Rapid quantitative detection method and system for lithium precipitation, storage medium and program product

Abstract

The invention discloses a lithium precipitation rapid quantitative detection method and system, a storage medium and a program product, and relates to the field of equipment specially suitable for material research and analys.The method comprises the steps that after ethyl alcohol of a preset volume is dropwise added to the surface of a to-be-detected pole piece, real-time concentration data of hydrogen is collected; determining a time-varying curve of the hydrogen concentration; determining the reaction activity of metal lithium on the surface of the to-be-detected pole piece based on the curve; compensating and correcting the real-time concentration data of the hydrogen according to the reaction activity; and converting the corrected hydrogen concentration data into a lithium quality detection result. The hydrogen generation container is the closed reaction bottle, the needle connected with the hydrogen sensor is inserted into the reaction bottle, the hydrogen concentration can be rapidly detected, the hydrogen concentration in different reaction bottles can be rapidly detected by inserting different reaction bottles, and the detection process is in a closed environment. The method is used for improving the precision of quantitative detection of precipitated lithium, so that the frequency of electrode replacement and calibration is reduced, and the continuity and reliability of detection data in the production process are improved.

Description

Technical Field

[0001] This application belongs to the field of equipment specifically applicable to material research and analysis, and particularly relates to a method, system, storage medium, and program product for rapid quantitative detection of lithium plating. Background Art

[0002] During the production and use of lithium-ion batteries, the monitoring and quantitative analysis of lithium plating phenomena are crucial. Lithium plating refers to the phenomenon that during the battery charging process, due to insufficient lithium-insertion space in the negative electrode, excessive resistance to lithium-ion insertion into the negative electrode, etc., lithium ions that cannot be inserted into the negative electrode gain electrons on the surface of the negative electrode to form metallic lithium. This phenomenon not only significantly shortens the battery cycle life and accelerates capacity attenuation, but the precipitated metallic lithium may also form abnormal connections inside the battery, leading to safety hazards such as short circuits.

[0003] In the related art, the surface of the battery electrode can be bombarded with ions by a mass spectrometer, and the mass-to-charge ratio and intensity of the generated secondary ions are analyzed to determine the content of metallic lithium on the electrode surface. This method has advantages such as high detection sensitivity and accurate quantification, and can provide reliable data support for the lithium plating amount in battery production.

[0004] However, due to the need to place the sample in a high-vacuum environment and perform ion sputtering treatment on the sample surface during the detection process, this destructive detection method makes it difficult to accurately capture the real-time dynamic changes in the lithium plating amount, especially the lithium plating behavior during the battery charge and discharge process is difficult to be monitored and evaluated in a timely manner. This reduces the accuracy of lithium plating quantitative detection, and then increases the frequency of electrode replacement and calibration, and reduces the continuity and reliability of the detection data during the production process. Summary of the Invention

[0005] This application provides a method, system, storage medium, and program product for rapid quantitative detection of lithium plating, which is used to improve the accuracy of lithium plating quantitative detection, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of the detection data during the production process.

[0006] In a first aspect, this application provides a method for rapid quantitative detection of lithium plating. After a preset volume of ethanol is dropped onto the surface of the electrode to be measured through a precision metering pump, real-time concentration data of hydrogen gas generated by the reaction of ethanol with metallic lithium on the surface of the electrode to be measured is collected by a sensor; Obtain a curve of hydrogen gas concentration changing with time according to the real-time concentration data of hydrogen gas; Determine the reaction activity of metallic lithium on the surface of the electrode to be measured based on the curve of hydrogen gas concentration changing with time; Compensate and correct the real-time concentration data of hydrogen gas according to the reaction activity to obtain corrected hydrogen gas concentration data; Convert the corrected hydrogen gas concentration data into a lithium mass detection result.

[0007] By adopting the above technical solution, real-time concentration data of hydrogen gas generated by the reaction of ethanol with metallic lithium on the surface of the electrode to be measured is collected by a sensor, and then a curve of the hydrogen gas concentration varying with time is obtained based on the real-time concentration data of hydrogen gas. Next, the reaction activity of metallic lithium on the surface of the electrode to be measured is determined based on the curve of the hydrogen gas concentration varying with time. The real-time concentration data of hydrogen gas is compensated and corrected according to the reaction activity to obtain corrected hydrogen gas concentration data. Finally, the corrected hydrogen gas concentration data is converted into a lithium mass detection result, which improves the detection efficiency, and the detection accuracy is improved by adopting the method of activity compensation. Furthermore, the frequency of electrode replacement and calibration is reduced, and the continuity and reliability of detection data in the production process are improved.

[0008] Combined with some embodiments of the first aspect, in some embodiments, after converting the corrected hydrogen gas concentration data into a lithium mass detection result, the method further includes: collecting a sequence of potential response signals of a lithium ion selective electrode during detection in a plurality of preset cycles; Dividing the sequence of potential response signals into a plurality of detection data segments at a preset time interval; Performing statistical calculations on the plurality of detection data segments to obtain the mean value, variance, and time correlation parameter of each detection data segment; Constructing an electrode response feature vector based on the mean value, variance, and time correlation parameter; Establishing a system drift function according to the electrode response feature vector; Calculating a correction matrix for the electrode response based on the system drift function, where the correction matrix includes a compensation coefficient for the attenuation of electrode performance; Applying the correction matrix to the currently collected potential response signal to obtain a corrected lithium mass detection result.

[0009] By adopting the above technical solution, by collecting and processing a sequence of potential response signals of a lithium ion selective electrode in a plurality of preset cycles, a systematic compensation mechanism for electrode performance attenuation is established. Through the statistical analysis of the detection data segments, the mean value, variance, and time correlation parameter are obtained, which accurately characterize the dynamic characteristic changes of the electrode response. The electrode response feature vector and system drift function constructed based on these characteristic parameters can quantitatively describe the attenuation law of electrode performance changing with time. The system compensates for the attenuation of electrode performance by calculating the correction matrix, reducing the measurement error caused by the attenuation of electrode performance during long-term use of the electrode. This data-driven correction method enables the system to adaptively adjust the measurement result when the electrode performance changes, improves the accuracy of lithium deposition quantitative detection, and further reduces the frequency of electrode replacement and calibration, improving the continuity and reliability of detection data in the production process.

[0010] In some embodiments in combination with some embodiments of the first aspect, an electrode response feature vector is constructed based on the mean, variance, and time correlation parameters, specifically including: Calculate the autocorrelation coefficient of each detection data segment, and the autocorrelation coefficient is used to characterize the degree of association between adjacent data points within the same detection data segment; Calculate the cross-correlation coefficient between adjacent detection data segments, and the cross-correlation coefficient is used to characterize the data change trend between different detection data segments; Perform a weighted combination of the autocorrelation coefficient and the cross-correlation coefficient to obtain a time series correlation eigenvalue; Arrange the mean, variance, and time series correlation eigenvalue in a preset dimension order to obtain an electrode response feature vector.

[0011] By adopting the above technical solution, the autocorrelation coefficient reflects the internal correlation between adjacent data points within the same data segment and can capture the short-term change characteristics of the electrode response. The cross-correlation coefficient reflects the change trend between different data segments and helps to identify the long-term change pattern of the electrode response. The time series correlation eigenvalue obtained by performing a weighted combination of these correlation coefficients, together with the mean and variance, forms a feature vector that can comprehensively characterize the dynamic characteristics of the electrode response, improving the recognition accuracy and sensitivity of the system to changes in electrode performance.

[0012] In some embodiments in combination with some embodiments of the first aspect, the system drift function is: ; In the above function, is the system drift function, and are the weights of the mean attenuation term, the variance influence term, the periodic drift term, the mean offset term, and the time correlation gradient term respectively, is the mean, is the variance, is the time correlation parameter, is the electrode response attenuation constant, is the variance influence index, is the periodic drift frequency, is the phase offset, is the time correlation attenuation coefficient, is the mean offset correction coefficient, is the time correlation gradient attenuation coefficient.

[0013] By adopting the above technical solutions, the mean attenuation term describes the change trend of the electrode baseline response, the variance influence term reflects the change in the degree of response fluctuation, the periodic drift term depicts the periodic change characteristics of the electrode response, the mean offset term characterizes the continuous offset of the response baseline, and the time correlation gradient term describes the dynamic change rate of the response characteristics. Each term is combined through different weight coefficients, and multiple attenuation constants and adjustment parameters are introduced, enabling the drift function to accurately describe the electrode performance changes at different time scales and improving the accuracy of the system's characterization of the electrode performance attenuation law.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the calculation function of the correction matrix is: ; In the above function, is the correction matrix, is the system drift function, is the non-linear compensation coefficient, is the drift threshold parameter, is the piecewise correction coefficient, is the response sensitivity parameter, is the characteristic time point, is the number of segments.

[0015] By adopting the above technical solutions, the first-order correction term based on the system drift function reflects the direct impact of performance attenuation, the second-order derivative integral term describes the acceleration effect of performance changes, the hyperbolic sine term provides non-linear compensation ability, and the hyperbolic tangent term realizes piecewise correction. This composite correction mechanism enables the system to automatically adjust the compensation intensity according to the attenuation characteristics of the electrode performance in different time periods. By introducing multiple adjustment parameters and characteristic time points, the correction matrix can adapt to different degrees of performance attenuation, and achieve precise compensation while maintaining signal continuity, improving the compensation accuracy and robustness of the system.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after obtaining the corrected lithium mass detection result, the method further includes: Performing discrete wavelet transform on the electrode response signal sequence to obtain characteristic components at different time scales; Calculating the covariance matrix of the characteristic components and decomposing the eigenvalues of the covariance matrix; Arranging the eigenvalues in descending order and establishing a cumulative contribution rate curve; Determining the main characteristic dimension according to the cumulative contribution rate and extracting the eigenvector corresponding to the main characteristic dimension; Constructing an orthogonal basis space using the eigenvector corresponding to the main characteristic dimension and calculating the projection coefficient of the response signal in the orthogonal basis space; Establish a feature reconstruction equation based on the projection coefficients, and determine the optimal reconstruction parameters based on the feature reconstruction equation.

[0017] By adopting the above technical solution, through performing discrete wavelet transform on the electrode response signal sequence, the system can separate and identify the characteristic components in the signal at different time scales. Furthermore, by calculating the covariance matrix of the characteristic components and decomposing its eigenvalues, an accurate quantitative characterization of the signal features is achieved. The method of determining the main feature dimension based on the cumulative contribution rate reduces the data redundancy, extracts the most representative feature vectors, and realizes the efficient extraction and dimensionality reduction of the signal features by calculating the projection coefficients of the response signal in the orthogonal basis space. The establishment of the feature reconstruction equation enables the system to accurately restore the key features of the original signal, improves the accuracy and reliability of the lithium mass detection result, enhances the system's ability to suppress interference signals, and makes the detection result have better stability and repeatability.

[0018] Combined with some embodiments of the first aspect, in some embodiments, an orthogonal basis space is constructed by using the feature vectors corresponding to the main feature dimension, and the projection coefficients of the response signal in the orthogonal basis space are calculated, specifically including: Construct the feature vectors corresponding to the main feature dimension into an orthogonal matrix; Calculate the singular value decomposition of the orthogonal matrix to obtain the left and right singular vectors; Determine the dimension transformation relationship of the orthogonal basis space based on the left and right singular vectors; Construct a mapping matrix from the response signal to the orthogonal basis space based on the dimension transformation relationship; Calculate the coordinate transformation of the response signal in the orthogonal basis space by using the mapping matrix; Perform normalization processing on the coordinate transformation result to obtain the projection coefficients.

[0019] By adopting the above technical solution, a mapping matrix from the response signal to the orthogonal basis space is constructed based on the dimension transformation relationship determined by the left and right singular vectors, so that the original signal can be optimally represented in the orthogonal basis space. This signal processing method based on orthogonal transformation realizes the standardized expression of signal features by performing normalization processing on the coordinate transformation result. This method reduces the scale difference between different dimensions, improves the uniformity and comparability of feature expression, enhances the system's adaptability to signal fluctuations, and makes the detection result of lithium mass more stable and reliable.

[0020] In a second aspect, an embodiment of the present application provides a rapid quantitative detection system for lithium plating, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the system, enabling the above system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, when the computer program product runs on the system, enabling the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a rapid quantitative detection method for lithium plating. By collecting and processing the potential response signal sequence of a lithium-ion selective electrode within multiple preset cycles, a systematic electrode performance decay compensation mechanism is established. Through statistical analysis of the detected data segment, the mean value, variance, and time correlation parameters are obtained, accurately characterizing the dynamic characteristic changes of the electrode response. The electrode response feature vector and system drift function constructed based on these characteristic parameters can quantitatively describe the decay law of the electrode performance over time. The system compensates for the electrode performance decay by calculating the correction matrix, reducing the measurement error caused by performance decay during the long-term use of the electrode. This data-driven correction method enables the system to adaptively adjust the measurement results when the electrode performance changes, improving the accuracy of lithium plating quantitative detection, and thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of the detection data during the production process.

[0024] 2. The present application provides a rapid quantitative detection method for lithium plating. The mean decay term describes the change trend of the electrode baseline response, the variance influence term reflects the change in the degree of response fluctuation, the periodic drift term characterizes the periodic change characteristics of the electrode response, the mean offset term represents the continuous offset of the response baseline, and the time correlation gradient term describes the dynamic change rate of the response characteristics. Each term is combined through different weight coefficients, and multiple decay constants and adjustment parameters are introduced, enabling the drift function to accurately describe the electrode performance changes at different time scales, and improving the accuracy of the system's characterization of the electrode performance decay law.

[0025] 3. The present application provides a rapid quantitative detection method for lithium plating. By performing discrete wavelet transform on the electrode response signal sequence, the system can separate and identify the characteristic components in the signal at different time scales. Furthermore, by calculating the covariance matrix of the characteristic components and decomposing its eigenvalues, an accurate quantitative characterization of the signal characteristics is achieved. The method of determining the main characteristic dimension based on the cumulative contribution rate reduces the data redundancy, extracts the most representative eigenvectors, and realizes the efficient extraction and dimensionality reduction of the signal characteristics by calculating the projection coefficients of the response signal in the orthogonal basis space. The establishment of the feature reconstruction equation enables the system to accurately restore the key features of the original signal, improves the accuracy and reliability of the lithium mass detection results, enhances the system's ability to suppress interference signals, and makes the detection results have better stability and repeatability. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic flow chart of a rapid quantitative detection method for lithium plating in an embodiment of the present application.

[0027] Figure 2 is another schematic flow chart of a rapid quantitative detection method for lithium plating in an embodiment of the present application.

[0028] Figure 3 is yet another schematic flow chart of a rapid quantitative detection method for lithium plating in an embodiment of the present application.

[0029] Figure 4 is a schematic structural diagram of the physical device of a rapid quantitative detection system for lithium plating provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in the present application refers to any and all possible combinations including one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0032] A rapid quantitative detection method for lithium deposition provided by this application is applicable to a rapid quantitative detection device for lithium deposition. The rapid quantitative detection device for lithium deposition includes a data collector (Type-c or Bluetooth communication), a hydrogen sensor (mileage: concentration 0-10%), and a reaction bottle (capacity 10 ml). The data collector is connected by a bendable rod in the middle of the sensor, and a needle is installed in the sensor and inserted into the sealed reaction bottle. The entire detection system is sealed, and all operations are premised on ensuring sealing, whether before or after inserting the needle or adding ethanol. The reaction bottle is sealed. After the needle is inserted into the sealed reaction bottle, the hydrogen concentration in the reaction bottle can be directly detected. The needle connected to the hydrogen sensor is inserted into the sealed reaction bottle, and the hydrogen concentration in the reaction bottle can be quickly detected. By inserting into different reaction bottles, the hydrogen concentration in different reaction bottles can be quickly detected.

[0033] The specific operation plan is as follows: 1. Place the reaction bottle (bacteria bottle) in the glove box and install the electrode sheet (the electrode sheet with lithium deposition). 2. Seal the reaction bottle with a rubber stopper and an aluminum lid, and tighten it with a capping pliers. 3. Take out the reaction bottle in a conventional laboratory environment. 4. Insert a syringe into the reaction bottle and add a certain amount of ethanol (The amount of water added determines how much gas space remains in the reaction bottle. For example, if the reaction bottle is 10 ml and 4 ml of ethanol is injected, the remaining gas space is 6 ml) 5. Quickly tape the opening where the syringe is inserted and wait for the ethanol to react with the lithium on the electrode sheet. 6. Transfer to the test position and insert the needle connected to the hydrogen sensor into the sealed reaction bottle. 7. Hydrogen is generated by the reaction, and the hydrogen concentration in the sealed system is detected by the hydrogen sensor, so as to analyze the corresponding lithium mass.

[0034] The logic of the rapid quantitative detection method for lithium deposition of this application is: dropping a quantitative amount of ethanol onto the electrode sheet with lithium deposition - the electrode sheet reacts with ethanol to generate hydrogen - detecting the hydrogen concentration - inversely inferring the lithium mass based on the hydrogen concentration.

[0035] Next, a specific example is used in combination with Figure 1 to describe a rapid quantitative detection method for lithium deposition in an embodiment of this application: Please refer to Figure 1 , which is a flow schematic diagram of a rapid quantitative detection method for lithium deposition in an embodiment of this application.

[0036] S101. After a preset volume of ethanol is dropped onto the surface of the electrode sheet to be measured through a precision metering pump, real-time concentration data of hydrogen generated by the reaction of ethanol with the metallic lithium on the surface of the electrode sheet to be measured is collected through a sensor. In this step, the system first drops a preset volume of ethanol onto the surface of the electrode to be tested through a precision metering pump. The preset volume can be set according to parameters such as the size and material properties of the electrode to be tested, so as to ensure that ethanol can evenly cover the surface of the electrode to be tested and react fully with it. The choice of ethanol is not limited to anhydrous ethanol, and other alcohol compounds that can react with metallic lithium to produce hydrogen, such as methanol, propanol, etc., can also be used.

[0037] After the ethanol is dropped onto the surface of the electrode to be tested, metallic lithium will chemically react with ethanol to generate hydrogen and lithium ethoxide compounds. The system collects real-time concentration data of the hydrogen generated during the reaction through a sensor. The sensor can be a gas concentration sensor, such as an electrochemical gas sensor, a catalytic combustion gas sensor, etc., or other sensors that can detect the hydrogen concentration in real time. The selection of the sensor needs to consider its performance indicators such as sensitivity, selectivity, response time, etc., to ensure the accuracy and real-time nature of the detection results.

[0038] S102. Obtain a curve of hydrogen concentration changing with time based on the real-time concentration data of hydrogen; After obtaining the real-time concentration data of hydrogen, the system needs to plot a curve of hydrogen concentration changing with time based on these data. By analyzing the shape and trend of the curve, the kinetic characteristics of the reaction between ethanol and metallic lithium can be understood, providing a basis for subsequent data processing and analysis.

[0039] During the process of plotting the curve, the system can adopt different data fitting and smoothing algorithms, such as the least squares method, spline interpolation method, etc., to reduce the influence of data noise and fluctuations, and improve the smoothness and readability of the curve. At the same time, the system can also perform operations such as segmenting and normalizing the curve according to needs to highlight the characteristic information of the curve and facilitate subsequent analysis.

[0040] In some cases, due to factors such as sensor performance and environmental interference, the obtained hydrogen concentration data may have outliers or data missing. To solve this problem, the system can adopt outlier detection and data repair algorithms, such as the 3σ criterion, Grubbs test, etc., to identify and eliminate abnormal data, and repair the missing data through methods such as interpolation and fitting to ensure the continuity and integrity of the curve.

[0041] S103. Determine the reactivity of metallic lithium on the surface of the electrode to be tested based on the curve of hydrogen concentration changing with time; Based on the curve of hydrogen concentration changing with time, the system can calculate the slope of the curve at different time periods and determine the reactivity of metallic lithium on the surface of the electrode to be tested according to the magnitude of the slope. The larger the slope of the curve, the faster the change in hydrogen concentration per unit time, that is, the higher the reaction rate of metallic lithium with ethanol and the stronger the reactivity.

[0042] To accurately calculate the curve slope, the system can adopt numerical differentiation algorithms such as the finite difference method, curve fitting method, etc. Considering that the curve may have noise and fluctuations, to improve the stability and reliability of slope calculation, the system can smooth the curve, such as the moving average method, Savitzky-Golay filtering, etc., to remove the influence of high-frequency noise. In addition, the system can also select appropriate time windows and step sizes according to needs to balance calculation efficiency and accuracy.

[0043] S104. Compensate and correct the real-time concentration data of hydrogen according to the reaction activity to obtain the corrected hydrogen concentration data; After determining the reaction activity of metallic lithium on the surface of the electrode to be measured, the system needs to compensate and correct the real-time concentration data of hydrogen according to the reaction activity. Due to differences in reaction activity, the hydrogen concentrations generated at different positions or different time periods may deviate, resulting in errors in the detection results. By introducing a reaction activity factor to correct the hydrogen concentration data, this deviation can be effectively reduced and the detection accuracy can be improved.

[0044] The specific method of compensation and correction can be selected according to the actual situation. Common methods include multiplicative correction, additive correction, exponential correction, etc. For example, the hydrogen concentration data can be divided by the reaction activity factor corresponding to the time period or position to obtain the corrected concentration data. The calculation of the correction factor can be based on reaction kinetic models, empirical formulas, etc., or obtained through experimental calibration.

[0045] First, the system can establish a quantitative relationship model between the hydrogen concentration and the reaction activity according to the reaction activity factor calculated in step S103. Common models include linear models, exponential models, logarithmic models, etc., and an appropriate model form can be selected according to the actual situation. For example, a linear model can be adopted, assuming that there is a linear relationship between the hydrogen concentration and the reaction activity, that is: C_corrected = C_measured / (1 + k * A) where C_corrected is the corrected hydrogen concentration, C_measured is the measured hydrogen concentration, A is the reaction activity factor, and k is the proportionality coefficient, which can be obtained through experimental calibration or theoretical calculation.

[0046] After establishing the quantitative relationship model, the system can calculate the corrected hydrogen concentration value according to the real-time collected hydrogen concentration data and the corresponding reaction activity factor. Specifically, for each sampling moment or sampling position, the system can divide the measured hydrogen concentration by the corresponding correction factor to obtain the corrected concentration value, that is: C_corrected(t) = C_measured(t) / (1 + k * A(t)) where t represents the sampling time or sampling position.

[0047] It should be noted that in practical applications, due to the large differences in the spatio-temporal distribution of reactivity, a single correction factor may not meet the accuracy requirements. To further improve the correction effect, the system can adopt strategies of segmented correction or local correction, that is, according to the distribution characteristics of reactivity, the electrode to be measured is divided into multiple regions or time periods, the correction factors are calculated for each region or time period respectively, and local correction is carried out. For example, the surface of the electrode to be measured can be divided into a central region and an edge region, the reactivity factors and correction coefficients of the two regions are calculated respectively, the hydrogen concentration data of each region are independently corrected, and finally the correction results of each region are combined to obtain the corrected concentration distribution of the entire electrode surface.

[0048] In addition, to further improve the correction accuracy and reliability, the system can also introduce an adaptive correction algorithm, and dynamically adjust the parameters of the correction factor and correction model according to the change of hydrogen concentration before and after correction to adapt to the change trend of reactivity. For example, algorithms such as Kalman filtering and adaptive weighting can be used to update the correction factor and model parameters in real time based on historical data and current measurement values, and smooth the correction results to reduce the influence of noise and fluctuations.

[0049] S105. Convert the corrected hydrogen concentration data into lithium mass detection results.

[0050] After obtaining the corrected hydrogen concentration data, the system needs to convert it into lithium mass detection results for subsequent analysis and application. According to the stoichiometric ratio of the reaction between metallic lithium and ethanol, a quantitative relationship between hydrogen concentration and lithium mass can be established to achieve data conversion.

[0051] During the conversion process, the system can adopt a pre-established standard curve or calculation formula to map the hydrogen concentration data to lithium mass values. The standard curve can be obtained by preparing a series of lithium ion standard solutions with known concentrations, measuring their corresponding hydrogen concentrations, and performing curve fitting. The calculation formula can be derived based on parameters such as the reaction chemical equation and thermodynamic equilibrium constant.

[0052] In the above embodiments, real-time concentration data of hydrogen gas generated by the reaction of ethanol with metallic lithium on the surface of the electrode to be measured is collected by a sensor. Then, a curve of the hydrogen gas concentration changing with time is obtained based on the real-time concentration data of hydrogen gas. Next, the reaction activity of metallic lithium on the surface of the electrode to be measured is determined based on the curve of the hydrogen gas concentration changing with time. The real-time concentration data of hydrogen gas is compensated and corrected according to the reaction activity to obtain corrected hydrogen gas concentration data. Finally, the corrected hydrogen gas concentration data is converted into a lithium mass detection result, which improves the detection efficiency, and the detection accuracy is improved by using the active compensation method. Furthermore, the frequency of electrode replacement and calibration is reduced, and the continuity and reliability of detection data in the production process are improved.

[0053] Next, a specific embodiment is used in combination with Figure 2 to describe another method for rapid quantitative detection of lithium deposition in the embodiments of the present application: Please refer to Figure 1 , which is another process schematic diagram of a method for rapid quantitative detection of lithium deposition in the embodiments of the present application.

[0054] S201. Collect a sequence of potential response signals during the detection process of a lithium ion selective electrode in a number of preset cycles; This step is to collect the potential response signals of the lithium ion selective electrode during the detection process. The system can connect the lithium ion selective electrode through a potential acquisition device, and continuously sample the potential signal of the electrode at a certain sampling frequency within a preset detection cycle to obtain a sequence of potential response signals. The preset cycle can be set according to actual detection requirements. For example, it can be set to 1 minute, 5 minutes, 10 minutes, etc. The sampling frequency can also be adjusted according to actual needs. Usually, it can be set to sample 10 times per second, 100 times per second, etc.

[0055] In addition to the above collection method, the system can also use other methods to collect potential response signals. For example, a segmented collection method can be adopted. Within each preset cycle, the collection time is divided into multiple segments, and a relatively high sampling frequency is used for collection within each segment, with a certain interval time left between segments. This collection method can reduce the amount of data to a certain extent while still being able to capture the key change information of the potential signal.

[0056] S202. Divide the sequence of potential response signals into multiple detection data segments according to a preset time interval; This step is to perform segmentation processing on the collected sequence of potential response signals. The system can divide the sequence of potential response signals into multiple detection data segments according to a preset time interval. The preset time interval can be set according to actual needs. For example, it can be set to 1 second, 5 seconds, 10 seconds, etc. Each divided detection data segment contains potential response signal data within a certain time range.

[0057] In addition to being segmented at fixed time intervals, the system can also adopt other segmentation methods. For example, according to the variation characteristics of the potential response signal, an adaptive segmentation method can be used. In the intervals where the potential signal changes significantly, a shorter segmentation interval is adopted, and in the intervals where the potential signal changes less, a longer segmentation interval is adopted. This segmentation method can better capture the key change information of the potential signal.

[0058] S203. Perform statistical calculations on multiple detected data segments to obtain the mean, variance, and time correlation parameters of each detected data segment; This step is to calculate the statistical features of the segmented detected data segments. The system can perform statistical analysis on each detected data segment to calculate the mean, variance, and time correlation parameters of the data segment. The mean reflects the average level of the potential signal within the data segment, the variance reflects the fluctuation of the potential signal within the data segment, and the time correlation parameter reflects the time variation characteristics of the potential signal within the data segment.

[0059] When calculating the mean and variance, the system can use the traditional arithmetic mean and sample variance formulas for calculation. For the time correlation parameter, the system can use the autocorrelation coefficient and cross-correlation coefficient for calculation. The autocorrelation coefficient reflects the time autocorrelation of the potential signal within the data segment, that is, the correlation degree between the data points and themselves at different time delays. The cross-correlation coefficient reflects the correlation of the potential signal between different data segments, that is, the correlation degree between different data segments at the same time point.

[0060] S204. Construct an electrode response feature vector based on the mean, variance, and time correlation parameters; The system constructs an electrode response feature vector based on the mean, variance, and time correlation parameters, specifically including: calculating the autocorrelation coefficient of each detected data segment, and the autocorrelation coefficient is used to characterize the correlation degree between adjacent data points within the same detected data segment; calculating the cross-correlation coefficient between adjacent detected data segments, and the cross-correlation coefficient is used to characterize the data change trend between different detected data segments; performing weighted combination on the autocorrelation coefficient and cross-correlation coefficient to obtain the time series correlation eigenvalue; arranging the mean, variance, and time series correlation eigenvalue in the preset dimension order to obtain the electrode response feature vector.

[0061] This step is to further process and fuse the statistical features to construct an electrode response feature vector. The system can combine the mean, variance, and time correlation parameters of each data segment into a multi-dimensional feature vector as the electrode response feature vector of the data segment. The dimension of the feature vector can be set according to actual needs. For example, the mean, variance, autocorrelation coefficient, and cross-correlation coefficient can be used as four dimensions to form a four-dimensional feature vector.

[0062] When constructing the feature vector, the system can assign different weights to different statistical features. For example, different weight coefficients can be given according to the influence degree of different features on the electrode response. The weight coefficients can be set empirically or automatically learned through data analysis and machine learning methods. By setting reasonable weights, the role of important features can be highlighted and the representation ability of the feature vector can be improved.

[0063] S205. Establish a system drift function according to the electrode response feature vector; The system establishes a system drift function according to the electrode response feature vector, and the system drift function is: ; In the above function, is the system drift function, and are respectively the weights of the mean attenuation term, the variance influence term, the periodic drift term, the mean offset term and the time correlation gradient term, is the mean, is the variance, is the time correlation parameter, is the electrode response attenuation constant, is the variance influence index, is the periodic drift frequency, is the phase offset, is the time correlation attenuation coefficient, is the mean offset correction coefficient, is the time correlation gradient attenuation coefficient.

[0064] This step is to establish a system drift function using the electrode response feature vector to describe the drift change law of the electrode response over time. The drift function can be expressed as a mathematical model with the time variable as the input and the drift amount of the electrode response as the output. The establishment of the drift function can be based on the analysis and fitting of the electrode response feature vector.

[0065] The system can use various methods to establish the drift function. For example, classical function fitting methods such as polynomial fitting, exponential function fitting, and Fourier series fitting can be used. Machine learning methods such as support vector machines and neural networks can also be used to construct a drift function model by learning the mapping relationship between the electrode response feature vector and time. The selection of an appropriate fitting method depends on the specific characteristics of the electrode response and the data distribution.

[0066] represents the combined influence of mean drift and variance drift, represents the attenuation of mean drift, represents the influence of variance drift; Represents the influence of periodic drift, is the time-correlation parameter, reflecting the periodic change characteristics of the electrode response. is the sine function, representing the waveform of the periodic drift.

[0067] Represents the cumulative effect of mean drift, represents the offset of the mean value relative to the initial value, is the time-weighting function of the cumulative effect.

[0068] Represents the influence of the change rate of the time-correlation parameter on the drift. is the change rate of the time-correlation parameter, is the attenuation function of the time difference.

[0069] Calculate the correction matrix of the electrode response based on the system drift function; The system calculates the correction matrix of the electrode response based on the system drift function, where the calculation function of the correction matrix is: ; In the above function, is the correction matrix, is the system drift function, is the non-linear compensation coefficient, is the drift threshold parameter, is the piecewise correction coefficient, is the response sensitivity parameter, is the characteristic time point, is the number of segments.

[0070] In this step, the established system drift function is used to calculate the correction matrix of the electrode response, which is used to correct the drift of the electrode response. The correction matrix is a time-dependent matrix, and its elements represent the correction coefficients required to correct the drift of the electrode response at different time points. The calculation of the correction matrix can be based on the mathematical form and parameter values of the system drift function.

[0071] When calculating the correction matrix, the system can adopt numerical integration methods to solve the function values of the drift function at different time points to obtain the corresponding correction coefficients. Common numerical integration methods include the trapezoidal method, Simpson's method, Romberg method, etc. The system can select a suitable numerical integration method according to the complexity of the drift function and the accuracy requirements.

[0072] Represents the linear correction term based on the system drift function.

[0073] Represents the integral correction term with cosine weighting for the second derivative of the system drift function.

[0074] Represents the non - linear correction term based on the hyperbolic sine function. is the hyperbolic sine function.

[0075] Represents the piece - wise correction term. is the hyperbolic tangent function, is the starting time of the

[0076] In practical applications, the drift of the electrode response may exhibit non - linear and time - varying characteristics, and a single drift function may not be able to fully describe the drift behavior. To solve this problem, the system can introduce a piece - wise correction strategy, divide the time axis into multiple intervals, and adopt different drift functions and correction matrices in each interval. Piece - wise correction can better capture the local characteristics of the electrode response drift and improve the accuracy of correction. At the same time, the system can also introduce an adaptive correction mechanism, dynamically adjust the parameters of the correction matrix according to the real - time changes of the electrode response, so as to adapt to the changes of the drift behavior.

[0077] S207: Apply the correction matrix to the currently collected potential response signal to obtain the corrected lithium mass detection result.

[0078] In this step, the calculated correction matrix is used to perform drift correction on the currently collected potential response signal to obtain the corrected lithium mass detection result. The correction process can be achieved by performing mathematical operations on the potential response signal and the correction matrix, such as matrix multiplication, matrix addition, etc.

[0079] When performing the correction operation, the system can adopt an efficient numerical calculation library or hardware acceleration technology, such as the BLAS (Basic Linear Algebra Subprograms) library, GPU (Graphics Processing Unit), etc., to improve the calculation efficiency and speed. At the same time, the system can also adopt the method of parallel computing, distribute the correction operation to multiple processing units to perform simultaneously, and further improve the calculation efficiency.

[0080] The corrected potential response signal can be converted into the corresponding lithium mass value through a pre - established concentration calibration curve or calibration model. The concentration calibration curve or calibration model can be obtained through experimental measurement or theoretical calculation methods, and they describe the quantitative relationship between the potential response signal and the lithium mass. The system can calculate the corresponding lithium mass value according to the amplitude of the corrected potential response signal, using the concentration calibration curve or calibration model, as the final detection result.

[0081] In the above embodiments, a systematic electrode performance decay compensation mechanism is established by collecting and processing the potential response signal sequences of the lithium-ion selective electrode within multiple preset cycles. Through the statistical analysis of the detected data segments, the mean value, variance, and time correlation parameters are obtained, accurately characterizing the dynamic characteristic changes of the electrode response. The electrode response feature vector and system drift function constructed based on these characteristic parameters can quantitatively describe the decay law of the electrode performance over time. The system compensates for the electrode performance decay by calculating the correction matrix, reducing the measurement error caused by the performance decay during the long-term use of the electrode. This data-driven correction method enables the system to adaptively adjust the measurement results when the electrode performance changes, improving the accuracy of the lithium plating quantitative detection, thereby reducing the frequency of electrode replacement and calibration, and enhancing the continuity and reliability of the detection data during the production process.

[0082] In the above embodiments, by establishing a system drift function and a correction matrix, the compensation for the electrode performance decay is realized, improving the accuracy of the lithium mass detection. However, in practical applications, the electrode response signal often also contains various complex noise and interference components, and these factors will affect the reliability of the measurement results. To further improve the detection accuracy, it is necessary to conduct a more in-depth feature analysis and signal processing on the electrode response signal. The following combines Figure 3 , and describes another lithium plating rapid quantitative detection method in the embodiments of the present application: Please refer to Figure 3 , which is another process schematic diagram of a lithium plating rapid quantitative detection method in the embodiments of the present application.

[0083] S301. Perform discrete wavelet transform on the electrode response signal sequence to obtain the feature components at different time scales; In this step, the system performs discrete wavelet transform processing on the obtained electrode response signal sequence. The discrete wavelet transform is a time-frequency analysis tool that can extract the local features of the signal at different time scales. By performing discrete wavelet transform on the signal sequence, the system can obtain the time-domain components reflecting the signal in different frequency ranges, thereby realizing the multi-scale decomposition of the signal. In addition to the discrete wavelet transform, other time-frequency analysis methods such as the short-time Fourier transform, Wigner-Ville distribution, etc. can also be used to extract the time-frequency features of the signal.

[0084] Specifically, the system can select appropriate wavelet basis functions, such as Daubechies wavelets, Symlets wavelets, etc., determine the decomposition level according to the characteristics of the signal, and then perform wavelet decomposition on the signal sequence. In each level of decomposition, the system decomposes the signal into a low-frequency approximation component and a high-frequency detail component through the stretching and translation of the wavelet basis function. Through multi-level decomposition, the system can obtain wavelet coefficients representing the characteristics of the signal at different time scales. These wavelet coefficients reflect the energy distribution of the signal in the corresponding frequency band and contain the local feature information of the signal.

[0085] S302. Calculate the covariance matrix of the feature components and decompose the eigenvalues of the covariance matrix; In this step, the system calculates the covariance matrix between the wavelet feature components obtained in the previous step. The covariance matrix describes the correlation and variation degree between different feature components and can be used to analyze the linear relationship between features. By calculating the covariance matrix, the system can quantify the statistical dependence between feature components at different time scales and provide a basis for subsequent feature selection and dimensionality reduction. In addition to the covariance matrix, other statistics such as the correlation coefficient matrix and the mutual information matrix can also be used to characterize the association between features.

[0086] Specifically, the system can organize the wavelet feature components into a matrix form, where each row represents a feature vector at a time point. Then, the system calculates the covariance matrix of the feature matrix, where each element of the covariance matrix represents the covariance between two feature components. Next, the system performs eigenvalue decomposition on the covariance matrix to obtain a set of eigenvalues and corresponding eigenvectors. The eigenvalues represent the variation degree of the covariance matrix, and the eigenvectors represent the main variation directions. By analyzing the magnitude and distribution of the eigenvalues, the system can evaluate the importance and redundancy of the features.

[0087] S303. Sort the eigenvalues in descending order and establish a cumulative contribution rate curve; In this step, the system sorts the eigenvalues of the covariance matrix obtained in the previous step in descending order, then calculates the cumulative contribution rate of each eigenvalue, and establishes a cumulative contribution rate curve. The cumulative contribution rate represents the proportion of the variance contribution of the first k eigenvalues to the total variance and reflects the information retention degree of the feature subspace. By analyzing the change trend of the cumulative contribution rate curve, the system can determine the number of main eigenvalues and provide a reference for subsequent feature selection. In addition to the cumulative contribution rate, other indicators for measuring the importance of features such as the divergence of eigenvalues and the ratio of eigenvalues can also be used to assist in feature selection.

[0088] Specifically, the system first sorts the eigenvalues in descending order so that the eigenvalues with larger variance contributions are in the front. Then, the system calculates the cumulative contribution rate of each eigenvalue, which is the ratio of the sum of the first k eigenvalues to the sum of all eigenvalues. Next, the system plots the cumulative contribution rate curve, with the number of eigenvalues on the horizontal axis and the corresponding cumulative contribution rate on the vertical axis. By observing the changing trend of the curve, the system can find the inflection point of the cumulative contribution rate, that is, the position where the curve tends to flatten. This inflection point often corresponds to the number of main eigenvalues, meaning that most of the data variance can be represented with fewer feature dimensions.

[0089] S304. Determine the main feature dimension according to the cumulative contribution rate, and extract the eigenvectors corresponding to the main feature dimension; In this step, the system determines the main feature dimension according to the cumulative contribution rate curve obtained in the previous step, that is, the number of feature dimensions that can represent most of the data variance. Then, the system extracts the eigenvectors corresponding to the main feature dimension as the basis vectors for constructing the feature subspace. By selecting the main feature dimension, the system can reduce the dimension of the data and remove noise while retaining the main information of the data, simplifying the subsequent analysis and modeling processes. In addition to the cumulative contribution rate, other dimension selection criteria such as AIC and BIC can also be used to determine the optimal main feature dimension.

[0090] Specifically, the system can determine the main feature dimension according to the inflection point position of the cumulative contribution rate curve or set a contribution rate threshold (such as 90%). For example, when the cumulative contribution rate reaches 90%, the corresponding number of eigenvalues is the main feature dimension. After determining the main feature dimension, the system selects the eigenvectors corresponding to the main feature dimension as the basis vectors of the feature subspace. These eigenvectors represent the main change directions of the data in the feature space and contain the key information of the data. By projecting the original data onto this feature subspace, the system can achieve dimensionality reduction representation and noise removal of the data.

[0091] S305. Use the eigenvectors corresponding to the main feature dimension to construct an orthogonal basis space, and calculate the projection coefficients of the response signal in the orthogonal basis space; The system uses the eigenvectors corresponding to the main feature dimension to construct an orthogonal basis space and calculate the projection coefficients of the response signal in the orthogonal basis space, specifically including: constructing the eigenvectors corresponding to the main feature dimension into an orthogonal matrix; calculating the singular value decomposition of the orthogonal matrix to obtain the left and right singular vectors; determining the dimension transformation relationship of the orthogonal basis space based on the left and right singular vectors; constructing a mapping matrix from the response signal to the orthogonal basis space based on the dimension transformation relationship; calculating the coordinate transformation of the response signal in the orthogonal basis space using the mapping matrix; and normalizing the coordinate transformation result to obtain the projection coefficients.

[0092] In this step, the system constructs an orthogonal basis space by using the eigenvectors corresponding to the main feature dimensions obtained in the previous step. The orthogonal basis space is a linear space composed of a set of orthogonal vectors and can be used for the orthogonal decomposition and projection representation of signals. By projecting the response signal onto this orthogonal basis space, the system can obtain the projection coefficients of the signal on each orthogonal basis vector, realizing the low-dimensional representation and feature extraction of the signal. In addition to the orthogonal basis constructed by the eigenvectors, other orthogonal bases such as Fourier basis and wavelet basis can also be used for the orthogonal decomposition of signals.

[0093] Specifically, the system first orthogonalizes the eigenvectors corresponding to the main feature dimensions, such as Schmidt orthogonalization or singular value decomposition, to obtain a set of orthogonal basis vectors. These orthogonal basis vectors form an orthogonal basis space, and each vector represents an orthogonal feature direction. Then, the system maps the response signal onto this orthogonal basis space and calculates the projection coefficients of the signal on each orthogonal basis vector. The calculation of the projection coefficients can be realized through inner product operations, indicating the component size of the signal in the corresponding feature direction. By representing the signal as a set of projection coefficients, the system realizes the low-dimensional representation and feature extraction of the signal, and can effectively remove noise and redundant information.

[0094] S306. Establish a feature reconstruction equation based on the projection coefficients and determine the optimal reconstruction parameters based on the feature reconstruction equation.

[0095] The system establishes a feature reconstruction equation based on the projection coefficients and determines the optimal reconstruction parameters based on the feature reconstruction equation, which specifically includes: arranging the projection coefficients according to the dimension of the orthogonal basis space to construct a coefficient matrix; establishing a feature reconstruction equation based on the coefficient matrix; calculating the condition number of the coefficient matrix of the feature reconstruction equation; dividing the feature reconstruction equation into a training set and a validation set; solving the reconstruction errors corresponding to different reconstruction parameter values on the training set; validating the reconstruction errors on the validation set, and selecting the parameter value that minimizes the reconstruction error as the optimal reconstruction parameter.

[0096] In this step, the system establishes a feature reconstruction equation based on the projection coefficients of the response signal on the orthogonal basis space obtained in the previous step. The feature reconstruction equation describes how to reconstruct the original signal by using the projection coefficients and reflects the decomposition and synthesis relationship of the signal on the orthogonal basis. By solving the feature reconstruction equation, the system can determine the optimal reconstruction parameters to achieve the high-precision restoration and feature representation of the signal. In addition to the reconstruction equation based on the projection coefficients, other reconstruction models such as sparse representation and dictionary learning can also be used for signal reconstruction and feature extraction.

[0097] Specifically, the system represents the response signal as a linear combination of orthogonal basis vectors, where the weight coefficient of each basis vector is the corresponding projection coefficient. By substituting the projection coefficients and orthogonal basis vectors into the feature reconstruction equation, the system obtains a system of linear equations that describes the signal reconstruction process. Then, the system solves the feature reconstruction equation through the least squares method or other optimization algorithms to obtain the optimal reconstruction parameters. These reconstruction parameters can be used for signal restoration and feature representation, reflecting the optimal approximation of the signal in the orthogonal basis space. By analyzing the characteristics such as sparsity and energy distribution of the reconstruction parameters, the system can further extract the key features and patterns of the signal.

[0098] In the above embodiments, by performing discrete wavelet transform on the electrode response signal sequence, the system can separate and identify the feature components in the signal at different time scales. Then, by calculating the covariance matrix of the feature components and decomposing its eigenvalues, an accurate quantitative characterization of the signal features is achieved. The method of determining the main feature dimension based on the cumulative contribution rate reduces the data redundancy, extracts the most representative feature vectors, and realizes the efficient extraction and dimensionality reduction of signal features by calculating the projection coefficients of the response signal in the orthogonal basis space. The establishment of the feature reconstruction equation enables the system to accurately restore the key features of the original signal, improves the accuracy and reliability of the lithium mass detection results, enhances the system's ability to suppress interference signals, and makes the detection results have better stability and repeatability.

[0099] The following describes the system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 4 for the schematic structural diagram of the physical device of a lithium deposition rapid quantitative detection system provided in the embodiments of the present application.

[0100] It should be noted that Figure 4 the structure of the system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0101] As Figure 4 shown, the system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage section 408 into the random access memory (RAM) 403, such as executing the methods in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0102] The following components are connected to the I / O interface 405: an input section 406 including a camera, an infrared sensor, etc.; an output section 407 including a liquid crystal display (LCD), a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 410 as needed so that a computer program read from it can be installed into the storage section 408 as needed.

[0103] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the present invention are executed.

[0104] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can 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), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0106] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist alone without being assembled into the system. The above storage medium carries one or more computer programs, and when the one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0107] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0108] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0109] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are 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 one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available media may be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, DVD), or semiconductor media (for example, solid state drive), etc.

[0110] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The aforementioned storage media include: various media such as ROM, random access memory (RAM), magnetic disks, or optical discs that can store program codes.

Claims

1. A rapid quantitative detection method for lithium precipitation, characterized in that: include: After a preset volume of ethanol is dripped onto the surface of the electrode to be tested by a precision metering pump, real-time concentration data of hydrogen generated by the reaction of the ethanol with the metallic lithium on the surface of the electrode to be tested is collected by a sensor; Obtaining a curve of hydrogen concentration changing with time according to the real-time concentration data of hydrogen; Determine the reaction activity of the metallic lithium on the surface of the electrode to be tested based on the curve of the change of the hydrogen concentration over time; Performing compensation correction on the real-time concentration data of hydrogen according to the reaction activity to obtain corrected hydrogen concentration data; The corrected hydrogen concentration data is converted into lithium mass detection results.

2. The method according to claim 1, characterized in that: After converting the corrected hydrogen concentration data into lithium quality detection results, the method further includes: Collecting a potential response signal sequence of the lithium ion selective electrode during a number of preset period detection processes; dividing the potential response signal sequence into a plurality of detection data segments according to preset time intervals; Performing statistical calculations on the multiple detection data segments to obtain a mean, variance and time correlation parameter of each detection data segment; constructing an electrode response feature vector based on the mean, the variance and the time correlation parameter; Establishing a system drift function according to the electrode response characteristic vector; Calculating a correction matrix of electrode response based on the system drift function, wherein the correction matrix includes compensation coefficients for electrode performance attenuation; The correction matrix is ​​applied to the currently collected potential response signal to obtain a corrected lithium quality detection result.

3. The method according to claim 2, characterized in that The constructing of the electrode response feature vector based on the mean, the variance and the time correlation parameter specifically includes: Calculating the autocorrelation coefficient of each of the detection data segments, wherein the autocorrelation coefficient is used to characterize the degree of association between adjacent data points in the same detection data segment; Calculating the mutual correlation coefficient between adjacent detection data segments, wherein the mutual correlation coefficient is used to characterize the data change trend between different detection data segments; Performing weighted combination on the autocorrelation coefficient and the mutual correlation coefficient to obtain a time series correlation characteristic value; The mean, the variance and the time series correlation eigenvalue are arranged in a preset dimensional order to obtain an electrode response eigenvector.

4. The method according to claim 2, characterized in that: The system drift function is: ； In the above function, is the system drift function, , , , and are the weight of the mean decay term, the weight of the variance influence term, the weight of the periodic drift term, the weight of the mean shift term and the weight of the time correlation gradient term. is the mean value, is the variance, is the time correlation parameter, the is the electrode response attenuation constant, is the variance impact index, is the periodic drift frequency, is the phase shift, the is the time-dependent attenuation coefficient, is the mean shift correction coefficient, is the time-dependent gradient attenuation coefficient.

5. The method according to claim 2, characterized in that: The calculation function of the correction matrix is: ； In the above function, is the correction matrix, is the system drift function, is the nonlinear compensation coefficient, is the drift threshold parameter, is the segment correction coefficient, is the response sensitivity parameter, is a characteristic time point, is the number of segments.

6. The method according to claim 2, characterized in that After obtaining the corrected lithium quality test result, the method further includes: Performing discrete wavelet transform on the electrode response signal sequence to obtain characteristic components at different time scales; Calculating a covariance matrix of the characteristic components and decomposing eigenvalues ​​of the covariance matrix; Arrange the characteristic values ​​in descending order and establish a cumulative contribution rate curve; Determine the main feature dimension according to the cumulative contribution rate, and extract the feature vector corresponding to the main feature dimension; An orthogonal basis space is constructed using the eigenvector corresponding to the main eigendimension, and a projection coefficient of the response signal in the orthogonal basis space is calculated; A characteristic reconstruction equation is established based on the projection coefficients, and an optimal reconstruction parameter is determined based on the characteristic reconstruction equation.

7. The method according to claim 6, characterized in that The step of constructing an orthogonal basis space by using the eigenvector corresponding to the main eigendimension and calculating the projection coefficient of the response signal in the orthogonal basis space specifically includes: Constructing the eigenvectors corresponding to the main eigendimension into an orthogonal matrix; Calculate the singular value decomposition of the orthogonal matrix to obtain left and right singular vectors; Determine a dimensional transformation relationship of an orthogonal basis space based on the left and right singular vectors; Constructing a mapping matrix from the response signal to the orthogonal basis space based on the dimensional transformation relationship; Calculating the coordinate transformation of the response signal in the orthogonal basis space using the mapping matrix; The coordinate transformation result is normalized to obtain a projection coefficient.

8. A rapid quantitative detection system for lithium deposition, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

Images