Pole piece flexibility quantitative evaluation method

Abstract

The invention discloses a pole piece flexibility quantitative evaluation method. The method comprises the following steps: acquiring a to-be-detected pole piece sample; placing the paper at a certain height above the identification area to carry out repeated folding operation for multiple times; acquiring a first image of the falling condition of the coating in the rectangular identification area and two images of the falling condition of the coating at the creases on the two sides of the pole piece; performing BGR color space transformation and gray processing operation on the initial image to obtain an intermediate image; carrying out binarization processing on the intermediate image, and distinguishing each pixel of the intermediate image by using different colors according to a set gray threshold range to obtain a processed image; and calculating the area ratio of the highlight red region relative to the identification region, and determining the accurate score of the material falling rate of the detected pole piece sample. The problem that the flexibility of the pole piece is difficult to accurately quantify is solved by accurately calculating the material falling rate of the pole piece in the flexibility judgment process according to a method for processing and identifying an image through a BGR channel by utilizing the coating falling conditions in the folding area of the pole piece and at the creases of the front side and the back side of the pole piece.

Description

Technical Field

[0001] The invention belongs to the technical field of flexibility evaluation, and in particular relates to a method for quantitatively evaluating the flexibility of a pole piece. Background Art

[0002] Driven by the development of algorithms and hardware progress, image processing technology has brought revolutionary changes in various scientific fields such as biomedicine, aerospace, vehicle-machine interaction, materials, etc. At present, digital image processing technology provides a powerful computing tool for research in materials and other fields. This method can clearly process modeling information and make the calculation process faster, more accurate and more convenient.

[0003] The flexibility of the coating on the pole piece of lithium-ion batteries has a huge impact on the key manufacturing processes of the battery, such as coating roller pressing, slitting and lamination. Therefore, in the research and development and production of lithium batteries, it is particularly important to accurately evaluate and test the flexibility of the pole piece. At present, most of the methods and patents for evaluating the flexibility of pole pieces in enterprises are mostly through manual folding and comparing the elasticity and brittleness of the pole piece. However, this method, which relies heavily on the experience of workers, has a large amount of subjective interference, lacks key quantitative indicators, and cannot compare and verify the test results, making it difficult to form a unified quantitative evaluation standard and system. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the current process of judging the flexibility of the pole piece is greatly influenced by subjective factors and the flexibility data cannot be quantified. A method for quantitatively evaluating the flexibility of the pole piece is provided, which accurately gives the flexibility value by identifying the material drop rate of the pole piece during the folding process, thereby solving the problem of quantitative calibration of the flexibility of the lithium-ion battery pole piece.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A method for quantitatively evaluating the flexibility of a pole piece, the pole piece comprising a metal foil and a coating applied on both sides thereof, the method comprising:

[0007] Step 1: Obtaining a sample of the electrode to be tested: wherein the sample of the electrode to be tested is obtained by cutting the electrode to be tested into a fixed size;

[0008] Step 2: Place the electrode sample to be tested at a certain height above the fixed-size identification area and fold it repeatedly for multiple times; the certain height refers to 10 to 35 cm, which can be adjusted appropriately according to the size of the identification area, and the multiple times are 1 to 15 times, which is determined according to the actual test conditions of the positive and negative electrodes of different models;

[0009] Step 3: Obtaining an initial image: Obtaining a first image of the coating drop in the above-mentioned identification area, a second image of the coating drop at the crease on the first surface of the pole piece, and a third image of the coating drop at the crease on the second surface of the pole piece;

[0010] Step 4: performing BGR color space transformation and grayscale processing operations on the acquired initial images (first, second and third images) to obtain grayscale intermediate images;

[0011] Step 5: Binarizing the intermediate images, and distinguishing each pixel in the intermediate image with different colors according to the grayscale threshold range set by the image recognition algorithm to obtain processed images, wherein the coating that has fallen off in the first image is marked as highlighted red, and the coating position that has fallen off at the fold of the second image and the third image is marked as highlighted red;

[0012] Step six: Calculate the area ratio of the highlighted red area marked in the processed image relative to the identification area, and combine the three images to determine the accurate score of the drop rate of the tested electrode sample. The flexibility score is represented by a combination of the drop rate of the corresponding area when the coating falls off during repeated bending operations of the electrode and the drop rate at the corresponding crease.

[0013] Furthermore, an image of the material drop of the electrode sample to be tested during the repeated folding operation is obtained, including:

[0014] In the step 3, an image of the coating of the electrode piece falling off within the rectangular area under the same number of folding times is obtained;

[0015] In the step three operation, images of coating shedding at the creases on the first and second surfaces of the electrode sheet under the same number of folding times are obtained.

[0016] Furthermore, the step 4 is specifically as follows:

[0017] Identify whether the initial image is within a preset pixel size range, and if the initial image is offset, move the entire image toward a preset center point;

[0018] Acquire all pixels in the image based on the initial image, and extract pixels from all the pixels in sequence;

[0019] Get the blue channel value (B), green channel value (G), and red channel value (R) of the extracted pixel, and use the pre-built weighted grayscale conversion formula to perform weighted summation on the B, G, and R values ​​of each pixel to obtain the corresponding grayscale value;

[0020] The grayscale values ​​are used to replace all pixel values ​​of the original color image to construct a single-channel grayscale intermediate image.

[0021] Furthermore, in step five, the intermediate image is subjected to threshold segmentation, and the grayscale threshold is set to X. If the grayscale value of a pixel in the grayscale intermediate image is ≤ the grayscale threshold X, the pixel is set to highlighted red; if the grayscale value of a pixel in the grayscale intermediate image is > the grayscale threshold X, the pixel is set to green, and the green pixel is set as the identification contrast area, and the red pixel is set as the dropout area.

[0022] Furthermore, in step six, the area ratio of the highlighted red area marked in the processed image relative to the identified area is calculated, including:

[0023] Calculate all pixels T in the first image after the processing 1 , set the pixel of the unit recognition area to t 1 The total number of pixels per unit area is calculated as: 1 =t 1 ×t 1 , traverse all pixels A marked as red in the first image after the processing 1 , calculate the drop rate of the first image according to the formula: D 1 =A 1 / T 1 .

[0024] Calculate all pixels T of the processed second image 2 , set the pixel of the unit recognition area at the crease to t 2 The total number of pixels at the crease is calculated as: 2 =1×t 2 , traverse all pixels A marked as red in the second image after the processing 2 , calculate the drop rate of the second image according to the formula: D 2 =A 2 / T 2 .

[0025] Calculate all pixels T of the processed third image 3 , set the pixel of the unit recognition area at the crease to t 3 The total number of pixels at the crease is calculated as: 3 =1×t 3 , traverse all pixels A marked as red in the third image after the processing 3 , calculate the drop rate of the third image according to the formula: D 3 =A 3 / T 3 .

[0026] Furthermore, in step 6, the drop rate of the three images of the electrode to be tested is accurately scored to determine the flexibility of the electrode sample to be tested, including: setting the drop rate weight of the first image to be w 1 , the drop rate weight of the second image is w 2 , the drop rate weight of the third image is w 3 , w 1 +w 2 +w 3 =1, set the number of folding times to n, and different folding times should also be assigned different weight coefficients. Let B n is the weight coefficient of the number of folds, ∑B n =1, the comprehensive drop rate of the electrode to be tested after n foldings D =∑ n [B n ×(w 1 ×D 1 +w 2 ×D 2 +w 3 ×D 3 )].

[0027] The beneficial effects of the present invention compared to the prior art are as follows: the present invention provides a method for quantitatively evaluating the flexibility of a pole piece based on image processing, which can acquire and preprocess images during the pole piece flexibility test, automatically identify the material drop area and perform calculations. The accuracy of the evaluation is only related to the image processing and recognition process, and the influence of human subjective factors is eliminated. In the actual test process, the precise flexibility score of each test pole piece can be quantified, and the test results of the pole pieces can be compared and verified with each other, forming a unified quantitative evaluation standard and simplifying the comparison system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart for implementing a method for quantitatively evaluating the flexibility of a pole piece provided in an embodiment of the present invention.

[0029] Figure 2 It is a schematic diagram of a method for detecting the flexibility of a pole piece provided in an embodiment of the present invention.

[0030] Figure 3 It is a flowchart of determining a recognition area and acquiring an image provided by an embodiment of the present invention.

[0031] Figure 4 It is a flowchart of the implementation of image recognition and image processing provided by an embodiment of the present invention.

[0032] Figure 5 It is a comparison diagram of the recognition area image before and after processing provided by an embodiment of the present invention. Specific implementation methods

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] The method of the present invention comprises the steps of obtaining a pole piece sample to be tested; placing the pole piece sample to be tested at a certain height above a white rectangular identification area of ​​a fixed size and performing repeated folding operations for multiple times; obtaining a first image of coating drop conditions in the above-mentioned rectangular identification area, as well as a second image of coating drop conditions at the crease of the first surface of the pole piece and a third image of coating drop conditions at the crease of the second surface of the pole piece; performing BGR color space transformation and grayscale processing operations on the obtained initial image to obtain a grayscale intermediate image; performing binarization processing on each of the intermediate images, and distinguishing each pixel in the intermediate image with different colors according to the grayscale threshold range set by an image recognition algorithm to obtain a processed image; calculating the area ratio of the highlighted red area marked in the processed image relative to the identification area, and combining the three images to determine an accurate score for the drop rate of the tested pole piece sample. The present invention utilizes the coating shedding in the folding area of ​​the pole piece and at the creases on the front and back sides of the pole piece during the flexibility judgment process to indirectly reflect its flexibility. According to the method of processing and identifying images through the BGR channel, the material shedding rate of the pole piece during the flexibility judgment process is accurately calculated to solve the problem that the flexibility of the pole piece is difficult to quantify accurately.

[0036] At present, most of the methods and patents for evaluating the flexibility of pole pieces in enterprises are mostly through manual folding and comparing the elasticity and brittleness of the pole pieces. However, this method, which relies heavily on the experience of workers, has a large interference of subjective factors, lacks key quantitative indicators, and cannot compare and verify the test results, making it difficult to form a unified quantitative evaluation standard and system. In view of this, the embodiment of the present invention provides a method for quantitative evaluation of the flexibility of pole pieces based on image processing, which can obtain and preprocess images during the flexibility test of lithium-ion battery pole pieces, automatically identify the material drop area and perform calculations, with reference to Figure 1 , Figure 1 : is a flow chart of a method for quantitatively evaluating the flexibility of a pole piece provided by an embodiment of the present invention. The present invention comprises the following steps:

[0037] Step 101: Take a pole piece sample to be tested.

[0038] The lithium-ion electrode sheet includes a metal foil and a coating coated on both sides thereof, and the metal foil is aluminum foil or copper foil, etc. Among them, the electrode sheet sample to be tested is obtained by cutting the lithium-ion battery electrode sheet to be tested into a fixed size. It should be noted that the length of the electrode sheet formed by cutting should be moderate, and should not be too long or too short. If the electrode sheet is too long, it will cause difficulties in the folding process. If the electrode sheet is too short, it may cause the electrode sheet to exceed its own brittle limit too early during the folding process and break, affecting the measurement. The shape of the test sample here is not required and is determined according to the actual test requirements. The shape can be any of rectangular, circular or square. It is best to be a shape that can still measure the length of the crease after the folding operation.

[0039] The lithium-ion battery pole piece sample in this embodiment is an aluminum foil coated with lithium iron phosphate slurry, and has undergone preparation processes such as rolling, drying, and slitting. The length of the rectangular pole piece is 50 to 150 mm and the width is 40 to 130 mm.

[0040] Step 102: Place the electrode sample to be tested at a certain height above the fixed-size identification area and perform repeated folding operations. The shape and size of the identification area are not required and are determined according to actual test requirements. The shape can be any of rectangle, circle, triangle, diamond or quadrilateral. It is best to be able to simply calculate the shape of its overall area. The color of the identification area should be as white as possible. Figure 2 , Figure 2 Schematic diagram of the electrode flexibility detection method provided in an embodiment of the present invention. 01 is a camera or camera element, which is used to shoot and obtain the image in the required step 103, 02 is the electrode to be tested in the folding, and 03 is a fixed-size identification area, in which the coating will fall off to varying degrees after folding.

[0041] The electrode tested according to the specific implementation method of the present invention needs to maintain consistent test conditions during the folding process, such as: testing under the same temperature and humidity environment, and in the process of preparing the electrode sample to be tested, it is necessary to ensure that the battery electrode thickness, double-sided coating thickness and surface density do not change.

[0042] Step 103, obtaining a first image of the coating drop in the above-mentioned identification area, a second image of the coating drop at the fold of the first surface of the pole piece, and a third image of the coating drop at the fold of the second surface of the pole piece;

[0043] In the specific embodiment of the present invention, all images are collected using Figure 2 01 camera or camera element, and acquire under uniform lens height, lens angle and light conditions.

[0044] For detailed information on how to obtain it, please refer to Figure 3 , Figure 3 This is a flowchart of determining the recognition area and acquiring the image provided by an embodiment of the present invention. The specific process is as follows:

[0045] (1) Determine the identification area and ensure that the electrode sample to be tested is repeatedly folded in half multiple times at a position directly above the identification area.

[0046] The recognition area in the specific embodiment of the present invention is rectangular in shape and has a size of 70×70 mm, and the folding operation is performed 10-15 cm above the recognition area.

[0047] (2) Obtain a first image of the coating peeling condition within the rectangular recognition area.

[0048] The first image is captured by Figure 2 The coating falling situation in the rectangular recognition area of ​​03, where the camera element lens should be parallel to the plane where the recognition area is located, and the first image must contain the entire rectangular recognition area.

[0049] (3) Determine the crease position of the electrode to be tested and set the crease recognition area.

[0050] (4) Obtain a second image of the coating peeling condition at the crease on the first surface of the pole piece.

[0051] (5) Obtain a third image showing the coating falling off at the crease on the second surface of the pole piece.

[0052] The pole pieces are mostly gray or dark (after rolling, they generally have surface reflective characteristics, etc.), so when acquiring the second and third images, the camera element lens should be parallel to the plane where the folds of the first and second surfaces are located. Since the shapes of the cut pole pieces may be different, the corresponding angles can be set according to the shape of the pole pieces to acquire the corresponding images, but the entire fold area must be included.

[0053] In step 103, in order to make the coating that has fallen off the pole piece in the first image more prominent in the rectangular identification area 03, the identification area photographed after folding should be made as white as possible, so that the coating that has fallen off the pole piece to be detected in the first image can be highlighted as much as possible in the identification area, so as to facilitate subsequent image recognition and processing.

[0054] Step 104: Perform BGR color space transformation and grayscale processing operations on the acquired initial image to obtain a grayscale intermediate image.

[0055] For detailed information on how to obtain it, please refer to Figure 4 , Figure 4 This is a flowchart of the implementation of image recognition and image processing provided by an embodiment of the present invention. The specific process is as follows:

[0056] (1) Identify whether the initial image is within a preset pixel size range. If the initial image is offset, move the entire image toward a preset center point.

[0057] The specific idea of ​​the moving image recognition code is to pre-construct a rectangular recognition area template, obtain the coordinate set of the identification points of the center point and boundary of the template, obtain the coordinates of the center point of the initial image, and if the initial image is offset, overlay the initial image on the pre-constructed rectangular recognition area with the center points of the two overlapping and overlaying each other.

[0058] (2) Obtain the blue channel value (B), green channel value (G), and red channel value (R) of the extracted pixel, and use the pre-built weighted grayscale conversion formula to perform weighted summation on the B, G, and R values ​​of each pixel to obtain the corresponding grayscale value.

[0059] All pixels in the initial image are acquired, and pixels are extracted sequentially from the all pixels.

[0060] The initial image is generally in color, and the color space conversion is performed using a color space conversion function in OpenCV, which is used to convert the image from the BGR color space to a grayscale image. Each pixel in the BGR color space contains corresponding blue, green, and red channels.

[0061] OpenCV conversion formula: Y = 0.299R + 0.587G + 0.114B

[0062] Where Y is the grayscale value, and R, G, and B are the pixel values ​​of the red, green, and blue channels, respectively. The weighting method used in the specific embodiment of the present invention is consistent with the human eye's perception of color brightness, with green having the largest weight and blue having the smallest weight.

[0063] (3) All pixel values ​​of the original color image are replaced by the grayscale values ​​to construct a single-channel grayscale intermediate image.

[0064] Step 105 , binarizing the intermediate images, and distinguishing each pixel in the intermediate image with different colors according to the gray threshold range set by the image recognition algorithm to obtain a processed image.

[0065] The coating that has fallen off in the first image will be marked in bright red, and the coating that has fallen off at the crease in the second and third images will be marked in bright red. The specific marking method is as follows:

[0066] (1) Binarization processing is to use the image binarization function cv2.threshold() provided by OpenCV to convert the BGR channel value of the current pixel into a binary image. Specifically, it usually converts a grayscale image into an image containing only two values ​​(usually 0 and 255).

[0067] (2) Threshold range: a pixel threshold is set. Areas with pixel values ​​greater than or less than the threshold are divided into two categories, which are used to distinguish the recognition area and the dropout area with different colors.

[0068] (3) Perform threshold segmentation on the intermediate image and set the grayscale threshold to X.

[0069] The grayscale threshold set in the specific embodiment of the present invention is 130. If the grayscale value of a pixel in the grayscale intermediate image is ≤ the grayscale threshold 130, the pixel is set to highlighted red; if the grayscale value of a pixel in the grayscale intermediate image is > the grayscale threshold 130, the pixel is set to green.

[0070] The rectangular identification area and the non-dropped areas on the front and back of the pole piece are marked in green. The dropped coating in the first image will be marked in highlighted red, and the locations of the dropped coating at the creases in the second and third images will be marked in highlighted red.

[0071] It is understandable that the grayscale threshold can be adjusted automatically, and the grayscale threshold of the grayscale intermediate image in the actual operation process can be set according to actual needs. Moreover, the shedding size of the electrode coating varies with the change of materials and processes, so the grayscale threshold used in the image processing process can be determined according to the actual acquired image. The larger the grayscale threshold, the more fine material shedding can be identified.

[0072] For a comparison of the results before and after processing, see Figure 5 , Figure 5 1 is a comparison diagram of the recognition area image before and after processing provided by the embodiment of the present invention. Among them, ① is before the first image processing, ② is after the first image processing, ③ is before the second or third image processing, and ④ is after the second or third image processing.

[0073] Step 106, calculating the area ratio of the highlighted red area marked in the processed image relative to the identification area, and combining the three images to determine an accurate score for the drop rate of the detected electrode sample.

[0074] The flexibility score is represented by a combination of a corresponding area drop rate and a corresponding crease drop rate when the coating of the lithium-ion battery electrode falls off during repeated bending operations.

[0075] Step 106 calculates all pixels T in the processed first image. 1 , set the pixel of the unit recognition area to t 1 The total number of pixels per unit area is calculated as: 1 =t 1 ×t 1 , traverse all pixels A marked as red in the first image after the processing1 , calculate the drop rate according to the formula: D 1 =A 1 / T 1 .

[0076] In a specific embodiment of the present invention, 1 is 1700.

[0077] In step 106, all pixels T of the processed second image are calculated. 2 , set the pixel of the unit recognition area at the crease to t 2 The total number of pixels at the crease is calculated as: 2 =1×t 2 , traverse all pixels A marked as red in the second image after the processing 2 , calculate the drop rate according to the formula: D 2 =A 2 / T 2 .

[0078] In a specific embodiment of the present invention, 2 is 350, and the second image is the crease of the first surface of the electrode to be tested, so the pixel width is 1.

[0079] In step 106, all pixels T of the processed third image are calculated. 3 , set the pixel of the unit recognition area at the crease to t 3 The total number of pixels at the crease is calculated as: 3 =1×t 3 , traverse all pixels A marked as red in the third image after the processing 3 , calculate the drop rate according to the formula: D 3 =A 3 / T 3 .

[0080] In a specific embodiment of the present invention, 3 is 350, and the third image is the crease of the first surface of the electrode to be tested, so the pixel width is 1.

[0081] In step 106, the drop rate of the three images of the electrode sample to be tested is accurately and quantifiably scored. 1 is the drop rate of the first image, D 2 is the drop rate of the second image, D 3 is the drop rate of the third image.

[0082] The flexibility evaluation is performed by comprehensively considering the quantifiable folding and shedding conditions of the electrode sample to be tested in the region, and the shedding conditions of the first and second surfaces of the electrode.

[0083] The specific content is: Let K be the flexibility judgment index, satisfying K = w 1 ×D 1 +w 2 ×D 2 +w 3 ×D 3 , where w 1 is the weight coefficient of the drop rate of the first image, w 2 is the drop rate weight coefficient of the second image, w 3 is the weight coefficient of the drop rate of the third image, w 1 +w 2 +w 3 =1.

[0084] In a specific embodiment of the present invention, 1 is 0.4, w 2 is 0.3, w 3 is 0.3.

[0085] In general, each electrode sample to be tested needs to be folded repeatedly for several times, let n be the number of folding times, and different folding times should also be given different weight coefficients, let B n is the weight coefficient of the number of folds, ∑B n =1.

[0086] In the specific embodiment of the present invention, the folding times n are 4, 6, and 8. 4 is 0.5, B 6 is 0.3, B 8 is 0.2.

[0087] The formula for calculating the comprehensive material drop rate in step 106 is as follows: D = ∑ n [B n ×(w 1 ×D 1 +w 2 ×D 2 +w 3 ×D 3 )].

[0088] Test pole piece A and test pole piece B are used in this embodiment. Test pole piece A and test pole piece B are aluminum foils coated with lithium iron phosphate slurry on both sides, and have undergone rolling, drying, slitting and other preparation processes. Both are cut into rectangles with a length of 110 mm and a width of 83 mm (excluding the pole ears). The double-sided surface density of pole piece A is 320 (g / m 2 ), the double-sided surface density of the pole piece B is 440 (g / m 2). The results of manual evaluation of flexibility are: A: good; B: good. This method identifies and calculates the comprehensive material loss rate of A: 17.0201%; the comprehensive material loss rate of B: 22.0652%. The results of flexibility evaluation are: A: 82.9799; B: 77.9348. The flexibility of A is better than that of B.

[0089] The processing object of the present invention is not limited to the selected lithium-ion battery pole pieces, but can be all types of pole pieces.

[0090] It should be noted that the terms "first", "second", "third", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar image objects and are only used for descriptive purposes, and do not have to be used to describe a specific order, or to be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features.

[0091] Those skilled in the art will appreciate that some of the steps in the various methods of the above embodiments can be completed by a PyThon program, which can be stored in a computer-readable storage medium, which can include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. The method for running the program can be various PyThon interpreters, integrated development environments, open source network applications, or interactive programming notebooks that can compile and run PyThon files.

[0092] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that for ordinary technicians in this field, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantitatively evaluating the flexibility of a pole piece, the pole piece comprising a metal foil and a coating applied on both sides thereof, characterized in that: The method is: Step 1: Obtaining a sample of the electrode to be tested: wherein the sample of the electrode to be tested is obtained by cutting the electrode to be tested into a fixed size; Step 2: Place the electrode sample to be tested at a certain height above the identification area of ​​fixed size and fold it repeatedly for multiple times; Step 3: Obtaining an initial image: Obtaining a first image of the coating drop in the above-mentioned identification area, a second image of the coating drop at the crease on the first surface of the pole piece, and a third image of the coating drop at the crease on the second surface of the pole piece; Step 4: performing BGR color space transformation and grayscale processing operations on the acquired initial image to obtain a grayscale intermediate image; Step 5: Binarizing the intermediate images, and distinguishing each pixel in the intermediate image with different colors according to the grayscale threshold range set by the image recognition algorithm to obtain processed images, wherein the coating that has fallen off in the first image is marked as highlighted red, and the coating position that has fallen off at the fold of the second image and the third image is marked as highlighted red; Step six: Calculate the area ratio of the highlighted red area marked in the processed image relative to the identification area, and combine the three images to determine the accurate score of the drop rate of the tested electrode sample. The flexibility score is represented by a combination of the drop rate of the corresponding area when the coating falls off during repeated bending operations of the electrode and the drop rate at the corresponding crease.

2. A method for quantitatively evaluating the flexibility of a pole piece according to claim 1, characterized in that: Obtain images of the material drop of the electrode sample to be tested during repeated folding operations, including: In the step 3, an image of the coating of the electrode piece falling off within the rectangular area under the same number of folding times is obtained; In the step three operation, images of coating shedding at the creases on the first and second surfaces of the electrode sheet under the same number of folding times are obtained.

3. A method for quantitatively evaluating the flexibility of a pole piece according to claim 1, characterized in that: The step 4 is specifically as follows: Identify whether the initial image is within a preset pixel size range, and if the initial image is offset, move the entire image toward a preset center point; Acquire all pixels in the image based on the initial image, and extract pixels from all the pixels in sequence; Get the blue channel value (B), green channel value (G), and red channel value (R) of the extracted pixel, and use the pre-built weighted grayscale conversion formula to perform weighted summation on the B, G, and R values ​​of each pixel to obtain the corresponding grayscale value; The grayscale values ​​are used to replace all pixel values ​​of the original color image to construct a single-channel grayscale intermediate image.

4. A method for quantitatively evaluating the flexibility of a pole piece according to claim 1, characterized in that: In step 5, the intermediate image is subjected to threshold segmentation, and the grayscale threshold is set to X. If the grayscale value of the pixel in the grayscale intermediate image is ≤ the grayscale threshold X, its pixel is set to highlighted red; if the grayscale value of the pixel in the grayscale intermediate image is greater than the grayscale threshold X, its pixel is set to green. The green pixels are set as the identification and contrast areas, and the red pixels are set as the dropout areas.

5. A method for quantitatively evaluating the flexibility of a pole piece according to claim 4, characterized in that: In step six, the area ratio of the highlighted red area marked in the processed image to the identified area is calculated, including: Calculate all pixels T1 in the processed first image, set the number of pixels in the unit identification area to t1, and the calculation formula for the total number of pixels in the unit area is: T1=t1×t1, traverse all pixels A1 marked as red in the processed first image, and calculate the drop rate of the first image according to the formula: D1=A1 / T1. Calculate all pixels T2 of the processed second image, set the number of pixels in the unit identification area at the crease to t2, and the calculation formula for the total number of pixels at the crease is: T2=1×t2, traverse all pixels A2 marked as red in the processed second image, and calculate the drop rate of the second image according to the formula: D2=A2 / T2. Calculate all pixels T3 of the processed third image, set the number of pixels in the unit identification area at the crease to t3, and the calculation formula for the total number of pixels at the crease is: T3=1×t3, traverse all pixels A3 marked as red in the processed third image, and calculate the drop rate of the third image according to the formula: D3=A3 / T3.

6. A method for quantitatively evaluating the flexibility of a pole piece according to claim 5, characterized in that: In step 6, the drop rate of the three images of the electrode to be tested is accurately scored to determine the flexibility of the electrode sample to be tested, including: setting the drop rate weight of the first image to w1, the drop rate weight of the second image to w2, and the drop rate weight of the third image to w3, w1+w2+w3=1, setting the number of folding times to n, and different folding times should also be assigned different weight coefficients, let B n is the weight coefficient of the number of folds, ∑B n =1, the comprehensive drop rate of the electrode to be tested after n foldings D =∑ n [B n ×(w1×D1+w2×D2+w3×D3)].

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