A method, system, storage medium and program product for rapid quantitative detection of lithium deposition

Abstract

A method, system, storage medium and program product for rapid quantitative detection of lithium deposition, relating to the field of equipment specifically suitable for material research and analysis. In this method, after a preset volume of ethanol is dripped onto the surface of the electrode to be tested, real-time concentration data of hydrogen is collected; a curve showing the change of hydrogen concentration over time is determined; the reaction activity of metallic lithium on the surface of the electrode to be tested is determined based on the curve; the real-time concentration data of hydrogen is compensated and corrected according to the reaction activity; and the corrected hydrogen concentration data is converted into a lithium quality detection result. In this application, the container for generating hydrogen is a closed reaction bottle, and the reaction bottle into which the needle connected to the hydrogen sensor is inserted can quickly detect the hydrogen concentration, and the insertion of different reaction bottles can quickly detect the hydrogen concentration in different reaction bottles, and the detection process is all in a closed environment. This application is used to improve the accuracy of quantitative detection of lithium deposition, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of detection data during the production process.

Description

Technical Field

[0001] The present application relates to the field of equipment specifically suitable for materials research and analysis, and in particular to a method, system, storage medium and program product for rapid quantitative detection of lithium deposition. Background Art

[0002] Monitoring and quantitative analysis of lithium plating are crucial during the production and use of lithium-ion batteries. Lithium plating occurs during battery charging when, due to insufficient lithium insertion space in the negative electrode or excessive resistance to lithium ion insertion, lithium ions that cannot be inserted into the negative electrode gain electrons on the negative electrode surface, forming metallic lithium. This phenomenon not only significantly shortens the battery's cycle life and accelerates capacity decay, but the deposited lithium metal can also form abnormal connections within the battery, causing safety hazards such as short circuits.

[0003] In related technologies, a mass spectrometer can be used to bombard the surface of a battery electrode with ions and analyze the mass-to-charge ratio and intensity of the generated secondary ions to determine the amount of metallic lithium on the electrode surface. This method has the advantages of high detection sensitivity and accurate quantitative analysis, and can provide reliable lithium precipitation data support for battery production.

[0004] However, since the sample needs to be placed in a high vacuum environment and the sample surface needs to be ion sputtered during the detection process, this destructive detection method makes it difficult to accurately capture the real-time dynamic changes in the amount of lithium deposition, especially the lithium deposition behavior during the battery charging and discharging process. It is difficult to monitor and evaluate it in a timely manner, which reduces the accuracy of quantitative detection of lithium deposition, thereby increasing the frequency of electrode replacement and calibration, and reducing the continuity and reliability of detection data during the production process. Summary of the Invention

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

[0006] In a first aspect, the present application provides a rapid quantitative detection method for lithium deposition. After a preset volume of ethanol is dripped onto the surface of a test electrode by a precision metering pump, a sensor is used to collect real-time concentration data of hydrogen generated by the reaction of ethanol with metallic lithium on the surface of the test electrode.

[0007] Obtain a curve showing changes in hydrogen concentration over time based on real-time hydrogen concentration data;

[0008] 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;

[0009] Compensate and correct the real-time concentration data of hydrogen according to the reaction activity to obtain corrected hydrogen concentration data;

[0010] Convert the corrected hydrogen concentration data into lithium mass detection results.

[0011] By adopting the above technical solution, the real-time concentration data of hydrogen generated by the reaction of ethanol and metallic lithium on the surface of the electrode to be tested is collected through a sensor, and then a curve of the hydrogen concentration changing with time is obtained based on the real-time concentration data of hydrogen. The reaction activity of the metallic lithium on the surface of the electrode to be tested is determined based on the curve of the hydrogen concentration changing with time. The real-time concentration data of hydrogen is compensated and corrected according to the reaction activity to obtain corrected hydrogen concentration data. Finally, the corrected hydrogen concentration data is converted into lithium quality detection results, thereby improving the detection efficiency. The activity compensation method is used to improve the detection accuracy, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of the detection data in the production process.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, 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 plurality of preset period detection processes;

[0013] dividing the potential response signal sequence into a plurality of detection data segments according to a preset time interval;

[0014] Performing statistical calculations on multiple detection data segments to obtain the mean, variance, and time correlation parameters of each detection data segment;

[0015] Construct electrode response feature vectors based on mean, variance, and time correlation parameters;

[0016] Establish a system drift function based on the electrode response characteristic vector;

[0017] Calculate the correction matrix of the electrode response based on the system drift function, the correction matrix includes the compensation coefficient of the electrode performance degradation;

[0018] The correction matrix is applied to the currently collected potential response signal to obtain the corrected lithium quality detection result.

[0019] By adopting the above technical solution, a systematic electrode performance attenuation compensation mechanism was established by collecting and processing the potential response signal sequence of the lithium-ion selective electrode within multiple preset cycles. Through statistical analysis of the detection data segments, the mean, variance and time-correlation parameters were obtained, accurately characterizing the dynamic characteristics of the electrode response. The electrode response eigenvector and system drift function constructed based on these characteristic parameters can quantitatively describe the attenuation law of electrode performance over time. The system compensates for the attenuation of electrode performance by calculating the correction matrix, reducing the measurement error caused by performance attenuation during 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, improves the accuracy of quantitative lithium plating detection, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of detection data during the production process.

[0020] In conjunction with some embodiments of the first aspect, in some embodiments, constructing an electrode response feature vector based on the mean, variance, and time correlation parameters specifically includes:

[0021] Calculate the autocorrelation coefficient of each detection data segment. The autocorrelation coefficient is used to characterize the degree of correlation between adjacent data points in the same detection data segment.

[0022] Calculate the mutual correlation coefficient between adjacent detection data segments. The mutual correlation coefficient is used to characterize the data change trend between different detection data segments.

[0023] The autocorrelation coefficient and the cross-correlation coefficient are weighted and combined to obtain the time series correlation characteristic value;

[0024] The mean, variance and time series correlation eigenvalues are arranged in the order of the preset dimensions to obtain the electrode response eigenvector.

[0025] By employing this technical solution, the autocorrelation coefficient reflects the inherent correlation between adjacent data points within the same data segment, capturing the short-term variations in electrode response. The cross-correlation coefficient reflects the variation trend between different data segments, helping to identify the long-term variation pattern of electrode response. The time series correlation eigenvalue, obtained by weighted combination of these correlation coefficients, together with the mean and variance to form a eigenvector, can comprehensively characterize the dynamic characteristics of the electrode response, improving the system's accuracy and sensitivity in identifying changes in electrode performance.

[0026] In conjunction with some embodiments of the first aspect, in some embodiments, the system drift function is: ;

[0027] In the above function, is the system drift function, and are the weights of the mean decay term, the variance influence term, the periodic drift term, the mean shift 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 shift, is the time-correlation attenuation coefficient, is the mean shift correction coefficient, is the time-dependent gradient attenuation coefficient.

[0028] By employing this technical solution, the mean decay term describes the changing trend of the electrode baseline response, the variance effect term reflects the degree of response fluctuation, the periodic drift term characterizes the periodic variation of the electrode response, the mean shift term characterizes the persistent shift of the response baseline, and the time-correlation gradient term describes the dynamic rate of change of the response characteristics. By combining these terms with different weighting coefficients and introducing multiple decay constants and adjustment parameters, the drift function can accurately describe changes in electrode performance at different time scales, improving the system's ability to accurately characterize electrode performance decay patterns.

[0029] In conjunction with some embodiments of the first aspect, in some embodiments, the calculation function of the correction matrix is: ;

[0030] 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 the characteristic time point, is the number of segments.

[0031] By adopting the above technical solution, the first-order correction term based on the system drift function reflects the direct impact of performance degradation, the second-order derivative integral term describes the acceleration effect of performance change, the hyperbolic sine term provides nonlinear compensation capability, and the hyperbolic tangent term implements piecewise correction. This composite correction mechanism enables the system to automatically adjust the compensation intensity based on the attenuation characteristics of electrode performance over different time periods. By introducing multiple adjustment parameters and characteristic time points, the correction matrix can adapt to different degrees of performance degradation and achieve precise compensation while maintaining signal continuity, improving the compensation accuracy and robustness of the system.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, after obtaining the corrected lithium quality test result, the method further includes:

[0033] Perform discrete wavelet transform on the electrode response signal sequence to obtain characteristic components at different time scales;

[0034] Calculate the covariance matrix of the eigencomponents and decompose the eigenvalues of the covariance matrix;

[0035] Arrange the eigenvalues in descending order and establish a cumulative contribution rate curve;

[0036] Determine the main feature dimension according to the cumulative contribution rate, and extract the feature vector corresponding to the main feature dimension;

[0037] The orthogonal basis space is constructed using the eigenvectors corresponding to the main eigendimensions, and the projection coefficients of the response signal in the orthogonal basis space are calculated.

[0038] A characteristic reconstruction equation is established based on the projection coefficients, and the optimal reconstruction parameters are determined based on the characteristic reconstruction equation.

[0039] By adopting the above technical solution and 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, and then achieve accurate quantitative characterization of the signal characteristics by calculating the covariance matrix of the characteristic components and decomposing their eigenvalues. The method of determining the main characteristic dimension based on the cumulative contribution rate reduces the redundancy of the data and extracts the most representative eigenvectors. By calculating the projection coefficients of the response signal in the orthogonal basis space, efficient extraction and dimensionality reduction of the signal characteristics are achieved. The establishment of the feature reconstruction equation enables the system to accurately restore the key features of the original signal, improve the accuracy and reliability of the lithium quality detection results, enhance the system's ability to suppress interference signals, and make the detection results more stable and repeatable.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, constructing an orthogonal basis space using eigenvectors corresponding to the main eigendimension and calculating the projection coefficients of the response signal in the orthogonal basis space specifically includes:

[0041] Construct the eigenvectors corresponding to the main eigendimensions into an orthogonal matrix;

[0042] Calculate the singular value decomposition of the orthogonal matrix to obtain the left and right singular vectors;

[0043] Determine the dimensional transformation relationship of the orthogonal basis space based on the left and right singular vectors;

[0044] Construct a mapping matrix from the response signal to the orthogonal basis space based on the dimensional transformation relationship;

[0045] The coordinate transformation of the response signal in the orthogonal basis space is calculated using the mapping matrix;

[0046] Normalize the coordinate transformation results to obtain the projection coefficients.

[0047] By employing this technical solution, a mapping matrix is constructed from the response signal to an orthogonal basis space based on the dimensional transformation relationship determined by the left and right singular vectors, enabling the original signal to be optimally represented in this orthogonal basis space. This signal processing method based on orthogonal transformation achieves a standardized representation of signal features by normalizing the coordinate transformation results to obtain projection coefficients. This method reduces scale differences between different dimensions, improves the uniformity and comparability of feature representation, and enhances the system's adaptability to signal fluctuations, resulting in more stable and reliable lithium quality detection results.

[0048] In a second aspect, an embodiment of the present application provides a rapid quantitative detection system for lithium deposition, which 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 enable the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0049] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a system, enables the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0050] In a fourth aspect, an embodiment of the present application provides a computer program product, which, when executed on a system, enables the system to execute the method described in any possible implementation manner in the first aspect.

[0051] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0052] 1. The present application provides a method for rapid quantitative detection of lithium plating, which establishes a systematic electrode performance attenuation compensation mechanism by collecting and processing the potential response signal sequence of lithium ion selective electrodes within multiple preset cycles. By statistical analysis of the detection data segments, the mean, variance and time correlation parameters are obtained, and the dynamic characteristics of the electrode response are accurately characterized. The electrode response characteristic vector and system drift function constructed based on these characteristic parameters can quantitatively describe the attenuation law of electrode performance over time. The system compensates for the attenuation of electrode performance by calculating the correction matrix, reducing the measurement error caused by performance attenuation during 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, improves the accuracy of quantitative detection of lithium plating, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of detection data during the production process.

[0053] 2. This application provides a rapid quantitative detection method for lithium deposition. The mean decay term describes the changing 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 shift term characterizes the continuous shift of the response baseline, and the time-correlation gradient term describes the dynamic change rate of the response characteristics. Each term is combined with different weight coefficients, and multiple decay constants and adjustment parameters are introduced, so that the drift function can accurately describe the changes in electrode performance at different time scales, improving the system's accuracy in describing the electrode performance decay law.

[0054] 3. The present application provides a method for rapid quantitative detection of lithium deposition. 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, and then calculate the covariance matrix of the characteristic components and decompose their eigenvalues to achieve accurate quantitative characterization of the signal characteristics. The method of determining the main feature dimension based on the cumulative contribution rate reduces the redundancy of the data, extracts the most representative eigenvectors, and achieves 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 quality detection results, enhances the system's ability to suppress interference signals, and makes the detection results more stable and repeatable. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of a method for rapid quantitative detection of lithium deposition in an embodiment of the present application.

[0056] Figure 2 This is another flow chart of a rapid quantitative detection method for lithium deposition in an embodiment of the present application.

[0057] Figure 3 This is another flow chart of a rapid quantitative detection method for lithium deposition in an embodiment of the present application.

[0058] Figure 4 This is a schematic diagram of the physical device structure of a rapid quantitative detection system for lithium deposition provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The terms used in the following examples 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 this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0060] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0061] The present application provides a rapid quantitative detection method for lithium deposition, which 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 10ml). The data collector is connected to the sensor by a flexible rod, and a needle is installed in the sensor to be inserted into a sealed reaction bottle. The entire detection system is sealed, and all operations are based on the premise of ensuring sealing, whether before or after inserting the needle or adding ethanol. The reaction bottle is sealed, and 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, which can quickly detect the hydrogen concentration in the reaction bottle, and the hydrogen concentration in different reaction bottles can be quickly detected by inserting different reaction bottles.

[0062] The specific operation plan is as follows:

[0063] 1. Place the reaction bottle (bacteria bottle) in the glove box and install the electrode (lithium precipitation electrode);

[0064] 2. Seal the reaction bottle with a rubber stopper and aluminum cap, and press it tightly with a capping pliers;

[0065] 3. Take out the reaction bottle in a regular laboratory environment;

[0066] 4. Insert the syringe into the reaction bottle and add a certain amount of ethanol

[0067] (The amount of water added determines how much gas space remains in the reaction bottle. For example, if the reaction bottle is 10ml and 4ml of ethanol is added, the remaining gas space is 6ml)

[0068] 5. Quickly tape the syringe insertion port and wait for the ethanol to react with the lithium on the electrode;

[0069] 6. Move to the test position and insert the needle connected to the hydrogen sensor into the sealed reaction bottle;

[0070] 7. The reaction produces hydrogen, and the concentration of hydrogen in the closed system is detected by a hydrogen sensor to analyze the corresponding lithium mass.

[0071] The logic of the rapid quantitative detection method for lithium deposition in the present application is: a certain amount of ethanol is added dropwise to the electrode for lithium deposition - the electrode reacts with the ethanol to produce hydrogen - the hydrogen concentration is detected - and the mass of lithium is inferred based on the hydrogen concentration.

[0072] The following uses an embodiment and combines Figure 1 , a method for rapid quantitative detection of lithium deposition in an embodiment of the present application is described:

[0073] See also Figure 1 , is a flow chart of a rapid quantitative detection method for lithium deposition in an embodiment of the present application.

[0074] S101, after a preset volume of ethanol is dripped onto the surface of the electrode to be tested by a precision metering pump, a sensor is used to collect real-time concentration data of hydrogen generated by the reaction of ethanol with metallic lithium on the surface of the electrode to be tested;

[0075] In this step, the system first drips a preset volume of ethanol onto the surface of the electrode to be tested using a precision metering pump. This preset volume can be set based on parameters such as the size and material properties of the electrode to be tested, ensuring that the ethanol evenly covers the surface and fully reacts with it. The choice of ethanol is not limited to anhydrous ethanol; other alcohols that react with lithium metal to produce hydrogen, such as methanol and propanol, can also be used.

[0076] When ethanol is dripped onto the surface of the electrode to be tested, metallic lithium reacts with the ethanol to produce hydrogen and lithium ethoxylate. The system uses a sensor to collect real-time concentration data on the hydrogen produced during the reaction. The sensor can be a gas concentration sensor, such as an electrochemical gas sensor, a catalytic combustion gas sensor, or other sensors capable of real-time hydrogen concentration detection. The selection of the sensor requires consideration of performance indicators such as sensitivity, selectivity, and response time to ensure accurate and timely test results.

[0077] S102. Obtaining a curve showing changes in hydrogen concentration over time based on the real-time hydrogen concentration data;

[0078] After acquiring real-time hydrogen concentration data, the system needs to plot a curve showing the change in hydrogen concentration over time. By analyzing the shape and trend of the curve, we can understand the kinetic characteristics of the reaction between ethanol and metallic lithium, providing a basis for subsequent data processing and analysis.

[0079] When plotting a curve, the system can employ various data fitting and smoothing algorithms, such as least squares and spline interpolation, to reduce the impact of data noise and fluctuations, improving the smoothness and readability of the curve. Furthermore, the system can segment and normalize the curve as needed to highlight its characteristic information and facilitate subsequent analysis.

[0080] In some cases, the acquired hydrogen concentration data may contain outliers or missing data due to factors such as sensor performance and environmental interference. To address this issue, the system can use outlier detection and data repair algorithms, such as the 3σ criterion and Grubbs test, to identify and remove outliers. It also repairs missing data through interpolation and fitting to ensure the continuity and integrity of the curve.

[0081] S103, determining the reaction activity of metallic lithium on the surface of the electrode to be tested based on a curve of hydrogen concentration changing with time;

[0082] Based on the curve of hydrogen concentration changing over time, the system can calculate the slope of the curve at different time periods and determine the reactivity of the metallic lithium on the surface of the electrode under test based on 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 and ethanol, and the stronger the reactivity.

[0083] To accurately calculate the slope of a curve, the system can employ numerical differentiation algorithms, such as the finite difference method and curve fitting. Considering the potential for noise and fluctuations in the curve, and to improve the stability and reliability of the slope calculation, the system can smooth the curve using methods such as moving averages and Savitzky-Golay filtering to remove the influence of high-frequency noise. Furthermore, the system can select an appropriate time window and step size as needed to balance computational efficiency and accuracy.

[0084] S104, performing compensation correction on the real-time hydrogen concentration data according to the reaction activity to obtain corrected hydrogen concentration data;

[0085] After determining the reactivity of the lithium metal on the electrode surface under test, the system needs to compensate and correct the real-time hydrogen concentration data based on the reactivity. Due to differences in reactivity, hydrogen concentrations generated at different locations or during different time periods may vary, leading to errors in the test results. By introducing a reactivity factor to correct the hydrogen concentration data, this deviation can be effectively reduced and detection accuracy can be improved.

[0086] The specific compensation correction method can be selected based on the actual situation. Common methods include multiplication, addition, and exponential correction. For example, the hydrogen concentration data can be divided by the reaction activity factor corresponding to the time period or location to obtain the corrected concentration data. The correction factor can be calculated based on reaction kinetic models, empirical formulas, etc., or obtained through experimental calibration.

[0087] First, the system can establish a quantitative relationship model between hydrogen concentration and reaction activity based on the reaction activity factor calculated in step S103. Common models include linear models, exponential models, logarithmic models, etc., and the appropriate model form can be selected according to the actual situation. For example, a linear model can be used, assuming that there is a linear relationship between hydrogen concentration and reaction activity, that is:

[0088] C_corrected = C_measured / (1 + k * A)

[0089] Among them, C_corrected is the corrected hydrogen concentration, C_measured is the measured hydrogen concentration, A is the reaction activity factor, and k is the proportional coefficient, which can be obtained through experimental calibration or theoretical calculation.

[0090] After establishing the quantitative relationship model, the system can calculate the corrected hydrogen concentration value based on the real-time collected hydrogen concentration data and the corresponding reaction activity factor. Specifically, for each sampling time or sampling location, the system can divide the measured hydrogen concentration by the corresponding correction factor to obtain the corrected concentration value, that is:

[0091] C_corrected(t) = C_measured(t) / (1 + k * A(t))

[0092] Where t represents the sampling time or sampling position.

[0093] It should be noted that in actual applications, due to the large differences in the spatiotemporal distribution of reaction activity, a single correction factor may not meet the accuracy requirements. In order to further improve the correction effect, the system can adopt a segmented correction or local correction strategy, that is, according to the distribution characteristics of the reaction activity, the electrode to be tested is divided into multiple areas or time periods, and the correction factor is calculated for each area or time period, and local correction is performed. For example, the surface of the electrode to be tested can be divided into a central area and an edge area, and the reaction activity factor and correction coefficient of the two areas are calculated respectively. The hydrogen concentration data of each area is independently corrected, and finally the correction results of each area are merged to obtain the corrected concentration distribution of the entire electrode surface.

[0094] Furthermore, to further improve correction accuracy and reliability, the system can incorporate adaptive correction algorithms. Based on changes in hydrogen concentration before and after correction, the system dynamically adjusts the correction factor and model parameters to accommodate changing trends in 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 measurements. The correction results can also be smoothed to minimize the impact of noise and fluctuations.

[0095] S105: Convert the corrected hydrogen concentration data into lithium quality detection results.

[0096] After obtaining the corrected hydrogen concentration data, the system needs to convert it into lithium mass detection results for subsequent analysis and application. Based on the stoichiometric ratio of the reaction between metallic lithium and ethanol, a quantitative relationship between hydrogen concentration and lithium mass can be established, thus achieving data conversion.

[0097] During the conversion process, the system can use a pre-established standard curve or calculation formula to map hydrogen concentration data to lithium mass values. The standard curve can be obtained by preparing a series of lithium ion standard solutions of known concentrations, measuring the 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.

[0098] In the above embodiment, the real-time concentration data of hydrogen generated by the reaction of ethanol and metallic lithium on the surface of the electrode to be tested is collected by a sensor, and then a curve of the change of hydrogen concentration over time is obtained based on the real-time concentration data of hydrogen. Then, based on the curve of the change of hydrogen concentration over time, the reaction activity of the metallic lithium on the surface of the electrode to be tested is determined, and the real-time concentration data of hydrogen is compensated and corrected according to the reaction activity to obtain corrected hydrogen concentration data. Finally, the corrected hydrogen concentration data is converted into a lithium quality detection result, thereby improving the detection efficiency, and the detection accuracy is improved by using the activity compensation method, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of the detection data during the production process.

[0099] The following uses an embodiment and combines Figure 2 , another rapid quantitative detection method for lithium deposition in the embodiment of the present application is described:

[0100] See also Figure 1 , is another flow chart of a rapid quantitative detection method for lithium deposition in an embodiment of the present application.

[0101] S201, collecting a potential response signal sequence of the lithium ion selective electrode during a plurality of preset period detection processes;

[0102] This step is to collect the potential response signal of the lithium-ion selective electrode during the detection process. The system can be connected to the lithium-ion selective electrode through a potential acquisition device. During the preset detection period, the potential signal of the electrode is continuously sampled at a certain sampling frequency to obtain a potential response signal sequence. The preset period can be set according to the actual detection needs, 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, and can usually be set to 10 samples per second, 100 samples per second, etc.

[0103] In addition to the aforementioned acquisition methods, the system can also employ other methods to collect potential response signals. For example, segmented acquisition can be employed. Within each preset cycle, the acquisition time is divided into multiple segments, each segment is sampled at a higher frequency, and a certain interval is maintained between segments. This acquisition method can reduce the amount of data to a certain extent while still capturing key changes in the potential signal.

[0104] S202, dividing the potential response signal sequence into a plurality of detection data segments according to a preset time interval;

[0105] This step segments the collected potential response signal sequence. The system can segment the potential response signal sequence into multiple detection data segments based on preset time intervals. The preset time interval can be set according to actual needs, for example, to 1 second, 5 seconds, 10 seconds, etc. Each segmented detection data segment contains the potential response signal data within a specific time range.

[0106] In addition to segmenting at fixed intervals, the system can also employ other segmentation methods. For example, it can employ adaptive segmentation based on the changing characteristics of the potential response signal, using shorter segmentation intervals in intervals with large potential signal changes and longer intervals in intervals with small potential signal changes. This segmentation method can better capture key changes in the potential signal.

[0107] S203, performing statistical calculations on the multiple detection data segments to obtain the mean, variance, and time correlation parameters of each detection data segment;

[0108] This step calculates the statistical characteristics of the segmented detection data segments. The system performs statistical analysis on each detection data segment, calculating 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 parameters reflect the temporal variation characteristics of the potential signal within the data segment.

[0109] When calculating the mean and variance, the system can use traditional arithmetic mean and sample variance formulas. For temporal correlation parameters, the system can use the autocorrelation coefficient and cross-correlation coefficient. The autocorrelation coefficient reflects the temporal autocorrelation of the potential signal within a data segment, that is, the degree of correlation between a data point and itself at different time delays. The cross-correlation coefficient reflects the correlation of the potential signals between different data segments, that is, the degree of correlation between different data segments at the same time point.

[0110] S204, constructing an electrode response feature vector based on the mean, variance and time correlation parameters;

[0111] The system constructs the electrode response characteristic vector based on the mean, variance and time correlation parameters, specifically including: calculating the autocorrelation coefficient of each detection data segment, which is used to characterize the degree of correlation between adjacent data points in the same detection data segment; calculating the cross-correlation coefficient between adjacent detection data segments, which is used to characterize the data change trend between different detection data segments; performing a weighted combination of the autocorrelation coefficient and the cross-correlation coefficient to obtain the time series correlation characteristic value; arranging the mean, variance and time series correlation characteristic value in the order of preset dimensions to obtain the electrode response characteristic vector.

[0112] This step further processes and fuses 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 multidimensional feature vector, which serves as the electrode response feature vector for that data segment. The dimensions 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 the four dimensions to form a four-dimensional feature vector.

[0113] When constructing a feature vector, the system can assign different weights to different statistical features. For example, different weight coefficients can be assigned based on the degree of influence of different features on the electrode response. Weight coefficients can be set empirically or automatically learned through data analysis and machine learning. By setting appropriate weights, the role of important features can be highlighted, improving the representational power of the feature vector.

[0114] S205, establishing a system drift function according to the electrode response characteristic vector;

[0115] The system establishes a system drift function based on the electrode response characteristic vector. The system drift function is: ;

[0116] In the above function, is the system drift function, and are the weights of the mean decay term, the variance influence term, the periodic drift term, the mean shift 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 shift, is the time-correlation attenuation coefficient, is the mean shift correction coefficient, is the time-dependent gradient attenuation coefficient.

[0117] This step uses the electrode response eigenvector to establish a system drift function, which describes the temporal drift of the electrode response. The drift function can be represented as a mathematical model with time as input and the electrode response drift as output. The drift function can be established by analyzing and fitting the electrode response eigenvector.

[0118] The system can use a variety of methods to establish the drift function, including classic function fitting methods such as polynomial fitting, exponential function fitting, and Fourier series fitting. Machine learning methods such as support vector machines and neural networks can also be used to build a drift function model by learning the mapping relationship between the electrode response feature vector and time. The appropriate fitting method depends on the specific characteristics of the electrode response and the data distribution.

[0119] represents the combined effect of mean shift and variance shift, represents the attenuation of mean shift, Indicates the effect of variance drift;

[0120] represents the influence of periodic drift, It is a time-dependent parameter that reflects the periodic variation characteristics of the electrode response. is a sinusoidal function, representing a waveform of periodic drift.

[0121] represents the cumulative effect of mean shift, represents the offset of the mean relative to the initial value, is the time weight function of the cumulative effect.

[0122] Indicates the effect of the rate of change of the time-dependent parameter on the drift. is the rate of change of the time-dependent parameter, is the decay function of the time difference.

[0123] Calculate the correction matrix of the electrode response based on the system drift function;

[0124] 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: ;

[0125] 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 the characteristic time point, is the number of segments.

[0126] This step uses the established system drift function to calculate the electrode response correction matrix, which is used to perform drift correction on the electrode response. The correction matrix is a time-dependent matrix whose elements represent the correction coefficients required to perform drift correction on 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.

[0127] When calculating the correction matrix, the system can use numerical integration to solve the drift function at different time points to obtain the corresponding correction coefficients. Common numerical integration methods include the trapezoidal method, Simpson's method, and Romberg's method. The system can select the appropriate numerical integration method based on the complexity and accuracy requirements of the drift function.

[0128] represents the linear correction term based on the system drift function.

[0129] It represents the cosine-weighted integral correction term for the second-order derivative of the system drift function.

[0130] represents the nonlinear correction term based on the hyperbolic sine function. is the hyperbolic sine function.

[0131] Represents a segment correction term. is the hyperbolic tangent function, For the The start time of a segment.

[0132] In practical applications, the drift of electrode responses may exhibit nonlinear and time-varying characteristics, and a single drift function may not be able to fully describe the drift behavior. To address this issue, the system can introduce a segmented correction strategy, dividing the time axis into multiple intervals and using a different drift function and correction matrix in each interval. Segmented correction can better capture the local characteristics of electrode response drift and improve the accuracy of correction. At the same time, the system can also introduce an adaptive correction mechanism, dynamically adjusting the parameters of the correction matrix based on the real-time changes in the electrode response to adapt to changes in drift behavior.

[0133] S207: Apply the correction matrix to the currently collected potential response signal to obtain a corrected lithium quality detection result.

[0134] This step uses the calculated correction matrix to perform drift correction on the currently acquired potential response signal, obtaining a corrected lithium quality test result. The correction process can be achieved by performing mathematical operations on the potential response signal and the correction matrix, such as matrix multiplication and matrix addition.

[0135] When performing correction operations, the system can utilize efficient numerical computation libraries or hardware acceleration technologies, such as the BLAS (Basic Linear Algebra Subprograms) library and the GPU (Graphics Processing Unit), to improve computational efficiency and speed. Furthermore, the system can employ parallel computing methods, distributing correction operations to multiple processing units for simultaneous execution, further improving computational efficiency.

[0136] The corrected potential response signal can be converted into a corresponding lithium mass value using a pre-established concentration calibration curve or calibration model. These concentration calibration curves or calibration models, derived through experimental measurement or theoretical calculation, describe the quantitative relationship between the potential response signal and lithium mass. Based on the amplitude of the corrected potential response signal, the system uses the concentration calibration curve or calibration model to calculate the corresponding lithium mass value as the final test result.

[0137] In the above embodiment, a systematic electrode performance attenuation compensation mechanism is established by collecting and processing the potential response signal sequence of the lithium ion selective electrode within multiple preset cycles. By statistically analyzing the detection data segments, the mean, variance and time correlation parameters are obtained, and the dynamic characteristics of the electrode response are accurately characterized. The electrode response characteristic vector and system drift function constructed based on these characteristic parameters can quantitatively describe the attenuation law of the electrode performance over time. The system compensates for the electrode performance attenuation by calculating the correction matrix, reducing the measurement error caused by performance attenuation during 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, improves the accuracy of quantitative detection of lithium plating, thereby reducing the frequency of electrode replacement and calibration, and improving the continuity and reliability of detection data during the production process.

[0138] The above embodiment achieves compensation for electrode performance degradation and improves the accuracy of lithium quality detection by establishing a system drift function and a correction matrix. However, in practical applications, the electrode response signal often contains various complex noise and interference components, which will affect the reliability of the measurement results. In order to further improve the detection accuracy, it is necessary to conduct more in-depth feature analysis and signal processing on the electrode response signal. Figure 3 , another rapid quantitative detection method for lithium deposition in the embodiment of the present application is described:

[0139] See also Figure 3 , is another flow chart of a rapid quantitative detection method for lithium deposition in an embodiment of the present application.

[0140] S301, performing discrete wavelet transform on the electrode response signal sequence to obtain characteristic components at different time scales;

[0141] In this step, the system performs discrete wavelet transform processing on the acquired electrode response signal sequence. Discrete wavelet transform is a time-frequency analysis tool that can extract local features of the signal at different time scales. By performing discrete wavelet transform on the signal sequence, the system can obtain time domain components that reflect the signal in different frequency ranges, thereby achieving multi-scale decomposition of the signal. In addition to discrete wavelet transform, other time-frequency analysis methods such as short-time Fourier transform and Wigner-Ville distribution can also be used to extract the time-frequency features of the signal.

[0142] Specifically, the system selects appropriate wavelet basis functions, such as Daubechies wavelets and Symlets wavelets, determines the number of decomposition layers based on the signal's characteristics, and then performs wavelet decomposition on the signal sequence. At each decomposition layer, the system decomposes the signal into low-frequency approximate components and high-frequency detail components by scaling and translating the wavelet basis functions. Through multi-layer decomposition, the system obtains wavelet coefficients that represent the signal's characteristics at different time scales. These wavelet coefficients reflect the signal's energy distribution within the corresponding frequency band and contain information about the signal's local characteristics.

[0143] S302, calculating the covariance matrix of the characteristic components, and decomposing the eigenvalues of the covariance matrix;

[0144] 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 between different feature components and can be used to analyze linear relationships between features. By calculating the covariance matrix, the system can quantify the statistical dependencies between feature components at different time scales, providing a basis for subsequent feature selection and dimensionality reduction. In addition to the covariance matrix, other statistical quantities such as the correlation coefficient matrix and the mutual information matrix can also be used to characterize the correlation between features.

[0145] Specifically, the system organizes the wavelet feature components into a matrix, with each row representing an eigenvector at a time point. The system then calculates the covariance matrix of the feature matrix, where each element of the covariance matrix represents the covariance between two eigencomponents. Next, the system performs eigenvalue decomposition on the covariance matrix, obtaining a set of eigenvalues and corresponding eigenvectors. Eigenvalues represent the degree of variation in the covariance matrix, while eigenvectors represent the primary direction of variation. By analyzing the size and distribution of eigenvalues, the system can assess the importance and redundancy of features.

[0146] S303, arranging the characteristic values in descending order and establishing a cumulative contribution rate curve;

[0147] In this step, the system arranges 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, reflecting the degree of information retention in the feature subspace. By analyzing the changing 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 feature importance, such as the divergence of eigenvalues and the ratio of eigenvalues, can also be used to assist feature selection.

[0148] Specifically, the system first arranges the eigenvalues in descending order so that the eigenvalues with larger variance contributions are at the front. Then, the system calculates the cumulative contribution rate of each eigenvalue, that is, the ratio of the sum of the first k eigenvalues to the sum of all eigenvalues. Next, the system draws a cumulative contribution rate curve, with the horizontal axis representing the number of eigenvalues and the vertical axis representing the corresponding cumulative contribution rate. 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 be flat. This inflection point often corresponds to the number of main eigenvalues, that is, most of the data variance can be represented by fewer feature dimensions.

[0149] S304, determining the main feature dimension according to the cumulative contribution rate, and extracting the feature vector corresponding to the main feature dimension;

[0150] In this step, the system determines the primary eigenvalue (DV) based on the cumulative contribution rate curve obtained in the previous step. This is the number of eigenvalues that can represent the majority of the data variance. The system then extracts the eigenvectors corresponding to the primary eigenvalues and uses them as basis vectors for constructing the feature subspace. By selecting the primary eigenvalue, the system can achieve dimensionality reduction and noise removal while preserving the data's key information, simplifying subsequent analysis and modeling. In addition to the cumulative contribution rate, other dimensionality selection criteria, such as AIC and BIC, can also be used to determine the optimal DV.

[0151] Specifically, the system can determine the primary feature dimension based on the inflection point of the cumulative contribution rate curve, or by setting a contribution rate threshold (such as 90%). For example, when the cumulative contribution rate reaches 90%, the corresponding number of eigenvalues is the primary feature dimension. After determining the primary feature dimension, the system selects the eigenvectors corresponding to the primary feature dimension as the basis vectors of the feature subspace. These eigenvectors represent the main direction of change of the data in the feature space and contain key information about the data. By projecting the original data onto this feature subspace, the system can achieve dimensionality reduction and noise removal.

[0152] S305, constructing an orthogonal basis space using the eigenvector corresponding to the main eigendimension, and calculating the projection coefficient of the response signal in the orthogonal basis space;

[0153] The system uses the eigenvectors corresponding to the main eigendimension to construct an orthogonal basis space and calculates the projection coefficients of the response signal in the orthogonal basis space, specifically including: constructing the eigenvectors corresponding to the main eigendimension into an orthogonal matrix; calculating the singular value decomposition of the orthogonal matrix to obtain left and right singular vectors; determining the dimensional 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 dimensional transformation relationship; using the mapping matrix to calculate the coordinate transformation of the response signal in the orthogonal basis space; and normalizing the coordinate transformation results to obtain the projection coefficients.

[0154] In this step, the system uses the eigenvectors corresponding to the main eigendimensions obtained in the previous step to construct an orthogonal basis space. The orthogonal basis space is a linear space composed of a set of orthogonal vectors, which can be used for orthogonal decomposition and projection representation of the signal. 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, thereby realizing low-dimensional representation and feature extraction of the signal. In addition to the orthogonal basis constructed by the eigenvectors, other orthogonal bases such as the Fourier basis and the wavelet basis can also be used for orthogonal decomposition of the signal.

[0155] Specifically, the system first performs orthogonal processing on the eigenvectors corresponding to the main feature dimension, such as Schmidt orthogonalization or singular value decomposition, to obtain a set of orthogonal basis vectors. These orthogonal basis vectors constitute an orthogonal basis space, and each vector represents an orthogonal feature direction. Then, the system maps the response signal to this orthogonal basis space and calculates the projection coefficient of the signal on each orthogonal basis vector. The calculation of the projection coefficient can be achieved through an inner product operation, which represents the size of the signal component in the corresponding feature direction. By representing the signal as a set of projection coefficients, the system realizes low-dimensional representation and feature extraction of the signal, which can effectively remove noise and redundant information.

[0156] S306 : Establish a characteristic reconstruction equation based on the projection coefficient, and determine optimal reconstruction parameters based on the characteristic reconstruction equation.

[0157] The system establishes a feature reconstruction equation based on the projection coefficients and determines the optimal reconstruction parameters based on the feature reconstruction equation, specifically including: arranging the projection coefficients according to the dimensions 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 error corresponding to different reconstruction parameter values on the training set; verifying the reconstruction error on the validation set, and selecting the parameter value that minimizes the reconstruction error as the optimal reconstruction parameter.

[0158] 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 use the projection coefficients to reconstruct the original signal 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 high-precision restoration and feature representation of the signal. In addition to the reconstruction equation based on projection coefficients, other reconstruction models such as sparse representation and dictionary learning can also be used for signal reconstruction and feature extraction.

[0159] 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 set of linear equations that describes the signal reconstruction process. Then, the system solves the feature reconstruction equation using 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 sparsity, energy distribution and other characteristics of the reconstruction parameters, the system can further extract the key features and patterns of the signal.

[0160] In the above embodiment, by performing a 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, and then achieve accurate quantitative characterization of the signal characteristics by calculating the covariance matrix of the characteristic components and decomposing their eigenvalues. The method of determining the main characteristic dimension based on the cumulative contribution rate reduces the redundancy of the data, extracts the most representative eigenvectors, and achieves 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 quality detection results, enhances the system's ability to suppress interference signals, and makes the detection results more stable and repeatable.

[0161] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 4 , which is a schematic diagram of the physical device structure of a rapid quantitative detection system for lithium deposition provided in an embodiment of the present application.

[0162] It should be noted that Figure 4 The structure of the system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0163] like Figure 4 As shown, the system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403. RAM 403 also stores various programs and data required for system operation. CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0164] The following components are connected to the I / O interface 405: an input section 406 including a camera, infrared sensor, and the like; an output section 407 including a liquid crystal display (LCD) and speakers; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. 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. Removable media 411, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, and the like, is installed in the drive 410 as needed, so that computer programs read from the removable media can be installed in the storage section 408 as needed.

[0165] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409 and / or installed from removable media 411. When executed by central processing unit (CPU) 401, the computer program performs the various functions defined in the present invention.

[0166] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0167] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0168] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiments, or may exist independently and not incorporated into the system. The storage medium carries one or more computer programs, and when executed by a processor of a system, the system implements the methods provided in the above embodiments.

[0169] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, 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.

[0170] As used in the above embodiments, the term “when…” may be interpreted as “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted as “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0171] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented 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 can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0172] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A rapid quantitative detection method for lithium deposition, 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, the sensor collects 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; Obtaining a curve showing changes in hydrogen concentration over time based on the real-time hydrogen concentration data; Determining the reactivity of the metallic lithium on the surface of the electrode to be tested based on the curve of the change of 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; Converting the corrected hydrogen concentration data into lithium mass detection results; collecting a potential response signal sequence of the lithium ion selective electrode during a plurality 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 plurality of 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 for electrode response based on the system drift function, wherein the correction matrix includes a compensation coefficient for electrode performance degradation; The correction matrix is applied to the currently collected potential response signal to obtain a corrected lithium quality detection result.

2. The method according to claim 1, characterized in that The constructing of the electrode response feature vector based on the mean, the variance and the time correlation parameter specifically includes the following steps: Calculating the autocorrelation coefficient of each detection data segment, wherein the autocorrelation coefficient is used to characterize the degree of correlation 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 a weighted combination on the autocorrelation coefficient and the cross-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.

3. The method according to claim 1, characterized in that The system drift function is: ； In the above function, is the system drift function, 、 、 、 and the aforementioned are the weights of the mean decay term, the variance influence term, the periodic drift term, the mean shift term, and the time correlation gradient term, respectively. is the mean, the is the variance, is the time correlation parameter, is the electrode response attenuation constant, is the variance influence index, is the periodic drift frequency, the is the phase shift, the is the time-dependent attenuation coefficient, is the mean shift correction coefficient, is the time-dependent gradient attenuation coefficient.

4. The method according to claim 1, wherein 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.

5. The method according to claim 1, characterized in that After obtaining the corrected lithium quality test result, the method further includes the following steps: 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; Determining a main feature dimension according to the cumulative contribution rate, and extracting a feature vector corresponding to the main feature dimension; constructing an orthogonal basis space using the eigenvector corresponding to the main eigendimension, and calculating the projection coefficient of the response signal in the orthogonal basis space; A characteristic reconstruction equation is established based on the projection coefficients, and an optimal reconstruction parameter is determined based on the characteristic reconstruction equation.

6. The method according to claim 5, characterized in that The method 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 the following steps: Constructing the eigenvectors corresponding to the main eigendimensions into an orthogonal matrix; Calculating 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 result of the coordinate transformation is normalized to obtain a projection coefficient.

7. 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 according to any one of claims 1 to 6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to perform the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to perform the method according to any one of claims 1 to 6.

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