A data analysis method, apparatus, device and medium

Abstract

The application discloses a data analysis method and device, equipment and medium, relates to the computer technical field, removes the background influence operation and intercepts the filter to the cyclic voltammetry curve data, obtains the intercept filter curve data, carries out data leveling to the cyclic voltammetry curve data, realizes leveling analysis, carries out range mean calculation to the intercept filter curve data, obtains high stage range mean and low stage range mean, calculates range mean difference value;The intercept filter curve data is carried out fitting straight line integral calculation, the cyclic voltammetry curve data is carried out integral calculation, obtains the cyclic voltammetry curve data integral, calculates integral difference value;The range mean difference value and integral difference value identifier are scanned, and the cyclic array sorting is carried out, the sorting index is obtained, the target cyclic voltammetry curve data is screened out, the analysis of the cyclic voltammetry curve data is realized, the data analysis accuracy and efficiency are improved, the data analysis scene diversity is increased, the calculation complexity of data analysis is reduced, and the time and resource loss are reduced.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a data analysis method, apparatus, device, and medium. Background Technology

[0002] SRECM (Super Resolution Electrochemical Microscopy) is an electrochemical instrument developed and manufactured by our research group. Compared with existing super-resolution electrochemical microscopes at home and abroad, it has a wider range of applications, can achieve full microscope mode, and can achieve lower spatiotemporal resolution. The data obtained from electrochemical experiments based on this instrument are more accurate and have a wider range than the experimental data obtained from other super-resolution electrochemical microscopes. However, some existing manual calculation data processing methods are not suitable for high-precision experimental results. For the existing method of manually calculating the charge integral, it is easy to make mistakes when the current is extremely small, and manual calculation is very complicated and time-consuming when there is a large amount of data to be calculated. For mechanical leveling, when using SRECM for experiments with a resolution of a few nanometers, it is impossible to ensure that the probe is absolutely parallel to the plane, and it is necessary to continue leveling after the measurement data. For the existing Euler angle conversion method, it is necessary to determine whether it is internal or external rotation based on the experimental results and then manually calculate it one by one, which is complicated and time-consuming.

[0003] As can be seen from the above, how to improve the accuracy and efficiency of data analysis, increase the diversity of data analysis scenarios, reduce the computational complexity of data analysis, and reduce time and resource consumption are problems that need to be solved in this field. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a data analysis method, apparatus, device, and medium that can improve the accuracy and efficiency of data analysis, increase the diversity of data analysis scenarios, reduce the computational complexity of data analysis, and reduce time and resource consumption. The specific solution is as follows:

[0005] Firstly, this application discloses a data analysis method, including:

[0006] Background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems are acquired. The cyclic voltammetry curve data are then subjected to background removal operation to obtain the cyclic voltammetry curve data after background removal. The cyclic voltammetry curve data after background removal is then truncated and filtered to obtain truncated and filtered curve data.

[0007] The coordinates of any three coordinates of the preset plane and the cyclic voltammetry curve data are used to calculate the leveling coordinates to obtain the leveled coordinates. The cyclic voltammetry curve data after removing the background influence is then leveled according to the leveled coordinates to obtain the leveled data, thereby realizing the leveling analysis of the cyclic voltammetry curve data.

[0008] The range mean is calculated for the extracted filtered curve data to obtain the range mean of the high stage and the range mean of the low stage, and the range mean difference between the range mean of the high stage and the range mean of the low stage is calculated.

[0009] The truncated filtered curve data is subjected to a fitting line integral calculation to obtain the fitting line integral, and the cyclic voltammetry curve data is subjected to an integral calculation to obtain the cyclic voltammetry curve data integral. The integral difference between the cyclic voltammetry curve data integral and the fitting line integral is calculated.

[0010] According to business requirements, the mean difference and integral difference of the range are scanned by identifiers, and the number of scans and the scan order are recorded. The scan order is sorted in a cyclic array based on the number of scans to obtain a sorting index. Target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

[0011] Optionally, the step of removing background interference from the cyclic voltammetry curve data to obtain the background-removed cyclic voltammetry curve data includes:

[0012] Based on the cyclic voltammetry curve data, the average background current and the average original current are calculated respectively to obtain the average background current and the average original current.

[0013] Calculate the difference between the average background current and the average original current, and use the cyclic voltammetry curve data and the difference to generate the cyclic voltammetry curve data after removing the background influence.

[0014] Optionally, the calculation of the background current average and the original current average based on the cyclic voltammetry curve data includes:

[0015] Determine whether the dimensions of the background data and the cyclic voltammetry curve data are consistent;

[0016] If the background data and the cyclic volt-ampere curve data have the same dimension, then the voltage data trend in the cyclic volt-ampere curve data is determined, and the cyclic volt-ampere curve data is segmented based on the voltage data trend to obtain the segmented cyclic volt-ampere curve data. The average background current and the average original current are calculated for the segmented cyclic volt-ampere curve data respectively.

[0017] If the dimensions of the background data and the cyclic voltammetry curve data are inconsistent, a warning message is generated and sent to the client.

[0018] Optionally, the step of truncating and filtering the cyclic voltammetry curve data after removing background influence to obtain truncated and filtered curve data includes:

[0019] Based on the voltage data trend in the cyclic voltammetry curve data, the cyclic voltammetry curve data after removing the background influence is truncated to obtain the truncated curve data.

[0020] The current values ​​in the extracted curve data are filtered to obtain the extracted filtered curve data.

[0021] Optionally, the step of calculating the range mean of the extracted filtered curve data to obtain the high-stage range mean and the low-stage range mean, and calculating the range mean difference between the high-stage range mean and the low-stage range mean, includes:

[0022] Based on the preset high-stage parameter truncation range and low-stage parameter truncation range, the truncation and filtering curve data is trunculated and the current value is denoised to obtain denoised current data. The range mean of the denoised current data is calculated to obtain the high-stage range mean and the low-stage range mean.

[0023] The difference between the mean of the higher-stage range and the mean of the lower-stage range is taken as the range mean difference.

[0024] Optionally, the step of performing a fitting line integral calculation on the truncated filtered curve data to obtain the fitting line integral includes:

[0025] The preset voltage range for fitting a straight line is input into the truncated filtered curve data to perform a straight line fitting, thereby obtaining a straight line function;

[0026] The preset integral voltage range is input into the linear function to obtain the integral of the fitted linear line.

[0027] Optionally, the step of performing identifier scanning on the mean difference of the range and the integral difference according to business requirements, recording the number of scans and the scan order, performing cyclic array sorting on the scan order based on the number of scans to obtain a sorting index, and filtering target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data includes:

[0028] The scanning arrangement type is determined based on business requirements; the scanning arrangement type includes horizontal scanning arrangement and vertical scanning arrangement.

[0029] According to the scanning arrangement type, the mean difference of the range and the integral difference are scanned by identifiers, and the number of scans and the scanning order are recorded. The scanning order is saved to the forward scanning order list, the scanning order is reversed, and the reversed scanning order is saved to the reverse scanning order list.

[0030] Based on the forward scan order list and the reverse scan order list, a cyclic array is sorted to obtain a sorting index, and target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data.

[0031] Secondly, this application discloses a data analysis device, comprising:

[0032] The curve data determination module is used to acquire background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems, remove background influence from the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data after removing background influence, and truncate and filter the cyclic voltammetry curve data after removing background influence to obtain truncate and filtered curve data.

[0033] The leveling analysis module is used to perform leveling coordinate calculations on any three coordinate information of a preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. The cyclic voltammetry curve data after removing background influence is leveled according to the leveled coordinate information to obtain leveled data, thereby realizing the leveling analysis of the cyclic voltammetry curve data.

[0034] The range mean difference calculation module is used to calculate the range mean of the truncated filtered curve data respectively to obtain the high-stage range mean and the low-stage range mean, and calculate the range mean difference between the high-stage range mean and the low-stage range mean.

[0035] The integral difference calculation module is used to perform a fitting line integral calculation operation on the intercepted filter curve data to obtain the fitting line integral, and to perform an integral calculation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data integral, and to calculate the integral difference between the cyclic voltammetry curve data integral and the fitting line integral.

[0036] The data analysis module is used to perform identifier scanning on the mean difference and integral difference of the range according to business requirements, record the number of scans and the scan order, sort the scan order in a cyclic array based on the number of scans to obtain a sorting index, and filter the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

[0037] Thirdly, this application discloses an electronic device, including:

[0038] Memory, used to store computer programs;

[0039] A processor is used to execute the computer program to implement the aforementioned data analysis method.

[0040] Fourthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed data analysis method.

[0041] As can be seen, this application provides a data analysis method, including acquiring background data and multiple sets of cyclic voltammetry curve data measured by a super-resolution electrochemical microscope system; removing background influence from the cyclic voltammetry curve data to obtain background-removed cyclic voltammetry curve data; truncating and filtering the background-removed cyclic voltammetry curve data to obtain truncated and filtered curve data; calculating leveling coordinates for any three coordinates of a preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information; performing data leveling on the background-removed cyclic voltammetry curve data according to the leveled coordinate information to obtain leveled data, thereby achieving leveling analysis of the cyclic voltammetry curve data; and calculating the range mean of the truncated and filtered curve data to obtain high... The system calculates the range mean difference between the high-stage range mean and the low-stage range mean; it performs a fitted line integral calculation on the truncated filtered curve data to obtain the fitted line integral, and performs an integral calculation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data integral, calculating the integral difference between the cyclic voltammetry curve data integral and the fitted line integral; it performs identifier scanning on the range mean difference and the integral difference according to business requirements, records the number of scans and the scan order, sorts the scan order in a cyclic array based on the number of scans to obtain a sorting index, and filters the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data to achieve the analysis of the cyclic voltammetry curve data. This application performs background removal operations on cyclic voltammetry (CVT) curve data to obtain background-free CVT curve data, thereby improving the accuracy of data analysis. It calculates and levels the coordinates of the CVT curve data and then levels the data according to the leveled coordinates, enabling leveling analysis of the CVT curve data. The background-free CVT curve data is then truncated and filtered, and the range mean is calculated for each truncated and filtered curve to determine the difference between the range mean of the high-stage and low-stage ranges. A fitted line integral is then calculated for the truncated and filtered curve data to determine the integral difference between the CVT curve data integral and the fitted line integral, improving data analysis efficiency. Finally, the range mean difference and integral difference are scanned to obtain a sorting index. Target CVT curve data corresponding to the sorting index is then selected from the CVT curve data to achieve CVT curve data analysis, increasing the diversity of data analysis scenarios, reducing computational complexity, and minimizing time and resource consumption. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0043] Figure 1 This is a flowchart of a data analysis method disclosed in this application;

[0044] Figure 2 This is a general flowchart of a data analysis process disclosed in this application;

[0045] Figure 3 This is a flowchart of another data analysis method disclosed in this application;

[0046] Figure 4 This is a software architecture diagram for data analysis disclosed in this application;

[0047] Figure 5 This application discloses an example of test data and background current.

[0048] Figure 6 This is a diagram showing the result of removing background current as disclosed in this application;

[0049] Figure 7 This is an example diagram illustrating a method for calculating the difference between average current values ​​disclosed in this application;

[0050] Figure 8 This is an example diagram of a line integral minus a linear integral disclosed in this application;

[0051] Figure 9 This is an example diagram of the difference calculation of cyclic voltammetry curves disclosed in this application;

[0052] Figure 10 This is a schematic diagram of the structure of a data analysis device disclosed in this application;

[0053] Figure 11 This application provides a structural diagram of an electronic device. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] SRECM is an electrochemical instrument developed and manufactured by our research group. Compared with existing super-resolution electrochemical microscopes at home and abroad, it has a wider range of applications, can achieve full microscope mode, and can achieve lower spatiotemporal resolution. The data obtained from electrochemical experiments using this instrument are more accurate and have a wider range than those obtained from other super-resolution electrochemical microscopes. However, some existing manual data processing methods are not suitable for high-precision experimental results. For existing methods of manually calculating charge integrals, manual calculation is prone to errors when the current is extremely small, and manual calculation is complex and time-consuming when there is a large amount of data to be calculated. For mechanical leveling, when using SRECM for experiments with a resolution of a few nanometers, it is impossible to ensure that the probe is perfectly parallel to the plane, and leveling is required after the measurement data. For existing Euler angle conversion methods, it is necessary to determine whether it is internal or external rotation based on the experimental results and then manually calculate it, which is complex and time-consuming. As can be seen from the above, how to improve the accuracy and efficiency of data analysis, increase the diversity of data analysis scenarios, reduce the computational complexity of data analysis, and reduce time and resource consumption are problems to be solved in this field.

[0056] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a data analysis method, which may specifically include:

[0057] Step S11: Acquire background data and multiple sets of cyclic voltammetry curve data measured by a super-resolution electrochemical microscope system; remove background influence from the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data after removing background influence; and truncate and filter the cyclic voltammetry curve data after removing background influence to obtain truncated and filtered curve data.

[0058] In this embodiment, background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems are acquired. Based on the cyclic voltammetry curve data, the average background current and the average original current are calculated to obtain the average background current and the average original current. The difference between the average background current and the average original current is calculated. The cyclic voltammetry curve data after removing the background influence is generated using the cyclic voltammetry curve data and the difference. The cyclic voltammetry curve data after removing the background influence is truncated and filtered to obtain truncated and filtered curve data.

[0059] The process for calculating the average background current and the average original current is as follows: First, determine if the dimensions of the background data and the cyclic volt-ampere curve data are consistent. If the dimensions are consistent, determine the voltage data trend in the cyclic volt-ampere curve data. Based on the voltage data trend, segment the cyclic volt-ampere curve data to obtain segmented cyclic volt-ampere curve data. Then, calculate the average background current and the average original current for the segmented cyclic volt-ampere curve data. If the dimensions are inconsistent, generate a warning message and send it to the client.

[0060] Specifically, the system imports background data and multiple sets of measured cyclic voltammetry curves. It determines if the dimensions of the background data and the cyclic voltammetry curves are the same. If they are different, a warning is issued and "Reference and data dimensions are different," requiring re-entry. If they are the same, the scan voltage and corresponding current of both are read separately. The voltage data trend is determined by judging the magnitude relationship between adjacent voltages. Then, the complete voltage data is read to segment the data according to the voltage increase and voltage decrease phases. Next, based on the cutting node index, it is determined whether the experimental data (i.e., the cyclic voltammetry curve data measured by the multiple sets of super-resolution electrochemical microscope systems) is the result of a complete scan. If it is a complete scan, the data is divided into two parts, with the voltage increase phase and the voltage decrease phase being of equal length. If it exceeds a complete scan, the data is divided into three parts. When the voltage data trend is first decreasing, the three parts are the decreasing-... The data length during the voltage increase-decrease sequence is greater than that during the voltage increase sequence. When the voltage trend is initially an increase, the three data segments are divided into increase-decrease-increase, with the data length during voltage increase being greater than that during voltage decrease. Then, the average background current is calculated. For a complete scan, the voltage values ​​during the increase and decrease phases are read separately, and the average background current during the increase and decrease phases at the same voltage is calculated. When the scan exceeds a complete cycle, only the first two segments of the decrease-increase-decrease or increase-decrease-increase sequence are used to calculate the average, avoiding a situation where the average of the three current values ​​is less than the average of the two current values. Then, the background current value is subtracted from the original current value at the corresponding voltage value, and the difference is restored to the order in which the data was read. Finally, the results are saved to obtain the cyclic voltammetry curve data after removing the background influence.

[0061] Step S12: Perform leveling coordinate calculations on any three coordinate information of the preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. Then, perform data leveling on the cyclic voltammetry curve data after removing background influence according to the leveled coordinate information to obtain leveled data, thereby realizing the leveling analysis of the cyclic voltammetry curve data.

[0062] In this embodiment, the specific leveling analysis steps are as follows: The plane equation is in the form of Ax + By + Cz + d = 0. Three points determine a plane. To obtain the specific function of the plane equation, three points are measured on the same level of the reference plane, and their coordinates A are input. xo A yo A zo B xo B yo B zo C xo C yo C zo Then calculate AC and BC using the following formula:

[0063] AC x =C xo -A xo ;

[0064] AC y =C yo -A yo ;

[0065] AC z =C zo -A zo ;

[0066] BC x =C xo -B xo ;

[0067] BC y =C yo -B yo ;

[0068] BC z =C zo -B zo ;

[0069] After obtaining AC and BC, the formula for calculating AB × AC is:

[0070] ABAC x =AC y *BC z -BC y *AC z ;

[0071] ABAC y =BC x *AC z -AC x *BC z ;

[0072] ABAC z =AC x *BCy -BC x *AC y ;

[0073] Then, use the result of AC×BC and the coordinates of point B to calculate b0;

[0074] b0 = -ABAC x *B xo -ABAC y *B yo -ABAC z *B zo ;

[0075] Then, for the angle θ between the reference plane and the sample plane, calculate cosθ and sinθ:

[0076]

[0077]

[0078] Next, input all measurement data x1, y1, z1, and find the point with the largest z-coordinate among all the data; use the coordinates of this point and AC×BC to calculate d. min :

[0079] d min =-z1*ABAC z -y1*ABAC y -x1*ABAC x -b0;

[0080] Then b0 and d min Adding them together gives b:

[0081] b = b0 + d min ;

[0082] Then, for each measurement, the new z0 coordinates are calculated using b, AC×BC, and their x and y coordinates:

[0083]

[0084] Finally, calculate the coordinates x2, y2, and z2 of the leveled point. For x2, use different expressions to calculate it when the x-coordinate of AC×BC takes different signs: x1, z1, cosθ, sinθ, and the new z0 coordinates.

[0085]

[0086] For y2, different expressions are used to calculate it when the ordinate of AC×BC takes different signs: y1, z1, cosθ, sinθ, and the new z0 coordinates.

[0087]

[0088] For z2, the calculation is performed using z1, cosθ, and the new z0 coordinates:

[0089] z2 = cosθ*(z0-z1);

[0090] Finally, the y2 obtained after leveling each measurement data point on the reference plane is saved.

[0091] Step S13: Calculate the range mean for the extracted filtered curve data to obtain the high-stage range mean and the low-stage range mean, and calculate the range mean difference between the high-stage range mean and the low-stage range mean.

[0092] In this embodiment, the filtered curve data is truncated and denoised based on a preset high-stage parameter truncation range and a low-stage parameter truncation range to obtain denoised current data. The range mean of the denoised current data is calculated to obtain the high-stage range mean and the low-stage range mean. The difference between the high-stage range mean and the low-stage range mean is taken as the range mean difference.

[0093] Specifically, the cyclic voltammetry curve data after background removal is imported, the scan voltage and corresponding current are read, and indices are recorded by judging the magnitude of adjacent voltages to extract the voltage increase phase from the data. The corresponding current data is also extracted based on the index, resulting in an overall shape similar to S. The current values ​​are then filtered. The filtered result will show an approximately stable shape in the lower left and upper right of the curve, yielding the extracted filtered curve data. Given the voltage range parameters low_left, low_right, high_left, and high_right for calculating the difference in the lower left and upper right, the filtered current... The data is truncated at corresponding positions. Then, noise is removed from the data in both stages. The first data point is judged as noise if it exceeds ±70% of the average value of the entire stage. Other data points are judged as noise if they exceed ±70% of the previous data point. If the previous data point is noise, the same criteria as the first data point are applied. The number of noise points in each stage is then counted. The average current value for each stage is calculated using the denoised current data. Finally, the average value of the high stage is subtracted from the average value of the low stage to obtain the final difference. Considering that not all experimenters using SRECM obtain cyclic voltammetry curves that are nearly S-shaped and stable in the lower left and upper right, and that some experimenters obtain skewed straight lines, an improved algorithm for calculating the difference in skewed stages is developed based on the above algorithm. The difference lies in the different noise reduction methods, as follows: For the obtained low_left to low_right and high_left to high_right data, firstly, a non-linear least squares method is used to fit the function, then the fitted current value is obtained by substituting it, and then noise is judged. When the current value exceeds ±70% of the fitted value, it is judged as noise. The subsequent process of calculating the mean and then the difference is the same as before.

[0094] Step S14: Perform a fitting line integral calculation on the intercepted filtered curve data to obtain the fitting line integral, and perform an integral calculation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data integral, and calculate the integral difference between the cyclic voltammetry curve data integral and the fitting line integral.

[0095] In this embodiment, the specific energy integration process is as follows: Import the cyclic volt-ampere curve data after removing background influences, read the scan voltage and corresponding current, find the stage of voltage increase, extract the voltage and corresponding current values ​​to obtain the extracted filtered curve data, input the voltage range of the fitted straight line, and extract the corresponding current according to the index of this range in the original voltage array; then perform straight line fitting to obtain a straight line function; then input the integration voltage interval, extract the corresponding voltage and current values ​​according to the index in the original array, and integrate along the given axis using the composite trapezoidal rule; then use the minimum and maximum values ​​of the integration interval to calculate the corresponding current value on the fitted straight line function, and calculate the area of ​​the trapezoid below the line; then use the calculated area below the curve minus the area of ​​the trapezoid below the line to obtain the final energy integration result (i.e., calculate the integration difference between the cyclic volt-ampere curve data integration and the fitted straight line integration).

[0096] Step S15: According to business requirements, perform identifier scanning on the mean difference of the range and the integral difference, and record the number of scans and the scanning order. Based on the number of scans, perform cyclic array sorting on the scanning order to obtain a sorting index. Filter the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

[0097] In this embodiment, the scanning arrangement type is determined according to business requirements; the scanning arrangement type includes horizontal scanning arrangement and vertical scanning arrangement; the range mean difference and the integral difference are scanned according to the scanning arrangement type, and the number of scans and the scanning order are recorded. The scanning order is saved to the forward scanning order list, the scanning order is reversed, and the reversed scanning order is saved to the reverse scanning order list; a cyclic array sort is performed based on the forward scanning order list and the reverse scanning order list to obtain a sorting index, and the target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data.

[0098] The specific scanning array sorting process is as follows: First, select whether to scan along the x-axis or the y-axis according to the experimental scanning method; then, according to the needs of different experimenters, read a set of data after batch calculation of the difference of cyclic voltammetry curves or batch calculation of energy integrals; read the string identifying the scanning position from each file name and save them sequentially in a list, named the file name list; find out how many data points are included in one scan according to the naming rules of the data; then calculate the total number of scans; construct two lists, list one (forward scan order list) stores the sequential results from 0 to the number of scans in one scan, and list two (reverse scan order list) stores the reverse order of list one; construct a loop, traversing from 0 to the total number of scans at intervals of 2, each time... When iterating through the scan count, both List 1 and List 2 are traversed once. When reading List 1 sequentially, the scan count plus the number in List 1 is used as the identifier of the scanned file. A new List 3 is created to store each identifier. When reading List 2 sequentially, the scan count + 1 plus the number in List 2 is used as the identifier of the scanned file. Each identifier is then stored in List 3. If the scan count + 1 exceeds the total number of scans, the loop is exited and List 2 is not traversed. For each identifier in List 3, its index in the filename list is found. Based on this index, the filename and corresponding data in the cyclic voltammetry curve data are read and stored in a new list. Finally, the filenames and corresponding data in the new list are concatenated and saved to obtain the target cyclic voltammetry curve data.

[0099] The general process structure of this application is as follows: Figure 2 As shown, the experimentally obtained cyclic voltammetry curve data were first subjected to background removal, then difference calculation and charge integration were performed. The results were then sorted according to the scanning method, and finally, the experimental data were leveled. The program directly implemented batch processing at each stage, and the trend of the cyclic voltammetry curve and the number of scans were taken into account during background removal, covering various cases such as voltage decreasing then increasing, increasing then decreasing then increasing, and increasing then decreasing then increasing, as well as cases with exactly one or more scans. Data corrected after background removal was used when calculating the difference of the cyclic voltammetry curves and the charge integration, resulting in more accurate results. Since the computer's default numerical sorting order cannot display the experimental data according to the scan array, the results after the difference of the cyclic voltammetry curves and the charge integration were programmed to sort according to the scanning method. During leveling, since mechanical leveling alone cannot guarantee absolute parallelism, geometric leveling is required to convert the measurement data into a formula to achieve leveling. Based on the measurement principle of the instrument, a software for geometric leveling was developed. The software takes into account the cases where the coordinate scale of the measurement point is extremely small and the angle between the reference plane and the sample plane takes different ranges. Different formulas are used to calculate and further reduce the error.

[0100] In this embodiment, background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems are acquired. Background interference is removed from the cyclic voltammetry curve data to obtain background-influence-free cyclic voltammetry curve data. The background-influence-free cyclic voltammetry curve data is then truncated and filtered to obtain truncated and filtered curve data. Leveling coordinates are calculated for any three coordinates of a preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. The background-influence-free cyclic voltammetry curve data is then leveled according to the leveled coordinate information to obtain leveled data, thus achieving leveling analysis of the cyclic voltammetry curve data. The range mean is calculated for the truncated and filtered curve data to obtain the high-stage range mean and... The low-stage range mean is calculated, and the difference between the range mean of the high-stage range and the low-stage range mean is calculated. A fitted line integral is calculated on the truncated filtered curve data to obtain the fitted line integral, and the cyclic voltammetry curve data is integrated to obtain the cyclic voltammetry curve data integral. The integral difference between the cyclic voltammetry curve data integral and the fitted line integral is calculated. According to business requirements, the range mean difference and the integral difference are scanned using identifiers, and the number of scans and the scan order are recorded. Based on the number of scans, the scan order is sorted in a cyclic array to obtain a sorting index. Target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data to achieve analysis of the cyclic voltammetry curve data. This application performs background removal operations on cyclic voltammetry (CVT) curve data to obtain background-free CVT curve data, thereby improving the accuracy of data analysis. It calculates and levels the coordinates of the CVT curve data and then levels the data according to the leveled coordinates, enabling leveling analysis of the CVT curve data. The background-free CVT curve data is then truncated and filtered, and the range mean is calculated for each truncated and filtered curve to determine the difference between the range mean of the high-stage and low-stage ranges. A fitted line integral is then calculated for the truncated and filtered curve data to determine the integral difference between the CVT curve data integral and the fitted line integral, improving data analysis efficiency. Finally, the range mean difference and integral difference are scanned to obtain a sorting index. Target CVT curve data corresponding to the sorting index is then selected from the CVT curve data to achieve CVT curve data analysis, increasing the diversity of data analysis scenarios, reducing computational complexity, and minimizing time and resource consumption.

[0101] See Figure 3 As shown in the figure, an embodiment of the present invention discloses a data analysis method, which may specifically include:

[0102] Step S21: Acquire background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems. Perform background removal operation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data after background removal. Based on the voltage data trend in the cyclic voltammetry curve data, truncate the cyclic voltammetry curve data after background removal to obtain truncated curve data. Filter the current value in the truncated curve data to obtain the truncated filtered curve data.

[0103] Step S22: Perform leveling coordinate calculations on any three coordinate information of the preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. Then, perform data leveling on the cyclic voltammetry curve data after removing background influence according to the leveled coordinate information to obtain leveled data, thereby realizing the leveling analysis of the cyclic voltammetry curve data.

[0104] Step S23: Calculate the range mean for the extracted filtered curve data to obtain the high-stage range mean and the low-stage range mean, and calculate the range mean difference between the high-stage range mean and the low-stage range mean.

[0105] Step S24: Input the preset fitting straight line voltage range into the intercepted filter curve data to perform straight line fitting to obtain a straight line function. Input the preset integral voltage range into the straight line function to obtain the fitting straight line integral. Perform integral calculation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data integral. Calculate the integral difference between the cyclic voltammetry curve data integral and the fitting straight line integral.

[0106] Step S25: According to business requirements, perform identifier scanning on the mean difference of the range and the integral difference, and record the number of scans and the scanning order. Based on the number of scans, perform cyclic array sorting on the scanning order to obtain a sorting index. Filter the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

[0107] Furthermore, the order of the three steps—difference of cyclic voltammetry curves, integral of charge, and balancing—in this application can be interchanged. Experimental data can be saved in different formats, such as txt files, which can be converted to XLSX files for further analysis. Besides cyclic voltammetry curve data, this method is also applicable to scenarios involving scanning linear voltammetry curves and current-time curves.

[0108] The software architecture or modular functionality of this application, such as Figure 4As shown, double-click the software, import a set of test data measured on the SRECM through the "Select Processing File" section, and import the background current data measured on the SRECM through the "Select Background Current" section. The code will then determine whether the dimension of the background current is the same as the dimension of the test data. If they are different, a warning box will pop up saying "The reference and data dimensions are different," and the program will output "The background current is different from the data dimension. Please select a background current of the same dimension" and end the program. Then you can re-enter the data. When the data is correct, the program will automatically determine whether the scanning voltage trend and scanning range are a complete circle. In different cases, the program will perform different processing, cutting the part with increased voltage and the part with decreased voltage. Whether it is a complete circle or a part that exceeds a circle, the data of the complete circle is used to calculate the average current of two cyclic voltammetry curves at a certain voltage. Then, the current value of the test data is subtracted from the calculated average value of the background current. After that, the data after cutting according to the voltage increase and decrease and removing the background are restored to the original sorting order, and the results are saved in a table. Figure 5 The curves in the diagram include test data and background current. Figure 6 The result after removing the background current shows that the curve trend becomes smoother from the sloping trend of the cyclic voltammetry curve data.

[0109] The data after background removal is read by entering the "data path", such as... Figure 7 As shown, this function calculates the difference between the average current value between high_left and high_right and the average current value between low_left and low_right. Input parameters "low_left", "low_right", "high_left", and "high_right", and send all these parameters into the main function to implement the specific functionality. First, it extracts the voltage increase portion from a complete set of data based on the magnitude relationship of adjacent voltages. Then, it smooths this portion of current data using the Savitzky-Golay filtering method. It extracts current values ​​from the low_left to low_right range and from the high_left to high_right range, and then performs noise reduction. Next, it averages the current values ​​corresponding to the denoised voltage range between low_left and low_right, and then averages the current values ​​corresponding to the denoised voltage range between high_left and high_right, subtracting the two to obtain the difference. Finally, it saves the filename of each set of data and the calculated difference to a table.

[0110] The software reads the background-removed data by inputting the "data path". When the software displays "Input Fitted Line Interval:", input "fitted interval min" and "fitted interval max". When the software displays "Please input integration interval:", input "integration interval min" and "integration interval max". A straight line is fitted using the data between the fitted interval min and the fitted interval max. The integral of the blue curve between the integrated interval min and the integrated interval max is calculated. Then, the area of ​​the trapezoid under the line within the integration interval is calculated. Subtracting the integral under the line from the line integral gives the result. Figure 8 The area of ​​the shaded region.

[0111] The results of the difference between the volt-ampere cycle curves or the results of the integral of the charge need to be arranged in the scanning order. First, choose whether to scan along the x-axis or the y-axis. (If scanning along the x-axis, assuming 21 data points are scanned at a time and a total of 21 scans are performed, the sorting would be 00, 01, ..., 020, 120, 119, ..., 10, 20, 21, ..., 220, 320, 319, ..., 1820, 1920, 1919, ..., 190, 200, ..., 2020; if scanning along the y-axis, assuming 21 data points are scanned at a time and a total of 10 scans are performed, the sorting would be 00, 10, ..., 200, 201, ..., 01, 02, 12, ..., 202, 203, 193, ..., 19, 09). Then, import the data using the "Select Processing File" option. Find and record the position identifier in the filename. Calculate how many data points are scanned at a time, record it as 'a', and then calculate the total number of scans, record it as 'b'. Next, a list is constructed to temporarily store the forward and reverse scan order of b data points scanned along the selected axis. Then, the sorting order of a scans is generated in a loop. Finally, the corresponding index is retrieved from the record position identifier list, the data under the corresponding index is read and the result of scanning along the selected axis is generated, and saved to a file.

[0112] For the step of subtracting cyclic voltammetry curves, the interval for calculating the mean of some experimental data tends to be stationary, but for other experimental data, the mean... Figure 9As shown, the data tends towards a sloping straight line. Therefore, for such data, we input "parameter low_left", "parameter low_right", "parameter high_left", and "parameter high_right". We then extract the current based on the range from low_left to low_right and the range from high_left to high_right, and perform noise reduction. During noise reduction, we first perform linear fitting on the data from low_left to low_right and high_left to high_right. Then, we determine noise points based on whether the original value exceeds ±70% of the fitted value. The calculation steps after noise reduction are the same as those when the mean interval tends to be stable.

[0113] This application proposes a complete data processing flow and converts the entire flow into code. The algorithm for the entire flow is written using the Python programming language to achieve batch automated processing of experimental data, replacing the manual calculation method. Compared with other experimental processing methods for electrochemical microscopy, this application proposes a flow for the SRECM instrument developed by the research group, which involves first removing the background, then performing a series of analyses, and then rearranging the experimental results according to the scanning order and leveling them. Furthermore, the leveling method is designed to be specific and suitable for the SRECM instrument. The technical effects are as follows: First, by taking into account the influence of microcurrents, background removal software is used to remove the background before data processing, ensuring the accuracy of subsequent processing. Second, it replaces manual calculation, realizing batch automated subtraction of volt-ampere cycle curves. Third, it enables batch calculation of energy integrals. To better allow experimenters to judge whether the linear fitting interval and integration interval are correct, a plotting function has been added to the software. Experimenters can input the fitting interval and integration interval based on the visualized volt-ampere cycle curve, making it more convenient and flexible. Fourth, in addition to mechanical leveling, geometric leveling is also performed after obtaining the measurement data. A leveling method suitable for SRECM is provided, and the influence of the calculation method when the angle between two planes is acute and obtuse is considered, making the leveled data more accurate. Finally, limited by the default number arrangement of computers, when plotting according to the scanning order, the software enables batch processing, quickly and accurately solving the problem.

[0114] In this embodiment, background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems are acquired. Background interference is removed from the cyclic voltammetry curve data to obtain background-influence-free cyclic voltammetry curve data. The background-influence-free cyclic voltammetry curve data is then truncated and filtered to obtain truncated and filtered curve data. Leveling coordinates are calculated for any three coordinates of a preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. The background-influence-free cyclic voltammetry curve data is then leveled according to the leveled coordinate information to obtain leveled data, thus achieving leveling analysis of the cyclic voltammetry curve data. The range mean is calculated for the truncated and filtered curve data to obtain the high-stage range mean and... The low-stage range mean is calculated, and the difference between the range mean of the high-stage range and the low-stage range mean is calculated. A fitted line integral is calculated on the truncated filtered curve data to obtain the fitted line integral, and the cyclic voltammetry curve data is integrated to obtain the cyclic voltammetry curve data integral. The integral difference between the cyclic voltammetry curve data integral and the fitted line integral is calculated. According to business requirements, the range mean difference and the integral difference are scanned using identifiers, and the number of scans and the scan order are recorded. Based on the number of scans, the scan order is sorted in a cyclic array to obtain a sorting index. Target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data to achieve analysis of the cyclic voltammetry curve data. This application performs background removal operations on cyclic voltammetry (CVT) curve data to obtain background-free CVT curve data, thereby improving the accuracy of data analysis. It calculates and levels the coordinates of the CVT curve data and then levels the data according to the leveled coordinates, enabling leveling analysis of the CVT curve data. The background-free CVT curve data is then truncated and filtered, and the range mean is calculated for each truncated and filtered curve to determine the difference between the range mean of the high-stage and low-stage ranges. A fitted line integral is then calculated for the truncated and filtered curve data to determine the integral difference between the CVT curve data integral and the fitted line integral, improving data analysis efficiency. Finally, the range mean difference and integral difference are scanned to obtain a sorting index. Target CVT curve data corresponding to the sorting index is then selected from the CVT curve data to achieve CVT curve data analysis, increasing the diversity of data analysis scenarios, reducing computational complexity, and minimizing time and resource consumption.

[0115] See Figure 10 As shown in the figure, an embodiment of the present invention discloses a data analysis device, which may specifically include:

[0116] The curve data determination module 11 is used to acquire background data and multiple sets of cyclic voltammetry curve data measured by a super-resolution electrochemical microscope system, perform background removal operation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data after background removal, and perform truncation filtering on the cyclic voltammetry curve data after background removal to obtain truncation filtered curve data.

[0117] The leveling analysis module 12 is used to perform leveling coordinate calculations on any three coordinate information of the preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information, and to perform data leveling on the cyclic voltammetry curve data after removing background influence according to the leveled coordinate information to obtain leveled data, so as to realize the leveling analysis of the cyclic voltammetry curve data.

[0118] The range mean difference calculation module 13 is used to calculate the range mean of the truncated filtered curve data respectively to obtain the high-stage range mean and the low-stage range mean, and calculate the range mean difference between the high-stage range mean and the low-stage range mean.

[0119] The integral difference calculation module 14 is used to perform a fitting line integral calculation operation on the intercepted filter curve data to obtain the fitting line integral, and to perform an integral calculation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data integral, and to calculate the integral difference between the cyclic voltammetry curve data integral and the fitting line integral.

[0120] The data analysis module 15 is used to perform identifier scanning on the mean difference of the range and the integral difference according to business requirements, record the number of scans and the scanning order, perform cyclic array sorting on the scanning order based on the number of scans to obtain a sorting index, and filter the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

[0121] In this embodiment, background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems are acquired. Background interference is removed from the cyclic voltammetry curve data to obtain background-influence-free cyclic voltammetry curve data. The background-influence-free cyclic voltammetry curve data is then truncated and filtered to obtain truncated and filtered curve data. Leveling coordinates are calculated for any three coordinates of a preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. The background-influence-free cyclic voltammetry curve data is then leveled according to the leveled coordinate information to obtain leveled data, thus achieving leveling analysis of the cyclic voltammetry curve data. The range mean is calculated for the truncated and filtered curve data to obtain the high-stage range mean and... The low-stage range mean is calculated, and the difference between the range mean of the high-stage range and the low-stage range mean is calculated. A fitted line integral is calculated on the truncated filtered curve data to obtain the fitted line integral, and the cyclic voltammetry curve data is integrated to obtain the cyclic voltammetry curve data integral. The integral difference between the cyclic voltammetry curve data integral and the fitted line integral is calculated. According to business requirements, the range mean difference and the integral difference are scanned using identifiers, and the number of scans and the scan order are recorded. Based on the number of scans, the scan order is sorted in a cyclic array to obtain a sorting index. Target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data to achieve analysis of the cyclic voltammetry curve data. This application performs background removal operations on cyclic voltammetry (CVT) curve data to obtain background-free CVT curve data, thereby improving the accuracy of data analysis. It calculates and levels the coordinates of the CVT curve data and then levels the data according to the leveled coordinates, enabling leveling analysis of the CVT curve data. The background-free CVT curve data is then truncated and filtered, and the range mean is calculated for each truncated and filtered curve to determine the difference between the range mean of the high-stage and low-stage ranges. A fitted line integral is then calculated for the truncated and filtered curve data to determine the integral difference between the CVT curve data integral and the fitted line integral, improving data analysis efficiency. Finally, the range mean difference and integral difference are scanned to obtain a sorting index. Target CVT curve data corresponding to the sorting index is then selected from the CVT curve data to achieve CVT curve data analysis, increasing the diversity of data analysis scenarios, reducing computational complexity, and minimizing time and resource consumption.

[0122] In some specific embodiments, the curve data determination module 11 may specifically include:

[0123] The average value calculation module is used to calculate the average background current and the average original current based on the cyclic voltammetry curve data, so as to obtain the average background current and the average original current.

[0124] The background removal module is used to calculate the difference between the average background current and the average original current, and to generate the cyclic voltammetry curve data after removing the background influence using the cyclic voltammetry curve data and the difference.

[0125] In some specific embodiments, the curve data determination module 11 may specifically include:

[0126] The judgment module is used to determine whether the dimensions of the background data and the cyclic voltammetry curve data are consistent;

[0127] The data segmentation module is used to determine the voltage data trend in the cyclic volt-ampere curve data if the background data and the cyclic volt-ampere curve data have the same dimension, and to segment the cyclic volt-ampere curve data based on the voltage data trend to obtain the segmented cyclic volt-ampere curve data. The module then performs background current average calculation and original current average calculation on the segmented cyclic volt-ampere curve data.

[0128] The warning module is used to generate a warning message and send the warning message to the client if the dimensions of the background data and the cyclic voltammetry curve data are inconsistent.

[0129] In some specific embodiments, the curve data determination module 11 may specifically include:

[0130] The truncating module is used to truncate the cyclic voltammetry curve data after removing the background influence based on the voltage data trend in the cyclic voltammetry curve data, so as to obtain truncated curve data.

[0131] The filtering module is used to filter the current values ​​in the truncated curve data to obtain the truncated filtered curve data.

[0132] In some specific embodiments, the range mean difference calculation module 13 may specifically include:

[0133] The range mean calculation module is used to perform range truncation and current value denoising operations on the truncated filter curve data based on the preset high-stage parameter truncation range and low-stage parameter truncation range to obtain denoised current data, and to calculate the range mean of the denoised current data to obtain the high-stage range mean and the low-stage range mean.

[0134] The range mean difference calculation module is used to take the difference between the high-stage range mean and the low-stage range mean as the range mean difference.

[0135] In some specific embodiments, the integral difference calculation module 14 may specifically include:

[0136] The linear fitting module is used to input a preset voltage range for fitting a straight line into the truncated filtered curve data to perform linear fitting and obtain a linear function.

[0137] The fitting line integral determination module is used to input a preset integration voltage range into the linear function to obtain the fitting line integral.

[0138] In some specific embodiments, the data analysis module 15 may specifically include:

[0139] The scanning arrangement type determination module is used to determine the scanning arrangement type according to business requirements; the scanning arrangement type includes horizontal scanning arrangement and vertical scanning arrangement.

[0140] The scanning order module is used to perform identifier scanning on the range mean difference and the integral difference according to the scanning arrangement type, record the number of scans and the scanning order, save the scanning order to the forward scanning order list, reverse the scanning order, and save the reversed scanning order to the reverse scanning order list.

[0141] The cyclic array sorting module is used to perform cyclic array sorting based on the forward scan order list and the reverse scan order list to obtain a sorting index, and to filter the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data.

[0142] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the data analysis method performed by the electronic device disclosed in any of the foregoing embodiments.

[0143] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0144] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0145] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. The operating system 221 can be Windows, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the data analysis method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the data analysis device from external devices, as well as data collected by its own input / output interface 25.

[0146] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0147] Furthermore, embodiments of this application also disclose a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the data analysis method steps disclosed in any of the foregoing embodiments.

[0148] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0149] The above provides a detailed description of the data analysis method, apparatus, device, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A data analysis method, characterized in that, include: Background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems are acquired. The cyclic voltammetry curve data are then subjected to background removal operation to obtain the cyclic voltammetry curve data after background removal. The cyclic voltammetry curve data after background removal is then truncated and filtered to obtain truncated and filtered curve data. The coordinates of any three coordinates of the preset plane and the cyclic voltammetry curve data are used to calculate the leveling coordinates to obtain the leveled coordinates. The cyclic voltammetry curve data after removing the background influence is then leveled according to the leveled coordinates to obtain the leveled data, thereby realizing the leveling analysis of the cyclic voltammetry curve data. The range mean is calculated for the extracted filtered curve data to obtain the range mean of the high stage and the range mean of the low stage, and the range mean difference between the range mean of the high stage and the range mean of the low stage is calculated. The truncated filtered curve data is subjected to a fitting line integral calculation to obtain the fitting line integral, and the cyclic voltammetry curve data is subjected to an integral calculation to obtain the cyclic voltammetry curve data integral. The integral difference between the cyclic voltammetry curve data integral and the fitting line integral is calculated. According to business requirements, the mean difference and integral difference of the range are scanned by identifiers, and the number of scans and the scan order are recorded. The scan order is sorted in a cyclic array based on the number of scans to obtain a sorting index. Target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

2. The data analysis method according to claim 1, characterized in that, The process of removing background interference from the cyclic voltammetry curve data to obtain background-removed cyclic voltammetry curve data includes: Based on the cyclic voltammetry curve data, the average background current and the average original current are calculated respectively to obtain the average background current and the average original current. Calculate the difference between the average background current and the average original current, and use the cyclic voltammetry curve data and the difference to generate the cyclic voltammetry curve data after removing the background influence.

3. The data analysis method according to claim 2, characterized in that, The calculation of the background current average and the original current average based on the cyclic voltammetry curve data includes: Determine whether the dimensions of the background data and the cyclic voltammetry curve data are consistent; If the background data and the cyclic volt-ampere curve data have the same dimension, then the voltage data trend in the cyclic volt-ampere curve data is determined, and the cyclic volt-ampere curve data is segmented based on the voltage data trend to obtain the segmented cyclic volt-ampere curve data. The average background current and the average original current are calculated for the segmented cyclic volt-ampere curve data respectively. If the dimensions of the background data and the cyclic voltammetry curve data are inconsistent, a warning message is generated and sent to the client.

4. The data analysis method according to claim 1, characterized in that, The step of truncating and filtering the cyclic voltammetry curve data after removing background influence to obtain truncated and filtered curve data includes: Based on the voltage data trend in the cyclic voltammetry curve data, the cyclic voltammetry curve data after removing the background influence is truncated to obtain the truncated curve data. The current values ​​in the extracted curve data are filtered to obtain the extracted filtered curve data.

5. The data analysis method according to claim 1, characterized in that, The step of calculating the range mean of the extracted filtered curve data to obtain the high-stage range mean and the low-stage range mean, and calculating the range mean difference between the high-stage range mean and the low-stage range mean, includes: Based on the preset high-stage parameter truncation range and low-stage parameter truncation range, the truncation and filtering curve data is trunculated and the current value is denoised to obtain denoised current data. The range mean of the denoised current data is calculated to obtain the high-stage range mean and the low-stage range mean. The difference between the mean of the higher-stage range and the mean of the lower-stage range is taken as the range mean difference.

6. The data analysis method according to claim 1, characterized in that, The step of performing a fitting line integral calculation on the truncated filtered curve data to obtain the fitting line integral includes: The preset voltage range for fitting a straight line is input into the truncated filtered curve data to perform a straight line fitting, thereby obtaining a straight line function; The preset integral voltage range is input into the linear function to obtain the integral of the fitted linear line.

7. The data analysis method according to any one of claims 1 to 6, characterized in that, The step of performing identifier scanning on the mean difference and integral difference of the range according to business requirements, recording the number of scans and the scan order, sorting the scan order in a cyclic array based on the number of scans to obtain a sorting index, and filtering the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data includes: The scanning arrangement type is determined based on business requirements; the scanning arrangement type includes horizontal scanning arrangement and vertical scanning arrangement. According to the scanning arrangement type, the range mean difference and the integral difference are scanned by identifiers, and the number of scans and the scanning order are recorded. The scanning order is saved to the forward scanning order list, the scanning order is reversed, and the reversed scanning order is saved to the reverse scanning order list. Based on the forward scan order list and the reverse scan order list, a cyclic array is sorted to obtain a sorting index, and target cyclic voltammetry curve data corresponding to the sorting index is selected from the cyclic voltammetry curve data.

8. A data analysis device, characterized in that, include: The curve data determination module is used to acquire background data and cyclic voltammetry curve data measured by multiple sets of super-resolution electrochemical microscopy systems, remove background influence from the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data after removing background influence, and truncate and filter the cyclic voltammetry curve data after removing background influence to obtain truncate and filtered curve data. The leveling analysis module is used to perform leveling coordinate calculations on any three coordinate information of a preset plane and the cyclic voltammetry curve data to obtain leveled coordinate information. The cyclic voltammetry curve data after removing background influence is leveled according to the leveled coordinate information to obtain leveled data, thereby realizing the leveling analysis of the cyclic voltammetry curve data. The range mean difference calculation module is used to calculate the range mean of the truncated filtered curve data respectively to obtain the high-stage range mean and the low-stage range mean, and calculate the range mean difference between the high-stage range mean and the low-stage range mean. The integral difference calculation module is used to perform a fitting line integral calculation operation on the intercepted filter curve data to obtain the fitting line integral, and to perform an integral calculation on the cyclic voltammetry curve data to obtain the cyclic voltammetry curve data integral, and to calculate the integral difference between the cyclic voltammetry curve data integral and the fitting line integral. The data analysis module is used to perform identifier scanning on the mean difference and integral difference of the range according to business requirements, record the number of scans and the scan order, sort the scan order in a cyclic array based on the number of scans to obtain a sorting index, and filter the target cyclic voltammetry curve data corresponding to the sorting index from the cyclic voltammetry curve data to realize the analysis of the cyclic voltammetry curve data.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the data analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the data analysis method as described in any one of claims 1 to 7.

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