System and method for measuring hydration degree of ionic polymer metal composite material

Abstract

The invention provides a system and a method for measuring the hydration degree of an ionic polymer metal composite material, and relates to the technical field of high polymer chemistry. The method comprises the following steps: firstly, obtaining an IPMC sample to be measured and a known hydration degree IPMC as a contrast; secondly, applying alternating current electric signals with different frequencies in a range of 0.01-100Hz to metal electrodes on two sides of the IPMC sample, recording current and voltage, generating a correction coefficient to adjust conductivity, and performing fast Fourier transform on the conductivity to obtain phase angles and amplitudes under different frequencies; a deep learning network is used, the phase angle and the amplitude of the known hydration degree IPMC are used as input, the hydration degree is used as a label for training, and finally a hydration degree measurement model is formed. And finally, inputting the phase angle and the amplitude of a to-be-tested sample into the model to obtain the hydration degree of the to-be-tested sample. According to the method, conductivity analysis and a deep learning technology are combined, and accurate evaluation of the IPMC hydration degree is realized.

Description

Technical Field

[0001] The invention relates to the technical field of polymer chemistry, in particular to a system and method for measuring the hydration degree of an ion polymer metal composite material. Background Art

[0002] In recent years, ionic polymer metal composites (IPMC) have been widely used in the fields of smart materials, sensors and actuators due to their excellent electrical response characteristics and flexibility. However, the hydration degree has a great influence on the performance of IPMC, and the change of hydration degree directly affects its conductivity and electromechanical response. Therefore, how to accurately and quickly measure and control the hydration degree of IPMC is a major technical challenge in current research. Existing hydration measurement methods usually rely on traditional physical and chemical methods for analysis, which is not only time-consuming and labor-intensive, but may also affect the performance of the sample, and lack the ability of real-time monitoring and analysis.

[0003] In the prior art, publication number CN109358096B discloses a method and system for measuring the hydration degree of an ionic polymer metal composite material, wherein an external excitation signal is input into a cantilever beam structure including an IPMC to obtain the deformation of the end of the IPMC, and the induced current generated by the IPMC is obtained according to the deformation. An amplitude-frequency signal and a phase-frequency signal are obtained based on the external excitation signal and the induced current, and the amplitude-frequency signal and the phase-frequency signal are substituted into a sensing dynamic model to obtain an ion diffusion coefficient, and the hydration degree of the IPMC is determined according to the ion diffusion coefficient.

[0004] The main problems with the above method are: it relies on the dynamic response of the external excitation signal and the resulting deformation, and is therefore sensitive to the mechanical properties of the material and the external environment. The selection and stability of the external excitation signal directly affect the accuracy of the entire scheme. In actual applications, changes in environmental conditions such as temperature and humidity will affect the performance of the IPMC and its response behavior, introducing more variables and uncertainties, resulting in reduced reliability in hydration measurement.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a system and method for measuring the hydration degree of an ionomer-metal composite material, so as to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A system for measuring the hydration degree of an ionomer-metal composite material, comprising the following specific steps:

[0009] A sample acquisition module is used to acquire an IPMC sample to be measured and acquire an IPMC with a known hydration degree;

[0010] The data measurement module is used to apply alternating current signals of different frequencies ranging from 0.01 to 100 Hz to the metal electrodes on both sides of the IPMC sample, and ensure that the alternating current signals of different frequencies act for the same time, generate a conductivity time series signal corresponding to the frequency, and record the current value and voltage value at multiple time points during the action time of the alternating current signal corresponding to the frequency, generate the current average value and the voltage average value within the frequency action time, and then generate the standard deviation of the current and voltage, generate a correction coefficient based on the standard deviation, correct the conductivity by the correction coefficient, and use the same method to process the IPMC with known hydration degree;

[0011] A data analysis module is used to perform fast Fourier transform on the conductivity time series signal of the alternating current signal at different frequencies, generate the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies according to the result of the fast Fourier transform, and use the same method to obtain the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies;

[0012] A model training module is used to generate a deep learning network, take the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies as input, take the hydration degree of the IPMC with known hydration degree as a label, train the deep learning network, and obtain a trained hydration degree measurement model;

[0013] The output measurement module is used to input the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies into the hydration measurement model to generate the hydration of the IPMC.

[0014] Furthermore, the formula for generating the conductivity of the IPMC sample at a specific frequency is:

[0015]

[0016] Among them, σ i (t) represents the conductivity of the IPMC sample at time t under the i-th electrical signal frequency, t represents the action time of the electrical signal frequency, i represents the index of the AC signal frequency, and i = 1, 2, ..., N, N represents the number of selected AC signal frequencies, I i (t) represents the current at time t under the frequency of the i-th electrical signal, V i (t) represents the voltage at time t under the i-th electrical signal frequency, L represents the distance between the electrodes on both sides of the IPMC sample, and A represents the contact area between the electrode and the IPMC sample.

[0017] Furthermore, the principle for correcting the conductivity by the correction coefficient is as follows:

[0018] The standard deviation of current and voltage is generated based on the formula:

[0019]

[0020] in, represents the average current value within the action time of the ith AC signal frequency, m represents the index of the time point within the action time, M represents the maximum value of the time point index, that is, the total number of current or voltage data collected at one frequency, I m represents the current value collected at the mth time point, V m represents the voltage value collected at the mth time point, represents the average voltage during the action time of the ith AC signal frequency, μ I,i It represents the standard deviation of the current during the action time of the ith AC signal frequency, μ V,i It represents the standard deviation of the voltage during the action time of the i-th AC signal frequency;

[0021] The correction factor is generated based on the formula:

[0022]

[0023] Among them, k I,i represents the current correction coefficient of the ith AC signal frequency, k V,i represents the voltage correction coefficient of the ith AC signal frequency, ∈ is a constant to prevent the denominator from being zero;

[0024] The conductivity correction is based on the formula:

[0025]

[0026] in, It represents the conductivity correction value at time t under the action of the i-th AC signal frequency.

[0027] Furthermore, the principle of fast Fourier transform is:

[0028] The formula for fast Fourier transform is:

[0029]

[0030] Among them, X i (f i ) represents the conductivity frequency domain signal of IPMC sample at the ith AC frequency, T represents the action time of an AC signal frequency, f irepresents the i-th frequency, and j represents the imaginary unit.

[0031] Furthermore, the principle for generating the phase angle and amplitude of conductivity at each frequency is:

[0032]

[0033] in, represents the phase angle of the ith alternating current frequency, f i Indicates the frequency of the ith alternating current, Im(X i (f i )) represents the imaginary part of the frequency domain signal, Re(X i (f i )) represents the real part of the frequency domain signal;

[0034]

[0035] Among them, A i (f i ) represents the amplitude of the i-th alternating current frequency.

[0036] The present invention also provides a method for measuring the hydration degree of an ionic polymer metal composite material. The method is performed by the above-mentioned system for measuring the hydration degree of an ionic polymer metal composite material, and the specific steps include:

[0037] Step 1: Obtain an IPMC sample to be measured and obtain an IPMC with known hydration degree;

[0038] Step 2: applying alternating current signals of different frequencies in the range of 0.01 to 100 Hz to the metal electrodes on both sides of the IPMC sample, and ensuring that the alternating current signals of different frequencies act for the same time, generating a conductivity time series signal corresponding to the frequency, and recording the current values ​​and voltage values ​​at multiple time points during the action time of the alternating current signal corresponding to the frequency, generating the current average value and the voltage average value within the frequency action time, and then generating the standard deviation of the current and voltage, generating a correction coefficient based on the standard deviation, correcting the conductivity by the correction coefficient, and using the same method to process the IPMC with known hydration degree;

[0039] Step 3: Perform fast Fourier transform on the conductivity time series signal of the AC signal at different frequencies, generate the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies according to the result of fast Fourier transform, and use the same method to obtain the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies;

[0040] Step 4: Generate a deep learning network, take the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies as input, take the hydration degree of the IPMC with known hydration degree as a label, train the deep learning network, and obtain a trained hydration degree measurement model;

[0041] Step 5: Input the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies into the hydration measurement model to generate the hydration of the IPMC.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention adjusts the IPMC material to a state close to the densest packing state, which can improve the mechanical strength and conductivity of the IPMC. In the subsequent conductivity measurement and hydration analysis, the performance of the IPMC sample is more stable and reliable, thereby improving the accuracy of the measurement results; by applying alternating current signals of different frequencies to the metal electrodes on both sides of the IPMC sample, the current and voltage of the sample at different frequencies are measured, which provides rich data support for the hydration analysis, and through detailed conductivity information, the relationship between hydration and conductivity can be better understood, thereby improving the accuracy of subsequent training of deep learning models.

[0044] The present invention also uses fast Fourier transform to remove redundant signals while converting time domain signals into frequency domain signals and extracting phase angles and amplitudes, thereby providing in-depth information about the electrical conductivity properties of the material and more clearly reflecting the impact of changes in hydration on conductivity; by training a deep learning network, the nonlinear relationship between phase angle and amplitude and hydration is captured. Compared with traditional linear models, deep learning network models perform better in processing complex data and are more timely. In the case of large-scale data sets, the training and output speeds of deep learning models are faster, and a large number of hydration measurements can be processed in a short time, which is suitable for industrial applications and real-time monitoring needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the system modules of an embodiment of the present invention;

[0046] Figure 2 The figure is a schematic diagram of the method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0048] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] See also Figure 1 , the present invention provides a technical solution:

[0051] A system for measuring the hydration degree of an ionomer-metal composite material, specifically comprising:

[0052] A sample acquisition module is used to acquire an IPMC sample to be measured and acquire an IPMC with a known hydration degree;

[0053] In this embodiment, IPMC is an ion polymer metal composite material, the IPMC sample is an IPMC material whose hydration degree is to be measured, and the IPMC with known hydration degree is an IPMC material used to train a deep learning model. The hydration degree is obtained by controlling IPMC to absorb or release water under different conditions. The principle is as follows:

[0054] Weigh the mass of the dried IPMC, recorded as M 干 Then, the dried IPMC was placed in deionized water, sealed and soaked for a period of time, and then the excess water on the surface was absorbed after being taken out. The mass of the wet IPMC was weighed and recorded as M 湿 The soaking time is determined according to the experimental requirements. Within a certain time range, the longer the soaking time, the 湿 The higher the hydration level, the

[0055] The data measurement module is used to apply alternating current signals of different frequencies ranging from 0.01 to 100 Hz to the metal electrodes on both sides of the IPMC sample, and ensure that the alternating current signals of different frequencies act for the same time, generate a conductivity time series signal corresponding to the frequency, and record the current value and voltage value at multiple time points during the action time of the alternating current signal corresponding to the frequency, generate the current average value and the voltage average value within the frequency action time, and then generate the standard deviation of the current and voltage, generate a correction coefficient based on the standard deviation, correct the conductivity by the correction coefficient, and use the same method to process the IPMC with known hydration degree;

[0056] In this embodiment, the principle for generating the conductivity of the IPMC sample is:

[0057] The formula for generating the conductivity of an IPMC sample at a specific frequency is:

[0058]

[0059] Among them, σ i (t) represents the conductivity of the IPMC sample at time t under the i-th electrical signal frequency, t represents the action time of the electrical signal frequency, i represents the index of the AC signal frequency, and i = 1, 2, ..., N, N represents the number of selected AC signal frequencies, I i (t) represents the current at time t under the frequency of the i-th electrical signal, V i (t) represents the voltage at time t under the i-th electrical signal frequency, L represents the distance between the electrodes on both sides of the IPMC sample, and A represents the contact area between the electrode and the IPMC sample.

[0060] Conductivity reflects the ability of IPMC sample to conduct current. The higher the conductivity, the stronger the material's ability to conduct electricity. Current reflects the magnitude of the alternating current passing through the electrodes on both sides of the IPMC sample at a specific electrical signal frequency. Voltage reflects the magnitude of the alternating voltage applied to both sides of the IPMC sample at a specific electrical signal frequency. The contact area A between the electrode and the sample reflects the size of the channel for current flow. Under the same current and voltage, the larger the contact area, the easier the current flows. The spacing L between the electrodes on both sides of the sample reflects the length of the path for current flow. The longer the spacing L, the greater the resistance to current flow. Conductivity is proportional to the magnitude of the alternating current passing through the electrodes on both sides of the sample and the contact area between the electrodes and the sample, and inversely proportional to the magnitude of the alternating voltage applied to both sides of the IPMC sample and the spacing L between the electrodes on both sides of the sample.

[0061] The principle on which conductivity is corrected by the correction factor is based on:

[0062] The standard deviation of current and voltage is generated based on the formula:

[0063]

[0064] in, represents the average current value within the action time of the ith AC signal frequency, m represents the index of the time point within the action time, M represents the maximum value of the time point index, that is, the total number of current or voltage data collected at one frequency, I m represents the current value collected at the mth time point, V m represents the voltage value collected at the mth time point, represents the average voltage during the action time of the ith AC signal frequency, μ I,i It represents the standard deviation of the current during the action time of the ith AC signal frequency, μ V,i It represents the standard deviation of the voltage during the action time of the i-th AC signal frequency;

[0065] The standard deviation of current and voltage reflects the volatility and consistency of current and voltage at a specific frequency. The smaller the standard deviation, the closer the measured value of current or voltage at this frequency is to the average value, the smaller the fluctuation during the measurement process, and the more stable and accurate the measurement result. A larger standard deviation indicates that there is greater noise or interference in the measurement.

[0066] The correction factor is generated based on the formula:

[0067]

[0068]

[0069] Among them, k I,i represents the current correction coefficient of the ith AC signal, k V,i represents the voltage correction coefficient of the ith AC signal, ∈ is a constant to prevent the denominator from being zero;

[0070] The correction coefficient is determined by proportional correction and is set as the ratio of the standard deviation to the mean value. ∈ is a constant to prevent the denominator from being 0, and its value is extremely small, ranging from 0.001 to 0.01. By comparing the standard deviation and the mean value, the correction coefficient can effectively reflect the uncertainty of the measurement.

[0071] The conductivity correction is based on the formula:

[0072]

[0073] in, It represents the conductivity correction value at time t under the action of the i-th AC signal frequency.

[0074] The conductivity correction formula reflects the correction result of conductivity by the corrected current and voltage by considering the influence of the uncertainty of current and voltage measurement in conductivity calculation;

[0075] A data analysis module is used to perform fast Fourier transform on the conductivity time series signal of the alternating current signal at different frequencies, generate the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies according to the result of the fast Fourier transform, and use the same method to obtain the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies;

[0076] The formula on which the Fast Fourier Transform is based is:

[0077]

[0078] Among them, X i (f i ) represents the conductivity frequency domain signal of IPMC sample at the ith AC frequency, T represents the action time of an AC signal frequency, f i represents the i-th frequency, and j represents the imaginary unit.

[0079] The phase angle and magnitude of the conductivity at each frequency are generated based on the principle:

[0080]

[0081] in, represents the phase angle of the ith alternating current frequency, f i Indicates the frequency of the ith alternating current, Im(X i (f i )) represents the imaginary part of the frequency domain signal, Re(X i (f i )) represents the real part of the frequency domain signal;

[0082]

[0083] Among them, A i (f i ) represents the amplitude of the i-th alternating current frequency. The phase angle represents the parameter of the phase difference between the current and voltage in the frequency domain signal, reflects the degree of delay of the current relative to the voltage, and describes the delay characteristics of the signal response. The larger the phase angle, the slower the current responds to the voltage; the amplitude reflects the conductivity strength of the material at a specific frequency. The higher the amplitude, the better the conductivity of the material at this frequency. As the hydration degree increases, the conductivity increases, thereby increasing the amplitude.

[0084] In IPMC materials, water molecules act as mobile ion carriers, increasing the ionic conductivity of the material. As the hydration degree increases, the conductivity increases and the amplitude increases. The change in hydration degree affects the ion mobility, thereby affecting the polarization effect of the material. When the hydration degree increases, the polarization effect increases, resulting in a decrease in the phase angle. At different frequencies, the change in the phase angle reflects the effect of the hydration degree on the dielectric effect of the material. At low frequencies, the phase angle is small, and at high frequencies, the phase angle increases, reflecting the delay in ion flow and the weakening of the polarization effect.

[0085] A model training module is used to generate a deep learning network, take the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies as input, take the hydration degree of the IPMC with known hydration degree as a label, train the deep learning network, and obtain a trained hydration degree measurement model;

[0086] In this embodiment, the deep learning network structure for the hydration measurement model is:

[0087] Input layer: contains 2 neurons, corresponding to the input phase angle and amplitude respectively;

[0088] The first hidden layer: contains 32 neurons, activated by the ReLU function;

[0089] The second hidden layer: contains 16 neurons, activated by the ReLU function;

[0090] Output layer: contains 1 neuron, outputs the hydration degree of IPMC, and is activated by a linear activation function;

[0091] The phase angle reflects the conductivity and response delay of IPMC materials and is an important indicator of the dielectric properties of the material. The moisture content in the IPMC material will affect the conductivity and thus the phase angle.

[0092] The amplitude represents the strength of the current signal and is directly related to the conductivity of the material. The conductivity of the material increases with the degree of hydration, which affects the amplitude.

[0093] The degree of hydration affects not only the conductivity, but also the flexibility, rigidity and response speed of IPMC, all of which are related to the phase angle and amplitude. Increased hydration increases the flexibility of the material, and flexible materials are better able to store and release energy when an external electric field is applied, resulting in a larger phase angle and amplitude, while materials with greater rigidity result in a smaller phase angle and amplitude. The degree of hydration affects the mobility of ions in the material, and ions move faster at high hydration levels. Therefore, the higher the hydration degree, the faster the response speed of the material. Materials with fast responses can quickly produce larger amplitudes when voltage is applied, resulting in more significant changes in the phase angle.

[0094] By taking the known phase angle and amplitude as input and the hydration level as output, the trained model can learn the complex nonlinear relationship between the two, thereby achieving the measurement of hydration level;

[0095] The phase angle, amplitude and corresponding hydration data under different hydration levels were collected and normalized. The data set was divided into a training set and a validation set at 80% and 20% respectively. The deep learning network was trained with the training set. After the training, the accuracy of the model was evaluated by the mean square error loss function. The model performance was evaluated with the validation set, and the network structure and hyperparameters were adjusted during the training process.

[0096] The output measurement module is used to input the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies into the hydration measurement model to generate the hydration of the IPMC.

[0097] See also Figure 2 The present invention also provides a method for measuring the hydration degree of an ionic polymer metal composite material. The method is performed by the above-mentioned system for measuring the hydration degree of an ionic polymer metal composite material, and the specific steps include:

[0098] Step 1: Obtain an IPMC sample to be measured and obtain an IPMC with known hydration degree;

[0099] Step 2: applying alternating current signals of different frequencies in the range of 0.01 to 100 Hz to the metal electrodes on both sides of the IPMC sample, and ensuring that the alternating current signals of different frequencies act for the same time, generating a conductivity time series signal corresponding to the frequency, and recording the current values ​​and voltage values ​​at multiple time points during the action time of the alternating current signal corresponding to the frequency, generating the current average value and the voltage average value within the frequency action time, and then generating the standard deviation of the current and voltage, generating a correction coefficient based on the standard deviation, correcting the conductivity by the correction coefficient, and using the same method to process the IPMC with known hydration degree;

[0100] Step 3: Perform fast Fourier transform on the conductivity time series signal of the AC signal at different frequencies, generate the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies according to the result of fast Fourier transform, and use the same method to obtain the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies;

[0101] Step 4: Generate a deep learning network, take the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies as input, take the hydration degree of the IPMC with known hydration degree as a label, train the deep learning network, and obtain a trained hydration degree measurement model;

[0102] Step 5: Input the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies into the hydration measurement model to generate the hydration of the IPMC.

[0103] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A system for measuring the hydration degree of an ionomer-metal composite material, characterized in that: The specific steps include: A sample acquisition module is used to acquire an IPMC sample to be measured and acquire an IPMC with a known hydration degree; The data measurement module is used to apply alternating current signals of different frequencies ranging from 0.01 to 100 Hz to the metal electrodes on both sides of the IPMC sample, and ensure that the alternating current signals of different frequencies act for the same time, generate a conductivity time series signal corresponding to the frequency, and record the current value and voltage value at multiple time points during the action time of the alternating current signal corresponding to the frequency, generate the current average value and the voltage average value within the frequency action time, and then generate the standard deviation of the current and voltage, generate a correction coefficient based on the standard deviation, correct the conductivity by the correction coefficient, and use the same method to process the IPMC with known hydration degree; A data analysis module is used to perform fast Fourier transform on the conductivity time series signal of the alternating current signal at different frequencies, generate the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies according to the result of the fast Fourier transform, and use the same method to obtain the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies; A model training module is used to generate a deep learning network, take the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies as input, take the hydration degree of the IPMC with known hydration degree as a label, train the deep learning network, and obtain a trained hydration degree measurement model; The output measurement module is used to input the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies into the hydration measurement model to generate the hydration of the IPMC.

2. The system for measuring the hydration degree of an ionic polymer metal composite material according to claim 1, characterized in that: The formula used by the data measurement module to generate the conductivity of the IPMC sample at a specific frequency is: Among them, σ i (t) represents the conductivity of the IPMC sample at time t under the i-th electrical signal frequency, t represents the action time of the electrical signal frequency, i represents the index of the AC signal frequency, and i = 1, 2, ..., N, N represents the number of selected AC signal frequencies, I i (t) represents the current at time t under the frequency of the i-th electrical signal, V i (t) represents the voltage at time t under the i-th electrical signal frequency, L represents the distance between the electrodes on both sides of the IPMC sample, and A represents the contact area between the electrode and the IPMC sample.

3. The system for measuring the hydration degree of an ionic polymer metal composite material according to claim 1, characterized in that: The principle on which the conductivity is corrected by the correction coefficient in the data measurement module is based on: The formulas used to generate the standard deviation of current and voltage are: in, represents the average current value within the action time of the ith AC signal frequency, m represents the index of the time point within the action time, and m represents the maximum value of the time point index, that is, the total number of current or voltage data collected at one frequency, I m represents the current value collected at the mth time point, V m represents the voltage value collected at the mth time point, represents the average voltage during the action time of the ith AC signal frequency, μ I,i It represents the standard deviation of the current during the action time of the ith AC signal frequency, μ V,i It represents the standard deviation of the voltage during the action time of the i-th AC signal frequency; The correction factor is generated based on the formula: Among them, k I,i represents the current correction coefficient of the ith AC signal frequency, k V,i represents the voltage correction coefficient of the ith AC signal frequency, ∈ is a constant to prevent the denominator from being zero; The conductivity correction is based on the formula: in, It represents the conductivity correction value at time t under the action of the i-th AC signal frequency.

4. The system for measuring the hydration degree of an ionic polymer metal composite material according to claim 3, characterized in that: The principle of fast Fourier transform is: The formula for fast Fourier transform is: Among them, X i (f i ) represents the conductivity frequency domain signal of IPMC sample at the ith AC frequency, T represents the action time of an AC signal frequency, f i represents the i-th frequency, and j represents the imaginary unit.

5. The system for measuring the hydration degree of an ionic polymer metal composite material according to claim 4, characterized in that: The phase angle and magnitude of the conductivity at each frequency are generated based on the principle: in, represents the phase angle of the ith alternating current frequency, f i Indicates the frequency of the ith alternating current, Im(X i (f i )) represents the imaginary part of the frequency domain signal, Re(X i (f i )) represents the real part of the frequency domain signal; Among them, A i (f i ) represents the amplitude of the i-th alternating current frequency.

6. A method for measuring the hydration degree of an ionomer-metal composite material, characterized in that: The method is performed by the hydration degree measuring system of the ionic polymer metal composite material according to any one of claims 1 to 5, and the specific steps include: Step 1: Obtain an IPMC sample to be measured and obtain an IPMC with known hydration degree; Step 2: applying alternating current signals of different frequencies in the range of 0.01 to 100 Hz to the metal electrodes on both sides of the IPMC sample, and ensuring that the alternating current signals of different frequencies act for the same time, generating a conductivity time series signal corresponding to the frequency, and recording the current values ​​and voltage values ​​at multiple time points during the action time of the alternating current signal corresponding to the frequency, generating the current average value and the voltage average value within the frequency action time, and then generating the standard deviation of the current and voltage, generating a correction coefficient based on the standard deviation, correcting the conductivity by the correction coefficient, and using the same method to process the IPMC with known hydration degree; Step 3: Perform fast Fourier transform on the conductivity time series signal of the AC signal at different frequencies, generate the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies according to the result of fast Fourier transform, and use the same method to obtain the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies; Step 4: Generate a deep learning network, take the phase angle and amplitude of the conductivity of the IPMC with known hydration degree at various frequencies as input, take the hydration degree of the IPMC with known hydration degree as a label, train the deep learning network, and obtain a trained hydration degree measurement model; Step 5: Input the phase angle and amplitude of the conductivity of the IPMC sample at various frequencies into the hydration measurement model to generate the hydration of the IPMC.

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