A method, system and electronic device for detecting the surface area of ​​a supercapacitor electrode

Abstract

The embodiments of the present application relate to the field of image recognition. The present application discloses a method, system and electronic device for detecting the surface area of ​​a supercapacitor electrode. The method includes: performing multi-layer discrete wavelet decomposition on the collected image, and nonlinearly enhancing the high-frequency coefficients of each layer according to different decomposition levels; reconstructing and denoising the enhanced image using a soft threshold method, and the threshold parameter is determined according to the decomposition level and the standard deviation of the noise; constructing circular structural elements of multiple scales, corresponding to different aperture feature extractions, and using a Gaussian kernel to calculate the morphological gradient for each scale; using a local window to calculate statistical features and determine the threshold, and weighted fusion of features of multiple scales, with the weight coefficient decreasing as the aperture increases. Through the scheme of the present application, the recognizability of microporous and mesoporous structures in the image can be improved, and accurate non-destructive detection of the electrode surface area can be achieved.

Description

Technical Field

[0001] The present application relates to the field of image recognition, and in particular to a method, system and electronic equipment for detecting the surface area of ​​a supercapacitor electrode. Background Art

[0002] With the rapid development of new energy technologies, supercapacitors, as an important energy storage device, have attracted much attention due to their high power density and long cycle life. In the research and development of supercapacitors, the specific surface area of ​​electrode materials is one of the key parameters that determine their energy storage performance. Accurately measuring the effective specific surface area of ​​electrode materials is of great significance for evaluating and optimizing the performance of supercapacitors. At present, the industry and academia generally use gas adsorption method (BET method) and electrochemical method (cyclic voltammetry) to measure the surface area of ​​supercapacitor electrode materials. In the prior art, the BET method is based on the principle of physical adsorption of gas molecules on the surface of the material for measurement. This method calculates the specific surface area of ​​the material using the BET equation by measuring the adsorption amount of gas molecules under different relative pressures. This method has the advantages of a solid theoretical basis and high measurement accuracy. The cyclic voltammetry method is to apply a periodic potential scan on the surface of the electrode material to measure the double-layer capacitance formed at the electrode / electrolyte interface, and then convert it to the electrochemical active area of ​​the electrode material. This method can achieve in-situ measurement, which is closer to the electrode state under actual working conditions.

[0003] However, these traditional measurement methods have significant technical defects in practical applications: First, as far as the BET method is concerned, its biggest limitation is that the measurement process requires destructive sampling of the sample. In the sample preparation process, mechanical force and vacuum degassing often cause irreversible deformation or collapse of the porous structure of the electrode material. Especially for materials with microporous (pore size less than 2 nanometers) and mesoporous (pore size between 2-50 nanometers) structures, this structural damage is more serious. Due to the relatively low mechanical strength of flexible electrode materials, it is very easy to change the pore structure during sampling and pretreatment, resulting in a large deviation between the measured specific surface area value and the effective surface area under actual use. Secondly, as far as cyclic voltammetry is concerned, although in-situ characterization of electrode materials can be achieved, there are still many challenges in actual measurement. The most prominent problem is that there is a significant electrochemical polarization effect under high scan rate conditions (usually greater than 50 mV / s). This polarization effect hinders the full formation of the double electric layer at the electrode / electrolyte interface, resulting in the measured capacitance value being lower than the actual value. What is even more complicated is that for electrode materials with multi-level pore structures, there are significant differences in the diffusion dynamics of electrolyte ions in pores of different scales. Specifically, the transmission resistance of ions in micropores is the largest, followed by mesopores, and the transmission resistance in macropores is the smallest. This kinetic difference makes it impossible to fully utilize some pore structures within a limited measurement time, resulting in the measured surface area value being smaller than the actual specific surface area of ​​the material. In addition, existing measurement methods also have problems such as long test cycles, complex operations, and high costs. These technical defects not only affect the accuracy and reliability of the measurement results, but also to a certain extent restrict the research and development efficiency and industrialization process of supercapacitor electrode materials.

[0004] Therefore, there is an urgent need for a technical solution that can improve the identifiability of microporous and mesoporous structures in images and achieve accurate nondestructive detection of the electrode surface area. Summary of the invention

[0005] One purpose of the present application is to provide a method, system and electronic device for detecting the surface area of ​​a supercapacitor electrode, at least to solve technical problems such as structural damage in the sample preparation process in the prior art leading to inaccurate detection results.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In a first aspect, some embodiments of the present application provide a method for detecting the surface area of ​​a supercapacitor electrode, comprising: positioning a supercapacitor electrode sample through a microscope stage, and using low-magnification and high-magnification microscopes for coarse adjustment and precise focusing in turn; using a ring lighting system to capture images of the sample at multiple preset lighting angles, and controlling the image brightness histogram peak by adjusting the exposure time; performing multi-layer discrete wavelet decomposition on the captured image, and performing nonlinear enhancement on the high-frequency coefficients of each layer according to different decomposition levels; reconstructing and denoising the enhanced image using a soft threshold method, and determining the threshold parameter based on the decomposition level and the noise standard deviation; constructing multiple circular structural elements of different scales, extracting features corresponding to different apertures, and using a Gaussian kernel to calculate the morphological gradient for each scale; using a local window to calculate statistical features and determine the threshold, and performing weighted fusion on features of multiple scales, and the weight coefficient decreases as the aperture increases.

[0008] In a possible implementation, the supercapacitor electrode sample is positioned by a microscope stage, and low-magnification and high-magnification microscopes are used in turn for coarse adjustment and precise focusing, including: placing the supercapacitor electrode sample at the center of the microscope stage; positioning the sample by a displacement device of the microscope; and using low-magnification and high-magnification microscopes in turn for coarse adjustment and precise focusing of the sample.

[0009] In a possible implementation, an annular lighting system is used to capture images of the sample at multiple preset lighting angles, and the peak value of the image brightness histogram is controlled by adjusting the exposure time, including: setting multiple light-emitting units in the annular lighting system to be evenly distributed along a circle, and adjusting the inclination angle and irradiation distance of the light-emitting units; capturing images of the sample at multiple different lighting angles; adjusting the exposure time of the image capture so that the peak value of the image brightness histogram is within a preset grayscale value range; and performing multiple image captures under each lighting condition for superposition and noise reduction.

[0010] In a possible implementation, the collected image is subjected to multi-layer discrete wavelet decomposition, and the high-frequency coefficients of each layer are nonlinearly enhanced according to different decomposition levels, including: performing multi-layer discrete wavelet decomposition on the collected original image to obtain low-frequency components and high-frequency components in multiple directions; including: ,in, Representation characteristics No. Layer discrete wavelet transform, Indicates The low-frequency component of the layer, Indicates The high-frequency component in the horizontal direction of the layer, Indicates The high frequency component in the vertical direction of the layer, Indicates The high-frequency components in the diagonal direction of the layer are enhanced; the high-frequency coefficients of each layer are nonlinearly enhanced according to different decomposition levels; when the absolute value of the coefficient exceeds the preset threshold, the amplitude is limited to avoid numerical overflow.

[0011] In a possible implementation, the enhanced image is reconstructed and denoised using a soft threshold method, and the threshold parameter is determined based on the decomposition level and the noise standard deviation, including: reconstructing and denoising the image using a soft threshold method; determining the soft threshold parameter based on the decomposition level; and calculating the noise standard deviation through the absolute deviation of the intermediate value of the high-frequency sub-band.

[0012] In a possible implementation, multiple circular structural elements of different scales are constructed, corresponding to different pore feature extractions, and a Gaussian kernel is used to calculate the morphological gradient for each scale, including: constructing multiple circular structural elements of different scales, corresponding to micropore, mesopore and macropore feature extractions respectively; calculating the morphological gradient for each scale, and processing it with a Gaussian kernel, wherein Gaussian kernel parameters adapted to the scale of the structural element are used.

[0013] In a possible implementation, a local window is used to calculate statistical features and determine a threshold, and features of multiple scales are weighted and fused, and the weight coefficient decreases as the aperture increases, including: using a local window to calculate local statistical features of the image; calculating a local threshold according to preset parameters; including: ,in, represents the local threshold, represents the local mean, Represents the morphological gradient value; weighted fusion is performed on features of multiple scales, and the weight coefficient decreases in sequence according to the aperture size.

[0014] In a possible implementation, nonlinear enhancement processing is performed on high-frequency coefficients of each layer according to different decomposition levels, including: ,in, represents the enhanced coefficient, represents the original high-frequency coefficients, represents the symbolic function, represents the power parameter, represents the gain factor, Represents the threshold parameter.

[0015] In the second aspect, the embodiments of the present application also provide a supercapacitor electrode surface area detection system for implementing the detection method as described in any of the above embodiments, the system comprising: a sampling unit and a computing unit; wherein the sampling unit is used to position the supercapacitor electrode sample through a microscope stage, and use low-magnification and high-magnification microscopes for coarse adjustment and precise focusing in turn; a ring lighting system is used to capture images of the sample at multiple preset illumination angles, and the image brightness histogram peak is controlled by adjusting the exposure time; the computing unit is used to perform multi-layer discrete wavelet decomposition on the captured image, and nonlinearly enhance the high-frequency coefficients of each layer according to different decomposition levels; the enhanced image is reconstructed and denoised using a soft threshold method, and the threshold parameter is determined according to the decomposition level and the noise standard deviation; multiple circular structure elements of different scales are constructed, corresponding to different aperture feature extractions, and a Gaussian kernel is used to calculate the morphological gradient for each scale; a local window is used to calculate statistical features and determine thresholds, and features of multiple scales are weighted fused, and the weight coefficient decreases as the aperture increases.

[0016] In the third aspect, an embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute the detection method described in any one of the above embodiments.

[0017] Compared with the prior art, the present application provides a method, system and electronic device for detecting the surface area of ​​a supercapacitor electrode. Through an improved wavelet transform method, the exponential term in the nonlinear enhancement function can adaptively adjust the degree of enhancement according to the coefficient amplitude, thereby avoiding the suppression of weak features by the traditional linear enhancement method. At the same time, the image features of different scales corresponding to micropores, mesopores and macropores can be processed separately through multi-scale decomposition. Furthermore, in some embodiments, by using morphological gradient information to guide the selection of thresholds, the defect of the traditional global threshold method being insensitive to local features is overcome. The exponential term in the adaptive threshold function can be dynamically adjusted according to the local gradient intensity to achieve accurate segmentation of pore structures of different scales. The multi-scale feature fusion strategy ensures the integrity and accuracy of the segmentation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0019] Figure 1 A schematic diagram of a process for detecting the surface area of ​​a supercapacitor electrode provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a module of a supercapacitor electrode surface area detection system provided in an embodiment of the present application;

[0021] Figure 3 An exemplary structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0023] Those skilled in the art can understand that the terms "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor represent the necessary logical order between them. It should also be understood that in the embodiments of the present disclosure, "multiple" can refer to two or more, and "at least one" can refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are an "or" relationship. It should also be understood that the description of each embodiment in the present disclosure emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one.

[0024] At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered part of the specification. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0025] 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.

[0026] First embodiment

[0027] Figure 1 A flow chart of a method for detecting the surface area of ​​a supercapacitor electrode provided in an embodiment of the present application. It should be understood that the surface area of ​​supercapacitor electrodes is currently mainly detected by gas adsorption method (BET method) and electrochemical method (cyclic voltammetry). These methods have specific technical difficulties in practical applications: (1) The BET method requires destructive sampling, which causes the porous structure of the electrode material to deform or collapse during the sampling process, affecting the measurement accuracy. Specifically, the micropore (<2nm) and mesopore (2-50nm) structures are prone to irreversible deformation during sample preparation, resulting in deviations in the surface area measurement value. (2) Although cyclic voltammetry can be used for in-situ measurement, there is an electrochemical polarization effect at a high scanning rate, resulting in the failure to fully form a double layer, making the measured surface area smaller than the actual value. At the same time, for electrode materials with complex multi-level pore structures, the diffusion kinetics of electrolyte ions in pores of different scales are significantly different, resulting in deviations between the measurement results and the actual specific surface area.

[0028] Based on the above situation, the current detection method has the following specific technical problems: (1) The polarization effect and ion diffusion dynamics differences in the electrochemical measurement process lead to distortion of the surface area measurement value. Specifically, under high scanning rate conditions (>50mV / s), the double electric layer at the electrode / electrolyte interface fails to form completely, resulting in the measured capacitance value being lower than the actual value; at the same time, the ion transport resistance in pores of different scales is significantly different (micropores>mesopores>macropores), making it impossible for the measurement results to accurately reflect the entire effective surface area. (2) Structural damage problems during sample preparation lead to inaccurate test results. During BET test sampling, mechanical force and vacuum degassing process will cause the original pore structure to deform or collapse. Especially for flexible electrode materials, this structural damage is more serious.

[0029] In view of the above problems, the present application discloses the following contents: Figure 1As shown, in step S101, the supercapacitor electrode sample is positioned by a microscope stage, and low-magnification and high-magnification microscopes are used in turn for coarse adjustment and precise focusing. This includes: placing the supercapacitor electrode sample at the center of the microscope stage; positioning the sample by a displacement device of the microscope; and coarse adjustment and precise focusing of the sample by low-magnification and high-magnification microscopes in turn.

[0030] In one embodiment, the image acquisition system may be composed of a portable digital microscope (AmScopeMD500 model is recommended, with a resolution of 2592×1944 pixels) and a ring LED lighting system. The ring lighting system uses 8 independently controllable LED light sources (color temperature 5500K±200K, luminous flux 85±5 lumens each), which are evenly distributed along the circumference, with an inclination angle set to 32.7° and a distance from the sample surface maintained at 47.3 mm.

[0031] In step S102, an annular lighting system is used to capture images of the sample at multiple preset illumination angles, and the peak value of the image brightness histogram is controlled by adjusting the exposure time. This includes: setting multiple light-emitting units in the annular lighting system to be evenly distributed along the circumference, and adjusting the inclination angle and illumination distance of the light-emitting units; capturing images of the sample at multiple different illumination angles; adjusting the exposure time of the image capture so that the peak value of the image brightness histogram is within the preset grayscale value range; and performing multiple image captures under each illumination condition for superposition and noise reduction.

[0032] Specifically, during image acquisition, the supercapacitor electrode sample (size not exceeding 20mm×20mm) is placed in the center of the microscope stage and accurately positioned using the XY displacement device attached to the microscope. First, coarse focus is performed at 100x magnification, and then switched to 500x magnification for precise focus. Images are collected at 8 different illumination angles, and the exposure time is set to 2.73ms to ensure that the peak of the image brightness histogram is within the grayscale value range of 128±15. Five images are collected under each illumination condition for subsequent superposition and noise reduction.

[0033] In step S103, the collected image is subjected to multi-layer discrete wavelet decomposition, and the high-frequency coefficients of each layer are nonlinearly enhanced according to different decomposition levels. This includes: performing multi-layer discrete wavelet decomposition on the collected original image to obtain low-frequency components and high-frequency components in multiple directions; including: ,in, Representation characteristics No. Layer discrete wavelet transform, Indicates The low-frequency component of the layer, Indicates The high-frequency component in the horizontal direction of the layer, Indicates The high frequency component in the vertical direction of the layer, Indicates The high-frequency components in the diagonal direction of the layer are enhanced; the high-frequency coefficients of each layer are nonlinearly enhanced according to different decomposition levels; when the absolute value of the coefficient exceeds the preset threshold, the amplitude is limited to avoid numerical overflow.

[0034] Furthermore, the high frequency coefficients of each layer are subjected to nonlinear enhancement processing according to different decomposition levels, including: ,in, represents the enhanced coefficient, represents the original high-frequency coefficients, represents the symbolic function, represents the power parameter, represents the gain factor, Represents the threshold parameter.

[0035] Specifically, the collected original image is preprocessed and enhanced. First, a 4-layer discrete wavelet decomposition is performed, using the Daubechies-4 wavelet basis function. (x,y), the k-th level decomposition can be expressed as: in is the low frequency component, Represents the high frequency components in the horizontal, vertical and diagonal directions respectively.

[0036] The high frequency coefficients of each layer are subjected to nonlinear enhancement processing, and the parameters in the enhancement function are adaptively adjusted according to different decomposition levels. In one embodiment:

[0037] First layer: α_1 = 1.37, β_1 = 0.82, T_1 = 23.6

[0038] Second layer: α_2 = 1.25, β_2 = 0.93, T_2 = 18.4

[0039] Third layer: α_3 = 1.12, β_3 = 1.05, T_3 = 14.2

[0040] Fourth layer: α_4 = 0.95, β_4 = 1.18, T_4 = 11.7

[0041] For each layer's high frequency coefficients , the enhanced coefficient The calculation is as follows: It should be noted that numerical overflow should be avoided during the processing. When the value exceeds the threshold (recommended to be 0.85 times the image dynamic range), amplitude limitation is required.

[0042] At step S104, the enhanced image is reconstructed and denoised using a soft threshold method, and the threshold parameter is determined according to the decomposition level and the noise standard deviation. This includes: reconstructing and denoising the image using a soft threshold method; determining the soft threshold parameter according to the decomposition level; and calculating the noise standard deviation through the absolute deviation of the intermediate value of the high-frequency sub-band.

[0043] Specifically, the soft threshold method can be used to suppress noise during image reconstruction. The decomposition level k is determined according to the following relationship: ,in is the noise standard deviation (which can be obtained by The absolute deviation of the subband median is estimated by dividing by 0.6745), where N is the total number of image pixels.

[0044] In step S105, a plurality of circular structural elements of different scales are constructed, corresponding to the extraction of features of different apertures, and a Gaussian kernel is used to calculate the morphological gradient for each scale. This includes: constructing a plurality of circular structural elements of different scales, corresponding to the extraction of features of micropores, mesopores and macropores, respectively; calculating the morphological gradient for each scale, and processing it with a Gaussian kernel, wherein a Gaussian kernel parameter adapted to the scale of the structural element is used.

[0045] It should be noted that multi-scale morphological gradient calculation and adaptive threshold segmentation are performed based on the enhanced image. First, three circular structural elements of different scales are constructed:

[0046] B_1: radius 2.3 pixels, corresponding to micropore feature extraction

[0047] B_2: radius 5.7 pixels, corresponding to mesopore feature extraction

[0048] B_3: radius 12.4 pixels, corresponding to macro hole feature extraction

[0049] For each scale s, the morphological gradient is calculated: , where the Gaussian kernel parameters σ are adapted to the scale of the structure element: σ_1 = 0.92, σ_2 = 1.84, σ_3 = 3.26.

[0050] In step S106, a local window is used to calculate statistical features and determine a threshold, and weighted fusion is performed on features of multiple scales, with the weight coefficient decreasing as the aperture increases. This includes: using a local window to calculate local statistical features of the image; calculating a local threshold according to preset parameters; including: ,in, represents the local threshold, represents the local mean, Represents the morphological gradient value; weighted fusion is performed on features of multiple scales, and the weight coefficient decreases in sequence according to the aperture size.

[0051] In one embodiment, the adaptive threshold function uses a local window of 17×17 pixels to calculate local statistical features. The threshold function parameters are set to: γ = 0.437, λ = 42.8. The local threshold calculation formula is: , when performing multi-scale feature fusion, the weight coefficient of each scale is set as follows:

[0052] w_1 = 0.452 (micropore feature)

[0053] w_2 = 0.328 (mesoporous characteristics)

[0054] w_3 = 0.220 (macro hole feature)

[0055] In addition, in actual applications, the ambient temperature should be maintained at 23±2℃, and the relative humidity should be controlled in the range of 45% to 65% to avoid airflow disturbance. The electrode surface should be kept clean and dry, and dust-free airflow (pressure 0.2MPa) can be used to gently blow away surface dust. For samples with strong porosity, it is recommended to stand for 1.5 hours in an environment with a vacuum degree of 1.33Pa before processing. The microscope should be calibrated before each test, and the spatial resolution should be calibrated using a standard grating (spacing 2μm), and the grayscale should be calibrated using an 18% grayscale standard card. The quality of the collected original image is evaluated to ensure that the clarity evaluation index MTF50 is greater than 0.35 and the signal-to-noise ratio is not less than 32dB. If the image quality does not meet the standard, it needs to be re-collected.

[0056] In an implementation scenario, taking a typical activated carbon electrode material as an example, the electrode sheet size is 15mm×15mm and the thickness is about 0.28mm. The specific operation process is as follows:

[0057] First, place the sample on the microscope stage and adjust the XY displacement so that the observation area is located at the center of the electrode. Set the microscope magnification to 500 times and focus by fine-tuning the hand wheel until a clear surface morphology is observed.

[0058] Turn on 8 LED light sources in sequence and collect 5 images under each lighting condition. The image acquisition parameters are set as follows: resolution 2592×1944 pixels, exposure time 2.73ms, gain value 1.2. The 5 images under the same lighting condition are superimposed and averaged to obtain 8 basic images after noise suppression.

[0059] The eight basic images are input into the wavelet transform preprocessing module. The Daubechies-4 wavelet basis is used to perform 4-layer decomposition, and the high-frequency coefficients of each layer are nonlinearly enhanced. For example, the high-frequency coefficients of the first layer , the enhancement formula is: When reconstructing the image, the noise threshold λ_1 is calculated to be about 12.4 (assuming the image size is 2592×1944, the estimated noise standard deviation σ = 2.8). After the image is reconstructed and enhanced, it can be clearly observed that the details of the surface microstructure are well preserved and enhanced.

[0060] Next, morphological gradient calculation and adaptive threshold segmentation are performed. Taking micropore feature extraction as an example, a circular structure element with a radius of 2.3 pixels is used to calculate the morphological gradient, and then the adaptive threshold is calculated in a local window of 17×17 pixels: , and finally perform multi-scale feature fusion to obtain the complete segmentation result: .

[0061] Through the above steps, the distribution of pore structures of different scales on the electrode surface can be obtained, and then the effective surface area of ​​the electrode can be calculated. The whole detection process takes about 15 minutes, the operation is simple, and the results are reliable. The enhancement of different scale features of the electrode surface image is achieved through the improved wavelet transform method. The exponential term in the nonlinear enhancement function can adaptively adjust the degree of enhancement according to the coefficient amplitude, avoiding the suppression of weak features by the traditional linear enhancement method. At the same time, the image features of different scales corresponding to micropores, mesopores and macropores can be processed separately through multi-scale decomposition. At the same time, the selection of thresholds is guided by morphological gradient information, which overcomes the defect of the traditional global threshold method that is insensitive to local features. The exponential term in the adaptive threshold function can be dynamically adjusted according to the local gradient intensity to achieve accurate segmentation of pore structures of different scales. The multi-scale feature fusion strategy ensures the integrity and accuracy of the segmentation results.

[0062] The step division of the above methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this application; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the protection scope of this patent.

[0063] Second embodiment

[0064] Figure 2 Schematic diagram of a module of a supercapacitor electrode surface area detection system provided in an embodiment of the present application. A supercapacitor electrode surface area detection system provided in this embodiment is used to implement the detection method of the above embodiment, such as Figure 2As shown, the system includes: a sampling unit 201 and a computing unit 202; wherein the sampling unit 201 is used to position the supercapacitor electrode sample through a microscope stage, and use low-magnification and high-magnification microscopes to perform coarse adjustment and precise focusing in turn; a ring lighting system is used to collect images of the sample at multiple preset illumination angles, and the image brightness histogram peak is controlled by adjusting the exposure time; the computing unit 202 is used to perform multi-layer discrete wavelet decomposition on the collected image, and nonlinearly enhance the high-frequency coefficients of each layer according to different decomposition levels; the enhanced image is reconstructed and denoised using a soft threshold method, and the threshold parameter is determined according to the decomposition level and the noise standard deviation; multiple circular structural elements of different scales are constructed, corresponding to different aperture feature extractions, and a Gaussian kernel is used to calculate the morphological gradient of each scale; a local window is used to calculate statistical features and determine thresholds, and features of multiple scales are weighted fused, and the weight coefficient decreases as the aperture increases.

[0065] It is not difficult to find that this embodiment is a system embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.

[0066] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0067] Third embodiment

[0068] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0069] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the detection method provided in any one or more of the above embodiments. Figure 3 An exemplary structural diagram of the electronic device is disclosed. Figure 3As shown, the electronic device includes: one or more processors 1101, a memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0070] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0071] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0072] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0073] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the detection method provided in any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0074] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0075] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0076] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0078] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0079] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. For example, an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device may be used to implement the embodiments. In some embodiments, the software program of the present application may be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) may be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present application may be implemented by hardware, for example, as a circuit that cooperates with a processor to perform various steps or functions.

[0080] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0081] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0082] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0083] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for detecting the surface area of ​​a supercapacitor electrode, characterized in that: include: The supercapacitor electrode sample is positioned using a microscope stage, and low-magnification and high-magnification microscopes are used in turn for coarse adjustment and precise focusing; Using a ring lighting system to collect images of the sample at multiple preset lighting angles, and controlling the peak value of the image brightness histogram by adjusting the exposure time; Perform multi-layer discrete wavelet decomposition on the collected image, and perform nonlinear enhancement on the high-frequency coefficients of each layer according to different decomposition levels; The method includes: performing multi-layer discrete wavelet decomposition on the collected original image to obtain low-frequency components and high-frequency components in multiple directions; the method includes: ,in, Representation characteristics No. Layer discrete wavelet transform, Indicates The high frequency component in the vertical direction of the layer, Indicates The high-frequency components in the diagonal direction of each layer are processed; the high-frequency coefficients of each layer are nonlinearly enhanced according to different decomposition levels; when the absolute value of the coefficient exceeds the preset threshold, the amplitude is limited to avoid numerical overflow; wherein, the high-frequency coefficients of each layer are nonlinearly enhanced according to different decomposition levels, including: ,in, represents the enhanced coefficient, represents the original high-frequency coefficients, represents the symbolic function, represents the power parameter, represents the gain factor, represents the threshold parameter; The enhanced image is reconstructed and denoised using the soft threshold method, and the threshold parameter is determined according to the decomposition level and the noise standard deviation; Construct multiple circular structural elements of different scales, corresponding to different aperture feature extractions, and use Gaussian kernel to calculate the morphological gradient at each scale; A local window is used to calculate statistical features and determine the threshold, and features of multiple scales are weighted fused, with the weight coefficient decreasing as the aperture increases.

2. The detection method according to claim 1, characterized in that: in, The supercapacitor electrode sample is positioned by the microscope stage, and low-magnification and high-magnification microscopes are used for coarse adjustment and fine focusing, including: Placing the supercapacitor electrode sample at the center of the microscope stage; Positioning the sample by means of a displacement device of the microscope; The sample is coarsely focused and finely focused using a low-magnification microscope and a high-magnification microscope, respectively.

3. The detection method according to claim 1, characterized in that: in, The sample is imaged at a plurality of preset illumination angles using a ring illumination system, and the image brightness histogram peak is controlled by adjusting the exposure time, including: Arrange multiple light-emitting units in the annular lighting system to be evenly distributed along the circumference, and adjust the inclination angle and irradiation distance of the light-emitting units; Collecting images of the sample at multiple different illumination angles; Adjusting the exposure time of the image acquisition so that the peak value of the image brightness histogram is within a preset grayscale value range; Multiple images were acquired under each illumination condition for superposition and noise reduction.

4. The detection method according to claim 1, characterized in that: in, The enhanced image is reconstructed and denoised using a soft threshold method, wherein the threshold parameter is determined according to the decomposition level and the noise standard deviation, including: Reconstructing and denoising the image using a soft threshold method; Determine the soft threshold parameter according to the decomposition level; The noise standard deviation is calculated by the absolute deviation from the median value of the high frequency subband.

5. The detection method according to claim 1, characterized in that: in, Construct multiple circular structural elements of different scales, corresponding to different aperture feature extraction, and use Gaussian kernel to calculate the morphological gradient of each scale, including: Construct multiple circular structural elements of different scales, corresponding to the extraction of micropores, mesopores and macropores; The morphological gradient is calculated for each scale and processed using a Gaussian kernel with parameters appropriate to the scale of the structural element.

6. The detection method according to claim 1, characterized in that: in, The local window is used to calculate the statistical features and determine the threshold, and the features of multiple scales are weighted and fused. The weight coefficient decreases as the aperture increases, including: The local statistical features of the image are calculated using a local window; Calculates a local threshold based on preset parameters; these include: ,in, represents the local threshold, represents the local mean, Represents the morphological gradient value; The features of multiple scales are weightedly fused, and the weight coefficients decrease in sequence according to the aperture size.

7. A supercapacitor electrode surface area detection system, used to implement the detection method according to any one of claims 1 to 6, characterized in that: The system comprises: a sampling unit and a calculation unit; wherein, The sampling unit is used to position the supercapacitor electrode sample through a microscope stage, and use low-magnification and high-magnification microscopes to perform coarse adjustment and precise focusing in turn; use a ring lighting system to collect images of the sample at multiple preset lighting angles, and control the image brightness histogram peak by adjusting the exposure time; The computing unit is used to perform multi-layer discrete wavelet decomposition on the collected image, and nonlinearly enhance the high-frequency coefficients of each layer according to different decomposition levels; reconstruct and reduce the noise of the enhanced image using a soft threshold method, and the threshold parameter is determined according to the decomposition level and the noise standard deviation; construct a plurality of circular structural elements of different scales, extract corresponding features of different apertures, and use a Gaussian kernel to calculate the morphological gradient of each scale; use a local window to calculate statistical features and determine the threshold, and perform weighted fusion on features of multiple scales, and the weight coefficient decreases as the aperture increases.

8. An electronic device, characterized in that: include: A processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, and the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the detection method according to any one of claims 1 to 6.

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