Electrolyte low-temperature conductivity test method, controller, medium and product

Abstract

The application discloses a kind of electrolyte low temperature conductivity test method, controller, medium and product, it is related to electric variable measurement field.Method includes: in response to test channel temperature reaches target low temperature value, obtains conductivity-time sequence and carries out differential processing, obtains first-order change rate sequence and second-order change acceleration sequence;When sequence meets the quasi-steady condition that first-order rate and second-order acceleration are less than preset threshold value, extract quasi-steady data segment;According to preset evolution function model, the quasi-steady data segment is nonlinearly fitted, and target evolution function is constructed;Finally, the limit value of function when time tends to infinity is calculated as the predicted conductivity.The application aims to solve the problem of long test cycle and non-steady state misjudgment caused by the long and slow change of thermal equilibrium process in traditional low temperature test, by multidimensional derivative determination and model extrapolation prediction, the intrinsic conductivity is accurately obtained when physical thermal equilibrium is not reached, and the detection efficiency and accuracy of electrolyte low temperature conductivity are improved.

Description

Technical Field

[0001] This application relates to the field of electrical variable measurement, and in particular to a method, controller, medium, and product for testing the low-temperature conductivity of electrolytes. Background Technology

[0002] The low-temperature ion transport characteristics of lithium-ion battery electrolytes directly determine the battery's power performance under cold conditions. Traditional conductivity testing processes typically place the conductivity cell containing the sample in a low-temperature environmental chamber, adjusting the ambient temperature to a preset point using a cooling medium before measurement. Because the thermal capacity and conductivity of the electrolyte and conductivity cell components differ from the ambient medium, relying solely on the feedback value from the temperature sensor built into the environmental chamber will cause the actual sample temperature to lag behind the set ambient temperature, introducing measurement errors and reducing the accuracy of the conductivity data in relation to the actual temperature.

[0003] To address the aforementioned temperature hysteresis issue, relevant technologies typically employ measurement methods based on long-term isothermal control and numerical stability assessment. This method, after the ambient temperature reaches a set value, maintains the electrolyte sample in static heat exchange for a predetermined duration. During this period, the system continuously collects conductivity readings and monitors their fluctuations. Only when the numerical fluctuation rate within a continuous time period falls below a preset fixed threshold is the system considered to have reached thermal equilibrium, and the data is recorded. This method, by monitoring numerical stability to assist in determining the thermal equilibrium state, corrects, to some extent, the measurement bias caused by the failure to eliminate macroscopic temperature differences.

[0004] However, in extremely low-temperature testing environments, the process of the electrolyte reaching thermal equilibrium exhibits an asymptotic characteristic with a very small rate of change. The fixed threshold logic of related technologies can easily misjudge this non-steady state as a steady state when the conductivity value changes slowly but has not yet reached its true endpoint, causing the recorded data to deviate from the sample's intrinsic conductivity. Setting an extremely small judgment threshold or an excessively long forced waiting time to avoid misjudgment increases the testing cycle and reduces the detection efficiency of low-temperature electrolyte conductivity testing. Summary of the Invention

[0005] This application provides a method, controller, medium, and product for testing the low-temperature conductivity of electrolytes, which improves the detection efficiency of low-temperature electrolyte conductivity testing while ensuring the accuracy of test data.

[0006] In a first aspect, this application provides a method for testing the low-temperature conductivity of an electrolyte, applied to the controller of a multi-channel electrolyte testing device. The multi-channel electrolyte testing device further includes multiple testing channels and corresponding data acquisition units. The method includes: responding to the ambient temperature within the testing channel reaching a target low temperature value, controlling the data acquisition unit to acquire the conductivity values ​​of the electrolyte sample within the testing channel as a function of time, generating a conductivity-time series; performing differential processing on the conductivity-time series to obtain a first-order rate-of-change sequence and a second-order acceleration sequence; and extracting the conductivity-time series when the conductivity-time series meets a preset quasi-steady-state condition. Within a preset time period, a quasi-steady-state data segment is used. The preset quasi-steady-state conditions include: within the preset time period, the absolute values ​​of all points in the first-order rate of change sequence are less than a preset first rate threshold, and the absolute values ​​of all points in the corresponding second-order acceleration sequence are less than a preset second convergence threshold. Based on a preset evolution function model, the quasi-steady-state data segment is nonlinearly fitted to determine the model parameters of the evolution function model. The model parameters are substituted into the evolution function model to construct the target evolution function. Based on the target evolution function, the limit value when the time variable tends to infinity is calculated, and the limit value is used as the predicted conductivity of the electrolyte sample at the target low temperature.

[0007] By employing the above technical solution, the controller first uses differential processing to convert discrete conductivity data into a first-order rate of change sequence and a second-order acceleration of change sequence characterizing the trend, thereby quantifying the thermodynamic evolution state of the electrolyte. Secondly, by setting dual convergence thresholds, the controller can eliminate seemingly stable but actually slowly drifting non-equilibrium stages, thus obtaining effective data segments. Finally, the controller uses an evolution function model to perform nonlinear fitting on this segment and, by calculating the limit value where time approaches infinity, predicts the intrinsic conductivity of the electrolyte before physical thermal equilibrium is fully achieved. In summary, this solution ensures that the test data accurately approximates the true thermal equilibrium value while shortening the low-temperature testing cycle and improving the detection efficiency of low-temperature electrolyte conductivity testing.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the preset evolution function model is a double exponential decay model, specifically including: ,in, This represents the conductivity value at time t, where t represents the time variable; The intercept parameter represents the limit value as the time variable approaches infinity; and These represent the first amplitude parameter and the second amplitude parameter, respectively, used to characterize the weights of the change in conductivity; The first time constant represents the interfacial heat exchange rate between the test channel and the electrolyte sample. The second time constant represents the internal thermal conduction rate of the electrolyte sample.

[0009] By employing the above-mentioned technical solution, this scheme utilizes the mathematical superposition property of double exponential functions to decompose the complex nonlinear trajectory of conductivity over time into a weighted combination of rapidly decaying and slowly decaying components. This simultaneously captures the initial sharp decline and the later gentle tailing phase in the data sequence, avoiding the underfitting problem of single-function models when handling cross-scale variations. Furthermore, based on the mathematical convergence rule that the exponential term inevitably approaches zero over time, the steady-state limit value and transient changes are decoupled at the algorithm level. In summary, this scheme improves the mathematical convergence and accuracy of predicting the conductivity of electrolyte samples at the target low temperature using a composite function model.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, nonlinear fitting is performed on quasi-steady-state data segments according to a preset evolution function model to determine the model parameters of the evolution function model. Specifically, this includes: performing initial fitting on the quasi-steady-state data segments corresponding to each test channel using the evolution function model to obtain the initial first time constant of each test channel; calculating the statistical characteristic values ​​of multiple initial first time constants as the final first time constant in the evolution function model; and, based on the final first time constant, performing constraint fitting again on the quasi-steady-state data segments corresponding to each test channel to obtain the final model parameters of each test channel. The model parameters include an intercept parameter, a first amplitude parameter, a second amplitude parameter, and a second time constant.

[0011] By adopting the above technical solution, the controller first performs an unconstrained initial fitting on each test channel to obtain an initial first time constant reflecting the hardware characteristics of each channel. Secondly, through the calculation of statistical eigenvalues, parameter deviations caused by random noise in individual channels are eliminated, and common interface heat exchange characteristic parameters shared by the multi-channel devices are extracted. Finally, the controller fixes this common first time constant and performs a secondary constrained fitting on each channel, forcing the algorithm to focus on solving the second time constant and limit value characterizing the bulk properties of the electrolyte. In summary, this solution suppresses overfitting in the multi-parameter fitting process, reduces the sensitivity of the calculation results to initial values, and improves the consistency and robustness of multi-channel test results.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of performing initial fitting on the quasi-steady-state data segments corresponding to each test channel using an evolution function model, the method further includes: calculating the numerical difference between the initial and final times of the quasi-steady-state data segments of each test channel, and determining the initial value of the amplitude parameter corresponding to each test channel based on the numerical difference; calculating the initial value of the first time constant corresponding to each test channel based on the time point when the first derivative of the quasi-steady-state data segment reaches its maximum value; and using the initial value of the amplitude parameter and the initial value of the first time constant as the iterative starting point for nonlinear fitting, and performing initial fitting on each test channel.

[0013] By adopting the above technical solution, the controller first analyzes the numerical difference of the quasi-steady-state data segment, directly extracting an approximate solution for the amplitude parameter from the physical data, avoiding blind guessing. Secondly, utilizing the time point where the maximum value of the first derivative occurs, and based on the inflection point characteristics of the double exponential function, the order of magnitude of the first time constant is quickly estimated. Finally, these values ​​calculated based on data characteristics are used as the starting point for nonlinear fitting iterations, making the optimization algorithm closer to the global optimum in the search space. In summary, this solution avoids the iteration process getting trapped in local minima or divergence, reduces the number of iterations required for regression calculations, and improves the controller's computational efficiency and fitting success rate when processing massive amounts of test data.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of substituting model parameters into the evolution function model to construct the target evolution function, the method further includes: calculating the residual sum of squares between the target evolution function and the quasi-steady-state data segment; if the residual sum of squares is greater than a preset confidence threshold, then controlling the data acquisition unit to extend the data acquisition time of the test channel, updating the quasi-steady-state data segment, and reconstructing the target evolution function until the residual sum of squares is not greater than the confidence threshold.

[0015] By employing the above technical solution, the controller calculates the sum of squared residuals after fitting, quantifying the degree of agreement between the target evolution function and the actual quasi-steady-state data. It then compares this degree of agreement with a confidence threshold to perform data quality checks. When the fitting effect is poor, the controller automatically extends the acquisition time and updates the data, providing more sample support for the model until the accuracy requirements are met. In summary, this solution dynamically adjusts the testing time based on the actual steady-state state of the samples, avoiding prediction bias caused by insufficient data and preventing misjudgment of atypical samples, thus achieving an adaptive balance between testing accuracy and efficiency.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the conductivity-time series is differentiated to obtain a first-order rate of change sequence and a second-order acceleration sequence. Specifically, this includes: sliding and truncating local data windows on the conductivity-time series based on a preset time step; performing polynomial regression fitting on the conductivity values ​​within each local data window to construct a continuous fitting function for the corresponding local data window; performing analytical differentiation on the continuous fitting function to calculate the first-order and second-order derivative values ​​at the target time point in the local data window; and generating a first-order rate of change sequence and a second-order acceleration sequence based on the time sequence of the first-order and second-order derivative values, respectively.

[0017] By adopting the above technical solution, the controller first uses a sliding window to segment the long sequence data into local segments, and performs polynomial regression fitting within each window. The smoothing properties of the polynomial are used to filter out high-frequency measurement noise, constructing a continuous fitting function. Finally, the continuous function is analytically differentiated, replacing direct difference operations on discrete data. In summary, this scheme effectively solves the problem of amplifying data noise in traditional numerical difference methods, improving the signal-to-noise ratio of the derivative sequence while preserving the subtle trends in conductivity.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of differentiating the conductivity-time series to obtain the first-order rate of change sequence and the second-order acceleration sequence, the method further includes: obtaining the reference conductivity value of the multi-channel electrolyte testing device at a standard ambient temperature; and normalizing and calibrating the conductivity-time series of each test channel based on the reference conductivity value.

[0019] By adopting the above technical solution, this solution eliminates the impact of multi-channel hardware inconsistency on test results, making the low-temperature conductivity of electrolyte measured by different channels comparable, and improving the overall metrological accuracy of the test equipment.

[0020] Secondly, this application provides a multi-channel electrolyte testing device, including a controller, multiple test channels and corresponding data acquisition units. The controller includes one or more processors and a memory. The memory is coupled to one or more processors and is used to store computer program code, which includes computer instructions. One or more processors call the computer instructions to cause the controller to execute the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium storing computer instructions that, when executed on a controller, cause the controller to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product, including a computer program or instructions that, when executed on a controller, cause the controller to perform the method described in the first aspect and any possible implementation thereof.

[0023] It is understood that the controller provided in the second aspect, the computer-readable storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. By adopting quasi-steady-state determination and nonlinear fitting prediction based on dual derivative thresholds, mathematical model extrapolation can be started when the data enters a stage of gradual change but not yet fully stable. This effectively solves the problem of long testing time and easy misjudgment of unsteady state caused by reliance on physical thermal equilibrium in related technologies, and thus achieves low-temperature conductivity prediction that balances high efficiency and high accuracy.

[0026] 2. Because a double exponential decay model is used as the evolution function model, the rapid decay segment and the slow tail segment of conductivity change are accurately fitted simultaneously using dual time scales. This effectively solves the fitting deviation problem caused by the difficulty of a single model in taking into account cross-scale change characteristics in related technologies, and thus achieves accurate analysis of the intrinsic conductivity limit value of the electrolyte.

[0027] 3. Because the method of calculating the initial parameter value based on the numerical difference and the extreme point of the derivative is adopted, it provides an iterative starting point close to the global optimum for nonlinear fitting. This effectively solves the problem in related technologies where the algorithm is prone to getting trapped in local minima or iterative divergence due to random selection of initial values, thereby achieving efficient convergence of fitting operations and stability of results. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of a multi-channel electrolyte testing device according to an embodiment of this application;

[0029] Figure 2 This is a schematic flowchart of a method for testing the low-temperature conductivity of an electrolyte in an embodiment of this application;

[0030] Figure 3 This is another schematic flowchart of a method for testing the low-temperature conductivity of an electrolyte in an embodiment of this application;

[0031] Figure 4 This is a schematic diagram of the physical device structure of the controller in an embodiment of this application. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] This application provides a method for testing the low-temperature conductivity of electrolytes, which is applied to the controller of a multi-channel electrolyte testing device.

[0035] Please see Figure 1 This is a schematic diagram of a multi-channel electrolyte testing device according to an embodiment of this application. The multi-channel electrolyte testing device 100 includes a controller 101, multiple test channels 102 and corresponding data acquisition units 103.

[0036] The multi-channel electrolyte testing equipment 100 is an automated testing device capable of simultaneously testing multiple groups of electrolyte samples in parallel. Through the synergy of hardware architecture and software algorithms, it achieves full-process automation from temperature control and data acquisition to intelligent prediction.

[0037] The controller 101 is the core computing and control center of the multi-channel electrolyte testing equipment 100. It is communicatively connected to multiple test channels 102 and data acquisition unit 103 to coordinate the operation of the entire testing system.

[0038] Multiple test channels 102 are physical spaces used to accommodate electrolyte samples to be tested. Each test channel 102 has an independent temperature-controlled environment or is placed in a unified low-temperature environment chamber, ensuring that the samples are under the set low-temperature conditions. The design of the test channels 102 allows multiple different electrolyte samples (e.g., different formulations or different batches) to be placed simultaneously without interference between channels, supporting high-throughput parallel testing.

[0039] The data acquisition unit 103 corresponds one-to-one with the test channel 102 (or is connected via multiplexing) and is used to acquire the electrochemical signals of the electrolyte sample in real time. Under the command of the controller 101, the data acquisition unit 103 measures the conductivity value of the sample in the test channel 102 at a preset sampling frequency (e.g., once every 5-30 seconds), and combines the measurement results with a precise timestamp to generate a conductivity-time series, which is transmitted to the controller 101 in real time for buffering and subsequent algorithm processing.

[0040] In some embodiments, the data acquisition unit 103 is also responsible for dynamically adjusting the acquisition duration based on feedback from the controller to ensure that the amount of data meets the requirements of fitting accuracy.

[0041] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is a flowchart illustrating a method for testing the low-temperature conductivity of an electrolyte in an embodiment of this application.

[0042] S201. In response to the ambient temperature in the test channel reaching the target low temperature value, the data acquisition unit is controlled to acquire the conductivity value of the electrolyte sample in the test channel over time, and generate a conductivity-time series.

[0043] Here, ambient temperature refers to the temperature value monitored in real time by the temperature sensor within the test channel; the target low temperature value represents the pre-set test temperature point; the conductivity value represents the ion transport capacity of the electrolyte sample at a specific moment; and the conductivity-time series refers to the set of conductivity measurements arranged in chronological order, recorded as follows: The data is in pairs format.

[0044] Specifically, after the test starts, the controller continuously monitors the feedback values ​​from the temperature sensors in each test channel. When the ambient temperature of a certain test channel reaches the target low temperature value (e.g., the deviation between the actual temperature and the target temperature is within ±0.5℃), the controller immediately triggers the data acquisition process for that channel. The controller sends an acquisition command to the corresponding data acquisition unit, setting the sampling frequency, for example, once every 5 to 30 seconds. The data acquisition unit continuously measures the complex impedance or conductivity of the electrolyte sample according to the set frequency and binds the measurement results with the corresponding timestamp for storage. The controller organizes the received data into a conductivity-time series in chronological order. This series is updated in real time and stored in the controller's buffer area, providing the raw data foundation for subsequent data processing.

[0045] In multi-channel parallel testing scenarios, the controller independently manages the data acquisition process of each channel to ensure that the difference in the temperature arrival time of each channel does not affect the accuracy of data recording.

[0046] Optionally, in some embodiments, after obtaining the conductivity-time series, the controller acquires the reference conductivity value of the multi-channel electrolyte testing device at a standard ambient temperature; and performs normalization calibration on the conductivity-time series of each test channel based on the reference conductivity value.

[0047] Specifically, before the formal low-temperature test, the controller first controls each test channel to perform a pre-test on the same standard electrolyte or their respective test samples at a standard ambient temperature to obtain the baseline conductivity value. Where i represents the test channel number. During the low-temperature test, the controller normalizes the conductivity-time series acquired from each channel using a ratio method. or difference method Calibration is performed, among which This is to account for the system bias of test channel i. This calibration mechanism can effectively eliminate hardware differences between multiple channels and improve the comparability and consistency of test results from different channels.

[0048] S202. Differentiate the conductivity-time series to obtain the first-order rate of change series and the second-order acceleration series.

[0049] Differential processing refers to the process of numerically differentiating discrete-time series data; the first-order rate of change sequence represents the set of first-order derivatives of conductivity with respect to time, denoted as... ,in This is used to characterize the rate of change of conductivity; the second-order acceleration sequence represents the set of second derivatives of conductivity with respect to time, recorded as... ,in It is used to characterize the convergence or divergence properties of the trend of electrical conductivity change.

[0050] Specifically, upon receiving the continuously updated conductivity-time series, the controller initiates the differential processing algorithm module in real time. For calculating the first-order rate of change, the controller employs numerical differentiation methods, such as forward differencing, backward differencing, or central differencing, to calculate the slope between adjacent data points. For calculating the second-order acceleration, the controller performs differential processing on the first-order rate of change series again, for example, using... or The calculation is performed in a specific manner. To suppress the influence of measurement noise on the differential result, the controller can first perform moving average filtering or smoothing filtering preprocessing on the original conductivity-time series before performing the differential operation. The time indices of the calculated first-order and second-order sequences, which are synchronized with the original time series, are stored in the controller's data processing buffer.

[0051] S203. When the conductivity-time series meets the preset quasi-steady-state conditions, extract the quasi-steady-state data segment of the conductivity-time series within the preset time length. The preset quasi-steady-state conditions include: within the preset time length, the absolute value of each point in the first-order rate of change sequence is less than the preset first rate threshold, and the absolute value of each point in the corresponding second-order acceleration sequence is less than the preset second convergence threshold.

[0052] Among them, the preset quasi-steady-state condition refers to the composite criterion used to determine whether the thermodynamic state of the electrolyte sample has entered the fitable range; the preset time length represents the width of the sliding time window used for quasi-steady-state determination, for example, set to 5 minutes to 15 minutes. The setting of this parameter needs to take into account the data sampling frequency and the characteristic time scale of the thermal relaxation process; the quasi-steady-state data segment refers to the continuous conductivity-time data subset that meets the quasi-steady-state condition; the first rate threshold represents the upper limit of the first-order rate of change, used to filter data segments with sufficiently slow changes. The setting of this threshold is based on the viscosity characteristics of the electrolyte at the target low temperature and the expected thermal equilibrium time, and is usually set to 0.01% / min to 0.1% / min of the conductivity value; the second convergence threshold represents the upper limit of the second-order acceleration, used to exclude oscillation or drift stages where the change trend has not yet stabilized. The setting of this threshold is usually 10% to 50% of the first rate threshold, used to ensure that the data not only changes slowly but also that the change trend has become gradual.

[0053] Specifically, after acquiring and processing the conductivity-time series and its derivative series, the controller initiates quasi-steady-state determination logic. The controller employs a sliding time window mechanism, backtracking data for a preset time period (e.g., the most recent 10 minutes) from the current moment, extracting all conductivity data points and their corresponding first and second-order derivative values ​​within that window. The controller then checks the absolute value of each point in the first-order rate of change sequence within the window. Are all values ​​less than the first rate threshold? Simultaneously, check the absolute value of each point in the corresponding second-order acceleration sequence. Are all values ​​less than the second convergence threshold?

[0054] The controller determines that the system has entered the quasi-steady-state region only when all data points within the window simultaneously meet both conditions. It then extracts all conductivity-time data pairs within that time window to form a quasi-steady-state data segment. This data segment is continuously updated as the time window slides, providing high-quality data samples for nonlinear fitting.

[0055] If any condition is not met, the controller continues to collect data and waits for the next decision cycle to ensure that the extracted data segments truly reflect the slow relaxation characteristics of the system rather than transient disturbances.

[0056] S204. Based on the preset evolution function model, perform nonlinear fitting on the quasi-steady-state data segment to determine the model parameters of the evolution function model.

[0057] The preset evolution function model refers to the mathematical expression used to describe the evolution of electrolyte conductivity over time; the model parameters represent the undetermined coefficients in the evolution function model.

[0058] The change in electrolyte conductivity at low temperatures is mainly affected by two thermal processes: rapid heat exchange at the interface between the cell wall and the electrolyte, and slow heat conduction within the electrolyte itself. The double-exponential model can account for these two physical processes; therefore, the pre-defined evolution function model is a double-exponential decay model, specifically including:

[0059]

[0060] in, This represents the conductivity value at time t, where t represents the time variable;

[0061] The intercept parameter represents the limit value as the time variable approaches infinity;

[0062] and These represent the first amplitude parameter and the second amplitude parameter, respectively, used to characterize the weights of the change in conductivity;

[0063] The first time constant represents the interfacial heat exchange rate between the test channel and the electrolyte sample.

[0064] The second time constant represents the internal thermal conduction rate of the electrolyte sample.

[0065] Specifically, after extracting the quasi-steady-state data segment, the controller calls the nonlinear fitting algorithm module to process the data segment. The controller takes the time variable t and the conductivity value k from the quasi-steady-state data segment as inputs and applies the double exponential decay model... As the fitting objective function, the controller first estimates the initial values ​​of the model parameters, for example, setting the initial value of y0 as the last conductivity value of the data segment. and The initial values ​​are set to be proportionally distributed based on the difference between the first and last parts of the data segment. , The initial values ​​are set to 10% and 50% of the data segment's time span, respectively. The controller employs nonlinear optimization methods such as the trust region algorithm or genetic algorithm to minimize the sum of squared residuals of the objective function through iterative calculation. Gradually adjust the parameter values ​​until convergence.

[0066] In each iteration, the controller updates the parameter estimates and calculates goodness-of-fit metrics such as the coefficient of determination or root mean square error. When the goodness-of-fit reaches a preset standard or the maximum number of iterations is reached, the controller outputs the final set of model parameters. This completes the nonlinear fitting process.

[0067] S205. Substitute the model parameters into the evolution function model to construct the target evolution function.

[0068] Specifically, the controller obtains the model parameter set from the nonlinear fitting output. Then, these specific values ​​are substituted into the general form of the double exponential decay model. For example, if the fit yields... Then the objective evolution function constructed by the controller is The controller stores the target evolution function in the data record of the current test channel and can optionally plot a comparison graph of the fitted curve and the measured data for quality verification.

[0069] S206. Based on the target evolution function, calculate the limit value when the time variable tends to infinity, and use the limit value as the predicted conductivity of the electrolyte sample at the target low temperature.

[0070] The predicted conductivity refers to the theoretical conductivity value of the electrolyte sample in a complete thermal equilibrium state at the target low temperature, obtained by extrapolation based on quasi-steady-state data.

[0071] Specifically, after constructing the target evolution function, the controller performs a limit analysis on that function. Due to the exponential function... As t approaches infinity, the value approaches zero, and the controller calculates... Therefore, the controller directly extracts the intercept parameter from the model parameters. This is the limit value as the time variable approaches infinity. The controller will use this limit value. The predicted conductivity of the electrolyte sample at the current target low temperature is marked and recorded in the test report along with the corresponding temperature value, goodness of fit, time constant, and other information. The controller then determines that the test at the current temperature point is complete, and without waiting for the actual achievement of physical thermal equilibrium, it can immediately start the test process for the next target low temperature value or end the entire test task.

[0072] In this embodiment, the quasi-steady-state condition determination based on first-order rate of change and second-order acceleration sequences is employed. Compared to traditional first-order rate of change determination, the introduction of second-order acceleration determination effectively identifies pseudo-steady-state conditions that appear to change slowly but are still within the nonlinear decay range, thus avoiding premature sampling. Furthermore, by combining an evolution function model with nonlinear fitting of quasi-steady-state data segments to calculate the limit value, the final steady-state value can be accurately calculated using a mathematical model even before the electrolyte sample has fully reached physical thermal equilibrium. This effectively solves the problems in related technologies where long periods of static waiting for thermal equilibrium lead to long testing cycles and susceptibility to non-steady-state errors, thereby achieving a simultaneous improvement in the efficiency and data accuracy of low-temperature conductivity testing.

[0073] The above embodiments highlight the basic process of low-temperature conductivity testing of electrolytes. Quasi-steady-state conditions are determined through differential processing, and limit values ​​are predicted using an evolution function model, achieving efficient testing without waiting for physical thermal equilibrium. In practical applications, to further improve the signal-to-noise ratio of differential data and the robustness of nonlinear fitting, more refined control is needed on the sliding window settings, differential algorithm details, and iterative strategies for fitting parameters during data processing to adapt to complex and changing testing environments.

[0074] Based on the above embodiments, the method provided in this embodiment will be described in further detail below. Please refer to... Figure 3 This is another flowchart illustrating a method for testing the low-temperature conductivity of an electrolyte in an embodiment of this application.

[0075] S301. In response to the ambient temperature in the test channel reaching the target low temperature value, the data acquisition unit is controlled to acquire the conductivity value of the electrolyte sample in the test channel over time, and generate a conductivity-time series.

[0076] This step is similar to the description of step S201 in the above embodiment, and will not be repeated here.

[0077] S302. Based on a preset time step, slide and capture a local data window on the conductivity-time series.

[0078] The preset time step represents the time interval at which the controller moves the sliding window on the conductivity-time series. The setting of this parameter needs to take into account both the real-time performance and smoothness of the derivative calculation, and is usually set to 1 to 5 times the data acquisition cycle. The local data window refers to a continuous subset of data with a fixed time span taken forward or backward from a certain moment as the center or endpoint. Its window width needs to be greater than the typical noise period of the conductivity signal to achieve effective filtering, and at the same time, it needs to be smaller than the characteristic time scale of the quasi-steady-state process to capture the true evolution trend.

[0079] Specifically, upon receiving the continuously updated conductivity-time series, the controller initiates a sliding window extraction procedure. Starting from the beginning of the conductivity-time series, the controller gradually moves the starting point of the window backward according to a preset time step, extracting a partial data window with each movement. For example, if the window width is set to 60 seconds and the time step is set to 10 seconds, the controller sequentially extracts data segments of 0-60 seconds, 10-70 seconds, and 20-80 seconds. The controller marks each extracted partial data window, records its corresponding time range and center time, and extracts all data points and timestamps within the window to form an independent data subset.

[0080] S303. Perform polynomial regression fitting on the conductivity values ​​within each local data window to construct a continuous fitting function for the corresponding local data window.

[0081] Among them, polynomial regression fitting refers to the mathematical process of finding a polynomial equation to approximate discrete data points using the principle of least squares; continuous fitting function refers to a mathematical expression obtained through fitting that can describe the continuous change of conductivity with time within a local data window.

[0082] Specifically, for each extracted local data window, the controller calls the built-in mathematical operation module to perform regression analysis. The controller establishes a low-order polynomial model, typically choosing a 2nd-order (parabolic) or 3rd-order (cubic) polynomial, in the form of... The controller substitutes the discrete data points within the window into the model and uses the least squares algorithm to find the coefficient set that minimizes the sum of squared errors. By determining these coefficients, the controller successfully constructed a continuous fitting function corresponding to the current local data window.

[0083] S304. Perform analytical differentiation on the continuous fitting function and calculate the first and second derivative values ​​at the target time point in the local data window.

[0084] The target time point usually refers to the center or end time of a local data window, representing the time node corresponding to that window; the first derivative value represents the slope of the continuous fitting function at the target time point, i.e., the rate of change; the second derivative value represents the curvature of the continuous fitting function at the target time point, i.e., the acceleration of change.

[0085] Specifically, the controller performs calculations using the analytical form of the constructed continuous fitting function. Since the derivative of a polynomial function has a clear algebraic form, the controller does not need to perform numerical differencing; instead, it directly calculates the product of the coefficients and the power. For example, for a third-order polynomial... The controller calculates the first derivative function according to the formula. Calculate the second derivative function according to the formula. Subsequently, the controller selects the target time point within the local data window (e.g., the last moment of the window). Substitute these values ​​into the above derivative formula to calculate the first and second derivative values ​​at that moment.

[0086] S305. Based on the time series of the first and second derivative values, generate the first-order rate of change sequence and the second-order acceleration sequence, respectively.

[0087] Here, the first-order rate of change sequence represents a data set composed of the first-order derivative values ​​corresponding to each local data window in chronological order, recorded as follows: The second-order acceleration sequence represents a data set composed of the second-order derivative values ​​corresponding to each local data window in chronological order, and is recorded as follows: , where n is the total number of local data windows.

[0088] Specifically, as the sliding window advances along the time axis, the controller continuously outputs a series of derivative values ​​corresponding to different moments. The controller allocates dedicated memory space to associate each first-order derivative value calculated in step S304 with its corresponding timestamp, appending them sequentially into an array to generate a first-order rate-of-change sequence describing the speed of change in conductivity. Simultaneously, the controller associates each second-order derivative value with its timestamp, appending them sequentially into another array to generate a second-order acceleration sequence describing the convergence of the conductivity change trend.

[0089] S306. When the conductivity-time series meets the preset quasi-steady-state conditions, extract the quasi-steady-state data segment of the conductivity-time series within the preset time length. The preset quasi-steady-state conditions include: within the preset time length, the absolute value of each point in the first-order rate of change sequence is less than the preset first rate threshold, and the absolute value of each point in the corresponding second-order acceleration sequence is less than the preset second convergence threshold.

[0090] This step is similar to step S203 in the above embodiments, and will not be repeated here.

[0091] S307. Calculate the numerical difference between the initial and final times of the quasi-steady-state data segment of each test channel, and determine the initial value of the amplitude parameter corresponding to each test channel based on the numerical difference.

[0092] Here, the initial time and the cutoff time refer to the start and end points of the quasi-steady-state data segment on the time axis, respectively; the initial value of the amplitude parameter refers to the first amplitude parameter in the evolution function model. Second amplitude parameter The initial guess value before iterative optimization.

[0093] Specifically, before entering the nonlinear fitting stage, the controller first performs feature analysis on the extracted quasi-steady-state data segment. The controller reads the conductivity value of the first data point of this segment. and the conductivity value of the last data point And calculate the difference between the two. This numerical difference reflects the main rate of conductivity decay during this cooling or equilibrium phase. Since the total change in conductivity from the initial state to the equilibrium state is jointly determined by the two exponential decay models, the controller uses this numerical difference based on physical experience. The weights are allocated according to the typical weights of fast and slow relaxation processes.

[0094] S308. Based on the time point when the first derivative of the quasi-steady-state data segment reaches its maximum value, calculate the initial value of the first time constant corresponding to each test channel.

[0095] The point at which the first derivative reaches its maximum value refers to the moment when the absolute value of the derivative in the first-order rate of change sequence corresponding to the quasi-steady-state data segment is the largest. This moment reflects the instant when the conductivity changes most drastically. The initial value of the first time constant represents the initial estimate of the first time constant used for nonlinear fitting iteration, which is estimated based on the time interval between the moment when the conductivity changes the fastest and the moment when the quasi-steady state begins.

[0096] Specifically, the controller iterates through all derivative values ​​and calculates their absolute values ​​from the first-order rate of change sequence corresponding to the quasi-steady-state data segments of each test channel, identifying the data point with the largest absolute value and its corresponding time point. The controller extracts the initial time of the quasi-steady-state data segment and calculates the time interval. Based on the theoretical characteristics of the double exponential decay model, the fast relaxation term... The first derivative in The value is near an extreme, so the controller uses this time interval as a rough estimate of the first time constant, setting it to... The controller stores the calculated initial value of the first time constant in the fitting parameter cache of each test channel.

[0097] When the conductivity changes extremely slowly, resulting in no obvious peak value in the first derivative, the controller adopts a backup strategy, using 10% to 20% of the time span of the quasi-steady-state data segment as the initial value of the first time constant.

[0098] S309. Using the initial values ​​of the amplitude parameter and the first time constant as the iterative starting point for nonlinear fitting, perform initial fitting on each test channel.

[0099] Specifically, after calculating the initial values ​​of the amplitude parameter and the first time constant, the controller initiates the initial fitting program for each test channel. The controller then uses the calculated values... , and Assemble the initial parameter vector, and simultaneously perform initial value estimation for other parameters, such as the initial value of the intercept parameter. The initial value of the second time constant is set as the average value of the last few points of the quasi-steady-state data segment. Set to 50% to 80% of the quasi-steady-state data segment time span. The controller will then display the complete initial parameter vector. As the starting point for iteration, the data is input into the nonlinear fitting algorithm module. The controller calls the Levenberg-Marquardt algorithm or the trust region algorithm to perform an initial fit on the quasi-steady-state data segments of each test channel, with the goal of minimizing the sum of squared residuals. During the iteration process, the controller monitors the goodness-of-fit index and the parameter update magnitude. When the convergence condition is met (e.g., the residual rate of change is less than...), the controller... The iteration terminates and the fitting result is output when the number of iterations reaches the upper limit.

[0100] S310. Using the evolution function model, the quasi-steady-state data segments corresponding to each test channel are initially fitted to obtain the initial first time constant of each test channel.

[0101] The initial first time constant refers to the first time constant value independently calculated by each test channel after the initial fitting is completed, which is used to characterize the specific interface thermal response speed of that channel.

[0102] Specifically, after performing an initial fit on each test channel, the controller extracts the first time constant parameter from the fitting results of each channel. The controller then iterates through the fitting outputs of all test channels and reads the optimized parameter vector for each channel. And extract the first time constant from it. The controller performs a validity check on the extracted initial first time constants of each test channel, discarding obviously abnormal fitting results. The controller then selects the initial first time constants of each test channel that pass the validity check. The set of constituent vectors is stored in the data cache, where m is the number of valid channels.

[0103] S311. Calculate the statistical characteristic values ​​of multiple initial first time constants as the final first time constants in the evolution function model.

[0104] Among them, statistical characteristic values ​​refer to representative values ​​obtained by statistical analysis of multiple initial first time constants. Commonly used statistical characteristic values ​​include arithmetic mean, median or weighted average. The final first time constant represents the fixed parameter value determined by cross-channel statistical analysis and used for constraint fitting of all test channels. It should be consistent in the same batch of tests.

[0105] Specifically, after acquiring the initial set of first time constants for each test channel, the controller first calculates the arithmetic mean of this set as a preliminary candidate for statistical feature values. To improve robustness, the controller simultaneously calculates the median by taking the value at the middle position after sorting the dataset. The controller compares the difference between the mean and the median. If the relative deviation is less than a preset threshold (e.g., 10%), the arithmetic mean is selected as the final first time constant; if the deviation is large, the median is selected to suppress the influence of outliers.

[0106] The controller can also calculate the standard deviation to assess the data dispersion. If the standard deviation is too large, a warning will be triggered indicating a possible inconsistency problem between channels. The controller stores the determined final first time constant as a fixed parameter in the global configuration and fixes this parameter in subsequent constraint fitting, no longer using it as an optimization variable.

[0107] If the dispersion of the calculated initial first time constants exceeds the preset tolerance (e.g., the variance is greater than X), the abnormal channel data is removed and the statistical feature value is recalculated, or the abnormal channel is fitted separately without constraints.

[0108] S312. Based on the final first time constant, constrain the quasi-steady-state data segments corresponding to each test channel again to obtain the final model parameters of each test channel. The model parameters include the intercept parameter, the first amplitude parameter, the second amplitude parameter, and the second time constant.

[0109] Constraint fitting refers to the regression process of optimizing the remaining parameters while fixing some model parameters (treating them as known constants); the final model parameters refer to the set of parameters determined after constraint optimization and used for the final prediction calculation.

[0110] Specifically, the controller reconfigures the nonlinear fitting algorithm, this time removing the first time constant term from the evolution function model. The values ​​determined in step S311 are fixed and no longer considered as variables to be optimized. The controller only applies to the remaining four parameters. The optimal solution is found through free iteration.

[0111] By reducing the number of unknowns and introducing high-confidence physical constraints, the controller can improve the stability and convergence accuracy of the fit, and in particular, it can more accurately resolve the second time constant representing the bulk properties of the electrolyte. and the intercept parameter representing the final truth value This allows us to obtain the final model parameters for each channel.

[0112] S313. Substitute the model parameters into the evolution function model to construct the target evolution function.

[0113] Optionally, in some embodiments, after constructing the target evolution function, the controller further calculates the sum of squared residuals between the target evolution function and the quasi-steady-state data segment;

[0114] If the sum of squared residuals is greater than the preset confidence threshold, the data acquisition unit is controlled to extend the data acquisition time of the test channel, update the quasi-steady-state data segment, and reconstruct the target evolution function until the sum of squared residuals is not greater than the confidence threshold.

[0115] S314. Based on the target evolution function, calculate the limit value when the time variable tends to infinity, and use the limit value as the predicted conductivity of the electrolyte sample at the target low temperature.

[0116] Steps S313 and S314 are similar to those described in steps S205 and S206 in the above embodiments, and will not be repeated here.

[0117] In this embodiment, by employing a polynomial regression differential processing based on a sliding local data window, and combining it with a double exponential decay model to optimize initial values ​​and constrain fitting of quasi-steady-state data segments, not only is the interference of measurement noise on the calculation of the rate of change eliminated, but the calculation accuracy of model parameters is also improved by utilizing statistical eigenvalue constraints. This effectively solves the problems of low signal-to-noise ratio of differential data and easy trapping in local optima or overfitting in multi-parameter fitting in related technologies, thereby improving the robustness and efficiency of electrolyte conductivity under complex low-temperature conditions.

[0118] The controller in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 4 This is a schematic diagram of the physical device structure of the controller in an embodiment of this application.

[0119] It should be noted that, Figure 4 The controller structure shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0120] like Figure 4 As shown, the controller includes a CPU 401, which can perform various appropriate actions and processes based on a program stored in the read-only memory ROM 402 or a program loaded from the storage section 408 into the random access memory RAM 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An I / O interface 405 is also connected to the bus 404.

[0121] The following components are connected to I / O interface 405: input section 406 including audio input devices, push-button switches, etc.; output section 407 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 408 including a hard disk, etc.; and communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0122] 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 / instructions carried on a computer-readable medium, the computer program / instructions containing computer program / instructions for performing the methods shown in the flowcharts. In such embodiments, the computer program / instructions can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by CPU 401, it performs the various functions defined in the present invention.

[0123] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can 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.

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0125] Specifically, the controller in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the electrolyte low-temperature conductivity testing method provided in the above embodiment.

[0126] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the controller described in the above embodiments; or it may exist independently and not assembled into the controller. The storage medium carries one or more computer programs that, when executed by a processor of the controller, cause the controller to implement the electrolyte low-temperature conductivity testing method provided in the above embodiments.

[0127] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0128] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0129] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above 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 method for testing the low-temperature conductivity of an electrolyte, characterized in that, A controller for a multi-channel electrolyte testing device, the multi-channel electrolyte testing device further comprising multiple testing channels and corresponding data acquisition units, the method comprising: In response to the ambient temperature in the test channel reaching the target low temperature value, the data acquisition unit is controlled to acquire the conductivity value of the electrolyte sample in the test channel over time, and generate a conductivity-time series. Differentiating the conductivity-time series yields a first-order rate-of-change sequence and a second-order acceleration-of-change sequence. When the conductivity-time series meets the preset quasi-steady-state conditions, extract the quasi-steady-state data segment of the conductivity-time series within the preset time length. The preset quasi-steady-state conditions include: within the preset time length, the absolute value of each point in the first-order rate of change sequence is less than the preset first rate threshold, and the absolute value of each point in the corresponding second-order acceleration sequence is less than the preset second convergence threshold. Based on the preset evolution function model, the quasi-steady-state data segment is nonlinearly fitted to determine the model parameters of the evolution function model; Substitute the model parameters into the evolution function model to construct the target evolution function; Based on the target evolution function, the limit value when the time variable tends to infinity is calculated, and the limit value is used as the predicted conductivity of the electrolyte sample at the target low temperature.

2. The method according to claim 1, characterized in that, The preset evolution function model is a double exponential decay model, specifically including: in, This represents the conductivity value at time t, where t represents the time variable; The intercept parameter represents the limit value as the time variable approaches infinity; and These represent the first amplitude parameter and the second amplitude parameter, respectively, used to characterize the weights of the change in conductivity; The first time constant represents the interfacial heat exchange rate between the test channel and the electrolyte sample. The second time constant represents the internal thermal conductivity rate of the electrolyte sample.

3. The method according to claim 2, characterized in that, The step of performing nonlinear fitting on the quasi-steady-state data segment according to a preset evolution function model to determine the model parameters of the evolution function model specifically includes: Using the evolution function model, the quasi-steady-state data segments corresponding to each test channel are initially fitted to obtain the initial first time constant of each test channel. Calculate the statistical characteristic values ​​of multiple initial first time constants, and use them as the final first time constants in the evolution function model; Based on the final first time constant, the quasi-steady-state data segments corresponding to each test channel are constrained and fitted again to obtain the final model parameters of each test channel. The model parameters include the intercept parameter, the first amplitude parameter, the second amplitude parameter, and the second time constant.

4. The method according to claim 3, characterized in that, Before the step of performing an initial fitting of the quasi-steady-state data segment corresponding to each of the test channels using the evolution function model, the method further includes: Calculate the numerical difference between the initial and final times of the quasi-steady-state data segment of each test channel, and determine the initial value of the amplitude parameter corresponding to each test channel based on the numerical difference; Based on the time point when the first derivative of the quasi-steady-state data segment reaches its maximum value, calculate the initial value of the first time constant corresponding to each of the test channels; The initial values ​​of the amplitude parameter and the first time constant are used as the iterative starting point for nonlinear fitting, and the initial fitting is performed on each of the test channels.

5. The method according to claim 4, characterized in that, After the step of substituting the model parameters into the evolution function model to construct the target evolution function, the method further includes: Calculate the sum of squared residuals between the target evolution function and the quasi-steady-state data segment; If the sum of squared residuals is greater than a preset confidence threshold, the data acquisition unit is controlled to extend the data acquisition time of the test channel, update the quasi-steady-state data segment, and reconstruct the target evolution function until the sum of squared residuals is not greater than the confidence threshold.

6. The method according to claim 1, characterized in that, The step of differentiating the conductivity-time series to obtain the first-order rate of change sequence and the second-order acceleration sequence specifically includes: Based on a preset time step, a local data window is slidably extracted on the conductivity-time series; Polynomial regression fitting is performed on the conductivity values ​​within each local data window to construct a continuous fitting function corresponding to the local data window; Analytical differentiation is performed on the continuous fitting function to calculate the first and second derivative values ​​at the target time point in the local data window; Based on the time sequence of the first-order derivative and the second-order derivative, a first-order rate of change sequence and a second-order acceleration sequence are generated, respectively.

7. The method according to claim 6, characterized in that, Before the step of differentiating the conductivity-time series to obtain the first-order rate of change sequence and the second-order acceleration sequence, the method further includes: Obtain the reference conductivity value of the multi-channel electrolyte testing equipment at standard ambient temperature; The conductivity-time series of each test channel is normalized and calibrated based on the reference conductivity value.

8. A multi-channel electrolyte testing device, comprising a controller, multiple test channels, and corresponding data acquisition units, characterized in that, The controller includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors invoking the computer instructions to cause the controller to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed on the controller, the controller causes the controller to perform the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are run on the controller, the controller causes the controller to perform the method as described in any one of claims 1-7.

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