023] Fourthly, embodiments of this application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the electrode detection method as described in the first aspect.

[0024] In the electrode detection method, electrode detection device, terminal equipment, and computer-readable storage medium of this application, the initial image after filtering is transformed into a color space to obtain an intermediate image in the HSV color space. For each abnormal parent type, abnormal pixels are determined based on the HSV values ​​of each pixel in the intermediate image and a preset HSV threshold range. The intermediate image after determining the abnormal pixels is then subjected to binary processing, which can accurately and efficiently determine at least one abnormal region corresponding to an abnormal parent type in the electrode. Then, at least one rectangular region is determined by rectangular template matching. The rectangular region is compared with preset parameters to determine the abnormal subtype of at least one abnormal region, so as to facilitate subsequent targeted adjustments based on the abnormal subtype. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below.

[0026] Figure 1 A schematic flowchart of an electrode detection method provided in an embodiment of this application;

[0027] Figure 2 This is a schematic diagram of the initial image of an electrode detection method provided in an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a binary image of an electrode detection method provided in an embodiment of this application;

[0029] Figure 4 A schematic diagram of the initial image and the target image for an electrode detection method provided in an embodiment of this application;

[0030] Figure 5 This is a schematic diagram of an electrode detection device provided in an embodiment of this application;

[0031] Figure 6 This is a schematic diagram of the composition of a terminal device provided in an embodiment of this application.

[0032] Figure label:

[0033] Electrode detection device 500, acquisition unit 501, transformation unit 502, first determination unit 503, second determination unit 504, third determination unit 505, calculation unit 506, fourth determination unit 507, and fifth determination unit 508;

[0034] Terminal device 600, bus 10, processor 30, memory 50, communication interface 70. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] The following descriptions of the embodiments are with reference to the accompanying illustrations, illustrating specific embodiments in which this application can be implemented. Directional terms used in this application, such as "up," "down," "front," "back," "left," "right," "inner," "outer," and "side," are merely for reference to the accompanying illustrations. Therefore, the directional terms used are for better and clearer explanation and understanding of this application, and are not intended to indicate or imply that the referred device or element must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting this application.

[0037] Furthermore, the serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. Unless otherwise specified, the terms "connection" and "linkage" used in this application include both direct and indirect connections (linkages).

[0038] With the promotion of new energy sources, new energy batteries are being used more and more widely. However, many defects exist in the battery electrodes produced. Since the quality of the electrodes determines the battery's performance and lifespan, it is necessary to inspect the electrodes to eliminate those with serious abnormalities. Typically, electrode anomaly detection involves photographing the electrodes to identify the location and cause of the anomaly. However, issues such as lighting and resolution during photography can easily lead to misjudgments of abnormal areas. Therefore, how to accurately and efficiently detect electrode anomalies has become an urgent problem to be solved.

[0039] To address the aforementioned problems, this application provides an electrode detection method. In this method, the filtered initial image undergoes color space transformation to obtain an intermediate image in the HSV color space. For each anomaly parent type, abnormal pixels are determined based on the HSV values ​​of each pixel in the intermediate image and a preset HSV threshold range. The intermediate image after determining the abnormal pixels is then subjected to binary processing. This accurately and efficiently identifies at least one abnormal region corresponding to an abnormal parent type in the electrode. Rectangular template matching is then performed on the at least one abnormal region to determine at least one rectangular area. The rectangular area is compared with preset parameters to determine the abnormal subtype of at least one abnormal region, facilitating subsequent targeted adjustments based on the abnormal subtype.

[0040] Please see Figure 1 , Figure 1 This is a schematic flowchart of an electrode detection method provided in an embodiment of this application. The electrode detection method includes the following steps S101-S104, wherein:

[0041] S101: Obtain the initial image of the polarimeter.

[0042] A battery cell is formed by winding multiple electrode sheets. For example, a battery cell includes an inner ring electrode sheet, a middle ring electrode sheet, and an outer ring electrode sheet. After disassembling the battery cell, obtaining the initial image of the multiple electrode sheets can be achieved by arranging the inner ring electrode sheet, the middle ring electrode sheet, and the outer ring electrode sheet in a row, and then using an electrode sheet detection device to take a panoramic photo of the arranged multiple electrode sheets. Taking photos of multiple electrode sheets at once reduces the impact of different shooting times and light on the quality of the initial image, thereby reducing the possibility of misjudging abnormal areas.

[0043] After the battery cell is disassembled, the number of abnormalities on the electrode plates is usually judged by the disassembly personnel based on their experience. When describing the abnormalities on the electrode plates, they usually use words such as "a lot" or "a little", which is not conducive to others analyzing and judging the location and cause of the abnormality on the electrode plates, nor can it be clear how serious the abnormality on the electrode plates is.

[0044] This application acquires abnormal regions corresponding to different parent types of anomalies in the electrode by obtaining an initial image of the electrode. This eliminates the difference in naked-eye recognition among different disassembly personnel. The information obtained from the initial image is used to accurately define the abnormal regions, improving the accuracy of abnormal region identification. Furthermore, when analyzing anomalies in multiple electrodes, only one initial image needs to be analyzed, resulting in fast analysis speed and effectively improving the efficiency of identifying abnormal regions.

[0045] S102: The initial image after filtering is transformed into an intermediate image by color space transformation. The color space of the intermediate image is the HSV color space, which includes the HSV values ​​corresponding to each pixel of the intermediate image.

[0046] For example, when filtering the initial image, at least one of the following filtering methods can be used: Gaussian filtering, median filtering, mean filtering, and directional filtering (Sobel filtering). One or more filtering methods can be selected according to actual needs.

[0047] As can be seen, using Gaussian filtering to process the initial image preserves the details of the edge regions, ensuring accurate identification of anomalous areas, especially since anomalous regions are prone to appear at the edges. Median filtering effectively protects the details of the edge regions (i.e., the four edges of the initial image). When identifying anomalous regions at the edges, such as those related to insufficient lithium, these anomalous regions are typically located at the edges of the electrode. Median filtering preserves these edge details, ensuring accurate identification of anomalous regions. Mean filtering improves the efficiency of the filtering process, thus enhancing the efficiency of anomalous region identification. Directional filtering (i.e., Sobel filtering) extracts edges during the filtering process, effectively improving the efficiency of identifying anomalous region edges.

[0048] Initial image filtering removes noise and ensures image quality. The HSV color space consists of hue, saturation, and value, and is an intermediate image within the HSV color space, with pixel values ​​determined by HSV values. Typically, the initial image obtained from a photograph is an RGB color space image. However, RGB images are significantly affected by lighting conditions, and directly using an initial RGB image for analyzing abnormal areas can easily lead to misjudgments.

[0049] S103: The intermediate image is binarized based on the HSV value of the intermediate image and the preset HSV threshold range to determine at least one abnormal region in the electrode corresponding to the abnormal parent type. The preset HSV threshold ranges corresponding to different abnormal parent types are different, and each abnormal parent type includes at least one abnormal subtype.

[0050] It should be noted that in this application, when determining abnormal regions of different abnormal parent types, target images corresponding to different abnormal parent types for the same polarimeter are output separately to avoid the possibility of overlapping or misjudgment of abnormal regions of different abnormal parent types. Specifically, a single target image may be marked with one or more abnormal regions belonging to one abnormal parent type, and the abnormal parent types marked in different target images may differ.

[0051] In one possible implementation, S103: The method for determining the abnormal region in the pole piece corresponding to the abnormal parent type by performing binary processing on the intermediate image based on the HSV value of the intermediate image and a preset HSV threshold range can be:

[0052] The abnormal pixel set is determined based on the HSV value of each pixel in the intermediate image and the preset HSV threshold range. One abnormal parent type corresponds to one abnormal pixel set, and the abnormal pixel set contains multiple pixels belonging to the same abnormal parent type.

[0053] The intermediate image is binary processed based on the set of abnormal pixels to obtain a binary image, where one set of abnormal pixels corresponds to one frame of binary image;

[0054] Based on the binary image and the set of abnormal pixels, at least one abnormal region corresponding to the abnormal parent type is determined.

[0055] When an abnormal region of a certain abnormal parent type is identified, the HSV values ​​of each pixel in the intermediate image are compared with the preset HSV threshold range corresponding to the abnormal parent type. Pixels whose HSV values ​​are within the preset HSV threshold range are marked as abnormal pixels and added to the abnormal pixel set.

[0056] It can be understood that an abnormal pixel set includes all abnormal pixels belonging to the parent type of that abnormality, and the abnormal pixels in the abnormal pixel set may constitute one or more abnormal regions.

[0057] As can be seen, judging whether a pixel belongs to an abnormal pixel by the HSV value of each pixel in the intermediate image can accurately and efficiently identify abnormal pixels, thereby improving the accuracy and efficiency of abnormal region identification.

[0058] In one possible implementation, the preset HSV threshold range includes a first threshold range, a second threshold range, and a third threshold range. The HSV color space includes H channels, S channels, and V channels. The first threshold range corresponds to the H channels, the second threshold range corresponds to the S channels, and the third threshold range corresponds to the V channels. The method for determining the set of abnormal pixels based on the HSV values ​​corresponding to each pixel in the intermediate image and the preset HSV threshold range can be as follows:

[0059] Pixels in the intermediate image whose H channel values ​​are within the first threshold range, S channel values ​​are within the second threshold range, and V channel values ​​are within the third threshold range are obtained to form an abnormal pixel set.

[0060] As can be seen, the HSV color space of the intermediate image is split into H, S, and V channels. The H channel corresponds to the first threshold range within the preset HSV threshold range, the S channel to the second threshold range, and the V channel to the third threshold range. For each pixel in the intermediate image, it is determined whether the H, S, and V channel values ​​are within their respective threshold ranges. For example, the S channel value at pixel (0,0) meets the second threshold range, the V channel value meets the third threshold range, while the H channel value is outside the first threshold range. Therefore, pixel (0,0) is not an abnormal pixel. Determining whether a pixel is an abnormal pixel by using different single channels of the HSV color space results in higher detection precision and accuracy, reducing the possibility of false positives in abnormal areas.

[0061] In one possible implementation, the pixel values ​​in the binary image include a first value and a second value, which are different. The method for obtaining a binary image by performing binary processing on an intermediate image based on a set of abnormal pixels can be as follows:

[0062] Set the pixel values ​​of all pixels in the intermediate image that belong to the abnormal pixel set to the first value;

[0063] The pixel values ​​of all pixels in the intermediate image that do not belong to the abnormal pixel set are set to the second value to obtain a binary image.

[0064] For example, assuming the initial image is an 8x8 image, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the initial image of an electrode detection method provided in this application embodiment. (0,0) represents the pixel in the first row and first column, (0,4) represents the pixel in the first row and fifth column, (1,0) represents the pixel in the second row and first column, ..., (7,7) represents the pixel in the eighth row and eighth column. Assuming the abnormal pixel set is {(0,0), (0,1), (1,0), (1,1), (2,0), (3,4), (3,5), (4,4), (4,5)}, after binary processing of the intermediate image based on the abnormal pixel set, the pixel values ​​of the pixels in the abnormal pixel set are all first values ​​(e.g., the first value is "0"), and the pixel values ​​of the pixels in the non-abnormal pixel set are second values ​​(e.g., the second value is "255"), resulting in a binary image, as shown below. Figure 3 As shown, Figure 3 This is a schematic diagram of a binary image of an electrode detection method provided in an embodiment of this application. Figure 3 The pixels (0,0), (0,1), (1,0), (1,1), and (2,0) can form an anomalous region. Figure 3The middle pixels (3,4), (3,5), (4,4) and (4,5) can form another anomalous region.

[0065] As can be seen, after determining the set of abnormal pixels corresponding to a certain parent type of anomaly, the pixel values ​​of all pixels in the set of abnormal pixels (i.e., abnormal pixels) are set to the first value, while the pixel values ​​of pixels that do not belong to the set of abnormal pixels are set to the second value. The first value and the second value are different. For example, the first value is "0" and the second value is "255". In the binary image, the abnormal area is represented as black and the non-abnormal area is represented as white. In this way, the abnormal area and the non-abnormal area can be intuitively distinguished through the binary image, which effectively improves the efficiency of determining the abnormal area.

[0066] In one possible implementation, determining at least one anomalous region corresponding to the anomalous parent type based on the binary image and the set of anomalous pixels includes:

[0067] Edge detection is performed on the binary image based on each pixel in the abnormal pixel set to obtain at least one abnormal region.

[0068] It can be seen that the pixel gray values ​​change between abnormal and non-abnormal regions in the binary image, and the abnormal and non-abnormal regions are represented by different colors. Further edge recognition is used to detect the contour of the abnormal region to facilitate the determination of the area size of the abnormal region. Then, the total area of ​​the abnormal region corresponding to a certain abnormal parent type in the initial image of the electrode is counted to improve the accuracy of abnormal region recognition.

[0069] In one possible implementation, after performing binary processing on the intermediate image based on the HSV value of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the pole piece corresponding to the abnormal parent type, the method further includes:

[0070] Calculate the moments for at least one outlier region;

[0071] Determine the centroid and area of ​​at least one anomalous region based on the moments of at least one anomalous region;

[0072] The number of at least one anomalous region is determined based on the centroid of at least one anomalous region.

[0073] As can be seen, after identifying the anomalous regions, the moments of each anomalous region are calculated to obtain its centroid and area. For example, the centroid of the anomalous region is obtained by calculating its zeroth and first moments. After obtaining the centroids, each anomalous region's centroid can be labeled onto the initial image. Specifically, when labeling the centroid of each anomalous region, the labeled centroid is placed at the centroid location within the anomalous region, allowing for intuitive data analysis to determine the electrode anomaly. When two anomalous regions are close together, their labeled centroids can be used to quickly distinguish them, avoiding misjudgments of the number of anomalous regions. Furthermore, the distribution of anomalous regions can be analyzed using their areas.

[0074] After calculating the moments of the anomalous region, the centroid and area of ​​the anomalous region can be marked in the target image. The target image can be an image in the same format as the initial image, such as an RGB image.

[0075] Here, the abnormal parent type is not limited to lithium-ion intercalation abnormalities, high water content, or electrode coating abnormalities, etc. Multiple abnormal parent types may exist for the same electrode, and correspondingly, multiple target images corresponding to these different abnormal parent types can be output; for example, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of the initial image and the target image of an electrode detection method provided in an embodiment of this application. Figure 4 In the image, the left image is the initial image and the right image is the target image. The abnormal region identified in the target image is the lithium ion embedding abnormal region. Here, the abnormal region identified in the target image may also be other abnormal parent types such as high water content, abnormal electrode coating, and other abnormalities.

[0076] In each target image, the centroid and area values ​​can be marked using fonts of different colors. Figure 4 In the target image corresponding to the lithium-ion embedding anomaly shown, the anomalous region at the edge is continuous and excessively large. When marking the area value, the width value of the excessively large anomalous region can be used. For example, a rectangle can be used to match the anomalous region at the edge and the width of the rectangle can be calculated. Specifically:

[0077] S104: Perform rectangular template matching on at least one abnormal region to determine at least one rectangular region corresponding to at least one abnormal region.

[0078] In one possible implementation, S104: The method for performing rectangular template matching on at least one abnormal region to determine at least one rectangular region corresponding to at least one abnormal region may be: performing rectangular template matching on the abnormal region based on the area of ​​the abnormal region and the set of abnormal pixels to determine the rectangular region corresponding to the abnormal region.

[0079] For each abnormal region, a rectangular template is matched based on the location and area of ​​the abnormal region corresponding to the set of abnormal pixels to determine the rectangular area of ​​the abnormal region, so as to determine the abnormal subtype of the abnormal region in the future.

[0080] After identifying the rectangular regions of the anomalies, the rectangular regions of each anomaly can be marked and bounded in the target image, such as... Figure 4 Rectangular areas A1, A2, and A3 in the right-hand image.

[0081] It is understandable that when performing rectangular template matching based on the location and area of ​​the abnormal region determined by the set of abnormal pixels, the least squares matching method or other matching methods can be used, and there is no limitation on this.

[0082] S105: Compare at least one rectangular area with a preset parameter value to determine the anomaly subtype of at least one abnormal area.

[0083] In one possible implementation, the electrode includes a negative electrode. When the electrode is a negative electrode, the preset parameter value includes a first parameter value, which is the average of the difference between the width of the preset electrode and the width of the preset electrode. The abnormal subtype of at least one abnormal region is determined by comparing at least one rectangular region with the preset parameter value, including: if the width of at least one rectangular region is greater than the first parameter value, then the abnormal subtype of at least one abnormal region is determined to be insufficient lithium intercalation in the negative electrode; if the width of at least one rectangular region is less than the first parameter value, then the abnormal subtype of at least one abnormal region is determined to be insufficient lithium intercalation in the negative electrode; if there is at least one rectangular region with a width greater than the first parameter value and at least one rectangular region with a width less than the first parameter value, then the abnormal subtype of at least one abnormal region is determined to be misalignment in the negative electrode.

[0084] It can be seen that when the electrode is a negative electrode, the preset parameter value can be the first parameter value, which can be the design reference value for standard positive and negative electrodes. That is, the preset negative electrode is a standard negative electrode, and the preset positive electrode is a standard positive electrode. The widths of the determined rectangular areas are compared with the first parameter value. If the width of at least one rectangular area is greater than the first parameter value, it indicates that the negative electrode is too wide and the positive electrode is too narrow. This indicates that the battery capacity is low, and during battery charging, the distance for lithium ions to move from the positive electrode to the negative electrode is long and the resistance is high. Lithium ions cannot properly insert into the negative electrode due to the long distance, resulting in insufficient lithium insertion in the negative electrode. In this case, the abnormal subtype of at least one abnormal region is determined to be insufficient lithium insertion in the negative electrode. If the width of at least one rectangular area is greater than the first parameter value, it indicates that the negative electrode is too narrow, meaning that the lithium ion insertion space in the negative electrode is insufficient. During battery charging, the lithium ion insertion space in the negative electrode is insufficient, and lithium plating occurs due to the insufficient space for lithium ions to extract from the positive electrode. In this case, the abnormal subtype of at least one abnormal region is determined to be insufficient lithium insertion in the negative electrode. Since misalignment of the negative electrode sheet affects the movement path of lithium ions and can lead to abnormal lithium ion embedding on the negative electrode sheet, if there are multiple rectangular regions, and at least one rectangular region has a width greater than the first parameter value, and at least one rectangular region has a width less than the first parameter value, it indicates that the negative electrode sheet is misaligned. In this case, the anomaly subtype of at least one abnormal region is determined to be negative electrode sheet misalignment. In summary, the anomaly subtype of the abnormal region is further determined based on the first parameter value of the negative electrode sheet, which facilitates the adjustment of the negative electrode sheet according to the anomaly subtype.

[0085] In one possible implementation, the preset parameter value further includes a second parameter value, which is a misalignment threshold for the electrode to be misaligned due to gravity sinking; when the electrode is a negative electrode, the abnormal subtype of at least one abnormal region is determined by comparing at least one rectangular area with the preset parameter value, including: obtaining the difference between the width of the rectangular area and the first parameter as the first difference; if the first difference is greater than the second parameter value, the abnormal subtype of at least one abnormal region is determined to be negative electrode misalignment.

[0086] It can be seen that the second parameter value can be the maximum misalignment threshold caused by the electrode sinking due to gravity. Here, the electrode is a negative electrode. When the electrode is a negative electrode, the difference between the rectangular area and the first parameter value is obtained as the first difference value, representing the misalignment size of the negative electrode. If the first difference value is greater than the second parameter value, it indicates that the misalignment size of the negative electrode exceeds the maximum misalignment threshold caused by gravity sinking, indicating an abnormal misalignment of the negative electrode. Therefore, it is determined that at least one abnormal region's abnormal subtype is negative electrode misalignment.

[0087] It is understandable that when the electrode is a negative electrode, the value of the second parameter can be the maximum misalignment threshold caused by the negative electrode sinking due to gravity.

[0088] In this application, after determining the anomaly subtype corresponding to the abnormal area, design adjustments or process modifications can be made for different anomaly subtypes. For example, if the anomaly subtype of the abnormal area is determined to be negative electrode misalignment based on the width of the rectangular area and the first parameter, the winding equipment of the machine can be modified, or the width dimension of the negative electrode can be modified. If the anomaly subtype of the abnormal area is determined to be negative electrode misalignment based on the first difference of the negative electrode and the second parameter, it is considered that the negative electrode is abnormal due to gravity. The winding time during core winding can be adjusted to reduce the occurrence of anomalies caused by the negative electrode's own gravity.

[0089] For example, when the abnormal subtype of the abnormal region is lithium plating abnormality or insufficient lithium intercalation abnormality of the negative electrode, the design parameters of the positive electrode and / or negative electrode can be adjusted to ensure that the design parameters of the positive electrode and negative electrode meet the standards.

[0090] The above describes in detail the method designed in the embodiments of this application. The electrode detection device designed in the embodiments of this application is provided below.

[0091] Please see Figure 5 , Figure 5 This is a schematic diagram of an electrode detection device 500 provided in an embodiment of this application. The electrode detection method provided in this application is applied to the electrode detection device 500. The electrode detection device 500 includes an acquisition unit 501, a transformation unit 502, a first determination unit 503, a second determination unit 504, and a third determination unit 505. The acquisition unit 501 is used to acquire an initial image of the electrode. The transformation unit 502 is used to perform color space transformation on the filtered initial image to obtain an intermediate image. The color space of the intermediate image is the HSV color space, which includes the HSV values ​​of each pixel in the intermediate image. The first determination unit 503 is used to perform binary processing on the intermediate image based on the HSV values ​​of the intermediate image and a preset HSV threshold range to determine the abnormal region in the electrode corresponding to the abnormal parent type. The preset HSV threshold ranges corresponding to different abnormal parent types are different, and each abnormal parent type includes at least one abnormal subtype. The second determination unit 504 is used to perform rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region. The third determining unit 505 is used to compare at least one rectangular area with a preset parameter value to determine the abnormal subtype of at least one abnormal area.

[0092] In one possible implementation, the first determining unit 503 is further configured to: determine an abnormal pixel set based on the HSV values ​​corresponding to each pixel in the intermediate image and a preset HSV threshold range, wherein an abnormal parent type corresponds to an abnormal pixel set, and the abnormal pixel set contains multiple pixels belonging to the same abnormal parent type; perform binary processing on the intermediate image based on the abnormal pixel set to obtain a binary image, wherein an abnormal pixel set corresponds to a binary image; and determine at least one abnormal region corresponding to the abnormal parent type based on the binary image and the abnormal pixel set.

[0093] In one possible implementation, the preset HSV threshold range includes a first threshold range, a second threshold range, and a third threshold range. The HSV color space includes an H channel, an S channel, and a V channel. The first threshold range corresponds to the H channel, the second threshold range corresponds to the S channel, and the third threshold range corresponds to the V channel. The first determining unit 503 is further configured to: acquire pixels in the intermediate image whose H channel values ​​are within the first threshold range, whose S channel values ​​are within the second threshold range, and whose V channel values ​​are within the third threshold range, to form an abnormal pixel set.

[0094] In one possible implementation, the pixel values ​​in the binary image include a first value and a second value, which are different. The first determining unit 503 is further configured to: set the pixel values ​​of all pixels in the intermediate image that belong to the abnormal pixel set to the first value; and set the pixel values ​​of all pixels in the intermediate image that do not belong to the abnormal pixel set to the second value, so as to obtain a binary image.

[0095] In one possible implementation, the first determining unit 503 is further configured to: perform edge recognition on the binary image based on each pixel in the abnormal pixel set to obtain the abnormal region.

[0096] In one possible implementation, the electrode detection device 500 further includes a calculation unit 506, a fourth determination unit 507, and a fifth determination unit 508. The calculation unit 506 calculates the moments of the abnormal regions. The fourth determination unit 507 determines the centroid and area of ​​the abnormal regions based on their moments. The fifth determination unit 508 determines the number of abnormal regions based on their centroids.

[0097] In one possible implementation, the second determining unit 504 is further configured to: perform rectangular template matching on the abnormal region based on the area of ​​the abnormal region and the set of abnormal pixels, and determine the rectangular area corresponding to the abnormal region.

[0098] In one possible implementation, the electrode includes a negative electrode. When the electrode is a negative electrode, the preset parameter value includes a first parameter value, which is the average value of the difference between the width of the preset negative electrode and the width of the preset positive electrode. The third determining unit 505 is further configured to: if the width of at least one rectangular region is greater than the first parameter value, determine that the abnormal subtype of at least one abnormal region is insufficient lithium intercalation of the negative electrode; if the width of at least one rectangular region is less than the first parameter value, determine that the abnormal subtype of at least one abnormal region is insufficient lithium intercalation of the negative electrode; if there is at least one rectangular region with a width greater than the first parameter value and at least one rectangular region with a width less than the first parameter value, determine that the abnormal subtype of at least one abnormal region is misalignment of the negative electrode.

[0099] In one possible implementation, the preset parameter value further includes a second parameter value, which is a misalignment threshold for the electrode to be misaligned due to gravity sinking. When the electrode is a negative electrode, the third determining unit 505 is further configured to: obtain the difference between the width of the rectangular area and the first parameter, as the first difference; if the first difference is greater than the second parameter value, then determine that at least one abnormal region's abnormal subtype is negative electrode misalignment.

[0100] The implementation of each unit of the electrode detection device 500 can also correspond to the corresponding description of the electrode detection method embodiment described in any embodiment of this application.

[0101] Please see Figure 6 , Figure 6 This is a schematic diagram of the composition of a terminal device 600 provided in an embodiment of this application. The terminal device 600 may include a processor 30, a memory 50, and a communication interface 70, wherein the processor 30, the memory 50, and the communication interface 70 are interconnected via a bus 10, the memory 50 is used to store a computer program, the computer program includes program instructions, and the processor 30 is configured to call the program instructions to execute the electrode detection method as described in any embodiment of this application.

[0102] As one implementation approach, the functionality of the communication interface 70 can be implemented using transceiver circuitry or a dedicated transceiver chip. The processor 30 can be implemented using a dedicated processing chip, processing circuitry, processor, or general-purpose chip.

[0103] For the concepts, explanations, detailed descriptions, and other steps related to the technical solutions provided in the embodiments of this application involved in this terminal device, please refer to the description of the method steps performed by the device in the foregoing method or other embodiments, which will not be repeated here.

[0104] As another implementation of this embodiment, a computer-readable storage medium is provided, on which instructions are stored, which, when executed, perform the method described in any embodiment of this application.

[0105] As another implementation of this embodiment, a computer program product containing instructions is provided, which, when executed, perform the method described in any embodiment of this application.

[0106] Those skilled in the art will understand that, for ease of explanation, Figure 6 Only one memory 50 and processor 30 are shown in the illustration. In a real terminal or server, multiple processors 30 and memory 50 may exist. The memory 50 may also be referred to as a storage medium or storage device, etc., and this embodiment does not impose any limitations on this.

[0107] It should be understood that in the embodiments of this application, the processor 30 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0108] It should also be understood that the memory 50 mentioned in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0109] It should be noted that when the processor 30 is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory (storage module) is integrated into the processor.

[0110] It should be noted that the memory 50 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0111] In addition to the data bus, bus 10 may also include a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as bus 10 in the diagram.

[0112] It should also be understood that the first, second, third, fourth and various numerical designations used herein are merely for descriptive convenience and are not intended to limit the scope of this application.

[0113] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0114] In implementation, each step of the above method can be completed by the integrated logic circuits in the hardware of the processor 30 or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly implemented by the hardware processor, or by a combination of hardware and software modules in the processor 30. The software modules can reside in random access memory 50, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 50, and the processor 30 reads information from memory 50 and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0115] In the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0116] Those skilled in the art will recognize that the various illustrative logical blocks (ILBs) and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0117] In the embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or modules may be electrical, mechanical, or other forms.

[0118] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0119] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0120] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0121] The above are some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for detecting electrode sheets, characterized in that, include: Obtain the initial image of the polarimeter; The filtered initial image is transformed into an intermediate image using a color space transformation. The intermediate image uses an HSV color space, which includes the HSV values ​​corresponding to each pixel in the intermediate image. The intermediate image is binary processed based on the HSV value of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the electrode corresponding to the abnormal parent type. The preset HSV threshold ranges corresponding to different abnormal parent types are different, and each abnormal parent type includes at least one abnormal subtype. Perform rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region; The at least one rectangular region is compared with a preset parameter value to determine the anomaly subtype of the at least one abnormal region. The electrode includes a negative electrode. When the electrode is the negative electrode, the preset parameter value includes a first parameter value, which is the average value of the difference between the width of the preset negative electrode and the width of the preset positive electrode. The step of comparing the at least one rectangular region with a preset parameter value to determine the anomaly subtype of the at least one abnormal region includes: If the width of at least one rectangular region is greater than the first parameter value, then the abnormal subtype of the at least one abnormal region is determined to be insufficient lithium intercalation of the negative electrode sheet; If the width of at least one rectangular region is less than the first parameter value, then the abnormal subtype of the at least one abnormal region is determined to be insufficient clamping of the negative electrode sheet; If at least one of the rectangular regions has a width greater than the first parameter value, and at least one of the rectangular regions has a width less than the first parameter value, then the abnormal subtype of the at least one abnormal region is determined to be the negative electrode misalignment abnormality.

2. The electrode detection method according to claim 1, characterized in that, The step of performing binary processing on the intermediate image based on the HSV value of the intermediate image and the preset HSV threshold range to determine at least one abnormal region in the pole piece corresponding to the abnormal parent type includes: An abnormal pixel set is determined based on the HSV value corresponding to each pixel in the intermediate image and the preset HSV threshold range, wherein one abnormal parent type corresponds to one abnormal pixel set, and the abnormal pixel set contains multiple pixels belonging to the same abnormal parent type. The intermediate image is binary processed according to the set of abnormal pixels to obtain a binary image, wherein one set of abnormal pixels corresponds to one frame of the binary image; The at least one abnormal region corresponding to the abnormal parent type is determined based on the binary image and the set of abnormal pixels.

3. The electrode detection method according to claim 2, characterized in that, The preset HSV threshold range includes a first threshold range, a second threshold range, and a third threshold range. The HSV color space includes an H channel, an S channel, and a V channel. The first threshold range corresponds to the H channel, the second threshold range corresponds to the S channel, and the third threshold range corresponds to the V channel. Determining the set of abnormal pixels based on the HSV values ​​corresponding to each pixel in the intermediate image and the preset HSV threshold range includes: Pixels in the intermediate image whose H channel values ​​are within the first threshold range, whose S channel values ​​are within the second threshold range, and whose V channel values ​​are within the third threshold range are obtained to form the abnormal pixel set.

4. The electrode detection method according to claim 2, characterized in that, The pixel values ​​in the binary image include a first value and a second value, wherein the first value and the second value are different. The step of performing binary processing on the intermediate image based on the set of abnormal pixels to obtain a binary image includes: Set the pixel values ​​of all pixels in the intermediate image that belong to the set of abnormal pixels to the first value; The pixel values ​​of all pixels in the intermediate image that do not belong to the abnormal pixel set are set to the second value to obtain the binary image.

5. The electrode detection method according to claim 2, characterized in that, Determining the at least one abnormal region corresponding to the abnormal parent type based on the binary image and the set of abnormal pixels includes: Edge recognition is performed on the binary image based on each pixel in the set of abnormal pixels to obtain at least one abnormal region.

6. The electrode detection method according to claim 2, characterized in that, After performing binary processing on the intermediate image based on the HSV value of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the pole piece corresponding to the abnormal parent type, the method further includes: Calculate the moments of the at least one anomalous region; The centroid and area of ​​the at least one anomalous region are determined based on the moments of the at least one anomalous region. The number of the at least one anomalous region is determined based on the centroid of the at least one anomalous region.

7. The electrode detection method according to claim 6, characterized in that, The step of performing rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region includes: Based on the area of ​​the abnormal region and the set of abnormal pixels, a rectangular template is matched to the abnormal region to determine the rectangular area corresponding to the abnormal region.

8. The electrode detection method according to claim 1, characterized in that, The preset parameter value also includes a second parameter value, which is the misalignment threshold of the electrode caused by gravity sinking. When the electrode is a negative electrode, the step of comparing the at least one rectangular region with a preset parameter value to determine the abnormal subtype of the at least one abnormal region includes: The difference between the width of the rectangular area and the first parameter is obtained as the first difference; If the first difference is greater than the second parameter value, then the abnormal subtype of the at least one abnormal region is determined to be the negative electrode misalignment.

9. An electrode testing device, characterized in that, include: The acquisition unit is used to acquire the initial image of the electrode. The transformation unit is used to transform the filtered initial image into an intermediate image by color space transformation. The color space of the intermediate image is the HSV color space, and the HSV color space includes the HSV values ​​of each pixel in the intermediate image. The first determining unit is used to perform binary processing on the intermediate image based on the HSV value of the intermediate image and a preset HSV threshold range to determine at least one abnormal region in the pole piece corresponding to the abnormal parent type, wherein the preset HSV threshold range corresponding to different abnormal parent types is different, and each abnormal parent type includes at least one abnormal subtype. The second determining unit is used to perform rectangular template matching on the at least one abnormal region to determine at least one rectangular region corresponding to the at least one abnormal region. The third determining unit is configured to compare the at least one rectangular region with a preset parameter value to determine the abnormal subtype of the at least one abnormal region; the electrode includes a negative electrode, and when the electrode is the negative electrode, the preset parameter value includes a first parameter value, the first parameter value being the average value of the difference between the width of the preset negative electrode and the width of the preset positive electrode; the third determining unit is further configured to: if the width of all at least one rectangular region is greater than the first parameter value, then determine the abnormal subtype of the at least one abnormal region as insufficient lithium intercalation of the negative electrode; if the width of all at least one rectangular region is less than the first parameter value, then determine the abnormal subtype of the at least one abnormal region as insufficient intercalation of the negative electrode; if there is at least one rectangular region with a width greater than the first parameter value and at least one rectangular region with a width less than the first parameter value, then determine the abnormal subtype of the at least one abnormal region as a misalignment abnormality of the negative electrode.

10. A terminal device, characterized in that, The device includes a processor and a memory interconnected thereto, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the electrode detection method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the electrode detection method as described in any one of claims 1 to 8.

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