Method, device, equipment, storage medium and product for detecting flatness of microstructure on back of battery

Abstract

The application relates to a battery back microstructure flatness detection method, device, equipment, storage medium and product. The method comprises the following steps: acquiring a back microstructure image of a battery to be detected, extracting a tower base position characteristic parameter of the battery to be detected from the back microstructure image; according to the tower base position characteristic parameter, performing back microstructure region division on the back microstructure image to obtain a plurality of back microstructure regions; according to tower base distribution characteristic parameters of the back microstructure regions, generating flatness evaluation indexes of the back microstructure regions; and performing flatness detection on the back microstructure of the battery to be detected according to the flatness evaluation indexes of the back microstructure regions. The method improves the detection accuracy of the flatness detection of the battery back microstructure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic cells, in particular to a method and device for detecting flatness of a back microstructure of a cell, a computer device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] With the continuous development of science and technology, photovoltaic cells have been widely used in people's lives. In order to improve the power generation efficiency, enhance the stability of the cell structure, and optimize the performance of the cell, it is necessary to detect the flatness of the back microstructure of the photovoltaic cell after the alkali etching process.

[0003] At present, the flatness of the back microstructure of the photovoltaic cell is detected by the tower base size of the photovoltaic cell obtained by testing under a 3D microscope. For example, in the case where the tower base size meets the flatness requirement, it is determined that the back microstructure of the photovoltaic cell is flat. However, since the tower base size can only reflect the flatness of the tower base, and the result of the tower base size is affected by the subjective consciousness of the tester, the flatness result of the back microstructure detected by relying on the tower base size is difficult to match the actual flatness of the back microstructure of the photovoltaic cell, and thus it is easy to occur that the flatness of the back microstructure of the photovoltaic cell is detected incorrectly. Therefore, the detection accuracy of the flatness of the back microstructure of the cell is low. SUMMARY

[0004] Therefore, it is necessary to provide a method and device for detecting flatness of a back microstructure of a cell, a computer device, a computer readable storage medium and a computer program product for improving the detection accuracy of the flatness of the back microstructure of the cell.

[0005] In a first aspect, the present application provides a method for detecting flatness of a back microstructure of a cell, comprising:

[0006] obtaining a back microstructure image of a cell to be detected, and extracting a tower base position characteristic parameter of the cell to be detected from the back microstructure image, wherein the tower base position characteristic parameter is used to represent the tower base position condition on the back microstructure of the cell to be detected;

[0007] dividing the back microstructure image into a plurality of back microstructure regions according to the tower base position characteristic parameter, to obtain the plurality of back microstructure regions;

[0008] generating a flatness evaluation index of each of the back microstructure regions according to a tower base distribution characteristic parameter of each of the back microstructure regions, wherein the tower base distribution characteristic parameter is used to represent the tower base distribution condition on each of the back microstructure regions.

[0009] According to the flatness evaluation index of each of the back microstructure regions, the flatness of the back microstructure of the battery to be detected is detected.

[0010] In one of the embodiments, the tower base position feature parameters include at least one first tower base position; the back microstructure region division of the back microstructure image is performed according to the tower base position feature parameters, and a plurality of back microstructure regions are obtained, which includes:

[0011] According to the first tower base positions, the overall tower base regions in which the first tower bases are distributed are located on the back microstructure image.

[0012] According to the tower base types of the first tower bases in the overall tower base regions, each of the back microstructure regions is divided from the overall tower base regions.

[0013] In one of the embodiments, the tower base distribution feature parameters include a tower base distribution number; the flatness evaluation index of each of the back microstructure regions is generated according to the tower base distribution feature parameters of each of the back microstructure regions, which includes:

[0014] The total amount of tower base distribution of the back microstructure of the battery to be detected is obtained.

[0015] The ratio of the tower base distribution number of each of the back microstructure regions to the total amount of tower base distribution is taken as the flatness evaluation index of each of the back microstructure regions.

[0016] In one of the embodiments, the tower base position feature parameters include at least one second tower base position; the back microstructure region division of the back microstructure image is performed according to the tower base position feature parameters, and a plurality of back microstructure regions are obtained, which includes:

[0017] According to the second tower base positions, the local tower base regions in which the second tower bases are distributed are located on the back microstructure image.

[0018] According to the region distribution positions of the second tower bases in the local tower base regions, the region position demarcation information of the back microstructure of the battery to be detected is generated.

[0019] According to the region position demarcation information, each of the back microstructure regions is divided from the back microstructure image.

[0020] In one of the embodiments, the tower base distribution characteristic parameters include at least one tower base identifier identifying the specified tower base of each of the back microstructure region distribution; and the generating the flatness evaluation index of each of the back microstructure region according to the tower base distribution characteristic parameters of each of the back microstructure region includes:

[0021] For any of the back microstructure regions, the specified tower bases of the back microstructure region distribution are combined in pairs according to the identification order between the tower base identifiers to obtain the total tower base region interval of the back microstructure region.

[0022] The flatness evaluation index of each of the back microstructure region is generated according to the total tower base region interval of each of the back microstructure region.

[0023] In one of the embodiments, the flatness detection of the back microstructure of the battery to be detected according to the flatness evaluation index of each of the back microstructure region includes:

[0024] The total amount of the back microstructure region and the specified tower base component of the specified tower base of each of the back microstructure region distribution are obtained.

[0025] The total amount of the specified tower base of the back microstructure to be detected is obtained by fusing the total amount of the back microstructure region and each of the specified tower base component, and the total interval of the tower base structure of the back microstructure to be detected is obtained by fusing each of the total interval of the tower base region.

[0026] The ratio between the total interval of the tower base structure and the total amount of the specified tower base is taken as the flatness coefficient of the back microstructure of the battery to be detected.

[0027] The flatness of the back microstructure of the battery to be detected is detected according to the flatness coefficient.

[0028] In a second aspect, the application further provides a flatness detection method and device of a back microstructure of a battery, which includes:

[0029] An acquisition module is configured to acquire a back microstructure image of a battery to be detected, and extract tower base position characteristic parameters of the battery to be detected from the back microstructure image, wherein the tower base position characteristic parameters are used to represent the tower base position condition on the back microstructure of the battery to be detected.

[0030] A division module is configured to divide the back microstructure image according to the tower base position characteristic parameters to obtain a plurality of back microstructure regions.

[0031] The generating module is configured to generate a flatness evaluation index of each of the back microstructure regions according to a tower base distribution characteristic parameter of each of the back microstructure regions, where the tower base distribution characteristic parameter is used to represent a tower base distribution condition on each of the back microstructure regions.

[0032] The flatness detection module is configured to perform flatness detection on the back microstructure of the battery to be detected according to the flatness evaluation index of each of the back microstructure regions.

[0033] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0034] The tower base position characteristic parameter is used to represent a tower base position condition on the back microstructure of the battery to be detected; the back microstructure image is divided into a plurality of back microstructure regions according to the tower base position characteristic parameter; a flatness evaluation index of each of the back microstructure regions is generated according to a tower base distribution characteristic parameter of each of the back microstructure regions, where the tower base distribution characteristic parameter is used to represent a tower base distribution condition on each of the back microstructure regions; and flatness detection is performed on the back microstructure of the battery to be detected according to the flatness evaluation index of each of the back microstructure regions.

[0035] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0036] The tower base position characteristic parameter is used to represent a tower base position condition on the back microstructure of the battery to be detected; the back microstructure image is divided into a plurality of back microstructure regions according to the tower base position characteristic parameter; a flatness evaluation index of each of the back microstructure regions is generated according to a tower base distribution characteristic parameter of each of the back microstructure regions, where the tower base distribution characteristic parameter is used to represent a tower base distribution condition on each of the back microstructure regions; and flatness detection is performed on the back microstructure of the battery to be detected according to the flatness evaluation index of each of the back microstructure regions.

[0037] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0038] acquiring a back microstructure image of a battery to be detected, extracting a tower base position characteristic parameter of the battery to be detected from the back microstructure image, wherein the tower base position characteristic parameter is used to represent a tower base position condition on the back microstructure of the battery to be detected; performing back microstructure region division on the back microstructure image according to the tower base position characteristic parameter to obtain a plurality of back microstructure regions; generating a flatness evaluation index of each back microstructure region according to a tower base distribution characteristic parameter of each back microstructure region, wherein the tower base distribution characteristic parameter is used to represent a tower base distribution condition on each back microstructure region; and performing flatness detection on the back microstructure of the battery to be detected according to the flatness evaluation index of each back microstructure region.

[0039] The battery back microstructure flatness detection method, device, computer equipment, computer readable storage medium and computer program product can first acquire the back microstructure image of the battery to be detected, extract a tower base position feature parameter representing the tower base position condition on the back microstructure of the battery to be detected from the back microstructure image, then divide the back microstructure image into a plurality of back microstructure regions through the tower base position feature parameter, and determine the flatness evaluation index of different back microstructure regions through a tower base distribution feature parameter representing the tower base distribution condition on each back microstructure region. That is, for each back microstructure region, the flatness evaluation index for quantitatively evaluating the flatness of the back microstructure is determined through the actual tower base distribution condition on the different back microstructure region. Finally, the flatness evaluation index of each back microstructure region can be used to detect the flatness of the back microstructure of the battery to be detected. Since the flatness evaluation index is determined depending on the tower base distribution feature parameter actually reflecting the tower base distribution condition of different back microstructure regions, and the flatness evaluation index is determined for different back microstructure regions of the back microstructure of the battery to be detected, that is, the flatness of the plurality of back microstructure regions of the battery to be detected is objectively detected, and the plurality of flatness evaluation indexes can be used to automatically and quantitatively detect the back microstructure of the battery to be detected. Therefore, the technical defect that the flatness of the back microstructure of the battery to be detected can only be detected by a single tower base size is overcome. The tower base size can only reflect the flatness of the tower base, and the result of the tower base size is affected by the subjective consciousness of the tester, so that the flatness of the back microstructure detected depending on the tower base size is difficult to match the actual flatness of the back microstructure of the photovoltaic cell, and the flatness detection error of the back microstructure of the photovoltaic cell is prone to occur. Therefore, the detection accuracy of the flatness detection of the back microstructure of the battery is improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed in the embodiments or related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1 A flowchart of a flatness detection method of a back microstructure of a battery in an embodiment;

[0042] Figure 2 A schematic diagram of a back microstructure image of a battery to be detected in a flatness detection method of a back microstructure of a battery in an embodiment;

[0043] Figure 3 Flow chart of the flatness detection method of the microstructure on the back surface of the battery in another embodiment;

[0044] Figure 4 Structure block diagram of the flatness detection device of the microstructure on the back surface of the battery in an embodiment;

[0045] Figure 5 Internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0047] First of all, it should be understood that the alkali texturing process is an important process in the production process of silicon solar cells. By accurately controlling the concentration, temperature and time of the alkali solution and other parameters, a pyramid structure with uniform shape, size and distribution can be prepared, thereby effectively controlling the reflectivity of the surface of the silicon wafer. At the same time, a tower base morphology will be formed on the microstructure on the back surface of the solar cell. In order to improve the power generation efficiency, enhance the stability of the battery structure and optimize the performance of the battery, etc., the flatness of the microstructure on the back surface of the solar cell needs to be detected. In order to detect the flatness of the microstructure on the back surface of the solar cell, the tower base size of the solar cell is usually tested by a 3D microscope, for example, the tower base side length of a single tower base is measured, and the tower base distribution and the quality of the entire surface on the microstructure on the back surface of the solar cell are reflected by using the tower base side length of a single tower base. However, the single tower base size can reflect the flatness of the tower base, but cannot objectively characterize the overall tower base morphology of the microstructure on the back surface. In addition, the tester is easily affected by subjective consciousness in the process of testing the tower base size, which leads to the difficulty in matching the flatness detection result of the microstructure on the back surface of the battery with the actual flatness of the microstructure on the back surface of the battery, i.e., the flatness detection of the microstructure on the back surface of the battery by using a single tower base size cannot objectively reflect the overall quality of the back surface structure of the battery. Therefore, there is an urgent need for a flatness detection method that can improve the detection accuracy of the flatness detection of the microstructure on the back surface.

[0048] In one embodiment, as Figure 1As shown, a battery back microstructure flatness detection method is provided. In this embodiment, the method is applied to a terminal, including but not limited to a personal computer, a notebook computer, a smart phone, a tablet computer, etc. The terminal includes an acquisition module, a division module, a generation module, and a flatness detection module. The acquisition module is used to acquire a back microstructure image of a battery to be detected. Tower base position characteristic parameters of the battery to be detected are extracted from the back microstructure image, wherein the tower base position characteristic parameters are used to represent the tower base position condition on the back microstructure of the battery to be detected. The division module is used to divide the back microstructure image according to the tower base position characteristic parameters, to obtain a plurality of back microstructure regions. The generation module is used to generate flatness evaluation indexes of the back microstructure regions according to tower base distribution characteristic parameters of the back microstructure regions, wherein the tower base distribution characteristic parameters are used to represent the tower base distribution condition on the back microstructure regions. The flatness detection module is used to detect the flatness of the back microstructure of the battery to be detected according to the flatness evaluation indexes of the back microstructure regions. In the process of detecting the flatness of the back microstructure of the battery to be detected, the tower base position characteristic parameters representing the tower base position condition on the back microstructure of the battery to be detected are extracted from the back microstructure image of the battery to be detected. Then, the back microstructure image is divided into a plurality of back microstructure regions based on the tower base position condition on the back microstructure of the battery to be detected. Then, for each back microstructure region, a corresponding flatness evaluation index is generated through the tower base distribution condition on the back microstructure region. Finally, the flatness of the back microstructure of the battery to be detected is detected through the flatness evaluation indexes of all the back microstructure regions. Since each flatness evaluation index is determined based on the tower base distribution characteristic parameters actually reflecting the tower base distribution condition of different back microstructure regions, and each back microstructure region of the back microstructure of the battery to be detected has a flatness evaluation index, i.e., the flatness of all the back microstructure regions of the battery to be detected is objectively detected, and the flatness of the back microstructure of the battery to be detected can be automatically and quantitatively detected through all the flatness evaluation indexes. Through the information interaction among the acquisition module, the division module, the generation module, and the flatness detection module, the flatness of the back microstructure of the battery to be detected can be objectively detected, thereby solving the technical problem of low flatness detection accuracy caused by detecting the flatness of the back microstructure of the battery through a single tower base size. It can be understood that the method can also be applied to a server and a system including a terminal and a server, and the interaction between the terminal and the server is realized. In this embodiment, the method includes the following steps 202 to 208.

[0049] At step 202, an image of the back microstructure of the battery to be detected is obtained, and a tower base position feature parameter of the battery to be detected is extracted from the image of the back microstructure, wherein the tower base position feature parameter is used to represent the tower base position on the back microstructure of the battery to be detected.

[0050] It should be noted that the image of the back microstructure is used to represent the back microstructure, which can be an initial image of the back microstructure of the battery obtained by identifying the back microstructure of the battery under a 3D microscope through an identification device, or an image obtained by processing the initial image of the back microstructure using image processing software, wherein the image processing software can be ImageJ, and in an implementable manner, referring to Figure 2 , Figure 2 As a schematic diagram of the image of the back microstructure of the battery to be detected, it can be understood that, since the tower base exists on the back microstructure of the battery to be detected, there will be a line profile representing the tower base topography on the image of the back microstructure, i.e., 21 is the tower base on the back microstructure, wherein the tower base topography of different tower bases can be the same or different, the tower base can be a square closed region composed of four equal length sides, or an open region composed of two or three length sides, etc., and the different tower bases can be independently distributed on different regions of the back microstructure or superimposedly distributed on the same region of the back microstructure, and the battery to be detected refers to the battery waiting for the flatness detection of the back microstructure, and the type of the battery is not limited in the embodiment.

[0051] It should be noted that the tower base position feature parameter is used to represent the tower base position on the back microstructure of the battery to be detected, and the tower base position feature parameter can be the coordinates of the tower base center point of a specified tower base, which can be specified by the staff before the flatness detection, for example, in an implementable manner, after obtaining the image of the back microstructure of the battery to be detected, a preset coordinate system is constructed on the image of the back microstructure, and the actual coordinates of the tower base center point of the specified tower base are read as the tower base position feature parameter.

[0052] As an example, step 202 includes: obtaining an initial image of the back microstructure of the battery to be detected, pre-processing the initial image of the back microstructure to obtain the image of the back microstructure of the battery to be detected, and extracting the coordinates of the tower base center point of the specified tower base of the battery to be detected from the image of the back microstructure.

[0053] At step 204, the image of the back microstructure is divided into a plurality of back microstructure regions according to the tower base position feature parameter.

[0054] It should be noted that due to the differences in the number and morphology of the tower bases of different structural regions on the back microstructure, the area flatness of different structural regions on the back microstructure also differs. Therefore, the tower base position characteristic parameter can be used to divide the back microstructure image of the battery to be detected into multiple back microstructure regions representing the morphology of the tower base. It should be understood that the number of back microstructure regions can be determined according to the requirements of flatness detection. For example, in an implementable manner, the specified tower base can be a tower base with a square closed region in the back microstructure image. That is, the back microstructure image is divided into back microstructure regions based on the coordinates of the center point of the tower base with a square closed region, and multiple back microstructure regions containing the tower base with a square closed region are obtained.

[0055] As an example, step 204 includes: dividing the back microstructure image into multiple back microstructure regions containing the specified tower base according to the coordinates of the center point of the specified tower base.

[0056] Step 206: generating a flatness evaluation index of each back microstructure region according to the tower base distribution characteristic parameter of each back microstructure region, wherein the tower base distribution characteristic parameter is used to represent the distribution of the tower base on each back microstructure region.

[0057] It should be noted that the flatness evaluation index is used to evaluate the area flatness of the corresponding back microstructure region, and the tower base distribution characteristic parameter is used to represent the distribution of the tower base on each back microstructure region. The tower base distribution characteristic parameter can be the number of tower bases or the distribution proportion of the tower base in the current back microstructure region. It should be understood that the distribution of the tower base in different back microstructure regions is different after the back microstructure is divided into regions, and the tower base distribution characteristic parameter can represent the distribution of the tower base in each back microstructure region. Therefore, the mapping relationship between the tower base distribution characteristic parameter and the flatness evaluation index can be used to map the distribution of the tower base in each back microstructure region. For example, in an implementable manner, assuming that 80% of the current back microstructure region has a tower base, the flatness evaluation index of the current back microstructure region is A1, and assuming that 60% of the current back microstructure region has a tower base, the flatness evaluation index of the current back microstructure region is A2, and so on. It should be understood that the greater the proportion of the tower base in the current back microstructure region, the lower the flatness evaluation index of the current back microstructure region.

[0058] As an example, step 206 comprises: determining the tower base proportion of each back microstructure according to the tower base distribution characteristic parameter of each back microstructure region, and querying the region flatness of each back microstructure region according to the tower base proportion of each back microstructure.

[0059] Step 208, performing flatness detection on the back microstructure of the battery to be detected according to the flatness evaluation index of each back microstructure region.

[0060] It should be noted that since the overall flatness of the back microstructure of the battery to be detected is fed back by each back microstructure region, the overall flatness of the back microstructure of the battery to be detected can be detected by integrating the region flatness of each back microstructure region. For example, in an implementable manner, assuming that the back microstructure image X of the battery to be detected is divided into four back microstructure regions X1, X2, X3 and X4, and the flatness evaluation indexes of X1, X2, X3 and X4 are x1, x2, x3 and x4 in turn, then the overall structure flatness of the back microstructure of the battery to be detected is: Wherein, x1 is the overall structure flatness of the back microstructure of the battery to be detected.

[0061] As an example, the flatness evaluation indexes of each back microstructure region are averaged to obtain the overall structure flatness of the back microstructure of the battery to be detected.

[0062] The method for detecting the flatness of the back microstructure of the battery comprises the following steps: firstly, preprocessing the initial back microstructure image of the battery to be detected to obtain a back microstructure image of the battery to be detected, and extracting the coordinates of the tower base center points of the specified tower bases on the back microstructure of the battery to be detected from the back microstructure image; then, dividing the back microstructure image into a plurality of microstructure regions through the coordinates of the tower base center points of the specified tower bases; then, generating flatness evaluation indexes of the back microstructure regions according to the tower base distribution characteristic parameters of the different back microstructure regions; and finally, obtaining the overall structural flatness of the back microstructure of the battery to be detected by integrating the flatness evaluation indexes of the different back microstructure regions, so as to realize the purpose of detecting the flatness of the back microstructure of the battery to be detected. Since the flatness evaluation indexes are determined according to the tower base distribution characteristic parameters that actually reflect the tower base distribution of the different back microstructure regions, and the different back microstructure regions of the back microstructure of the battery to be detected are all determined to have flatness evaluation indexes, that is, the flatness of the plurality of back microstructure regions of the battery to be detected is objectively detected, and the purpose of automatically and quantitatively detecting the back microstructure of the battery to be detected can be realized through the plurality of flatness evaluation indexes, instead of only detecting the flatness of the back microstructure of the battery through a single tower base size. Therefore, the technical defect that the flatness of the back microstructure of the battery is detected by the tower base size, which can only reflect the flatness of the tower base and is affected by the subjective consciousness of the tester, so that the flatness of the back microstructure detected by the tower base size cannot match the actual flatness of the back microstructure of the photovoltaic battery, and the flatness of the back microstructure of the photovoltaic battery is easily detected incorrectly, is overcome. Therefore, the detection accuracy of the flatness of the back microstructure of the battery is improved.

[0063] In one embodiment, as shown in FIG. 1A, the tower base position characteristic parameters include at least one first tower base position. Figure 3 According to the tower base position characteristic parameters, the back microstructure image is divided into a plurality of back microstructure regions, comprising:

[0064] Step 302: According to the first tower base positions, the overall tower base region in which the first tower bases are distributed is located on the back microstructure image.

[0065] It should be noted that the flatness of the microstructure of the back surface of the battery to be detected is affected by the number and morphology of the towers, and then by distinguishing the first image area where the towers exist and the second image area where the towers do not exist in the microstructure image of the back surface, the size of the uneven image area affected by the towers and the size of the flat image area unaffected by the towers can be obtained from the overall perspective, so that the flatness of the microstructure of the back surface of the battery to be detected can be reflected from the overall perspective. Therefore, the first tower position is used to represent the position of the specified type of tower. For example, in an implementable manner, it is assumed that the towers with two or more edge lengths will affect the flatness of the microstructure of the back surface of the battery to be detected, and then the overall tower area of the microstructure image of the back surface affected by the towers and not flat can be defined by at least one tower position. Whether any tower on the microstructure image of the back surface is a tower of a specified type can be determined based on the closure rate of the tower. For example, it is assumed that 4 edges of a tower on the microstructure image of the back surface are complete, and the closure rate of the tower is defined as 100%. It is assumed that 3 edges of a tower on the microstructure image of the back surface are complete, and the closure rate of the tower is defined as 75%. It is assumed that 2 edges of a tower on the microstructure image of the back surface are complete, and the closure rate of the tower is defined as 50%. In an implementable manner, the towers with a closure rate greater than or equal to 50% on the microstructure image of the back surface can be regarded as towers of a specified type, and then the overall tower area on the microstructure image of the back surface can be located. The overall tower area refers to the image area occupied by the towers affecting the flatness of the microstructure of the back surface.

[0066] As an example, step 302 includes: collectively regarding the area covered by the first tower corresponding to each first tower position as the overall tower area distributed on the microstructure image of the back surface.

[0067] Step 304: According to the tower type of the first tower in the overall tower area, each microstructure area of the back surface is divided from the overall tower area.

[0068] It should be noted that towers with different closure rates can be understood as different types of towers. For example, a first tower with a closure rate of 50% is a type of tower, a first tower with a closure rate of 75% is a type of tower, and a first tower with a closure rate of 100% is a type of tower. Because the influence degree of different types of first towers in the overall tower area is different, the tower area with different influence degrees can be divided into different tower microstructure areas of the back surface.

[0069] As an example, according to the tower type of the first tower in the overall tower area, the overall tower area is divided to obtain a plurality of microstructure areas of the back surface.

[0070] In the embodiment, in the process of dividing the back microstructure image of the battery to be detected, first, the overall tower base region in which the first tower bases are distributed is located based on the first tower base position on the back microstructure image, that is, the overall tower base region which is uneven due to the influence of the tower base is located in the back microstructure image, and then the back microstructure region is divided in the overall tower base region by the tower base type of the first tower base, so that the purpose of dividing the back microstructure region of different tower base types affecting the flatness of the back microstructure image is achieved, thereby laying a foundation for improving the detection accuracy of the flatness detection of the battery back microstructure.

[0071] In one embodiment, the tower base distribution characteristic parameter includes the number of tower base distributions; and the flatness evaluation index of each back microstructure region is generated according to the tower base distribution characteristic parameter of each back microstructure region, including:

[0072] The total amount of tower base distribution of the back microstructure of the battery to be detected is obtained; and the ratio of the number of tower base distributions of each back microstructure region to the total amount of tower base distribution is taken as the flatness evaluation index of each back microstructure region.

[0073] It should be noted that for the back microstructure region of different tower base types affecting the flatness of the back microstructure, the proportion of the number of tower bases of this type in the entire back microstructure image can be counted, that is, the influence of the first tower base of this tower base type on the flatness of the back microstructure of the battery to be detected can be obtained, that is, by the ratio between the number of tower base distributions of different back microstructure regions and the total amount of tower base distribution of the back microstructure of the battery to be detected, the flatness of the corresponding back microstructure region can be accurately evaluated, for example, in an implementable manner, the first tower bases with a closure rate of 50%, 75% and 100% are counted in the back microstructure image, and three different types of back microstructure regions are divided in the back microstructure image, and the proportion of the above three types of tower bases in the entire back microstructure image corresponding to the field of view is calculated, so that the flatness evaluation index reflecting the three different types of back microstructure regions can be obtained.

[0074] As an example, the tower base distribution image features are extracted from the back microstructure image of the battery to be detected, the total amount of tower base distribution of the back microstructure of the battery to be detected is counted according to the tower base distribution image features; for any back microstructure region, the number of tower base distributions of the back microstructure region is counted, and the ratio of the number of tower base distributions to the total amount of tower base distribution is taken as the flatness evaluation index of the back microstructure region.

[0075] In the embodiment, for each back microstructure region, the number of tower base distributions on the back microstructure region and the total number of tower base distributions on the back microstructure image are obtained respectively by statistics, and the ratio of the number of tower base distributions to the total number of tower base distributions is taken as the flatness evaluation of the corresponding back microstructure region. Thus, the flatness of different back microstructure regions can be quantitatively detected from the overall number distribution dimension, that is, the number of tower base distributions objectively reflects the flatness of each back microstructure region on the back microstructure. Therefore, the detection accuracy of the flatness detection of the battery back microstructure is further improved.

[0076] In one embodiment, the tower base position feature parameter includes at least one second tower base position; according to the tower base position feature parameter, the back microstructure image is divided into a plurality of back microstructure regions, including:

[0077] According to the second tower base positions, local tower base regions in which the second tower bases are distributed are located on the back microstructure image; according to the regional distribution positions of the second tower bases in the local tower base regions, regional position boundary information of the battery back microstructure to be detected is generated; and according to the regional position boundary information, the back microstructure regions are divided from the back microstructure image.

[0078] It should be noted that, in the process of dividing the back microstructure image into regions, different types of tower bases do not have a linear impact on the flatness of the battery back microstructure to be detected, and since there are many types of tower bases, it is difficult to completely count all types of tower bases. Therefore, a fixed region division method can be used to divide the back microstructure image, and the flatness of different fixed regions is evaluated to finally evaluate the overall flatness of the battery back microstructure to be detected. For example, in one implementable way, the back microstructure image can be divided into four equal back microstructure regions by a four-quadrant division method.

[0079] It should be noted that the second tower base refers to a tower base whose region is divided into different back microstructure regions. The regional distribution position of the second tower base can locate the region boundary of different back microstructure regions on the back microstructure image, wherein the regional distribution position is used to identify the regional distribution of the second tower base, and the regional position boundary information is used to identify the position of the region boundary. For example, in one implementable way, half of the second tower base is located in a first region and half is located in a second region. The regional distribution position of the second tower base can be used to determine the position of the region boundary line of the battery back microstructure to be detected. Finally, the back microstructure image can be divided according to the position of the region boundary line to obtain a plurality of back microstructure regions.

[0080] As an example, according to the second tower base positions, a local tower base region in which the second tower bases are distributed is located on the back microstructure image, wherein the local tower base region can be understood as a tower base region on the back microstructure image which is jointly constituted by the tower bases of the second tower bases; according to the region distribution positions of the second tower bases in the local tower base region, a region boundary position of the back microstructure of the battery to be detected is determined; and the back microstructure image is divided by the region boundary position, to obtain a plurality of back microstructure regions.

[0081] In the embodiment, first, a local tower base region in which the second tower bases are distributed is located on the back microstructure image through the second tower base positions of the second tower bases, that is, a local tower base region of the second tower bases belonging to a plurality of back microstructure regions is located on the back microstructure image, and then, region position boundary information of the back microstructure of the battery to be detected is obtained based on the region distribution positions of the second tower bases in the local tower base region, and finally, the back microstructure image is divided into a plurality of back microstructure regions depending on the region position boundary information, that is, the back microstructure image is divided into back microstructure regions based on the specified region position boundary information, so that the back microstructure regions independently representing the flatness of part of the structure of the back microstructure to be detected are obtained, and then, the region flatness of the back microstructure region can be completely and objectively reflected from the overall tower base distribution through the tower base distribution characteristic parameters of the back microstructure region, so as to lay a foundation for improving the detection accuracy of the flatness evaluation index of the back microstructure region.

[0082] In one embodiment, the tower base distribution characteristic parameters include at least one tower base identifier of a specified tower base identifying each back microstructure region distribution; and a flatness evaluation index of each back microstructure region is generated according to the tower base distribution characteristic parameters of each back microstructure region, including:

[0083] For any back microstructure region, the specified tower bases of the back microstructure region distribution are combined in pairs according to the identifier order between the tower base identifiers, to obtain a total tower base region distance of the back microstructure region; and a flatness evaluation index of each back microstructure region is generated according to the total tower base region distance of each back microstructure region.

[0084] It should be noted that after the back microstructure image is divided into a plurality of back microstructure regions, the flatness of the back microstructure region is reflected by the total distance of the tower base region of different back microstructure regions, wherein the total distance of the tower base region is the sum of the tower base distances obtained by fusing a plurality of specified tower bases distributed in the back microstructure region under a preset fusion mode. For example, in an implementable manner, the back microstructure image is first divided into four quadrants to obtain four back microstructure regions of the quadrants, and a preset number of specified tower bases are selected in each back microstructure region of the quadrant, wherein the number of specified tower bases selected in different back microstructure regions of the quadrant can be the same or different. For example, six specified tower bases are selected for each of the four back microstructure regions of the quadrants, and the six specified tower bases are sorted to obtain specified tower base ①, specified tower base ②, specified tower base ③, specified tower base ④, specified tower base ⑤, and specified tower base ⑥ in each back microstructure region. Then, the tower base distance fusion is performed in a two-by-two combination manner to obtain L1, L2, L3, L4, L5, and L6, wherein L1 is the tower base distance between specified tower base ① and specified tower base ②, L2 is the tower base distance between specified tower base ② and specified tower base ③, L3 is the tower base distance between specified tower base ③ and specified tower base ④, L4 is the tower base distance between specified tower base ④ and specified tower base ⑤, L5 is the tower base distance between specified tower base ⑤ and specified tower base ⑥, and L6 is the tower base distance between specified tower base ⑥ and specified tower base ①. The tower base distance between any two specified tower bases can be the tower base distance between the first tower base center point of specified tower base Y1 and the second tower base center point of specified tower Y2. The sum of L1, L2, L3, L4, L5, and L6 is taken as the total distance L0 of the tower base region, and the ratio between the total distance L0 of the tower base region and the number of specified tower bases distributed in the back microstructure region is taken as the flatness evaluation index of the back microstructure region.

[0085] As an example, for any back microstructure region, the specified tower bases distributed in the back microstructure region are fused in a two-by-two combination manner according to the identification order between the tower base identifications to obtain the total distance of the tower base region of the back microstructure region. The total distance of the tower base region is taken as the flatness evaluation index of the back microstructure region.

[0086] In the embodiment, in the process of generating the flatness evaluation index of the back microstructure area, the total interval of the tower base area corresponding to the preset number of specified tower bases in the back microstructure area is characterized, so as to evaluate the flatness of the back microstructure area from the overall tower base distribution dimension, that is, the flatness evaluation index characterized in the above manner intuitively reflects the flatness of the back microstructure area, and therefore, further lays a foundation for improving the detection accuracy of the flatness detection of the battery back microstructure.

[0087] In one embodiment, the flatness detection of the battery back microstructure to be detected is performed according to the flatness evaluation index of each back microstructure area, including:

[0088] The total amount of the back microstructure area corresponding to each back microstructure area and the specified tower base component of the specified tower base distributed in each back microstructure area are obtained; the total amount of the specified tower base of the battery back microstructure to be detected is obtained by fusing the total amount of the back microstructure area and each specified tower base component, and the total interval of the tower base structure of the battery back microstructure to be detected is obtained by fusing the total interval of each tower base area; the ratio between the total interval of the tower base structure and the total amount of the specified tower base is taken as the flatness coefficient of the battery back microstructure to be detected; and the flatness detection of the battery back microstructure to be detected is performed according to the flatness coefficient.

[0089] It should be noted that after obtaining the total interval of the tower base area of different back microstructure areas, the total interval of the tower base structure of the battery back microstructure to be detected can be obtained by using the total interval of the tower base area distribution, that is, the interval index reflecting the influence of the tower base on the flatness of the battery back microstructure to be detected from the tower base distribution dimension is obtained, and the total amount of the specified tower base is obtained by using the specified tower base component of different back microstructure areas, that is, the interval index reflecting the influence of the tower base on the flatness of the battery back microstructure to be detected from the tower base number dimension is obtained, and then the flatness coefficient of the battery back microstructure to be detected is obtained by fusing the indexes of the two different dimensions, and the flatness degree of the battery back microstructure to be detected can be quantified by the flatness coefficient, and finally the accurate detection of the battery back microstructure to be detected can be realized by relying on the flatness coefficient.

[0090] It should be noted that the flatness coefficient is used to characterize the overall flatness of the battery back microstructure to be detected, for example, in one implementable manner, assuming that the back microstructure image is divided into four quadrants, the total amount of the back microstructure area is 4, and the total interval of the tower base area of different back microstructure areas is , , and , the specified tower base component is y, and the expression of the flatness coefficient is as follows:

[0091]

[0092] wherein, is a flatness coefficient, is a total distance of the tower base region of the back microstructure region in the first quadrant, is a total distance of the tower base region of the back microstructure region in the second quadrant, is a total distance of the tower base region of the back microstructure region in the third quadrant, is a total distance of the tower base region of the back microstructure region in the fourth quadrant, and y is a specified tower base component.

[0093] As an example, the total amount of the back microstructure region corresponding to each back microstructure region and the specified tower base component of the specified tower base distributed in each back microstructure region are obtained; the product of the total amount of the back microstructure region and each specified tower base component is taken as the total amount of the specified tower base of each back microstructure to be detected, and the sum of each total distance of the tower base region is taken as the total distance of the tower base structure of the back microstructure to be detected; the ratio between the total distance of the tower base structure and the total amount of the specified tower base is taken as the flatness coefficient of the back microstructure to be detected; in the case that the flatness coefficient is greater than a preset flatness coefficient threshold, it is determined that the back microstructure to be detected is flat, and in the case that the flatness coefficient is less than or equal to the preset flatness coefficient threshold, it is determined that the back microstructure to be detected is not flat, wherein the preset flatness coefficient threshold can be 0.5 or 0.6, etc.

[0094] In the embodiment, the total distance of the tower base structure obtained by fusing the total distance of the tower base region of different back microstructure regions and the total amount of the specified tower base corresponding to each back microstructure region, and the ratio between the total distance of the tower base structure and the total amount of the specified tower base is taken as the flatness coefficient of the back microstructure to be detected, and finally the flatness detection of the back microstructure to be detected depends on the flatness coefficient, that is, the number of towers and the distribution of towers are fused, and the flatness of the back microstructure to be detected is detected, so that the detection accuracy of the flatness detection of the back microstructure of the battery is further improved.

[0095] It can be understood that, taking the TOPcon battery micro as an example, the back passivation structure of TOPCon is its core, and to ensure the uniformity of the tunneling oxide layer, not only the uniformity of the LP furnace tube thermal field and temperature field, but also the less production time, and a more flat back surface morphology as the basis for producing the tunneling oxide layer are needed; it is more conducive to the uniformity control of the tunneling oxide layer; in actual application, when the microstructure of the back surface of the battery is flat, Uoc and FF will have obvious gain, and when there are too many tower bases in the back surface microstructure image and the tower bases overlap together, the flatness coefficient will be below 0.5, that is, when detecting the flatness of the microstructure of the back surface of the battery, the larger the flatness coefficient, the more flat the back surface, wherein the tower base is the pit in the back surface microstructure image, and the remaining area is the flat area outside the pit, if the tower base ratio is smaller, the microstructure of the back surface of the battery is more flat, and the flat area is more flat, which is more conducive to the uniformity control of the tunneling oxide layer; in addition, the more tower bases with a closing rate of 50%, even the fish scale shape, reflects that the additive is too strong, and the process needs to be adjusted, for example, the amount of additive.

[0096] Since each flatness evaluation index is determined depending on the tower base distribution characteristic parameter actually reflecting the tower base distribution of different back surface microstructure regions, and each back surface microstructure of the battery to be detected determines a flatness evaluation index, that is, the flatness of the multiple back surface microstructure regions of the battery to be detected is objectively detected, and then through multiple flatness evaluation indexes, the purpose of automatically and quantitatively detecting the back surface microstructure of the battery to be detected can be achieved, rather than only through a single tower base size to detect the flatness of the back surface microstructure of the battery, so the technical defects that the tower base size can only reflect the flatness of the tower base, and the result of the tower base size is affected by the subjective consciousness of the tester, resulting in that the flatness of the back surface microstructure detected by the tower base size is difficult to match the actual flatness of the back surface microstructure of the photovoltaic battery, and then the flatness detection error of the back surface microstructure of the photovoltaic battery is prone to occur, are overcome, and therefore the detection accuracy of the flatness detection of the back surface microstructure of the battery is improved.

[0097] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately executed with at least some of the other steps or steps or stages in the other steps.

[0098] Based on the same inventive concept, the embodiments of the present application also provide a battery back microstructure flatness detection device for implementing the battery back microstructure flatness detection method described above. The problem-solving implementation scheme provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more battery back microstructure flatness detection device embodiments provided below can refer to the limitations of the battery back microstructure flatness detection method described above, and will not be repeated here.

[0099] In one exemplary embodiment, as shown in Figure 4 A battery back microstructure flatness detection device is provided, comprising: an acquisition module 401, a division module 402, a generation module 403, and a flatness detection module 404, wherein:

[0100] The acquisition module 401 is configured to acquire a back microstructure image of a battery to be detected, and extract a tower base position feature parameter of the battery to be detected from the back microstructure image, wherein the tower base position feature parameter is used to represent a tower base position condition on the back microstructure of the battery to be detected.

[0101] The division module 402 is configured to divide the back microstructure image into a plurality of back microstructure regions according to the tower base position feature parameter.

[0102] The generation module 403 is configured to generate a flatness evaluation index of each of the back microstructure regions according to a tower base distribution feature parameter of each of the back microstructure regions, wherein the tower base distribution feature parameter is used to represent a tower base distribution condition on each of the back microstructure regions.

[0103] The flatness detection module 404 is configured to detect the flatness of the back microstructure of the battery to be detected according to the flatness evaluation index of each of the back microstructure regions.

[0104] In one of the embodiments, the tower base position feature parameters include at least one first tower base position; the division module 402 is further configured to:

[0105] According to each of the first tower base positions, locate the overall tower base regions where each of the first tower bases is distributed on the back microstructure image;

[0106] According to the tower base types of the first tower bases in the overall tower base regions, divide each of the back microstructure regions from the overall tower base regions.

[0107] In one of the embodiments, the tower base distribution feature parameters include a tower base distribution number; the generation module 403 is further configured to:

[0108] Obtain a total amount of tower base distributions of the back microstructure of the battery to be detected;

[0109] Take a ratio of the tower base distribution number of each of the back microstructure regions to the total amount of tower base distributions as the flatness evaluation index of each of the back microstructure regions.

[0110] In one of the embodiments, the tower base position feature parameters include at least one second tower base position; the division module 402 is further configured to:

[0111] According to each of the second tower base positions, locate the local tower base regions where each of the second tower bases is distributed on the back microstructure image;

[0112] According to the regional distribution positions of the second tower bases in the local tower base regions, generate regional position demarcation information of the back microstructure of the battery to be detected;

[0113] According to the regional position demarcation information, divide each of the back microstructure regions from the back microstructure image.

[0114] In one of the embodiments, the tower base distribution feature parameters include at least one tower base identifier identifying the specified tower bases distributed in each of the back microstructure regions; the generation module 403 is further configured to:

[0115] For any of the back microstructure regions, according to the identifier sequence between each of the tower base identifiers, perform tower base spacing fusion on the specified tower bases distributed in the back microstructure region in a two-by-two combination manner to obtain a total tower base region spacing of the back microstructure region;

[0116] According to the total tower base region spacing of each of the back microstructure regions, generate the flatness evaluation index of each of the back microstructure regions.

[0117] In one of the embodiments, the flatness detection module 404 is further configured to:

[0118] obtaining a total amount of the back microstructure regions corresponding to each of the back microstructure regions and a specified tower base component of a specified tower base distributed in each of the back microstructure regions;

[0119] obtaining a total amount of the specified tower base of the back microstructure of the battery to be detected by fusing the total amount of the back microstructure regions and each of the specified tower base components, and obtaining a total interval of the tower base structure of the back microstructure of the battery to be detected by fusing each of the total intervals of the tower base regions;

[0120] taking a ratio between the total interval of the tower base structure and the total amount of the specified tower base as a flatness coefficient of the back microstructure of the battery to be detected;

[0121] performing flatness detection on the back microstructure of the battery to be detected according to the flatness coefficient.

[0122] Each of the modules in the flatness detection device for the back microstructure of the battery can be realized by software, hardware, or a combination thereof. Each of the modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform operations corresponding to each of the modules.

[0123] In an exemplary embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram thereof can be as shown in Figure 5 The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program is executed by the processor to implement a flatness detection method for a back microstructure of a battery. Those skilled in the art can understand that Figure 5The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] In an embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0125] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0126] In an embodiment, a computer program product is provided, including a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0128] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0129] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for detecting the flatness of the microstructure on the back of a battery, characterized in that, The method includes: A microstructure image of the back side of the battery to be tested is obtained, and the tower base position feature parameters of the battery to be tested are extracted from the microstructure image of the back side. The tower base position feature parameters are used to characterize the position of the tower base on the microstructure of the back side of the battery to be tested. Based on the tower base location feature parameters, the back microstructure image is divided into back microstructure regions to obtain multiple back microstructure regions; Based on the tower base distribution characteristic parameters of each of the back microstructure regions, a flatness evaluation index for each of the back microstructure regions is generated, wherein the tower base distribution characteristic parameters are used to characterize the distribution of tower bases located on each of the back microstructure regions; The flatness of the back microstructure of the battery under test is detected based on the flatness evaluation index of each of the aforementioned back microstructure regions. The tower base position feature parameter includes at least one second tower base position. The back microstructure image is then divided into back microstructure regions based on the tower base position feature parameter, resulting in multiple back microstructure regions, including: Based on the location of each second tower base, local tower base regions of each second tower base are located and distributed on the back microstructure image; based on the regional distribution location of the second tower bases in the local tower base regions, regional location boundary information of the back microstructure of the battery to be tested is generated; based on the regional location boundary information, each back microstructure region is divided from the back microstructure image, wherein the tower base distribution feature parameters include at least one tower base identifier identifying the distribution of each back microstructure region; generating a flatness evaluation index for each back microstructure region based on the tower base distribution feature parameters of each back microstructure region includes: For any of the aforementioned back microstructure regions, the designated tower bases distributed in the back microstructure region are combined in pairs according to the identification order among the tower base identifiers to obtain the total tower base area spacing of the back microstructure region; based on the total tower base area spacing of each of the aforementioned back microstructure regions, a flatness evaluation index for each back microstructure region is generated.

2. The method according to claim 1, characterized in that, The tower base location feature parameters include at least one first tower base location; based on the tower base location feature parameters, the back microstructure image is divided into back microstructure regions to obtain multiple back microstructure regions, including: Based on the location of each of the first tower bases, the overall tower base area of ​​each of the first tower bases is located on the microstructure image on the back side; Based on the base type of the first base in the overall base region, each of the back microstructure regions is divided from the overall base region.

3. The method according to claim 1, characterized in that, The tower base distribution characteristic parameters include the number of tower bases; the step of generating a flatness evaluation index for each of the back microstructure regions based on the tower base distribution characteristic parameters includes: Obtain the total distribution of the tower base of the microstructure on the back of the battery under test; The ratio of the number of tower base distributions in each of the aforementioned back microstructure regions to the total number of tower base distributions is used as the flatness evaluation index for each of the aforementioned back microstructure regions.

4. The method according to claim 1, characterized in that, The step of performing flatness detection on the back microstructure of the battery under test based on the flatness evaluation index of each of the back microstructure regions includes: Obtain the total number of back microstructure regions that are common to all the back microstructure regions and the specified tower base component of the distribution of each back microstructure region in a specified tower base. By fusing the total amount of the back microstructure region and each of the specified tower base components, the total amount of the specified tower base of the back microstructure of the battery under test is obtained; and by fusing the total spacing of each of the tower base regions, the total spacing of the tower base structure of the back microstructure of the battery under test is obtained. The ratio between the total spacing of the tower base structure and the total number of the specified tower bases is used as the flatness coefficient of the microstructure on the back of the battery under test. The flatness of the microstructure on the back of the battery under test is detected based on the flatness coefficient.

5. A device for detecting the flatness of the microstructure on the back of a battery, characterized in that, The device includes: The acquisition module is used to acquire a microstructure image of the back side of the battery to be tested, and extract the tower base position feature parameters of the battery to be tested from the microstructure image of the back side, wherein the tower base position feature parameters are used to characterize the position of the tower base on the microstructure of the back side of the battery to be tested. The segmentation module is used to segment the back microstructure image into back microstructure regions based on the tower base location feature parameters, thereby obtaining multiple back microstructure regions. The generation module is used to generate a flatness evaluation index for each of the back microstructure regions based on the tower base distribution characteristic parameters of each of the back microstructure regions, wherein the tower base distribution characteristic parameters are used to characterize the tower base distribution on each of the back microstructure regions. The flatness detection module is used to detect the flatness of the back microstructure of the battery under test according to the flatness evaluation index of each of the back microstructure regions, wherein the tower base position feature parameter includes at least one second tower base position; the division module is further used for: Based on the location of each second tower base, local tower base regions of each second tower base are located and distributed on the back microstructure image; based on the regional distribution location of the second tower bases in the local tower base regions, regional location boundary information of the back microstructure of the battery to be detected is generated; based on the regional location boundary information, each back microstructure region is divided from the back microstructure image, wherein the tower base distribution feature parameters include at least one tower base identifier identifying the distribution of each back microstructure region; the generation module is further configured to: For any of the aforementioned back microstructure regions, the designated tower bases distributed in the back microstructure region are combined in pairs according to the identification order among the tower base identifiers to obtain the total tower base area spacing of the back microstructure region; based on the total tower base area spacing of each of the aforementioned back microstructure regions, a flatness evaluation index for each back microstructure region is generated.

6. The apparatus according to claim 5, characterized in that, The tower base location feature parameters include at least one first tower base location; the division module is further used for: Based on the location of each first tower base, locate the overall tower base region of each first tower base distributed on the back microstructure image; based on the tower base type of the first tower base in the overall tower base region, divide the back microstructure region from the overall tower base region.

7. The apparatus according to claim 5, characterized in that, The tower base distribution characteristic parameters include the number of tower bases; the generation module is also used for: Obtain the total number of base distributions in the microstructure on the back of the battery to be tested; use the ratio of the number of base distributions in each microstructure region on the back to the total number of base distributions as the flatness evaluation index for each microstructure region on the back.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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