Low-power-consumption magnetic induction fault target disturbance analysis method based on biological electromagnetic principle

Abstract

The invention discloses a low-power-consumption magnetic induction fault target disturbance analysis method based on a biological electromagnetic principle, and the method comprises the steps: transmitting two sine wave signals of the same-frequency and same-phase voltage through an excitation module, enabling one signal to enter a power amplifier module through a low-pass filter for signal conversion and amplification, and then inputting the signal into a coil module; differential signals generated by the coil module are further amplified and then input into a multiplexer MUX, the MUX selectively transmits detection signals to an analog phase discrimination module and a digital phase discrimination module, and salt solutions with different conductivities are placed in a system object field area of the coil module. A series of modulation signals of different frequency bands and corresponding reference signals are collected in a frequency sweeping mode, and a data set is obtained; and training the data set based on a distance correlation coefficient improved LSTM regression prediction model. According to the method, low-power-consumption detection on different target disturbance state precision requirements is achieved, operation is easy, and the detection effect is accurate.

Description

Technical Field

[0001] The invention belongs to the field of magnetic induction tomography detection, and in particular relates to a low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles. Background Art

[0002] Bioelectromagnetism is a discipline that studies electrical, magnetic and electromagnetic phenomena in biological tissues, including the behavior of excitable tissues, current and potential in organisms, magnetic fields inside and outside the body, the response of biological cells to electromagnetic fields, and the electromagnetic properties of biological tissues. There are charged ions and biomacromolecules in organisms. These charges and dipoles generate bioelectric currents driven by external electromagnetic fields or membrane potentials. Water occupies an important position in organisms and exists in the form of dipoles. Ion channels and potential differences on cell membranes are the basis of cell electrical activity. Different tissues and organs have different electrical conductivity and magnetic permeability. Bioelectromagnetism has a wide range of applications in medical diagnosis (such as electrocardiogram, electroencephalogram and electromyogram), body fat analysis, tumor detection, neural regulation and rehabilitation therapy. Through experimental research and computational models, it has promoted the development of medical technology and the diagnosis and treatment of diseases.

[0003] Compared with the technology based on bioelectromagnetic principles, CT (computed tomography) and MRI (magnetic resonance imaging) are two different medical imaging technologies. For example, the invention patent with patent number CN201880086909.X proposes a CT method for scanning objects in an X-ray security inspection system. CT uses the ability of X-rays to penetrate different tissues of the human body and generates tomographic images of internal structures by detecting the attenuation of the rays, providing high-resolution images. However, CT uses X-rays, which are ionizing radiation, and has certain radiation risks to the human body, and is not suitable for frequent inspections. For example, the invention patent with patent number CN202010794127.0 proposes a medical image diagnostic device, in which MRI uses strong magnetic fields and radio frequency pulses to generate detailed images of the internal structure of the human body through the nuclear magnetic resonance (NMR) phenomenon, especially in soft tissue imaging. It has excellent resolution and is widely used in the diagnosis of neurological diseases and cardiovascular diseases. MRI does not use ionizing radiation and does not cause ionizing damage to human tissues. It is suitable for long-term and repeated inspections, but the equipment is expensive and the scanning time is long, which is not suitable for some patients.

[0004] Considering the close connection between electric and magnetic fields, and the fact that magnetic fields are the result of the movement of electric charges in matter and have little effect on biological tissues, compared to CT and MRI, magnetic induction methods require almost zero ambient light and are not easily affected by instrument performance and processing speed. Compared to CT, magnetic induction does not pose an ionizing radiation risk to the human body; compared to MRI, magnetic induction is not limited by equipment cost and scanning time, and has lower power consumption. Therefore, by using the principle of magnetic induction and Maxwell's equations, and applying an external magnetic field to obtain relevant electrical property data, high-throughput and rapid analysis of conductivity, magnetic permeability, density, etc. in the target area can be performed.

[0005] At present, most magnetic induction tomography devices on the market have the problem of high power consumption, which limits their application in portable and long-term monitoring. Therefore, the present invention proposes a low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles, which obtains the conductivity data of the target area by using a single-channel magnetic induction tomography measurement device. The device selects different phase discrimination methods through a multiplexer (MUX). Analog phase discrimination has lower power consumption than digital phase discrimination, but its accuracy is not as high as that of digital phase discrimination. For different application scenarios, the most suitable phase discrimination method can be selected through MUX to balance power consumption and accuracy. The low power consumption characteristic not only reduces the operating cost of the equipment, but also makes the device more practical and efficient in various application environments. Finally, by acquiring relevant physical and chemical indicators to build a prediction model, the prediction model is used to perform corresponding analysis and detection, so as to achieve rapid and accurate detection of disturbances in the target area. Summary of the invention

[0006] The purpose of the present invention is to propose a low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles, so as to achieve low-power detection of different target disturbance state accuracy requirements, with simple operation and accurate detection effect.

[0007] The technical concept of the present invention is to first design an excitation module to ensure that the module can send two high-frequency and extremely low phase-shift sinusoidal wave signals of the same frequency and phase voltage, and then pass one of the sinusoidal wave signals through a low-pass filter into the power amplifier module, so that the sinusoidal current is large enough to excite the coil module. The design of the coil module consists of two parts: simulation design and manufacturing. It is necessary to control relevant parameters to ensure that the simulated inductance is consistent with the measured inductance. Then, a differential amplifier circuit is designed to further amplify the differential signal generated by the coil module, and the amplified signal is input into the MUX. The MUX can selectively transmit the detection signal to the analog phase-locked module and the digital phase-locked module as needed, ultimately effectively ensuring that power consumption is reduced while meeting functional requirements, improving portability and long-term monitoring requirements.

[0008] The technical solutions adopted by the present invention to achieve the above-mentioned invention object are as follows:

[0009] A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles comprises the following steps:

[0010] S1: construct a high-frequency multi-channel excitation module with extremely low phase offset rate, which can send two single-ended sinusoidal wave signals of the same frequency and phase with high frequency and extremely low phase offset rate. The first sinusoidal wave signal enters step S2, and the second sinusoidal wave signal is recorded as a reference signal;

[0011] S2: Build a power amplifier module with an integrated low-pass filter, pass the first sine wave signal in step S1 through a low-pass filter to remove high-frequency aliasing and noise, and then enter the power amplifier module to convert the sine wave signal into a differential signal and amplify the signal;

[0012] S3: Simulate and manufacture a coil module and a differential amplifier module. The coil module includes a receiving coil and a transmitting coil that are arranged in parallel and spaced apart. The differential signal amplified in step S2 is input to the transmitting coil. The transmitting coil generates a magnetic field signal when powered on. The receiving coil can achieve the effect of magnetoelectricity under the action of the magnetic field signal. The receiving coil outputs a sinusoidal wave signal of voltage, which is further amplified by the differential amplifier module, and the differential signal is converted into a single-ended signal to obtain a modulated signal.

[0013] S4: a series of salt solutions with different conductivity are prepared, and the salt solutions are placed in the system field area of ​​the coil module in step S3, that is, the middle position between the transmitting coil and the receiving coil. According to the operations of steps S1-S3, a series of modulation signals of different frequency bands under the interference of each conductive salt solution are collected by frequency sweeping, and corresponding reference signals are collected to obtain corresponding data sets;

[0014] S5: Build a low-power phase detection module, design a low-power phase detection module combining analog phase detection and digital phase detection through a multiplexer MUX, calculate the phase difference between the two signals in the data set of step S4 through analog phase detection or digital phase detection, and obtain a data set about the corresponding relationship between phase difference and conductivity;

[0015] S6: construct an LSTM regression prediction model improved based on the distance correlation coefficient, divide the data set obtained in step S5 into a training set and a test set, and use the constructed prediction model for training; when the conductivity of the salt solution to be tested is subsequently tested, the phase difference is first tested according to the method of steps S1-S5, and then input into the trained prediction model to obtain the corresponding salt solution conductivity.

[0016] Preferably, the step S1 specifically includes a high-frequency multi-channel excitation module with an extremely low phase shift rate, including: a main control chip, a DDS (direct digital frequency synthesizer), and a low-pass filter. The main control chip uses STMF103RC, and the DDS uses an AD9959 chip with a maximum operating bandwidth of 200MHz, supporting linear frequency, amplitude and phase scanning. The main control STMF103RC sends instructions for the frequency control word (FTW), phase shift word (POW) and amplitude scaling factor (ASF) to the DDS through the SPI interface to set the required output parameters. After the setting is completed, by sending an I / O update signal, the DDS updates the register value to the internal buffer, thereby generating an accurate output signal. The DDS uses the following three formulas to calculate the output frequency, phase shift, and amplitude of the output signal:

[0017]

[0018] Where FTW is the frequency control word, f out is the desired output frequency, f sys is the system clock frequency of the DDS.

[0019]

[0020] Where POW is the phase offset word and φ is the desired phase offset.

[0021] ASF=r I ×2 10 (3)

[0022] Where ASF is the amplitude scaling factor, r I It is the ratio of the required output current to the maximum output current.

[0023] Preferably, in step S1, the phase shift rate of the sinusoidal wave signal sent by the high-frequency multi-channel excitation module is 0.001°-0.0001°.

[0024] Preferably, the step S2 specifically includes:

[0025] S2.1: Build a passive low-pass filter. After the direct digital frequency synthesizer (DDS) system, build a 200MHz Butterworth low-pass filter of the 9th order. The main purpose is to remove high-frequency aliasing and noise and improve the spectral purity of the signal. The signal output by the DDS may contain unwanted high-frequency components and harmonics, which will cause spectral distortion. The 200MHz Butterworth low-pass filter of the 9th order effectively filters out these high-frequency components and improves the signal quality through its smooth frequency response (i.e., the amplitude is stable in the passband and decays rapidly after the cutoff frequency). Its transfer function is shown in the following formula:

[0026]

[0027] Among them, ω c =2πf c is the angular frequency of the cutoff frequency, f c is the cutoff frequency, n is the filter order which is 9, s is the complex frequency of the input signal entering the low-pass filter, and H(s) is the transfer function, which is the core of the filter and defines how the input signal changes in the frequency domain after passing through the filter.

[0028] S2.2: Build two-stage power amplifiers. The first stage power amplifier uses AD8138, and the second stage power amplifier uses AD8131. First, use AD8138 to convert the single-ended sine wave signal generated by DDS into a differential signal and perform gain amplification (for example, amplify it by 10 times). Then, AD8131 further amplifies the differential signal (for example, amplify it by 2 times) to maintain the characteristics of the differential signal and increase the output amplitude. Through the combination of these two stages of amplifiers, the input signal can be effectively amplified by a large multiple (for example, 20 times), so that the current of the amplified sine wave is large enough to excite the coil, while ensuring high accuracy and low distortion. The gain of AD8138 is shown in Formula 5, and the differential mode gain of AD8131 is shown in Formula 6.

[0029]

[0030] Among them, G N is the gain, R F1 and R G1 are the feedback resistor and input resistor in the AD8138 peripheral circuit.

[0031]

[0032] Among them, V OUT and V IN are the differential mode output voltage and single-ended input voltage of AD8138, R F2 and R G2 are the feedback resistor and input resistor in the AD8131 peripheral circuit.

[0033] Preferably, the step S3 specifically includes: simulating and manufacturing a coil module to ensure that the inductance of the coil simulation can first meet the system requirements, and the receiving coil and the transmitting coil are both set with relevant parameters according to the following formula:

[0034]

[0035] Where L is the inductance of the coil, r is the average radius of the coil, N is the number of turns of the coil, l is the length of the coil, and d is the thickness of the coil.

[0036] In addition, in order to ensure that the coil module generates a magnetic field of sufficient strength, it is necessary to measure the actual inductance of the receiving coil, transform the differential input coil of the receiving coil into a single-ended input coil through loop connection, and then use a network analyzer to measure the inductance and impedance in different frequency domains. By measuring the actual inductance, the power of the coil module can be adjusted.

[0037] S3.3: In order to improve the stability of the system, a specific device is 3D printed to fix the receiving coil and the transmitting coil.

[0038] Preferably, a differential amplifier module in step S3 adopts a differential detection method that can effectively avoid common-mode signal interference, reduce ground noise and system noise, and improve the reliability and anti-interference ability of weak signal detection of the magnetic induction coil. A differential amplifier chip with a very high common-mode rejection ratio is selected to convert the differential signal into a single-ended signal, which is convenient for a series of subsequent processing.

[0039] Preferably, the construction of the low-power phase detection module in step S5 specifically includes:

[0040] S5.1: Build a MUX selector with a fan-out of 2 and place it in the front stage of the module to enable the control signal to be output by the main control chip;

[0041] S5.2: The MUX selector connects the analog phase detector and the digital phase detector through a circuit. The MUX selector selects the analog phase detector or the digital phase detector to calculate the phase difference according to the detection field. The digital phase detector is selected in the field with high detection accuracy, and the analog phase detector is selected in the field with low detection accuracy. Taking digital phase detector as an example, the phase difference between two signals can be calculated by executing a digital signal processing algorithm. The algorithm usually includes calculating the Fourier transform of the two signals to extract the phase information, or using a time domain cross-correlation method to estimate the phase difference. In this way, the digital phase detector module can accurately measure the phase difference of high-speed signals and use the results for further signal processing or analysis.

[0042] Preferably, in step S4, a series of salt solutions with different conductivity are prepared, which is a series of aqueous salt solutions with different concentrations, and the salt is sodium chloride.

[0043] Preferably, the LSTM regression prediction model with improved distance correlation coefficient in step S6 includes the following contents:

[0044] S6.1: a series of modulation signals of different frequency bands under the interference of each conductive salt solution and corresponding reference signals are collected by means of frequency scanning. Under the interference of each conductive salt solution, voltage signals of i different frequency bands are scanned to obtain phase difference data sets of 30 frequency bands. Data is collected once every 1s in each frequency band, and the total collection time is b seconds. Therefore, the phase difference data set of each frequency band is composed of b columns of phase difference data.

[0045] In order to obtain the frequency band data that can best represent the highest correlation between conductivity and phase difference data, it is necessary to analyze the degree of correlation of the distance correlation coefficient for each frequency band. The correlation degree calculation formula is as follows:

[0046]

[0047] Where, dCor(X,Y) is the distance correlation coefficient, dCov(X,Y) is the distance covariance, dVar(X) and dVar(Y) are the distance variances of X and Y; X is a column of phase difference data in b columns of data under the corresponding frequency band, and Y is the different conductivities corresponding to a salt solution; a and b are both positive integers between 10 and 1000;

[0048] The data set for the conductivity of the salt solution is defined as Y = [y1 y2 … y a ] T , define the phase difference data matrix of salt solution conductivity in frequency band i as W = [w i,1 w i,2 … w i,a ] T , where w i,1 It represents the phase difference data [Δσ i,1 Δσ i,2 … Δσ i,b ]; define the phase difference data of column b of the conductivity of a salt solution in the corresponding frequency band as X = [x 1,j x 2,j … x a,j ] T ;

[0049] S6.2: The phase difference data corresponding to the frequency band with the highest total correlation value calculated and counted according to S6.1, i.e., the largest distance correlation coefficient, is taken as the target phase difference data;

[0050] S6.3: Define the target phase difference data matrix obtained by S6.2 as:

[0051]

[0052] The above phase difference data D a，b as the data set input, and the salt solution conductivity Y in S6 as the data set output, μ X =E(X),μ Y =E(Y), and use LSTM to build a conductivity regression prediction model for training.

[0053] S6.4: The network structure of the LSTM regression prediction model starts with the input phase difference data, and first embeds the data through the embedding layer. The main function of the embedding layer is to map the input high-dimensional discrete data to a low-dimensional continuous vector space, and then convert the complex phase difference data into a form that the model can better handle. Through data embedding, the model can capture the potential relationships and patterns in the input data, reduce the sparsity of the data, and make the subsequent feature extraction process more effective. In addition, the embedding layer can also help the model maintain consistency when processing data of different dimensions and improve the generalization ability of the model. After the data is embedded, the data will be input into the bidirectional LSTM (Bi-LSTM) layer for time series feature extraction. LSTM (Long Short-Term Memory Network) is a neural network structure that is particularly suitable for processing time series data and can effectively capture long-term dependencies in the data. By introducing two LSTM units, forward and backward, Bi-LSTM can simultaneously consider the contextual information of the time series, that is, not only pay attention to past data, but also pay attention to future data. This design enables the model to better capture global context features when processing phase difference data, thereby extracting richer and more meaningful time series features. These extracted time series features will be used as inputs for subsequent layers for further feature processing and model prediction. Through the combination of the embedding layer and Bi-LSTM, the network structure can fully utilize the characteristics of time series data, improve the ability to process complex data and the accuracy of conductivity prediction.

[0054] S6.5: After the extracted features are processed by the hidden layer, they are passed to the self-attention layer to capture the global context information and generate attention weights. These weights are applied to the features and finally output the predicted conductivity results through the classification layer. This model combines bidirectional LSTM and self-attention mechanism to improve the feature extraction and prediction capabilities of time series data.

[0055] Preferably, the step S6 specifically includes: using the training set to train the prediction model, and using the test set to test the performance of the trained prediction model. And based on the determination coefficient R between the predicted value and the actual value of the test set 2 And RMSE are used as indicators to determine the effectiveness of the model.

[0056] The invention provides a low-power magnetic induction tomography target disturbance analysis device and method based on bio-electromagnetic principle, comprising: a high-frequency multi-channel excitation module, a low-power phase detection module, and an improved LSTM regression prediction model.

[0057] The high-frequency multi-channel excitation module. First, a high-frequency multi-channel excitation source with an extremely low phase shift rate is constructed to ensure the accuracy and consistency of the signal. Next, a power amplifier circuit with an integrated low-pass filter is built so that the amplified sinusoidal current is large enough to effectively excite the coil. Then, by simulating and manufacturing the coil module, a specific 3D printing device is designed to fix the receiving coil and the transmitting coil, thereby improving the stability and accuracy of the system. These steps are combined to achieve a high-precision excitation module.

[0058] The low-power detection phase-lock module uses MUX to adaptively control the phase-lock circuit for application scenarios with different conductivities and different precision requirements. This module can greatly reduce the high power consumption problem caused by the current digital phase-lock magnetic induction tomography equipment, and can also increase the problem of fewer application scenarios of the current analog phase-lock magnetic induction tomography equipment. By using a low-power magnetic induction tomography measurement device to collect phase difference data of 200 salt solutions with different conductivity within a specified time, the data consists of data from 30 frequency bands, from 5MHz to 34MHz, and the data of each frequency band consists of 256 values, and then a phase difference data set is constructed through these data.

[0059] The improved LSTM regression prediction model. The correlation between phase difference and conductivity is calculated to obtain the frequency band data with the highest correlation. Specifically, since each conductivity salt solution has corresponding conductivity data of 30 frequency bands, in order to obtain the frequency band data that can best represent the strongest correlation between phase difference data and conductivity. It is necessary to perform a distance correlation coefficient correlation analysis on each frequency band respectively, calculate and count the frequency band data with the highest total correlation value as the target conductivity data, and divide the training set and test set into 7:3 ratio according to interval sampling. LSTM is used to construct a conductivity regression prediction model, and the phase difference data of the corresponding target obtained by the detection device is input as the test set to obtain the conductivity of the corresponding numbered salt solution. On this basis, the R between the actual value and the measured value is calculated. 2 and RMSE.

[0060] The beneficial effects of the present invention are:

[0061] (1) A high-frequency multi-channel excitation module with extremely low phase shift rate is used, combined with a power amplifier circuit with an integrated low-pass filter to achieve high-precision signal excitation and ensure signal accuracy and consistency. The stability and accuracy of the system are improved through the fixed design of the simulated and manufactured coil module and the 3D printing device.

[0062] (2) Through the MUX adaptive control phase detection circuit, the analog and digital phase detection methods can be flexibly selected, which significantly reduces the system power consumption and is suitable for a variety of application scenarios with different conductivity and accuracy requirements. The analog phase detection has lower power consumption, while the digital phase detection has higher accuracy. The selection of the multiplexer allows the optimal balance between power consumption and accuracy to be found in different application environments.

[0063] (3) The conductivity is predicted by using an improved LSTM regression prediction model based on the distance correlation coefficient. By calculating the correlation between the phase difference and the conductivity, the frequency band data with the highest correlation with the conductivity is accurately extracted, and an LSTM regression prediction model is constructed to significantly improve the accuracy of the conductivity prediction. This model performs well in processing complex, multi-band data, helping to achieve accurate analysis and detection of disturbance states in the target area.

[0064] (4) Through low-power design and efficient data processing, the device is suitable for portable and long-term monitoring applications, reducing equipment operating costs and improving application convenience and sustainability. The overall system design not only performs well in laboratory environments, but also has strong practicality and is widely used in medical diagnosis, environmental monitoring and other fields, providing an effective solution for low-power, high-precision target disturbance analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the process structure of a low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principle of the present invention;

[0066] Figure 2 It is a scatter plot between the predicted value and the actual value of the conductivity of the physical and chemical index;

[0067] Figure 3 Schematic diagram of the prediction model network structure of the present invention. DETAILED DESCRIPTION

[0068] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0069] Reference Figures 1 to 3 , a low-power magnetic induction tomography target disturbance analysis device and method based on bio-electromagnetic principle, comprising the following steps:

[0070] S1: Construct a high-frequency multi-channel excitation module with extremely low phase deviation rate, including:

[0071] Build a high-frequency multi-channel excitation module with extremely low phase offset rate, including: main control chip, DDS (direct digital frequency synthesizer), low-pass filter. The main control chip uses STMF103RC, and the DDS uses AD9959 chip with a maximum operating bandwidth of 200MHz, supporting linear frequency, amplitude and phase scanning. The main control chip STMF103RC sends instructions for frequency control word (FTW), phase offset word (POW) and amplitude scaling factor (ASF) to DDS through the SPI interface to set the required output parameters. After completing the setting, by sending the I / O update signal, the DDS updates the register value to the internal buffer to generate an accurate output signal. The DDS uses the following three formulas to calculate the output frequency, phase offset, and amplitude of the output signal:

[0072]

[0073] Where FTW is the frequency control word, f out is the desired output frequency, f sys is the system clock frequency of the DDS.

[0074]

[0075] Where POW is the phase offset word and φ is the desired phase offset.

[0076] ASF=r I ×2 10 (3)

[0077] Where ASF is the amplitude scaling factor, r I It is the ratio of the required output current to the maximum output current.

[0078] The phase shift rate of the sine wave signal sent by the high-frequency multi-channel excitation module is 0.0001°.

[0079] Step S1: The high-frequency multi-channel excitation module can send two single-ended sinusoidal signals of the same frequency and phase with a high frequency and extremely low phase shift rate. The first sinusoidal signal enters step S2, and the second sinusoidal signal is recorded as a reference signal.

[0080] S2: Build a power amplifier module with an integrated low-pass filter so that the amplified sinusoidal current is large enough to excite the coil, including:

[0081] Build a passive low-pass filter and build a 200MHz Butterworth low-pass filter 9th order after the direct digital frequency synthesizer (DDS) system, mainly to remove high-frequency aliasing and noise and improve the spectral purity of the signal. The signal output by DDS may contain unwanted high-frequency components and harmonics, which will cause spectral distortion. The 200MHz Butterworth low-pass filter 9th order effectively filters out these high-frequency components and improves signal quality through its smooth frequency response (i.e., the amplitude is stable in the passband and decays rapidly after the cutoff frequency). Its transfer function is shown in the following formula:

[0082]

[0083] Among them, ω c =2πf c is the angular frequency of the cutoff frequency, f c is the cutoff frequency, n is the filter order which is 9, s is the complex frequency of the input signal entering the low-pass filter, and H(s) is the transfer function, which is the core of the filter and defines how the input signal changes in the frequency domain after passing through the filter.

[0084] Build two-stage power amplifiers, the first stage power amplifier uses AD8138, and the second stage power amplifier uses AD8131. First, use AD8138 to convert the single-ended sine wave signal generated by DDS into a differential signal and amplify it with a gain of 10 times. Then, AD8131 further amplifies the differential signal with a gain of 2 times, maintaining the characteristics of the differential signal and increasing the output amplitude. Through the combination of these two stages of amplifiers, the input signal can be effectively amplified by 20 times, so that the current of the amplified sine wave is large enough to excite the coil, while ensuring high accuracy and low distortion. The gain of AD8138 is shown in Formula 5, and the differential mode gain of AD8131 is shown in Formula 6.

[0085]

[0086] Among them, G N is the gain, R F1 and R G1 are the feedback resistor and input resistor in the AD8138 peripheral circuit.

[0087]

[0088] Among them, V OUT and V IN are the differential mode output voltage and single-ended input voltage of AD8138, R F2 and R G2 are the feedback resistor and input resistor in the AD8131 peripheral circuit.

[0089] In step S2, the first sine wave signal in step S1 is passed through a low-pass filter to remove high-frequency aliasing and noise, and then enters a two-stage power amplifier to convert the sine wave signal into a differential signal and amplify the signal.

[0090] S3: Simulate and manufacture the coil module, design a specific 3D printing device to fix the receiving coil and the receiving coil to improve the system stability, including:

[0091] Simulate and manufacture the coil module to ensure that the inductance of the coil simulation can first meet the system requirements. The relevant parameters of the receiving coil and the transmitting coil are set using the following formula:

[0092]

[0093] Where L is the inductance of the coil, r is the average radius of the coil, N is the number of turns of the coil, l is the length of the coil, and d is the thickness of the coil.

[0094] To determine the actual inductance of the finished receiving coil, first, the differential input coil needs to be looped to form a single-ended input coil (the differential input coil of the receiving coil is converted into a single-ended input coil through loop connection), and then the network analyzer is used to measure the inductance and impedance in different frequency domains. The receiving coil and the transmitting coil are both PCB boards. When they are placed vertically to the ground, the coil radiation reaches the strongest. In order to improve the stability of the system, a specific 3D printing device is printed to fix the receiving coil and the transmitting coil.

[0095] The differential signal amplified in step S2 is input to the transmitting coil of S3. When the transmitting coil is energized, a magnetic field signal is generated. Under the action of the magnetic field signal, the receiving coil can achieve the effect of magnetoelectricity, and the receiving coil outputs a sinusoidal wave signal of voltage.

[0096] S4: Build a differential amplifier module to input the differential amplification signal generated by the detection coil into the subsequent module, i.e., the low-power phase detection module, which specifically includes:

[0097] A differential amplifier module uses a differential detection method that can effectively avoid common-mode signal interference, reduce ground noise and system noise, and improve the reliability and anti-interference ability of weak signal detection of magnetic induction coils. A differential amplifier chip with a very high common-mode rejection ratio is selected to convert the differential signal of S3 into a single-ended signal, which is convenient for a series of subsequent processing.

[0098] S5: Prepare a series of NaCl aqueous solutions with different mass concentrations to obtain a series of salt solutions with different conductivity (use a conductivity meter to measure the conductivity of the salt solution to obtain the actual conductivity data of the salt solution). The salt solution is placed in the system field area of ​​the coil module in step S3, that is, the middle position between the transmitting coil and the receiving coil. According to the operations of steps S1-S4, a series of modulation signals of different frequency bands under the interference of each conductive salt solution are collected by frequency scanning and the corresponding reference signal is collected to obtain the corresponding data set;

[0099] When the modulation signal is acquired in step S5, two modulation signals are acquired in the same frequency band, the first is an empty field modulation signal in which no salt solution is placed in the system field area, and the second is a conductivity interference modulation signal in which a salt solution is placed in the system field area;

[0100] S6: Build a low-power phase detection module. Use MUX to design a low-power phase detection module that combines analog phase detection and digital phase detection. Specifically, it includes:

[0101] Build a MUX selector with a fan-out of 2 and place it in the front stage of the analog phase detection and digital phase detection. The MUX selector connects the analog phase detection and the digital phase detection through a circuit. The MUX selector selects analog phase detection or digital phase detection to calculate the phase difference according to the detection field. The digital phase detection is selected in the field with high detection accuracy, and the analog phase detection is selected in the field with low detection accuracy. The digital phase detection calculates the phase difference between the two signals by executing a digital signal processing algorithm. The algorithm usually includes calculating the Fourier transform of the two signals to extract the phase information, or using a time domain cross-correlation method to estimate the phase difference. In this way, the digital phase detection module can accurately measure the phase difference of the high-speed signal and use the result for further signal processing or analysis.

[0102] S7: Construct an LSTM (Long Short-Term Memory) regression prediction model based on the improved distance correlation coefficient, including:

[0103] S7.1: A series of modulation signals of different frequency bands under the interference of each conductive salt solution and the corresponding reference signals are collected by means of frequency scanning. Under the interference of each conductive salt solution, 30 voltage signals of different frequency bands are scanned, and the 30 frequency bands are from 5MHz to 34MHz, and phase difference data sets of 30 frequency bands are obtained. When data is collected under the interference of each conductive salt solution, data is collected once every 1s in each frequency band, and the total collection time is 256s, that is, a total of 256 data are collected, so that the phase difference data set of each frequency band is composed of 256 columns of phase difference data.

[0104] The following two groups of experimental data were collected in each frequency band: 1) experimental group, collecting the modulation signal when a saline solution was placed in the system field area; 2) blank control group: no saline solution was placed in the system field area, and the empty field modulation signal was collected.

[0105] The phase difference is the phase difference between the phase of the conductivity interference modulation signal minus the phase of the empty field modulation signal and the phase of the reference signal.

[0106] Since the system will scan the phase difference data of 30 frequency bands, in order to obtain the frequency band data that can best represent the highest correlation between conductivity and phase difference data, it is necessary to analyze the degree of distance correlation coefficient for each frequency band. The correlation calculation formula is as follows:

[0107]

[0108] Among them, dCor(X,Y) is the distance correlation coefficient, dCov(X,Y) is the distance covariance, dVar(X) and dVar(Y) are the distance variances of X and Y. X is a column of phase difference data in the 256 columns of data under the corresponding frequency band, and Y is the different conductivity corresponding to 200 salt solutions.

[0109] Define the conductivity of the salt solution as Y = [y1 y2 … y 200 ] T , define the phase difference data matrix of salt solution conductivity in frequency band i as W = [w i,1 w i,2 … w i,200 ] T , where w i,1 It represents the phase difference data [Δσ i,1 Δσ i,2 … Δσ i,256 ]. Define the phase difference data of the jth column of the 256 columns of data of the conductivity of 200 salt solutions in the corresponding frequency band as X = [x 1,j x 2,j … x 200,j ] T .

[0110] The frequency band data with the highest total correlation value calculated and counted in this way is used as the target phase difference data, that is, the phase difference data corresponding to the frequency band with the largest distance correlation coefficient is calculated as the target phase difference data.

[0111] The acquired target phase difference data is defined as:

[0112]

[0113] The above phase difference data D200,256 As the data set input, the salt solution conductivity Y in S6 is used as the data set output. The training set and the test set are divided into 7:3, and 10 consecutive samples are regarded as an independent subset. The 1st, 5th, and 10th samples in each subset are selected as the test set. X =E(X),μ Y =E(Y), and the rest are used as training sets.

[0114] The network structure specifically includes a bidirectional LSTM (Bi-LSTM) layer to extract the phase difference features corresponding to the time series. After the extracted features are processed by the hidden layer, they are passed to the self-attention layer to capture the global context information and generate attention weights. These weights are applied to the features and finally output the predicted conductivity results through the classification layer. This model combines bidirectional LSTM and self-attention mechanisms to improve the feature extraction and prediction capabilities of phase difference data. Figure 3 shown.

[0115] The low-power detection phase-lock module uses MUX to adaptively control the phase-lock circuit for application scenarios with different conductivities and different precision requirements. This module can greatly reduce the high power consumption problem caused by the current digital phase-lock magnetic induction tomography equipment, and can also increase the problem of fewer application scenarios of the current analog phase-lock magnetic induction tomography equipment. By using a low-power magnetic induction tomography measurement device, the phase difference data of 200 salt solutions with different conductivity are collected within a specified time. The data consists of data from 30 frequency bands, from 5MHz to 34MHz, and the data of each frequency band consists of 256 values, and then a phase difference data set is constructed through these data.

[0116] S8: Based on the prediction model in S7, the phase difference data of the corresponding target obtained by the detection device is input to obtain the conductivity of the corresponding salt solution, specifically including:

[0117] The training set in S7 is used to train the prediction model in S7, and the test set in S7 is used to test the performance of the model in S7. The coefficient of determination R between the predicted value and the actual value of the test set is calculated. 2 And RMSE are used as indicators to determine the effectiveness of the model.

[0118] The improved LSTM regression prediction model. The correlation between phase difference and conductivity is calculated to obtain the frequency band data with the highest correlation. Specifically, since each conductivity salt solution has corresponding conductivity data of 30 frequency bands, in order to obtain the frequency band data that can best represent the strongest correlation between phase difference data and conductivity. It is necessary to perform a distance correlation coefficient correlation analysis on each frequency band respectively, calculate and count the frequency band data with the highest total correlation value as the target conductivity data, and divide the training set and test set into 7:3 ratio according to interval sampling. LSTM is used to construct a conductivity regression prediction model, and the phase difference data of the corresponding target obtained by the detection device is input as the test set to obtain the conductivity of the corresponding numbered salt solution. On this basis, the R between the actual value and the measured value is calculated. 2 and RMSE.

[0119] The relationship between the predicted phase difference and the conductivity can be visualized by using the prediction model of the present invention, and a scatter plot between the predicted value and the actual value of the conductivity of the physical and chemical index can be obtained. Figure 2 .

[0120] The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the embodiments. The protection scope of the present invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles, characterized in that The following steps are involved: S1: construct a high-frequency multi-channel excitation module with extremely low phase offset rate, which can send two single-ended sinusoidal wave signals of the same frequency and phase with high frequency and extremely low phase offset rate. The first sinusoidal wave signal enters step S2, and the second sinusoidal wave signal is recorded as a reference signal; S2: Build a power amplifier module with an integrated low-pass filter, pass the first sine wave signal in step S1 through a low-pass filter to remove high-frequency aliasing and noise, and then enter the power amplifier module to convert the sine wave signal into a differential signal and amplify the signal; S3: Simulate and manufacture a coil module and a differential amplifier module. The coil module includes a receiving coil and a transmitting coil that are arranged in parallel and spaced apart. The differential signal amplified in step S2 is input to the transmitting coil. The transmitting coil generates a magnetic field signal when powered on. The receiving coil can achieve the effect of magnetoelectricity under the action of the magnetic field signal. The receiving coil outputs a sinusoidal wave signal of voltage, which is further amplified by the differential amplifier module, and the differential signal is converted into a single-ended signal to obtain a modulated signal. S4: a series of salt solutions with different conductivity are prepared, and the salt solutions are placed in the system field area of ​​the coil module in step S3, that is, the middle position between the transmitting coil and the receiving coil. According to the operations of steps S1-S3, a series of modulation signals of different frequency bands under the interference of each conductive salt solution are collected by frequency sweeping, and corresponding reference signals are collected to obtain corresponding data sets; S5: Build a low-power phase detection module, design a low-power phase detection module combining analog phase detection and digital phase detection through a multiplexer MUX, calculate the phase difference between the two signals in the data set of step S4 through analog phase detection or digital phase detection, and obtain a data set about the corresponding relationship between phase difference and conductivity; S6: construct an LSTM regression prediction model improved based on the distance correlation coefficient, divide the data set obtained in step S5 into a training set and a test set, and use the constructed prediction model for training; when the conductivity of the salt solution to be tested is subsequently tested, the phase difference is first tested according to the method of steps S1-S5, and then input into the trained prediction model to obtain the corresponding salt solution conductivity.

2. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles as claimed in claim 1, characterized in that The high-frequency multi-channel excitation module with extremely low phase offset rate in step S1 includes a main control chip, a direct digital frequency synthesizer DDS and a low-pass filter. The main control chip sends instructions of frequency control word FTW, phase offset word POW and amplitude scaling factor ASF to DDS, sets the required output parameters, and DDS updates the register value to the internal buffer to generate an accurate output signal. DDS uses the following three formulas to calculate the output frequency, phase offset, and amplitude of the output signal: Where FTW is the frequency control word, f out is the desired output frequency, f sys is the system clock frequency of the DDS; Where POW is the phase offset word and φ is the required phase offset; ASF=r I ×2 10 (3) Where ASF is the amplitude scaling factor, r I It is the ratio of the required output current to the maximum output current.

3. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principle as claimed in claim 1, characterized in that In step S1, the phase shift rate of the sinusoidal wave signal sent by the high-frequency multi-channel excitation module is 0.001°-0.0001°.

4. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles as claimed in claim 1, characterized in that In step S2, the transfer function of the low-pass filter to remove high-frequency aliasing and noise is as follows: Among them, ω c =2πf c is the angular frequency of the cutoff frequency, f c is the cutoff frequency, n is the order of the filter, s is the complex frequency of the input signal entering the low-pass filter; H(s) is the transfer function, which is the core of the filter and defines the changes in the input signal after passing through the filter in the frequency domain; The construction of the power amplifier module includes building a front and rear two-stage power amplifier. The first stage power amplifier converts the single-ended sinusoidal wave signal generated in step S1 into a differential signal and performs gain amplification. The second stage power amplifier further performs gain amplification on the differential signal of the previous stage, maintains the characteristics of the differential signal and increases the output amplitude. Through the combination of these two stages of power amplifiers, the current of the amplified sinusoidal wave is large enough to excite the coil. The gain amplification function of the first-stage power amplifier is shown in Formula 5, and the differential-mode gain function of the second-stage power amplifier is shown in Formula 6: In formula 5, G N is the gain, R F1 and R G1 They are respectively the feedback resistor and the input resistor in the peripheral circuit of the first-stage power amplifier; In formula 6, V OUT and V IN are the differential mode output voltage and single-ended input voltage of the first-stage power amplifier, R F2 and R G2 They are respectively the feedback resistor and input resistor in the peripheral circuit of the second-stage power amplifier.

5. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principle as claimed in claim 1, characterized in that The specific steps of step S3 include: simulating and manufacturing the coil module to ensure that the inductance of the coil simulation can first meet the system requirements, and the receiving coil and the transmitting coil are both set with relevant parameters according to the following formula: Where L is the inductance of the coil, r is the average radius of the coil, N is the number of turns of the coil, l is the length of the coil, and d is the thickness of the coil.

6. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principle as claimed in claim 1, characterized in that When the modulation signal is acquired in step S4, two modulation signals are acquired in the same frequency band, the first one is an empty field modulation signal in which no salt solution is placed in the system object field area, and the second one is a conductivity interference modulation signal in which a salt solution is placed in the system object field area; The phase difference in step S5 is the difference between the phase of the conductivity interference modulation signal minus the phase of the empty field modulation signal and the phase of the reference signal.

7. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principles as claimed in claim 1, characterized in that Step S5: Building a low-power phase-detection module specifically includes: S5.1: Build a MUX selector with a fan-out of 2 and place it in the front stage of the module to enable the control signal to be output by the main control chip; S5.2: The MUX selector connects the analog phase detection and the digital phase detection through the circuit. The MUX selector selects the analog phase detection or the digital phase detection to calculate the phase difference according to the detection field. The digital phase detection is selected in the field with high detection accuracy, and the analog phase detection is selected in the field with low detection accuracy.

8. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principle as claimed in claim 1, characterized in that In step S4, a series of salt solutions with different conductivity are prepared, which is to prepare a series of aqueous salt solutions with different concentrations, and the salt is sodium chloride.

9. A low-power magnetic induction tomography target disturbance analysis method based on bio-electromagnetic principle as claimed in claim 1, characterized in that The LSTM regression prediction model improved by the distance correlation coefficient includes the following contents: S6.1: a series of modulation signals of different frequency bands under the interference of each conductive salt solution and corresponding reference signals are collected by means of frequency scanning. Under the interference of each conductive salt solution, voltage signals of i different frequency bands are scanned to obtain phase difference data sets of 30 frequency bands. Data is collected once every 1s in each frequency band, and the total collection time is b seconds. Therefore, the phase difference data set of each frequency band is composed of b columns of phase difference data. In order to obtain the frequency band data that can best represent the highest correlation between conductivity and phase difference data, it is necessary to analyze the degree of correlation of the distance correlation coefficient for each frequency band. The correlation degree calculation formula is as follows: Where, dCor(X,Y) is the distance correlation coefficient, dCov(X,Y) is the distance covariance, dVar(X) and dVar(Y) are the distance variances of X and Y; X is a column of phase difference data in b columns of data under the corresponding frequency band, and Y is the different conductivities corresponding to a salt solution; a and b are both positive integers between 10 and 1000; The data set for the conductivity of the salt solution is defined as Y = [y1 y2…y a ] T , define the phase difference data matrix of salt solution conductivity in frequency band i as W = [w i,1 w i,2 …w i,a ] T , where w i,1 It represents the phase difference data [Δσ i,1 Δσ i,2 …Δσ i,b ]; define the phase difference data of column b of the conductivity of a salt solution in the corresponding frequency band as X = [x 1,j x 2,j … x a,j ] T ; S6.2: The phase difference data corresponding to the frequency band with the highest total correlation value calculated and counted according to S6.1, i.e., the largest distance correlation coefficient, is taken as the target phase difference data; S6.3: Define the target phase difference data matrix obtained by S6.2 as: The above phase difference data D a，b as the data set input, and the salt solution conductivity Y in S6 as the data set output, μ X =E(X),μ Y =E(Y), and use LSTM to build a conductivity regression prediction model for training.

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