Method and system for testing electrical properties of coaxial double-electrode friction nanogenerator yarn

Abstract

The application belongs to the technical field of electric variable measurement, and particularly relates to a coaxial double-electrode friction nanometer power generation yarn electrical property test method and system. The method comprises the following steps: controlling the power generation yarn to perform calibration pre-excitation, collecting a reference charge amount, and constructing a response surface mapping function; performing calibration pre-excitation again, collecting current temperature and humidity and actually measuring the reference charge, and calculating a multiplicative environmental correction factor based on the mapping function; using a recursive least square algorithm with a forgetting factor to update a local gradient online, and calculating an additive dynamic reference drift amount; performing formal test excitation to obtain a formal test transfer charge amount; calculating a compensated surface charge density, and completing electrical property test. The application introduces a decoupling and compensation means for environmental factors and material historical state in the test method, so as to obtain a test result which can truly reflect the intrinsic charge transfer capacity of the material and has strong comparability under different times and environments.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement technology. More specifically, this invention relates to a method and system for testing the electrical properties of coaxial dual-electrode triboelectric nanofiber yarns. Background Technology

[0002] In the electrical performance testing of triboelectric nanofiber yarns, surface charge density is a key parameter for measuring the material's power generation capability. Existing testing methods typically involve a linear motor driving the yarn through a fixed-stroke contact-separation motion, using an electrometer to collect the output voltage waveform, and converting the peak voltage into transferred charge using a built-in integrating capacitor. The surface charge density is then calculated by combining this with the effective contact area.

[0003] However, ambient temperature and humidity have a significant nonlinear coupling effect on the charge transfer process at the triboelectric interface. Without a quantitative decoupling model, the charge measured under different environmental conditions varies considerably, directly leading to insufficient comparability of test results between batches. Furthermore, during long-term testing or continuous operation, the surface chemical groups of the coaxial dual-electrode triboelectric nanofiber yarn undergo irreversible evolution due to oxidation, physical adsorption, and other factors, causing a slow additive drift in intrinsic charge density. Conventional testing procedures lack online tracking and compensation mechanisms for this material aging effect, resulting in a non-negligible systematic bias in long-term experimental data, which impairs the accuracy and consistency of electrical performance evaluation.

[0004] In summary, how to effectively eliminate the multiplicative interference caused by environmental temperature and humidity fluctuations on surface charge density measurement, while adaptively correcting long-term reference drift caused by material aging, has become a pressing technical problem to be solved in this field. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for testing the electrical performance of a coaxial dual-electrode triboelectric nanofiber yarn, employing the following technical solution: comprising the following steps: under a controlled temperature and humidity environment, controlling the power generation yarn to perform calibration pre-excitation, collecting the reference charge output by the power generation yarn, and constructing a response surface mapping function; controlling the power generation yarn to perform the calibration pre-excitation, collecting the current temperature and humidity, and obtaining the measured reference charge; based on the response surface mapping function, calculating the ratio of the current environment predicted charge to the reference environment predicted charge, obtaining a multiplicative environmental correction factor; based on the measured charge of each calibration pre-excitation, using a recursive least squares algorithm with a forgetting factor to update the local gradient of the response surface mapping function online, calculating the additive dynamic reference drift; controlling the power generation yarn to perform formal test excitation, obtaining the formal test transferred charge; subtracting the additive dynamic reference drift from the formal test transferred charge and dividing by the multiplicative environmental correction factor, combined with the effective contact area, to calculate the compensated surface charge density, thereby completing the electrical performance test of the power generation yarn.

[0007] Preferably, the calibration pre-excitation includes: controlling the linear motor to drive the generator yarn to perform micro-separation and contact actions with a stroke amplitude of a preset percentage of the regular formal excitation stroke, and repeating this action for a preset number of cycles; the separation speed of the micro-separation and contact actions is consistent with the separation speed of the formal test excitation.

[0008] Preferably, the method for obtaining the reference charge includes: using an electrometer connected in series with the generator yarn to collect the voltage waveform output by the generator yarn during the calibration pre-excitation process, extracting the peak voltage within a stable period from the voltage waveform, and multiplying the peak voltage by the preset integrating capacitance parameter of the electrometer to obtain the reference charge.

[0009] Preferably, the construction of the response surface mapping function includes:

[0010] At predetermined temperature and humidity grid points, the reference charge quantities under different ambient temperatures and relative humidities are collected to form a sample set; using the first-order terms of ambient temperature, the first-order terms of relative humidity, the second-order terms of ambient temperature, the second-order terms of relative humidity, and the interaction terms of temperature and humidity as input features, the sample set is trained using a partial least squares regression algorithm to obtain the response surface mapping function.

[0011] Preferably, the reference environment includes a preset reference temperature value and a preset reference relative humidity value.

[0012] Preferably, the method for calculating the local gradient includes: establishing a local linear tangent plane model at the current operating point of the response surface mapping function, wherein the local linear tangent plane model includes a local gradient coefficient vector composed of the local reference charge intercept, local temperature sensitivity, and local humidity sensitivity; using the measured charge of each calibration pre-excitation as an observation, and updating the local gradient coefficient vector online using a recursive least squares recursive formula with a forgetting factor.

[0013] Preferably, the calculation of the additive dynamic reference drift includes: determining the difference between the measured reference charge of the most recent calibration pre-excitation and the predicted reference charge of the response surface mapping function under the current temperature and humidity as the additive dynamic reference drift; or, taking the moving average of the drift values ​​obtained from the most recent calibration pre-excitations as the additive dynamic reference drift.

[0014] Preferably, obtaining the amount of transferred charge in the formal test includes: during the formal test excitation process, using an electrometer to collect the voltage waveform generated by the generator yarn, extracting the peak voltage of the voltage waveform, and multiplying the peak voltage by the preset integrating capacitance parameter of the electrometer to obtain the amount of transferred charge in the formal test.

[0015] Preferably, the effective contact area is the electrode projection area of ​​the power-generating yarn, which is obtained by optical microscopic image analysis and is pre-input as a fixed parameter.

[0016] Secondly, this application also discloses an electrical performance testing system for coaxial dual-electrode triboelectric nanofiber yarn, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the electrical performance testing method for coaxial dual-electrode triboelectric nanofiber yarn described above is implemented.

[0017] The embodiments of the present invention have at least the following beneficial effects:

[0018] 1. By designing a calibration pre-excitation with an extremely short stroke and the same origin as the formal test, and combining partial least squares regression to establish a response surface model of temperature, humidity, and reference charge, quantitative decoupling of environmental influences was achieved. A multiplicative correction factor was generated using the ratio of predicted reference charge under the current environment and the reference environment, uniformly reducing the measured charge in any environment to standard conditions. This eliminated batch-to-batch measurement bias caused by temperature and humidity fluctuations, improving the cross-batch comparability and evaluation accuracy of surface charge density testing for power-generating yarns.

[0019] 2. A recursive least squares algorithm with a forgetting factor is introduced to update the local gradient of the response surface online. The additive dynamic reference drift is calculated in real time based on the measured charge of each calibration pre-excitation. By subtracting this drift from the formal test charge, the slow evolution of intrinsic charge density caused by material aging is adaptively compensated, overcoming the shortcomings of traditional static compensation in tracking material performance degradation and ensuring the consistency and accuracy of long-term test data. Attached Figure Description

[0020] Figure 1 The flowchart illustrates the steps of the method and system for testing the electrical properties of coaxial dual-electrode triboelectric nanofibers in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] Reference Figure 1 The electrical performance testing method for coaxial dual-electrode triboelectric nanofiber yarn includes steps S1-S5, as detailed below:

[0024] S1: Under controlled temperature and humidity conditions, the power generation yarn is controlled to perform calibration pre-excitation, the reference charge output by the power generation yarn is collected, and the response surface mapping function is constructed.

[0025] Ambient temperature and humidity have a nonlinear coupling effect on charge transfer in coaxial dual-electrode triboelectric nanofiber yarns. The charge measured under different temperature and humidity conditions varies greatly, resulting in insufficient comparability between batches of test results without environmental correction. Therefore, it is necessary to perform calibration pre-excitation under controlled temperature and humidity and obtain the reference charge, and then construct a response surface mapping function that can reflect the quantitative relationship between temperature and humidity and charge.

[0026] The calibration pre-excitation is performed by a linear motor driving the generator yarn. The separation contact stroke amplitude is taken as an empirical value of 5%, which is the proportion relative to the regular formal test excitation stroke. This value is a preset parameter. The process is repeated for 3 cycles, and this value is also a preset parameter. The separation speed is consistent with the separation speed used in the formal test excitation, thereby ensuring the similarity of the electric field evolution during the interface separation process. At the same time, the small stroke of 5% minimizes mechanical disturbance, which can generate a charge signal that can be reliably detected by the electrometer, while reducing the wear on the material surface to a very low level.

[0027] During each calibration pre-excitation process, an electrometer connected in series with the generator yarn collects the output voltage waveform, and the peak calibration pre-excitation voltage within the stable period is extracted using a peak detection algorithm. The unit is volt (V), and the reference charge is... Through the formula: In the formula The integrated capacitor parameter built into the electrometer is taken as an empirical value. F, preset parameters, can be adjusted according to the specific electrometer model. The unit is coulomb (C).

[0028] To obtain the sample set required for constructing the response surface, samples of the power-generating yarn were placed in an environmental test chamber at a temperature of [temperature value missing]. Temperature range 15°C to 35°C, relative humidity Within a range of 20% to 80%, grid points are set with a temperature step of 5 degrees Celsius and a humidity step of 10%RH. The temperature unit is degrees Celsius. These are dimensionless values ​​expressed as percentages. The ranges and step sizes mentioned above are preset parameters and can be adjusted according to the temperature and humidity sensitive range of the material being tested. After the sample has fully equilibrated at each grid point, a calibration pre-excitation is performed and the real-time temperature is recorded. relative humidity and the corresponding reference charge This forms a sample point.

[0029] The training set consists of all grid point samples, and the response surface mapping function is trained using the partial least squares regression algorithm. Input feature is the first-order term of temperature. Humidity (one item) Temperature quadratic term Humidity secondary term Interaction items with temperature and humidity The output is the predicted baseline charge. The algorithm handles feature collinearity by extracting the latent variables that best explain the input-output covariance. The principal component count is determined to be 3 through leave-one-out cross-validation and used as a fixed parameter of the model. The root mean square error of the trained model does not exceed 2% of the full-scale baseline charge. The response surface mapping function... It is stored in the host computer as a quantitative description of the reference charge behavior of the power-generating yarn under the current environment.

[0030] S2: Control the power generation yarn to perform calibration pre-excitation, collect the current temperature and humidity, and obtain the measured reference charge.

[0031] Before conducting formal electrical performance tests on the coaxial dual-electrode triboelectric nanofiber yarn, the temperature and relative humidity of the laboratory environment have usually changed compared to the controlled conditions when constructing the response surface, and there are still instantaneous fluctuations in environmental quantities during the test. These changes cause the charge transfer amount to drift after coupling through the triboelectric interface. Therefore, before applying the formal mechanical excitation, it is necessary to perform a calibration pre-excitation in situ, the same as in S1, and simultaneously collect the ambient temperature and relative humidity at this moment.

[0032] The calibration pre-excitation was still performed by a linear motor driving the generator yarn to perform a micro-separation contact action. The stroke amplitude was taken as an empirical value of 5%, which is the proportion relative to the stroke of the regular formal test excitation. The preset parameters and the number of repetition cycles were taken as an empirical value of 3. The preset parameters and the separation speed were the same as the separation speed of the formal test excitation. Since the separation speed determines the rate of change of the interface electric field, the same speed makes the evolution law of the interface electric field during the calibration pre-excitation consistent with that during the formal test. At the same time, the mechanical wear on the material surface is minimal under small stroke.

[0033] During the calibration pre-excitation process, an electrometer connected in series with the generator yarn continuously acquires the output voltage waveform. After pre-filtering with a low-pass filter, a peak detection algorithm is used to extract the calibration pre-excitation peak voltage within the stable period. The peak detection algorithm is a well-known technology and will not be described in detail here. The measured value of the current calibration reference charge in volts (V). We obtain it from the following formula:

[0034] In the formula, The integrated capacitor parameter built into the electrometer is taken as an empirical value. F, preset parameters The unit is coulomb. Simultaneously, a temperature and humidity sensor placed inside the test chamber collects the current ambient temperature. The units are degrees Celsius and relative humidity. Dimensionless, thus, before initiating formal test excitation, a set of data characterizing the charge output characteristics of the power-generating yarn under the current environment is obtained, including the current temperature and humidity. , and measured reference charge .

[0035] S3: Based on the response surface mapping function, calculate the ratio of the current environment's predicted charge to the reference environment's predicted charge to obtain the multiplicative environment correction factor.

[0036] Before testing, the pre-excitation calibration was completed and the current ambient temperature was obtained. With relative humidity Then, the constructed response surface mapping function is called. ,by and As input, calculate the predicted charge amount for the current environment. Its expression is: In the formula, The reference charge is the model-predicted amount of charge under the current ambient temperature and humidity, in coulombs. The unit is Celsius; This is a dimensionless relative humidity.

[0037] The reference environment is preset to the reference temperature. Celsius and reference relative humidity %, these two reference values ​​are preset parameters.

[0038] Call the same mapping function ,by and As input, the predicted charge quantity of the reference environment is obtained. Its expression is: In the formula, The reference charge is the model's predicted charge under reference standard conditions, in coulombs.

[0039] Since the reference temperature and humidity are fixed values The mapping function can be calculated and stored all at once after its construction, and then read directly during online testing.

[0040] Based on the two predicted charge quantities mentioned above, the multiplicative environmental correction factor... Defined as the ratio of the predicted charge in the current environment to the predicted charge in the reference environment: In the formula, is a dimensionless multiplicative coefficient. When When this occurs, it indicates that the predicted baseline charge under current environmental conditions is higher than that of the reference environment, meaning the current environment is more favorable for charge transfer; when... The opposite is true at other times.

[0041] get Then, the value is temporarily stored in the host computer.

[0042] Since the calibration pre-excitation and the formal test share the same contact area of ​​the same power-generating yarn sample, and both are performed under the same environmental conditions, this multiplicative factor can quantitatively characterize the overall scaling effect of environmental temperature and humidity fluctuations on charge output capability.

[0043] S4: Based on the measured charge of each calibration pre-excitation, the local gradient of the response surface mapping function is updated online using a recursive least squares algorithm with a forgetting factor, and the additive dynamic reference drift is calculated.

[0044] During long-term testing, the surface chemical groups of the coaxial dual-electrode triboelectric nanofiber yarn undergo irreversible evolution due to oxidation, adsorption, and other reasons, resulting in a slow additive drift in intrinsic charge density. This drift deviates from the predicted value of the original response surface model under the current temperature and humidity, and the deviation changes cumulatively over time. The static response surface model cannot adaptively track this gradual change. Therefore, a recursive least squares algorithm with a forgetting factor is adopted. Before each test cycle, the measured data of previous calibration pre-excitations are used to recursively update the local gradient of the response surface near the current operating point online, and the additive dynamic baseline drift is calculated accordingly.

[0045] After obtaining new calibration pre-excitation data, the current ambient temperature and humidity are used as the latest operating point. A local linear tangent plane model is established at this point to approximate the local behavior of the response surface near this point. The model expression is as follows:

[0046] In the formula, The reference charge predicted by the local linear model, in coulombs; The local reference charge intercept; For local temperature sensitivity; For localized humidity sensitivity; and The first The ambient temperature and relative humidity were collected during the pre-excitation of the calibration. The temperature unit is degrees Celsius (°C), and the relative humidity is dimensionless. and The coefficients for temperature and humidity variables corresponding to the prediction points. , , Constructing the local gradient coefficient vector Its initial value Take the zero vector, which is initialized to the preset parameters.

[0047] The measured reference charge of each calibration pre-excitation As an observation, a regression vector is constructed by combining the temperature and humidity difference between two adjacent working points. To drive RLS to perform online recursion and regression vector Defined as: The temperature and humidity differences between the current observation point and the previous working point contain information about local gradient changes. Therefore, the regression vector can effectively update the model parameters. The RLS recursive update process is implemented using the following three formulas. ; ; In the formula, Let be the gain vector, and be Dimension, dimensionless, is used to determine the magnitude of the contribution of the current observation error to the parameter update; for The auxiliary matrix is ​​dimensionless and inversely correlated with the input autocorrelation matrix; its initial value is... , for identity matrix Get experience points Preset parameters to ensure that the initial matrix is ​​non-singular; Forgetting factor, take empirical value Dimensionless, preset parameters, used to apply exponential decay weights to historical data to track the slow drift caused by material aging; For the first The measured reference charge quantity for the pre-excitation calibration is in coulombs. Through the above recursion, the local gradient coefficient vector... It updates online sequentially, adaptively reflecting the local behavior of the response surface near the latest operating point.

[0048] It should be noted that before substituting the temperature and relative humidity data into the matrix operation of the recursive least squares algorithm described above, the temperature difference and humidity difference data need to be normalized or dimensionless (e.g., divided by their respective preset reference step sizes) to eliminate the influence of different physical dimensions on the matrix iteration operation. Each physical quantity in the formula uses its dimensionless value in the calculation. Range normalization (Min-Max Normalization), Z-score standardization, or engineering per-unit processing can be used. Preferably, this embodiment uses a per-unit processing method, that is, dividing the temperature difference by a preset temperature reference step size (e.g., 1°C) and the humidity difference by a preset humidity reference step size (e.g., 1%RH), thereby converting the differences with physical units into pure numerical values.

[0049] After completing the online update of the local gradient, the additive dynamic reference drift is calculated. The basic calculation method is to use the reference charge measured in the most recent calibration pre-excitation. Mapping function with original response surface Predicted values ​​under the same temperature and humidity Difference, i.e. In the formula, This represents the single drift amount, measured in coulombs. To reduce measurement noise, the most recent... The moving average of the drift values ​​is used as the current additive dynamic baseline drift value. ,Right now ;in, The length of the sliding window is set to an empirical value of 3, using preset parameters, resulting in... This is the additive dynamic baseline drift that characterizes the current material aging effect.

[0050] S5: Control the power generation yarn to perform formal test excitation and obtain the formal test transferred charge amount; subtract the additive dynamic reference drift amount from the formal test transferred charge amount and divide by the multiplicative environmental correction factor, and combine with the effective contact area to calculate the compensated surface charge density, so as to complete the electrical performance test of the power generation yarn.

[0051] Multiplicative environment correction factor Additive dynamic reference drift With all conditions met and the power-generating yarn in the current test environment, the linear motor drives the coaxial dual-electrode triboelectric nano-power-generating yarn to perform formal test excitation. This excitation is a conventional full-stroke contact-separation cycle, with the separation speed consistent with the calibration pre-excitation. The number of excitation cycles is taken as an empirical value of 5, and the preset parameters are used.

[0052] The electrometer collects the output voltage waveform during the formal test excitation process, and the peak voltage of the formal test excitation within the stable period is extracted using a peak detection algorithm. The unit is volts (V), used to formally test the amount of transferred charge. It is obtained through the same calculation method as the reference charge, i.e. In the formula, The integrated capacitor parameter built into the electrometer is taken as an empirical value. F, preset parameters The unit is coulomb.

[0053] An electrometer is set up and connected in series with the generating yarn to collect voltage waveforms and output the reference charge and the charge transferred during the formal test. A temperature and humidity sensor is set up to collect the ambient temperature and relative humidity values ​​of the test environment in real time. Since the formal test excitation and the calibration pre-excitation are performed under the same environmental conditions and in the same contact area with the generating yarn, the instrument response characteristics for charge transfer are consistent in both cases. Therefore, the same sensor can be used directly. Perform the conversion.

[0054] Because temperature and humidity in the current environment cause multiplicative scaling of charge transfer, and material aging causes additive shift, the multiplicative environmental correction factor... Additive dynamic reference drift The magnitudes of these two effects were quantitatively characterized separately, therefore... Compensation will be provided.

[0055] from Subtract the additive dynamic reference drift Remove the reference offset caused by material aging, and then divide the difference by the multiplicative environmental correction factor. The charge is normalized to the reference environment to obtain the equivalent compensation charge. : In the formula, The equivalent charge after compensation is expressed in coulombs. This is the additive dynamic reference drift, expressed in coulombs. It is a dimensionless multiplicative environmental correction factor.

[0056] Effective contact area of ​​coaxial dual-electrode triboelectric nanofibers The electrode projection area is obtained through optical microscopic image analysis and measurement. Its value is determined after the power-generating yarn is prepared and input into the host computer as a fixed parameter. The unit is square meters (m²).

[0057] Compensated equivalent charge This reflects the net transferred charge after removing environmental and aging interferences, divided by... The compensated surface charge density is obtained. : In the formula, To compensate for the surface charge density.

[0058] The surface charge density has already accounted for measurement biases caused by environmental temperature and humidity fluctuations and material aging, and can be directly used for cross-sectional comparison of test data from different batches and dates without the need for additional corrections to environmental conditions or test history.

[0059] This invention also discloses an electrical performance testing system for coaxial dual-electrode triboelectric nanofiber yarn, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the electrical performance testing method for the coaxial dual-electrode triboelectric nanofiber yarn of this invention. The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface; their configuration and functions are known in the art and will not be described further here.

[0060] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for testing the electrical properties of coaxial dual-electrode triboelectric nanofiber yarn, characterized in that, Includes the following steps: Under controlled temperature and humidity conditions, the power generation yarn is controlled to perform calibration pre-excitation, the reference charge output by the power generation yarn is collected, and a response surface mapping function is constructed. The generator yarn is controlled to perform the calibration pre-excitation, the current temperature and humidity are collected, and the measured reference charge is obtained; Based on the response surface mapping function, the ratio of the current environmental predicted charge to the reference environmental predicted charge is calculated to obtain the multiplicative environmental correction factor; Based on the measured charge of each calibration pre-excitation, the local gradient of the response surface mapping function is updated online using a recursive least squares algorithm with a forgetting factor, and the additive dynamic reference drift is calculated. The power-generating yarn is controlled to perform a formal test excitation to obtain the formal test transferred charge amount; the formal test transferred charge amount is subtracted from the additive dynamic reference drift amount and divided by the multiplicative environmental correction factor, and combined with the effective contact area to calculate the compensated surface charge density, so as to complete the electrical performance test of the power-generating yarn.

2. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The calibration pre-excitation includes: The linear motor drives the generator yarn to perform micro-separation and contact actions with a stroke amplitude of a preset percentage of the normal formal excitation stroke, and repeats this action for a preset number of cycles; the separation speed of the micro-separation and contact actions is consistent with the separation speed of the formal test excitation.

3. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The method for obtaining the reference charge includes: using an electrometer connected in series with the generator yarn to collect the voltage waveform output by the generator yarn during the calibration pre-excitation process, extracting the peak voltage within a stable period from the voltage waveform, and multiplying the peak voltage by the preset integrating capacitance parameter of the electrometer to obtain the reference charge.

4. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The function for constructing the response surface mapping includes: At predetermined temperature and humidity grid points, the reference charge quantities under different ambient temperatures and relative humidity are collected to form a sample set; Using the first-order terms of ambient temperature, the first-order terms of relative humidity, the second-order terms of ambient temperature, the second-order terms of relative humidity, and the interaction term between temperature and humidity as input features, the sample set is trained using the partial least squares regression algorithm to obtain the response surface mapping function.

5. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The reference environment includes a preset reference temperature value and a preset reference relative humidity value.

6. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The method for calculating the local gradient includes: A local linear tangent plane model is established at the current operating point of the response surface mapping function. The local linear tangent plane model includes a local gradient coefficient vector consisting of the local reference charge intercept, local temperature sensitivity, and local humidity sensitivity. The measured charge from each calibration pre-excitation is used as the observation, and the local gradient coefficient vector is updated online using a recursive least squares formula with a forgetting factor.

7. The method for testing the electrical properties of coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The calculation of the additive dynamic reference drift includes: The difference between the measured reference charge of the most recent calibration pre-excitation and the predicted reference charge of the response surface mapping function under the current temperature and humidity is determined as the additive dynamic reference drift. Alternatively, the moving average of the drift values ​​obtained from the most recent calibration pre-excitations can be used as the additive dynamic reference drift value.

8. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The process of obtaining the formal test transferred charge includes: During the formal test excitation process, an electrometer is used to collect the voltage waveform generated by the power-generating yarn, the peak voltage of the voltage waveform is extracted, and the peak voltage is multiplied by the preset integrating capacitance parameter of the electrometer to obtain the amount of transferred charge in the formal test.

9. The method for testing the electrical properties of the coaxial dual-electrode triboelectric nanofiber yarn according to claim 1, characterized in that, The effective contact area is the electrode projection area of ​​the power-generating yarn, which is obtained by optical microscopic image analysis and is pre-input as a fixed parameter.

10. An electrical performance testing system for coaxial dual-electrode triboelectric nanofiber yarn, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the method for testing the electrical properties of coaxial dual-electrode triboelectric nanofiber yarn according to any one of claims 1-9.

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