A system and method for measuring the hydration level of an ionic polymer metal composite

Abstract

The application provides a kind of measurement system and method for hydration degree of ion polymer metal composite, and relates to the technical field of polymer chemistry.The IPMC sample to be measured and the known hydration degree IPMC as a control are first obtained;Second, the alternating current signal of different frequencies ranging from 0.01 to 100 Hz is applied to the metal electrodes on both sides of the IPMC sample, the current and voltage are recorded, the correction coefficient is generated to adjust the conductivity, and the fast Fourier transform is used for the conductivity to obtain the phase angle and amplitude at different frequencies.The phase angle and amplitude of the known hydration degree IPMC are used as input, and the hydration degree is used as label to train the deep learning network, and finally a hydration degree measurement model is formed.Finally, the phase angle and amplitude of the sample to be tested are input into the model to obtain the hydration degree.The method combines conductivity analysis and deep learning technology to realize accurate evaluation of the hydration degree of IPMC.

Description

Technical Field

[0001] This invention relates to the field of polymer chemistry, specifically to a system and method for measuring the hydration degree of ionomer-metal composite materials. Background Technology

[0002] In recent years, ionomer-metal composites (IPMCs) have been widely used in smart materials, sensors, and actuators due to their excellent electrical response properties and flexibility. However, hydration level has a significant impact on the performance of IPMCs, and changes in hydration level directly affect their electrical conductivity and electromechanical response. Therefore, how to accurately and rapidly measure and control the hydration level of IPMCs is a major technical challenge in current research. Existing hydration level measurement methods typically rely on traditional physical and chemical analysis, which is not only time-consuming and labor-intensive but may also affect the performance of the sample, and lacks the ability for real-time monitoring and analysis.

[0003] In the prior art, CN109358096B discloses a method and system for measuring the hydration degree of ion-polymer metal composite materials. An external excitation signal is input to a cantilever beam structure containing IPMC to obtain the deformation at the end of the IPMC. The induced current generated by the IPMC is obtained based on the deformation. The amplitude frequency signal and the phase frequency signal are obtained based on the external excitation signal and the induced current. The amplitude frequency signal and the phase frequency signal are substituted into the sensing dynamic model to obtain the ion diffusion coefficient. The hydration degree of the IPMC is determined based on 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 practical applications, changes in environmental conditions such as temperature and humidity will affect the performance and response behavior of IPMC, introducing more variables and uncertainties, which will reduce the reliability of hydration measurement.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a system and method for measuring the hydration degree of ionomer metal composite materials, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A system for measuring the hydration degree of an ionomer-metal composite material, comprising the following steps: The sample acquisition module is used to acquire the IPMC sample to be measured and to obtain the IPMC with known hydration level. 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, ensuring that the alternating current signals of different frequencies act for the same duration, generating a conductivity time-series signal corresponding to the frequency, and recording the current and voltage values ​​at multiple time points within the duration of the alternating current signal corresponding to the frequency, generating the average current and average voltage within the duration of the frequency, and then generating the standard deviation of the current and voltage, generating a correction coefficient based on the standard deviation, correcting the conductivity through the correction coefficient, and using the same method to process IPMC with known hydration degree; The data analysis module is used to perform fast Fourier transform on the conductivity time-series signal of AC signal at different frequencies. Based on the results of the fast Fourier transform, the phase angle and amplitude of the conductivity of IPMC sample at various frequencies are generated. The same method is used to obtain the phase angle and amplitude of the conductivity of IPMC with known hydration at various frequencies. The model training module is used to generate a deep learning network. It takes the phase angle and amplitude of the conductivity of the known IPMC at various frequencies as input and the hydration degree of the known IPMC as a label to train the deep learning network, thus obtaining 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 degree of the IPMC.

[0008] Furthermore, the formula used to generate the conductivity of the IPMC sample at a specific frequency is as follows: in, Let represent the conductivity of the IPMC sample at time t under the i-th electrical signal frequency, where t represents the duration of the electrical signal frequency. The index representing the frequency of the alternating current signal, and , Indicates the number of selected AC signal frequencies. This represents the current at time t at the frequency of the i-th electrical signal. This represents the voltage at time t at the frequency of the i-th electrical signal. This indicates the distance between the electrodes on both sides of the IPMC sample. This indicates the contact area between the electrode and the IPMC sample.

[0009] Furthermore, the principle underlying the correction of conductivity using a correction factor is as follows: The formulas used to determine the standard deviations of the generated current and voltage are as follows: in, This represents the average current during the duration of the i-th alternating current signal frequency. Indicates the index of a point in time within the effective time period. This represents the maximum value of the time point index, i.e., the total number of current or voltage data collected at a given frequency. This represents the current value collected at the m-th time point. This represents the voltage value collected at the m-th time point. This represents the average voltage during the duration of the i-th alternating current signal frequency. This represents the standard deviation of the current during the duration of the i-th alternating current signal frequency. This represents the standard deviation of the voltage during the duration of the i-th alternating current signal frequency. The formula used to generate the correction coefficient is: in, This represents the current correction factor for the i-th AC signal frequency. This represents the voltage correction factor for the i-th AC signal frequency. To prevent constants with a denominator of 0; The formula used to correct for conductivity is: in, This represents the conductivity correction value at time t under the influence of the i-th AC signal frequency.

[0010] Furthermore, the principle underlying the Fast Fourier Transform is as follows: The formula used to perform the Fast Fourier Transform is: in, Let T represent the frequency domain signal of the conductivity of the IPMC sample at the i-th alternating current frequency, where T represents the duration of the alternating current signal frequency. This represents the frequency of the i-th alternating current signal. It represents the imaginary unit.

[0011] Furthermore, the principle underlying the generation of the phase angle and amplitude of conductivity at each frequency is as follows: in, This represents the phase angle of the i-th alternating current frequency. This represents the imaginary part of a frequency domain signal. Represents the real part of a frequency domain signal; in, This represents the amplitude of the i-th alternating current frequency.

[0012] This invention also provides a method for measuring the hydration degree of ionomer metal composite materials. The method is executed by the aforementioned system for measuring the hydration degree of ionomer metal composite materials, and the specific steps include: Step 1: Obtain the IPMC sample to be measured and obtain the IPMC with known hydration level; Step 2: 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, ensuring that the alternating current signals of different frequencies act for the same duration, generate a conductivity time-series signal corresponding to that frequency, and record the current and voltage values ​​at multiple time points within the duration of the alternating current signal corresponding to the frequency, generate the average current and average voltage within the duration of the frequency, and then generate the standard deviation of the current and voltage. Based on the standard deviation, generate a correction coefficient, correct the conductivity using the correction coefficient, and use the same method to process IPMC with known hydration. Step 3: Perform Fast Fourier Transform on the conductivity time-series signal of AC signal at different frequencies, generate the phase angle and amplitude of the conductivity of IPMC sample at various frequencies based on the results of Fast Fourier Transform, and use the same method to obtain the phase angle and amplitude of the conductivity of IPMC with known hydration at various frequencies. Step 4: Generate a deep learning network. Use the phase angle and amplitude of the conductivity of the known IPMC at various frequencies as input, and the hydration degree of the known IPMC as the label to 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 degree of the IPMC.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention adjusts IPMC material to a near-dense packing state, which improves the mechanical strength and conductivity of IPMC. In subsequent conductivity measurements and hydration analysis, the IPMC sample exhibits more stable and reliable performance, 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, providing rich data support for hydration analysis. Furthermore, through detailed conductivity information, the relationship between hydration and conductivity can be better understood, thereby improving the accuracy of subsequent deep learning model training.

[0014] This invention also utilizes Fast Fourier Transform (FFT) to remove redundant signals while converting time-domain signals into frequency-domain signals and extracting phase angle and amplitude, thereby providing deeper information about the material's electrical conductivity properties and more clearly demonstrating the impact of hydration changes on conductivity. By training a deep learning network, the nonlinear relationship between phase angle and amplitude and hydration is captured. Compared to traditional linear models, deep learning network models perform better when processing complex data and are more timely. In the case of large-scale datasets, deep learning models have faster training and output speeds, enabling them to process a large number of hydration measurements in a short time, making them suitable for industrial applications and real-time monitoring needs. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system modules in an embodiment of the present invention; Figure 2 This is a schematic diagram of the method flow of an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example: Please see Figure 1 The present invention provides a technical solution: A system for measuring the hydration degree of an ionomer-metal composite material, specifically comprising: The sample acquisition module is used to acquire the IPMC sample to be measured and to obtain the IPMC with known hydration level. In this embodiment, IPMC is an ion-polymer metal composite material, and the IPMC sample is the IPMC material with the hydration degree to be measured. The IPMC with known hydration degree is the IPMC material used for training deep learning models. The hydration degree is obtained by controlling the absorption or release of water by IPMC under different conditions. The principle behind this is as follows: Weigh the dried IPMC and record the mass as follows: The dried IPMC was then placed in deionized water, sealed, and soaked for a period of time. After removal, excess water was removed from the surface, and the mass of the IPMC in the wet state was measured and recorded as follows. The soaking time is determined according to the experimental requirements. Within a certain time range, the longer the soaking time, the better. The higher the hydration level, the higher the degree of hydration. .

[0019] 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, ensuring that the alternating current signals of different frequencies act for the same duration, generating a conductivity time-series signal corresponding to the frequency, and recording the current and voltage values ​​at multiple time points within the duration of the alternating current signal corresponding to the frequency, generating the average current and average voltage within the duration of the frequency, and then generating the standard deviation of the current and voltage, generating a correction coefficient based on the standard deviation, correcting the conductivity through the correction coefficient, and using the same method to process IPMC with known hydration degree; In this embodiment, the principle underlying the generation of the conductivity of the IPMC sample is as follows: The formula used to generate the conductivity of an IPMC sample at a specific frequency is as follows: in, Let represent the conductivity of the IPMC sample at time t under the i-th electrical signal frequency, where t represents the duration of the electrical signal frequency. The index representing the frequency of the alternating current signal, and , Indicates the number of selected AC signal frequencies. This represents the current at time t at the frequency of the i-th electrical signal. This represents the voltage at time t at the frequency of the i-th electrical signal. This indicates the distance between the electrodes on both sides of the IPMC sample. This indicates the contact area between the electrode and the IPMC sample.

[0020] Electrical conductivity reflects the ability of an 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 of the IPMC sample at a specific electrical signal frequency, while voltage reflects the magnitude of the alternating voltage applied across the IPMC sample at a specific electrical signal frequency. The contact area between the electrodes and the sample is also important. This reflects the size of the current flow path. Under the same current and voltage, the larger the contact area, the easier it is for the current to flow. The distance between the electrodes on both sides of the sample... Reflects the path length and spacing of the current flow. The longer the current flow, the greater the resistance to current flow. The conductivity is directly proportional to the magnitude of the alternating current passing through the electrodes on both sides of the sample and the distance between the electrodes on both sides of the sample, and inversely proportional to the magnitude of the alternating voltage applied to both sides of the IPMC sample and the contact area between the electrodes and the sample.

[0021] The principle behind correcting conductivity using a correction factor is as follows: The formulas used to determine the standard deviations of the generated current and voltage are as follows: in, This represents the average current during the duration of the i-th alternating current signal frequency. Indicates the index of a point in time within the effective time period. This represents the maximum value of the time point index, i.e., the total number of current or voltage data collected at a given frequency. This represents the current value collected at the m-th time point. This represents the voltage value collected at the m-th time point. This represents the average voltage during the duration of the i-th alternating current signal frequency. This represents the standard deviation of the current during the duration of the i-th alternating current signal frequency. This represents the standard deviation of the voltage during the duration of the i-th alternating current signal frequency. 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 that 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. The formula used to generate the correction coefficient is: in, This represents the current correction factor for the i-th AC signal. This represents the voltage correction factor for the i-th AC signal. To prevent constants with a denominator of 0; The correction factor is determined by proportional adjustment and is set as the ratio of the standard deviation to the mean. To prevent the denominator from being a constant and to ensure that the value is extremely small, it can be set to 0.001~0.01. By comparing the standard deviation and the mean, the correction coefficient can effectively reflect the measurement uncertainty. The formula used to correct for conductivity is: in, This represents the conductivity correction value at time t under the influence of the i-th AC signal frequency.

[0022] The correction formula for conductivity reflects the correction result of conductivity by adjusting the current and voltage after considering the uncertainty of current and voltage measurements in conductivity calculation. The data analysis module is used to perform fast Fourier transform on the conductivity time-series signal of AC signal at different frequencies. Based on the results of the fast Fourier transform, the phase angle and amplitude of the conductivity of IPMC sample at various frequencies are generated. The same method is used to obtain the phase angle and amplitude of the conductivity of IPMC with known hydration at various frequencies. The formula upon which the Fast Fourier Transform is based is: in, Let T represent the frequency domain signal of the conductivity of the IPMC sample at the i-th alternating current frequency, where T represents the duration of the alternating current signal frequency. This represents the frequency of the i-th alternating current signal. It represents the imaginary unit.

[0023] The principle underlying the generation of the phase angle and amplitude of conductivity at each frequency is as follows: in, This represents the phase angle of the i-th alternating current frequency. This represents the imaginary part of a frequency domain signal. Represents the real part of a frequency domain signal; in, This represents the amplitude of the i-th alternating current frequency. The phase angle is a parameter representing the phase difference between current and voltage in a frequency domain signal. It 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 of a material at a specific frequency. The higher the amplitude, the better the conductivity of the material at that frequency. Furthermore, the conductivity increases with increasing hydration, which in turn increases the amplitude.

[0024] In IPMC materials, water molecules act as mobile ion carriers, increasing the material's ionic conductivity. As the degree of hydration increases, the conductivity and amplitude also increase. Changes in the degree of hydration affect ion mobility, thus influencing the material's polarization effect. When the degree of hydration increases, the polarization effect is enhanced, leading to a decrease in the phase angle. Furthermore, the changes in the phase angle at different frequencies reflect the influence of the degree of hydration on the material's dielectric effect. At low frequencies, the phase angle is smaller, while at high frequencies, the phase angle increases, reflecting the delay in ion flow and the weakening of the polarization effect. The model training module is used to generate a deep learning network. It takes the phase angle and amplitude of the conductivity of the known IPMC at various frequencies as input and the hydration degree of the known IPMC as a label to train the deep learning network, thus obtaining a trained hydration degree measurement model. In this embodiment, the deep learning network structure for the hydration measurement model is as follows: Input layer: Contains 2 neurons, corresponding to the input phase angle and amplitude respectively; The first hidden layer contains 32 neurons, which are activated by the ReLU function; The second hidden layer contains 16 neurons, which are activated by the ReLU function; Output layer: Contains 1 neuron, outputting the IPMC hydration level, activated by a linear activation function; The phase angle reflects the conductivity and response delay of IPMC materials and is an important indicator of the dielectric properties of materials. The moisture content in IPMC materials will affect the conductivity and thus the phase angle. 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, thus affecting the amplitude. Hydration not only affects conductivity but also the flexibility, rigidity, and response speed of IPMCs, all of which are related to phase angle and amplitude. Increased hydration improves material flexibility, and more flexible materials can better store and release energy under an applied electric field, resulting in larger phase angles and amplitudes. Conversely, more rigid materials tend to have smaller phase angles and amplitudes. Hydration also affects the mobility of ions within the material. Higher hydration levels result in faster ion movement, leading to a faster response speed. Materials with faster response speeds can quickly generate larger amplitudes when voltage is applied, resulting in more significant phase angle changes.

[0025] By using the known phase angle and amplitude as input and the degree of hydration as output, the trained model can learn the complex nonlinear relationship between the two, thereby enabling the measurement of the degree of hydration. Phase angle, amplitude, and corresponding hydration level data under different hydration levels were collected and normalized. The dataset was divided into training and validation sets at 80% and 20% respectively. The deep learning network was trained using the training set, and the accuracy of the model was evaluated using the mean squared error loss function after training. The model performance was evaluated using the validation set, and the network structure and hyperparameters were adjusted during the training process.

[0026] 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 degree of the IPMC.

[0027] Please see Figure 2 The present invention also provides a method for measuring the hydration degree of ion-polymer metal composite materials. The method is executed by the aforementioned system for measuring the hydration degree of ion-polymer metal composite materials, and the specific steps include: Step 1: Obtain the IPMC sample to be measured and obtain the IPMC with known hydration level; Step 2: 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, ensuring that the alternating current signals of different frequencies act for the same duration, generate a conductivity time-series signal corresponding to that frequency, and record the current and voltage values ​​at multiple time points within the duration of the alternating current signal corresponding to the frequency, generate the average current and average voltage within the duration of the frequency, and then generate the standard deviation of the current and voltage. Based on the standard deviation, generate a correction coefficient, correct the conductivity using the correction coefficient, and use the same method to process IPMC with known hydration. Step 3: Perform Fast Fourier Transform on the conductivity time-series signal of AC signal at different frequencies, generate the phase angle and amplitude of the conductivity of IPMC sample at various frequencies based on the results of Fast Fourier Transform, and use the same method to obtain the phase angle and amplitude of the conductivity of IPMC with known hydration at various frequencies. Step 4: Generate a deep learning network. Use the phase angle and amplitude of the conductivity of the known IPMC at various frequencies as input, and the hydration degree of the known IPMC as the label to 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 degree of the IPMC.

[0028] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0029] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0030] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A system for measuring the hydration degree of an ionomer-metal composite material, characterized in that, The specific steps include: The sample acquisition module is used to acquire the IPMC sample to be measured and to obtain the IPMC with known hydration level. 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, ensuring that the alternating current signals of different frequencies act for the same duration, generating a conductivity time-series signal corresponding to the frequency, and recording the current and voltage values ​​at multiple time points within the duration of the alternating current signal corresponding to the frequency, generating the average current and average voltage values ​​within the duration of the frequency, and then generating the standard deviation of the current and voltage, generating a correction coefficient based on the standard deviation, correcting the conductivity through the correction coefficient, and using the same method to process IPMC with known hydration degree; The data analysis module is used to perform fast Fourier transform on the conductivity time-series signal of AC signal at different frequencies. Based on the results of the fast Fourier transform, the phase angle and amplitude of the conductivity of IPMC sample at various frequencies are generated. The same method is used to obtain the phase angle and amplitude of the conductivity of IPMC with known hydration at various frequencies. The model training module is used to generate a deep learning network. It takes the phase angle and amplitude of the conductivity of the known IPMC at various frequencies as input and the hydration degree of the known IPMC as a label to train the deep learning network, thus obtaining 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. The principle behind correcting conductivity using a correction factor is as follows: The formulas used to determine the standard deviations of the generated current and voltage are as follows: in, This represents the average current during the duration of the i-th alternating current signal frequency. Indicates the index of a point in time within the effective time period. This represents the maximum value of the time point index, i.e., the total number of current or voltage data collected at a given frequency. This represents the current value collected at the m-th time point. This represents the voltage value collected at the m-th time point. This represents the average voltage during the duration of the i-th alternating current signal frequency. This represents the standard deviation of the current during the duration of the i-th alternating current signal frequency. This represents the standard deviation of the voltage during the duration of the i-th alternating current signal frequency. The formula used to generate the correction coefficient is: in, This represents the current correction factor for the i-th AC signal frequency. This represents the voltage correction factor for the i-th AC signal frequency. To prevent constants with a denominator of 0; The formula used to correct for conductivity is: in, This represents the conductivity correction value at time t under the influence of the i-th AC signal frequency.

2. The system for measuring the hydration degree of an ionomer-metal composite material according to claim 1, characterized in that: The formula used in the data measurement module to generate the conductivity of the IPMC sample at a specific frequency is as follows: in, Let represent the conductivity of the IPMC sample at time t under the i-th electrical signal frequency, where t represents the duration of the electrical signal frequency. The index representing the frequency of the alternating current signal, and , Indicates the number of selected AC signal frequencies. This represents the current at time t at the frequency of the i-th electrical signal. This represents the voltage at time t at the frequency of the i-th electrical signal. This indicates the distance between the electrodes on both sides of the IPMC sample. This indicates the contact area between the electrode and the IPMC sample.

3. The system for measuring the hydration degree of an ionomer-metal composite material according to claim 1, characterized in that: The principle underlying the Fast Fourier Transform is as follows: The formula used to perform the Fast Fourier Transform is: in, Let T represent the frequency domain signal of the conductivity of the IPMC sample at the i-th alternating current frequency, where T represents the duration of the alternating current signal frequency. This represents the frequency of the i-th alternating current signal. It represents the imaginary unit.

4. The system for measuring the hydration degree of an ionomer-metal composite material according to claim 3, characterized in that: The principle underlying the generation of the phase angle and amplitude of conductivity at each frequency is as follows: in, This represents the phase angle of the i-th alternating current frequency. This represents the imaginary part of a frequency domain signal. Represents the real part of a frequency domain signal; in, This represents the amplitude of the i-th alternating current frequency.

5. A method for measuring the hydration degree of an ionomer-metal composite material, characterized in that, The method is performed by the hydration degree measurement system of the ion-polymer metal composite material according to any one of claims 1-4, and the specific steps include: Step 1: Obtain the IPMC sample to be measured and obtain the IPMC with known hydration level; Step 2: 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, ensuring that the alternating current signals of different frequencies act for the same duration, generate a conductivity time-series signal corresponding to that frequency, and record the current and voltage values ​​at multiple time points within the duration of the alternating current signal corresponding to the frequency, generate the average current and average voltage within the duration of the frequency, and then generate the standard deviation of the current and voltage. Based on the standard deviation, generate a correction coefficient, correct the conductivity using the correction coefficient, and use the same method to process IPMC with known hydration. Step 3: Perform Fast Fourier Transform on the conductivity time-series signal of AC signal at different frequencies, generate the phase angle and amplitude of the conductivity of IPMC sample at various frequencies based on the results of Fast Fourier Transform, and use the same method to obtain the phase angle and amplitude of the conductivity of IPMC with known hydration at various frequencies. Step 4: Generate a deep learning network. Use the phase angle and amplitude of the conductivity of the known IPMC at various frequencies as input, and the hydration degree of the known IPMC as the label to 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 degree of the IPMC.

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