Method, device, equipment and medium for detecting dispersion degree of battery slurry components

Abstract

The present application provides a method, device, equipment and medium for detecting the dispersion degree of battery slurry components, which belongs to the field of battery technology, and particularly relates to the field of battery detection technology, wherein the battery slurry is pre-treated to retain the original microstructure, and the method comprises: obtaining an element distribution map, which is obtained by scanning the original microstructure in the battery slurry; inputting the element distribution map into a preset image processing software to perform image processing to obtain a quantitative dispersion index of each element; and determining the uniform dispersion degree detection result of each component in the battery slurry according to the quantitative dispersion index of all elements. The present application can quantitatively analyze the dispersion degree of each component in the battery slurry, so as to efficiently and accurately control the slurry quality during the battery manufacturing process, thereby improving the quality of battery products.

Description

Technical Field

[0001] The present application relates to the field of battery technology, in particular to the field of battery detection technology, and specifically to a method, device, equipment and medium for detecting the dispersion degree of battery slurry components. Background Art

[0002] In the battery manufacturing process, the preparation of battery slurry is a key step, and the quality of battery slurry directly affects the performance and life of the battery. Battery slurry is usually composed of active materials, conductive agents, adhesives and solvents, among which the uniform dispersion of conductive agents and adhesives (such as polyvinylidene fluoride PVDF) has a decisive influence on battery performance. The uniformity of the dispersion of the conductive agent determines the quality of the conductive network of the electrode, while the uniformity of the dispersion of the adhesive affects the bonding strength of the electrode material and the cycle stability of the battery. Therefore, it is necessary to detect the uniform dispersion of the battery slurry components to improve the quality of the finished battery.

[0003] At present, in the prior art, the method for detecting the dispersion degree of components in battery slurry is direct observation or physical property testing, which leads to the problem of low accuracy of the detection results of the dispersion degree of battery slurry components, making it difficult to control the slurry instructions with high efficiency and high precision during the battery manufacturing process, thereby affecting the quality of the produced batteries. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment and medium for detecting the dispersion degree of battery slurry components, which can quantitatively analyze the dispersion degree of each component in the battery slurry, so as to facilitate high-efficiency and high-precision control of the slurry quality during the battery manufacturing process, thereby improving the quality of battery products.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting the dispersion degree of battery slurry components, wherein the battery slurry is pretreated to retain the original microstructure, and the method comprises:

[0006] Obtaining an element distribution map, wherein the element distribution map is obtained by scanning the in-situ microstructure in the battery slurry;

[0007] Inputting the element distribution map into a preset image processing software to perform image processing to obtain a quantitative dispersion index of each element;

[0008] The uniform dispersion degree test result of each component in the battery slurry is determined based on the quantitative dispersion index of all elements.

[0009] In a possible implementation, the battery slurry is prepared by mixing a positive electrode active material, a conductive agent, a binder, and a solvent in a preset ratio.

[0010] In a possible implementation, the battery slurry is pretreated to retain the original microstructure, and the pretreatment includes: subjecting the battery slurry to a cryo-electron microscopy drying method.

[0011] In a possible implementation, the element distribution map is input into a preset image processing software to perform image processing to obtain a quantitative element dispersion index of each element, including: inputting the element distribution map into a preset image processing software to perform denoising processing to obtain a first distribution map; performing edge detection processing on the first distribution map to obtain edge boundary data of the first distribution map; performing image center point calculation processing on the first distribution map according to the edge boundary data of the first distribution map to obtain image center point data of the first distribution map; performing threshold segmentation evaluation processing on the image center point data of the first distribution map and the preset sample center point data to determine whether the image center point data of the first distribution map meets the matching conditions with the preset sample data, and if so, performing numerical analysis processing on the image center point data of the first distribution map to obtain a quantitative dispersion index.

[0012] In one possible implementation, the center point data includes multiple center points; accordingly, the image center point data of the first distribution map is numerically analyzed to obtain a quantitative dispersion index, including: connecting all center points and dividing them into multiple triangles; calculating the areas of all triangles according to the center point data, and sorting the areas of all triangles to obtain a triangle area sorting set; when it is detected that the total number of multiple triangles is 2n, determining n area differences according to the triangle area sorting set; determining an absolute fluctuation value according to the n area differences; determining a relative fluctuation value according to the n area differences and the absolute fluctuation value; and determining a quantitative dispersion index according to the absolute fluctuation value and the relative fluctuation value.

[0013] In a possible implementation, the calculation formula for determining n area differences based on the sorted set of triangle areas is:

[0014]

[0015] In the formula, i is a natural number greater than 0, is the difference between the area of ​​the 2i-th triangle and the area of ​​the i-th triangle, where is the area of ​​the 2i-th triangle, is the area of ​​the i-th triangle;

[0016] According to the n area differences, the calculation formula for determining the absolute fluctuation value is:

[0017]

[0018] In the formula, is the absolute fluctuation value, n is the number of area differences, is the mean of the normal distribution, is the mean of n area differences;

[0019] According to the n area differences, the calculation formula for determining the relative fluctuation value is:

[0020]

[0021] In the formula, RSD is the relative fluctuation value, is the absolute fluctuation value, is the mean of the n area differences.

[0022] In a possible implementation, after sorting the areas of all triangles to obtain a sorted set of triangle areas, the method further includes: when it is detected that the total number of multiple triangles is 2n+1, determining a first difference between the area of ​​the 2n+1th triangle and the area of ​​the 2nth triangle and a second difference between the area of ​​the 2nd triangle and the area of ​​the 1st triangle according to the sorted set of triangle areas; comparing the first difference and the second difference to delete the area of ​​the 2n+1th triangle or the area of ​​the 1st triangle corresponding to the smaller one of the first difference and the second difference, so that the number of triangle areas in the sorted set of triangle areas is 2n, and executing the step of determining n area differences according to the sorted set of triangle areas.

[0023] In a possible implementation, before inputting the element distribution map into a preset image processing software to perform denoising, the method further includes: inputting the element distribution map into a preset image processing software to perform image grayscale processing to obtain an element grayscale distribution map.

[0024] In a possible implementation, the method further includes: obtaining a microstructure detection result of each element in the battery slurry in response to an intuitive evaluation operation of the user, wherein the microstructure detection result is obtained by the user after observing the battery slurry through a field emission scanning electron microscope.

[0025] In a second aspect, an embodiment of the present application provides a device for detecting the dispersion degree of battery slurry components, wherein the battery slurry is pretreated to retain the original microstructure, and the device comprises:

[0026] An acquisition module, used for acquiring an element distribution map, wherein the element distribution map is obtained by scanning the in-situ microstructure in the battery slurry;

[0027] An image processing module, used for inputting the element distribution map into a preset image processing software to perform image processing and obtain a quantitative dispersion index of each element;

[0028] The test result generating module is also used to determine the test result of the uniform dispersion degree of each component in the battery slurry according to the quantitative dispersion index of all elements.

[0029] In a third aspect, an embodiment of the present application further provides an electronic device, including: at least one processor and a memory;

[0030] The memory stores computer-executable instructions;

[0031] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method for detecting the dispersion degree of battery slurry components as described in the first aspect above.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer-executable instruction. When a processor executes the computer-executable instruction, the method for detecting the degree of dispersion of battery slurry components as described in the first aspect above is implemented.

[0033] The embodiments of the present application provide a method, device, equipment and medium for detecting the dispersion degree of battery slurry components, wherein the method first scans the pre-treated battery slurry to obtain an element distribution map, inputs it into a preset image processing software to perform image processing, obtains a quantitative dispersion index of each element, and then determines the uniform dispersion degree detection result of each component in the battery slurry based on the quantitative dispersion index of all elements. This realizes an intuitive and quantitative analysis of the dispersion degree of key components in the battery slurry, optimizes the dispersion degree of the components of the battery slurry, and the quantitative dispersion index obtained by the image processing software reduces human errors, making the detection results more objective and accurate, so as to facilitate efficient and high-precision control of the slurry quality during the battery manufacturing process, thereby improving the quality of battery products. It helps to improve the battery charging and discharging performance and its cycle stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0035] Figure 1 A schematic diagram of an application scenario of a method for detecting the degree of dispersion of battery slurry components provided in an embodiment of the present application;

[0036] Figure 2A schematic diagram of a process for detecting the dispersion degree of battery slurry components provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of the grayscale processing process of the element distribution map provided in the embodiment of the present application;

[0038] Figure 4 A first distribution map after edge detection processing provided in an embodiment of the present application;

[0039] Figure 5 A schematic diagram of the structure of a device for detecting the dispersion degree of battery slurry components provided in an embodiment of the present application;

[0040] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] At present, although the existing technology also uses energy dispersion spectroscopy (EDS), electron microscopy and other means to analyze the components of battery slurry, it lacks direct quantification and evaluation of dispersion, and cannot provide more detailed and accurate evaluation data for improving the performance of battery materials.

[0043] In order to solve the above technical problems, the embodiments of the present application provide the following technical concepts for solving the problems: a field emission scanning electron microscope is used in combination with energy dispersive X-ray spectroscopy (Mapping) technology, and image processing technology is combined to quantitatively analyze the distribution uniformity of battery slurry components such as carbon conductive agents and polyvinylidene fluoride (PVDF) additives, thereby achieving more accurate and detailed slurry component evaluation data, providing for the performance improvement of battery materials.

[0044] Figure 1 A schematic diagram of an application scenario of the method for detecting the dispersion degree of battery slurry components provided in an embodiment of the present application, such as Figure 1 As shown, it includes: a terminal 101 and a server 102.

[0045] Among them, the terminal 101 is used for users to view the detection process and detection results for detecting the dispersion degree of battery slurry components. The server 102 is installed with image processing software, and the server 102 is used to transmit the element distribution map from the cloud, other servers or mobile terminals, and execute each step of the dispersion degree detection method of the battery slurry components through the image processing software, and finally obtain the uniform dispersion degree detection result.

[0046] Figure 2 This is a flow chart of a method for detecting the dispersion degree of battery slurry components provided in an embodiment of the present application. The execution subject of this embodiment may be Figure 1 The server 102 in the illustrated embodiment may also be other computer-related devices, and this embodiment is not particularly limited. The battery slurry is pre-treated to retain the original microstructure.

[0047] In an optional embodiment of the present application, the battery slurry is prepared by mixing the positive electrode active material, the conductive agent, the binder and the solvent in a preset ratio.

[0048] In this embodiment, the preset ratio can be a weight ratio value between the components of the battery slurry. In this embodiment, the positive electrode active material includes but is not limited to: one or more of lithium cobalt oxide, lithium manganese oxide, lithium nickel oxide, lithium nickel cobalt manganese oxide, lithium iron phosphate, lithium manganese iron phosphate, lithium vanadium phosphate, lithium vanadium oxyphosphate, and lithium nickel cobalt aluminum oxide.

[0049] In an optional embodiment of the present application, the preset ratio of each component in the battery slurry can be: 98-100 parts of lithium nickel cobalt manganese oxide, 0.35-0.4 parts of carbon nanotubes, 0.6-0.8 parts of conductive carbon black, 0.7-0.9 parts of polyvinylidene fluoride and 25-30 parts of N-methylpyrrolidone by weight. It should be noted that the preset ratio of each component in the battery slurry includes but is not limited to the preset ratio disclosed in the above embodiment.

[0050] In this embodiment, the mixing and preparation of battery slurry specifically includes the following steps:

[0051] First, lithium nickel cobalt manganese oxide (NCM), carbon nanotubes (CNT), conductive carbon black, polyvinylidene fluoride (PVDF), and N-methylpyrrolidone (NMP) components are selected in a preset weight ratio of (100): (0.37): (0.73): (0.8): (26), and the mixture is made fully uniform by high-speed stirring.

[0052] In an optional embodiment of the present application, the battery slurry is pretreated to retain the original microstructure, and the pretreatment is: the battery slurry is subjected to a cryo-electron microscopy drying method.

[0053] In this embodiment, the cryo-EM drying method refers to placing a drop of uniformly mixed battery slurry on a copper mesh, rapidly freezing it with liquid nitrogen, and then sublimating and drying it in a low-temperature environment to remove the solvent.

[0054] like Figure 2 As shown, the method for detecting the dispersion degree of the battery slurry components includes:

[0055] S201: Obtain an element distribution map, where the element distribution map is obtained by scanning the in-situ microstructure in the battery slurry.

[0056] In this embodiment, the scanning of the in-situ microstructure in the battery slurry can be obtained by scanning the battery slurry sample placed on the sample stage by an energy dispersive X-ray spectrometer. The element distribution map can be a distribution map of each element contained in the battery slurry, for example: obtaining a distribution map of elements such as carbon (C), cobalt (Co), and fluorine (F).

[0057] S202: Input the element distribution map into a preset image processing software to perform image processing to obtain a quantitative dispersion index of each element.

[0058] In this embodiment, the preset image processing software may be image processing software that performs grayscale processing, edge segmentation, etc. The element distribution diagram of each element in the battery slurry may be input as an original image into the preset image processing software to automatically perform image processing operations.

[0059] Specifically, in an optional embodiment of the present application, step S202 includes:

[0060] S202a: Input the element distribution map into a preset image processing software to perform denoising processing to obtain a first distribution map.

[0061] In this embodiment, the denoising process can be performed by using an erosion algorithm to remove pixels in the element distribution map that do not meet a specific condition as noise points, thereby obtaining a first distribution map after the noise points are removed.

[0062] In an optional embodiment of the present application, before step S202a, the method further includes: inputting the element distribution map into a preset image processing software to perform image grayscale processing to obtain an element grayscale distribution map.

[0063] Figure 3 A schematic diagram of the grayscale processing process of the element distribution map provided in an embodiment of the present application.

[0064] like Figure 3 As shown, the original element distribution map (left picture) reflects the distribution of manganese element. The pixel points in the image are transformed into the element grayscale distribution map of manganese element with grayscale values ​​after grayscale processing (right picture).

[0065] In this embodiment, image grayscale processing refers to the process of converting color images such as element distribution maps into grayscale images (i.e., converting the RGB value of each pixel in the image into a grayscale value). Grayscale images only have brightness information but no color information. The pixel value of a grayscale image usually represents the brightness level.

[0066] S202b: Perform edge detection processing on the first distribution graph to obtain edge boundary data of the first distribution graph.

[0067] Figure 4 This is a first distribution map after edge detection processing provided in an embodiment of the present application.

[0068] In this embodiment, edge detection processing can be a process of using an edge detection algorithm to find edge boundaries in the first distribution map. The principle of edge detection algorithm for edge segmentation is mainly based on significant changes in pixel grayscale values ​​or color values ​​in the image. The edge is the boundary between different areas in the image, providing a basis for subsequent image analysis, segmentation and other operations. The edge boundary data of the first distribution map refers to the set of all edge pixel points in the first distribution map, such as Figure 4 shown.

[0069] S202c: Perform image center point calculation processing on the first distribution map according to the edge boundary data of the first distribution map to obtain image center point data of the first distribution map.

[0070] In this embodiment, the image center point calculation process refers to the process of dividing the first distribution map into multiple regions using edge boundary data, and obtaining the center point coordinates of each region using the edge pixel coordinates of each region.

[0071] S202d: Perform threshold segmentation evaluation processing on the image center point data of the first distribution map and the preset sample center point data to determine whether the image center point data of the first distribution map meets the matching conditions with the preset sample data. If so, perform numerical analysis processing on the image center point data of the first distribution map to obtain a quantitative dispersion index.

[0072] In this embodiment, the threshold segmentation evaluation process refers to performing a threshold segmentation process on the center point data of the first distribution map and then performing a foolproof evaluation process, for example: using the DICE judgment method to determine whether the image center point data of the first distribution map matches the preset sample data.

[0073] Exemplarily, there are two groups of data: one group is the actual segmentation result, ie, preset sample data, and the other group is the segmentation result generated by the threshold segmentation process, ie, the image center point data segmentation data of the first distribution diagram.

[0074] At this time, the DICE coefficient needs to be calculated: The calculation formula of the DICE coefficient is:

[0075]

[0076] In the formula, A is a pixel set in the segmented area in the preset sample data, and B is a pixel set in the segmented area in the image center point data of the first distribution map after threshold segmentation. represents the size of the intersection of the two sets (i.e. the number of pixels that are correctly segmented together), Represents the size of the union of two sets (that is, the sum of the number of pixels in each segmentation does not include the number of repeated pixels).

[0077] Then set a threshold: Set a threshold for the DICE coefficient according to the requirements or standards of the application. This threshold represents the minimum standard for you to consider the segmentation result "good enough" or "acceptable". Compare the DICE coefficient with the threshold: Compare the calculated DICE coefficient with the set threshold. If the DICE coefficient is greater than or equal to the threshold, the segmentation result is considered acceptable or of high enough quality. If the DICE coefficient is less than the threshold, the segmentation result is considered to need improvement or is not accepted. If the segmentation results meet the requirements, these segmentation results can be further used for analysis or subsequent processing.

[0078] In an optional embodiment of the present application, if the segmentation result does not meet the requirements, it may be necessary to adjust the segmentation algorithm or parameters, and re-segment and evaluate. Iterative optimization processing can also be performed: that is, according to the feedback, the segmentation algorithm or parameters are iteratively adjusted, and the above process is repeated until a satisfactory segmentation effect is achieved.

[0079] Based on the above embodiment, in an optional embodiment of the present application, the center point data includes multiple center points. Accordingly, in step S202d:

[0080] The image center point data of the first distribution graph is numerically analyzed and processed to obtain a quantitative dispersion index, including:

[0081] d1: Connect all center points and divide them into multiple triangles.

[0082] d2: Calculate the areas of all triangles based on the center point data, and sort the areas of all triangles to obtain a sorted set of triangle areas.

[0083] In this embodiment, the sorting process of the areas of all triangles may be a process of sorting from small to large.

[0084] d3: When the total number of detected triangles is 2n, n area differences are determined based on the sorted set of triangle areas.

[0085] In this embodiment, determining n area differences refers to the area differences of n specific pairs of triangles among the 2n triangles. For example: assuming that the battery slurry is a positive electrode battery material, and the positive electrode battery material is formed by a mixture of large particles and small particles, then the corresponding 2n triangle area distributions obtained after segmentation must have a bimodal normal function, that is, there must be two areas in the most concentrated triangular area. For example: one area is 2 square centimeters, and the other area is 0.5 square centimeters. Therefore, as shown in the sample data, there are 50 samples of 2 square centimeters and 50 samples of 0.5 square centimeters. In order to avoid an area difference of 0, the area difference of the specific n pairs of triangles can be the area difference between the 2nth triangle and the nth triangle, the area difference between the 2n-1th triangle and the n-1th triangle, and so on. A total of n area differences can be determined.

[0086] d4: Determine the absolute fluctuation value based on n area differences.

[0087] d5: Determine the relative fluctuation value based on the n area differences and the absolute fluctuation value.

[0088] d6: Determine the quantitative dispersion index based on the absolute fluctuation value and the relative fluctuation value.

[0089] In this embodiment, the process from step d1 to step d6 is a process of normalizing the dispersion index.

[0090] In an optional embodiment of the present application, the calculation formula of step d3 is:

[0091]

[0092] In the formula, i is a natural number greater than 0, is the difference between the area of ​​the 2i-th triangle and the area of ​​the i-th triangle, where is the area of ​​the 2i-th triangle, is the area of ​​the i-th triangle;

[0093] Based on n area differences, the calculation formula for determining the absolute fluctuation value is:

[0094]

[0095] In the formula, is the absolute fluctuation value, n is the number of area differences, is the mean of the normal distribution, is the mean of n area differences;

[0096] According to n area differences, the calculation formula for determining the relative fluctuation value is:

[0097]

[0098] In the formula, RSD is the relative fluctuation value, is the absolute fluctuation value, is the mean of the n area differences.

[0099] In this embodiment, the value obtained through the above calculation process can be used as the quantitative dispersion index of the element. , and The units are all square centimeters. , and RSD are dimensionless values.

[0100] For example: Figure 3 and Figure 4 The manganese element shown in the figure has a quantitative dispersion index of 0.242, which is used as a reference standard to determine whether the components in the battery slurry are evenly dispersed. Based on the above embodiment, in an optional embodiment of the present application, after step d2, it also includes:

[0101] d7: When the total number of detected triangles is 2n+1, a first difference between the area of ​​the 2n+1th triangle and the area of ​​the 2nth triangle and a second difference between the area of ​​the second triangle and the area of ​​the first triangle are determined according to the sorted set of triangle areas.

[0102] d8: Compare the first difference and the second difference to delete the 2n+1th triangle area or the 1st triangle area corresponding to the smaller difference between the first difference and the second difference, so that the number of triangle areas in the triangle area sorted set is 2n, and execute the step of determining n area differences based on the triangle area sorted set.

[0103] S203: Determine the uniform dispersion degree test result of each component in the battery slurry according to the quantitative dispersion index of all elements.

[0104] In this embodiment, the quantitative dispersion index of all elements in the battery slurry is combined with the ratio of each component in the battery slurry to complete the final evaluation of the uniform dispersion degree of the slurry components and generate a uniform dispersion degree detection result.

[0105] In summary, the method for detecting the degree of dispersion of battery slurry components provided in the embodiment of the present application first inputs the element distribution map obtained by scanning the pre-treated battery slurry into the preset image processing software to perform image processing, obtains the quantitative dispersion index of each element, and then determines the uniform dispersion degree detection result of each component in the battery slurry based on the quantitative dispersion index of all elements. It realizes the intuitive and quantitative analysis of the degree of dispersion of key components in the battery slurry, optimizes the component dispersion of the battery slurry, and the quantitative dispersion index obtained by the image processing software reduces human errors, making the detection results more objective and accurate, so as to facilitate the efficient and high-precision control of the slurry quality during the battery manufacturing process, thereby improving the quality of the battery product. It helps to improve the battery charging and discharging performance and its cycle stability.

[0106] Based on the above embodiments, a method for detecting the dispersion degree of battery slurry components provided in an optional embodiment of the present application also includes: Step A: In response to the user's intuitive evaluation operation, a microstructure detection result of each element in the battery slurry is obtained, wherein the microstructure detection result is obtained by the user after observing the battery slurry through a field emission scanning electron microscope.

[0107] In this embodiment, the user places the battery slurry sample on the sample stage of the field emission scanning motor and observes it from a top-down direction. The dispersion degree test results of each component in the battery slurry can be intuitively evaluated through the microscopic structure. The test results are somewhat subjective, and the user can make a comprehensive judgment based on the quantitative dispersion index to improve the accuracy of the judgment.

[0108] Figure 5 A schematic diagram of a device for detecting the dispersion degree of battery slurry components provided in an embodiment of the present application, wherein the battery slurry is pre-treated to retain the original microstructure, and the device comprises: an acquisition module 51, an image processing module 52 and a detection result generation module 53.

[0109] The acquisition module 51 is used to acquire an element distribution map, which is obtained by scanning the in-situ microstructure in the battery slurry.

[0110] The image processing module 52 is used to input the element distribution diagram into a preset image processing software to perform image processing to obtain a quantitative dispersion index of each element.

[0111] The test result generating module 53 is also used to determine the test result of the uniform dispersion degree of each component in the battery slurry according to the quantitative dispersion index of all elements.

[0112] In an optional embodiment of the present application, the image processing module 52 is specifically used to: input the element distribution map into the preset image processing software to perform denoising processing to obtain a first distribution map; perform edge detection processing on the first distribution map to obtain edge boundary data of the first distribution map; perform image center point calculation processing on the first distribution map according to the edge boundary data of the first distribution map to obtain image center point data of the first distribution map; perform threshold segmentation evaluation processing on the image center point data of the first distribution map and the preset sample center point data to determine whether the image center point data of the first distribution map meets the matching conditions with the preset sample data, and if so, perform numerical analysis processing on the image center point data of the first distribution map to obtain a quantitative dispersion index.

[0113] In an optional embodiment of the present application, the center point data includes a plurality of center points;

[0114] Correspondingly, the image processing module 52 is also specifically used to: connect all the center points and divide them into multiple triangles; calculate the areas of all triangles based on the center point data, and sort the areas of all triangles to obtain a triangle area sorting set; when it is detected that the total number of multiple triangles is 2n, determine n area differences based on the triangle area sorting set; determine the absolute fluctuation value based on the n area differences; determine the relative fluctuation value based on the n area differences and the absolute fluctuation value; determine a quantitative dispersion index based on the absolute fluctuation value and the relative fluctuation value.

[0115] In an optional embodiment of the present application, the image processing module 52 is used to determine the calculation formula of n area differences according to the triangle area sorting set:

[0116]

[0117] In the formula, i is a natural number greater than 0, is the difference between the area of ​​the 2i-th triangle and the area of ​​the i-th triangle, where is the area of ​​the 2i-th triangle, is the area of ​​the i-th triangle;

[0118] Based on n area differences, the calculation formula for determining the absolute fluctuation value is:

[0119]

[0120] In the formula, is the absolute fluctuation value, n is the number of area differences, is the mean of the normal distribution, is the mean of n area differences;

[0121] According to n area differences, the calculation formula for determining the relative fluctuation value is:

[0122]

[0123] In the formula, RSD is the relative fluctuation value, is the absolute fluctuation value, is the mean of the n area differences.

[0124] In an optional embodiment of the present application, the image processing module 52 is further used for: when it is detected that the total number of multiple triangles is 2n+1, determining a first difference between the area of ​​the 2n+1th triangle and the area of ​​the 2nth triangle and a second difference between the area of ​​the second triangle and the area of ​​the first triangle according to the triangle area sorting set; performing comparison processing on the first difference and the second difference to delete the 2n+1th triangle area or the 1st triangle area corresponding to the smaller difference between the first difference and the second difference, so that the number of triangle areas in the triangle area sorting set is 2n, and executing the step of determining n area differences according to the triangle area sorting set.

[0125] In an optional embodiment of the present application, the image processing module 52 is further used to: input the element distribution map into a preset image processing software to perform image grayscale processing to obtain an element grayscale distribution map.

[0126] In an optional embodiment of the present application, the detection result generation module 53 is also used to: respond to the user's intuitive evaluation operation, obtain the microstructure detection result of each element in the battery slurry, wherein the microstructure detection result is obtained by the user after observing the battery slurry through a field emission scanning electron microscope.

[0127] The battery slurry component dispersion degree detection device provided in this embodiment can be used to implement the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be repeated here.

[0128] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the electronic device includes: at least one processor 601 and a memory 602 .

[0129] The memory 602 is used to store computer-executable instructions.

[0130] The processor 601 is used to execute the computer-executable instructions stored in the memory 602 to implement the various steps involved in the above method embodiment. For details, please refer to the relevant description in the above method embodiment.

[0131] Optionally, the memory 602 may be independent or integrated with the processor 601 .

[0132] When the memory 602 is independently provided, the electronic device further includes a bus 603 for connecting the memory 602 and the processor 601 .

[0133] The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method for detecting the dispersion degree of battery slurry components is implemented.

[0134] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the above method for detecting the degree of dispersion of battery slurry components.

[0135] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0136] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0137] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The unit composed of the above modules may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0138] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method of each embodiment of the present application.

[0139] It should be understood that the above processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0140] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0141] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0142] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0143] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0144] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0145] The present invention is intended to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solution from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting the dispersion degree of battery slurry components, characterized in that: The battery slurry is pre-treated to retain the in-situ microstructure, the method comprising: Obtaining an element distribution map, wherein the element distribution map is obtained by scanning the in-situ microstructure in the battery slurry; Inputting the element distribution map into a preset image processing software to perform denoising processing to obtain a first distribution map; Performing edge detection processing on the first distribution map to obtain edge boundary data of the first distribution map; Performing image center point calculation processing on the first distribution map according to the edge boundary data of the first distribution map to obtain image center point data of the first distribution map; Performing threshold segmentation evaluation processing on the image center point data of the first distribution map and the preset sample center point data to determine whether the image center point data of the first distribution map meets the matching condition with the preset sample data, and if so, performing numerical analysis processing on the image center point data of the first distribution map to obtain a quantitative dispersion index of each element; The uniform dispersion degree test result of each component in the battery slurry is determined based on the quantitative dispersion index of all elements.

2. The method according to claim 1, characterized in that The battery slurry is prepared by mixing the positive electrode active material, the conductive agent, the binder and the solvent in a preset ratio.

3. The method according to claim 1, characterized in that The battery slurry is pretreated to retain the original microstructure, wherein the pretreatment comprises: subjecting the battery slurry to a cryo-electron microscopy drying method.

4. The method according to claim 1, characterized in that: The center point data includes a plurality of center points; Accordingly, the performing of numerical analysis on the image center point data of the first distribution graph to obtain a quantitative dispersion index includes: Connect all the center points and divide them into multiple triangles; Calculating the areas of all triangles according to the center point data, and sorting the areas of all triangles to obtain a sorted set of triangle areas; When the total number of detected triangles is 2n, n area differences are determined based on the sorted set of triangle areas; Determining an absolute fluctuation value according to the n area differences; Determining a relative fluctuation value according to the n area difference values ​​and the absolute fluctuation value; A quantitative dispersion index is determined based on the absolute fluctuation value and the relative fluctuation value.

5. The method according to claim 4, characterized in that The calculation formula for determining n area differences based on the sorted set of triangle areas is: In the formula, i is a natural number greater than 0, is the difference between the area of ​​the 2i-th triangle and the area of ​​the i-th triangle, where is the area of ​​the 2i-th triangle, is the area of ​​the i-th triangle; According to the n area differences, the calculation formula for determining the absolute fluctuation value is: In the formula, is the absolute fluctuation value, n is the number of area differences, is the mean of the normal distribution, is the mean of n area differences; According to the n area differences, the calculation formula for determining the relative fluctuation value is: In the formula, RSD is the relative fluctuation value, is the absolute fluctuation value, is the mean of the n area differences.

6. The method according to claim 4, characterized in that After sorting the areas of all triangles to obtain a sorted set of triangle areas, the method further includes: When the total number of the detected triangles is 2n+1, a first difference between the area of ​​the 2n+1th triangle and the area of ​​the 2nth triangle and a second difference between the area of ​​the second triangle and the area of ​​the first triangle are determined according to the triangle area sorting set; The first difference and the second difference are compared to delete the 2n+1th triangle area or the 1st triangle area corresponding to the smaller difference between the first difference and the second difference, so that the number of triangle areas in the triangle area sorted set is 2n, and the step of determining n area differences based on the triangle area sorted set is executed.

7. The method according to claim 1, characterized in that Before inputting the element distribution map into a preset image processing software for denoising, the method further includes: The element distribution map is input into a preset image processing software to perform image grayscale processing to obtain an element grayscale distribution map.

8. The method according to any one of claims 1 to 7, characterized in that Also includes: In response to the user's intuitive evaluation operation, a microstructure detection result of each element in the battery slurry is obtained, wherein the microstructure detection result is obtained after the user observes the battery slurry through a field emission scanning electron microscope.

9. A device for detecting the dispersion degree of battery slurry components, characterized in that: The battery slurry is pre-treated to retain the in-situ microstructure, and the apparatus comprises: An acquisition module, used for acquiring an element distribution map, wherein the element distribution map is obtained by scanning the in-situ microstructure in the battery slurry; An image processing module is used to input the element distribution map into a preset image processing software to perform denoising processing to obtain a first distribution map; perform edge detection processing on the first distribution map to obtain edge boundary data of the first distribution map; perform image center point calculation processing on the first distribution map according to the edge boundary data of the first distribution map to obtain image center point data of the first distribution map; perform threshold segmentation evaluation processing on the image center point data of the first distribution map and the preset sample center point data to determine whether the image center point data of the first distribution map meets the matching condition with the preset sample data, and if so, perform numerical analysis processing on the image center point data of the first distribution map to obtain a quantitative dispersion index of each element; The test result generating module is used to determine the test result of the uniform dispersion degree of each component in the battery slurry according to the quantitative dispersion index of all elements.

10. An electronic device, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method for detecting the dispersion degree of battery slurry components according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the method for detecting the dispersion degree of battery slurry components according to any one of claims 1 to 8 is implemented.

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