Measurement impedance correction method and device based on adaptive identification of circuit distributed parameters

Abstract

This invention relates to a measurement impedance correction method and device based on adaptive identification of circuit distributed parameters. The method includes the following steps: obtaining two amplitudes Z1 and Z2 obtained by measuring the impedance of two precision resistors R1 and R2 with known resistance values ​​at a set excitation frequency; based on amplitude Z1, obtaining the value of distributed capacitance C0 using a simplified circuit model, and obtaining the values ​​of distributed capacitances C1 and C2 using a global probability search algorithm based on amplitude Z2; constructing a measurement circuit model corresponding to the measurement circuit based on the distributed capacitances C0, C1, and C2; and correcting the directly obtained measurement impedance using a global optimization algorithm based on the measurement circuit model. Compared with the prior art, this invention has the advantages of effectively improving measurement performance.

Description

Technical Field

[0001] This invention pertains to electrical impedance tomography, specifically an impedance measurement method, and more particularly to a measurement impedance correction method and device based on adaptive identification of circuit distributed parameters. Background Technology

[0002] Electrical impedance tomography (EIT) is a radiation-free, non-invasive imaging technique suitable for long-term dynamic monitoring and widely used in medical applications such as lung detection, brain imaging, and breast cancer screening. Measuring the impedance of biological tissues at different frequencies yields information about their electrical properties, such as conductivity, which can be used for disease diagnosis and biological tissue identification. At excitation frequencies above 200 kHz, the capacitance effect of the cell membrane weakens, allowing the excitation current to pass through the cell membrane and enter the cell interior, resulting in more biological tissue characteristics in the measured electrical properties. However, when the excitation frequency exceeds 200 kHz, the presence of parasitic parameters in the measurement circuit, such as the capacitance between chip pins and ground, the on-resistance of multiplexers, and the capacitance between transmission lines and ground, causes the measured impedance value to include parasitic parameters, leading to increased measurement errors. When the frequency remains constant, the higher the impedance, the greater the influence of parasitic parameters on the measurement results. The accuracy and stability of the impedance directly affect the quality of the imaging.

[0003] Traditional methods typically use fixed circuit model parameters, which can lead to measurement errors and imaging artifacts. Systems using fixed parameters are also highly sensitive to noise and model mismatches, reducing measurement robustness and accuracy. Therefore, it is necessary to update the circuit model parameters based on the current measurement state to improve measurement accuracy. Patent document CN105976411A (application number: CN201610332979.1) discloses a method that uses the ratio of the average of multiple measured voltage amplitudes on the measured electrode to the sum of the average voltage amplitudes on all electrodes as the normalization correction parameter for the transfer characteristics of the corresponding electrode measurement module. This method simplifies the nonlinear measurement equivalent circuit transfer function into a linear relationship, neglecting parasitic parameters on the measurement channel, such as chip pin capacitance to ground and switch on-resistance. It cannot cope with changes in measurement state, and the corrected value deviates from the theoretical value.

[0004] Therefore, in order to improve the measurement accuracy and stability of the measurement system, a self-identification method for circuit distributed parameters that can be updated in real time is needed. Summary of the Invention

[0005] The purpose of this invention is to provide a measurement impedance correction method and device based on adaptive identification of circuit distributed parameters that can effectively improve measurement performance.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A measurement impedance correction method based on adaptive identification of circuit distributed parameters includes the following steps:

[0008] The measurement circuit obtains two amplitudes Z1 and Z2 by performing impedance measurements on two precision resistors R1 and R2 with known resistance values ​​at a set excitation frequency.

[0009] Based on amplitude Z1, a simplified circuit model is used to obtain the value of distributed capacitance C0, and based on amplitude Z2, a global probability search algorithm is used to obtain the values ​​of distributed capacitance C1 and C2.

[0010] A measurement circuit model corresponding to the measurement circuit is constructed based on the distributed capacitances C0, C1, and C2.

[0011] Based on the measurement circuit model, the directly obtained measurement impedance is corrected using a global optimization algorithm.

[0012] Furthermore, the resistance value of the precision resistor R1 is less than 50Ω.

[0013] Furthermore, the set excitation frequency is 1MHz.

[0014] Furthermore, the value of the distributed capacitance C0 is obtained based on the amplitude Z1 and the amplitude of the measured impedance obtained from the channel measurement voltage difference and the sampling current.

[0015] Furthermore, the global probabilistic search algorithm is a simulated annealing algorithm.

[0016] Furthermore, when estimating the values ​​of distributed capacitances C1 and C2 using the simulated annealing algorithm, the objective function expression is as follows:

[0017] Minimize E(S) = ||Z t_s (C1,C2)|-|Z2|| 2 Among them, Z t_s (C1,C2) are the simulation values ​​of the circuit model calculated based on the solution S=(C1,C2), representing the impedance values ​​obtained by dividing the voltage difference directly measured by the measuring circuit by the excitation current value.

[0018] Furthermore, the global optimization algorithm is a differential evolution algorithm.

[0019] Furthermore, the correction process using the differential evolution algorithm includes:

[0020] Initialize the algorithm parameters, randomly generate a population, and decompose the calibrated measured impedance into resistance and capacitance values, using the calibrated resistance and capacitance values ​​as individuals in the population;

[0021] Calculate the fitness of each individual in the population;

[0022] Mutation, crossover, and selection operations are performed on individuals in the population to select individuals with better fitness to enter the next generation. This process continues until the termination condition is met. The individual with the best fitness in the current population is output as the optimal solution, and the corrected measurement impedance is obtained based on the optimal solution.

[0023] The present invention also provides a computer-readable storage medium, characterized in that it includes one or more programs executable by one or more processors of an electronic device, said one or more programs including instructions for performing the measurement impedance correction method based on adaptive identification of circuit distributed parameters as described above.

[0024] The present invention also provides an electronic device including one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the measurement impedance correction method based on adaptive identification of circuit distributed parameters as described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. This invention constructs a correction model between the measured impedance and the impedance value to be measured with respect to the excitation frequency, which includes capacitance, inductance and resistance. The correction coefficient has a nonlinear relationship with the excitation frequency, thereby constructing a nonlinear measurement model that is more in line with reality and improving the identification performance.

[0027] 2. This invention can adaptively identify circuit distributed parameters at any time when recalibration is required without disassembling the circuit, effectively handling changes in measurement state and exhibiting good adaptability. This invention achieves self-identification of the distributed parameters of the measurement circuit, enabling identification of parameters on the board during measurement, reducing human intervention and interference from external factors, and improving the system's automation level.

[0028] 3. The method of this invention can self-identify the distributed parameters of a circuit within a frequency range of 200kHz to 1MHz, with low computational complexity. It can quickly and accurately correct the measured impedance based on the circuit model, achieving precise impedance measurement and thus improving the accuracy of bioimpedance data reconstruction. This invention realizes the correction of measured impedance based on a circuit model with identified distributed parameters, raising the excitation frequency for accurate impedance measurement to 1MHz, making it applicable to a wider range of high-frequency applications and facilitating the accurate acquisition and analysis of the electrical properties of biological tissues above 200kHz.

[0029] 4. The present invention has a better correction effect on larger impedances and does not require changes to the existing measurement channel circuit design, making it more conducive to integration into the measurement system. It has important application value and good application prospects in image reconstruction such as electrical impedance tomography. Attached Figure Description

[0030] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0031] Figure 2 The flowchart below shows the global probability search algorithm used in Embodiment 1 of the present invention to obtain C1 and C2.

[0032] Figure 3 This is a flowchart of the method for correcting and measuring impedance according to Embodiment 1 of the present invention;

[0033] Figure 4 An equivalent model of the measurement circuit containing distributed circuit parameters established when measuring impedance using the method of the present invention;

[0034] Figure 5 To calculate the circuit equivalent model when the distributed capacitance C0 is calculated using the method of the present invention;

[0035] Figure 6 The bar graphs are for the relative residuals between the simulated and measured values ​​of the equivalent circuit model obtained by the method of the present invention at 500 kHz, 800 kHz and 1 MHz, where (6a) is 500 kHz, (6b) is 800 kHz and (6c) is 1 MHz.

[0036] Figure 7 The box plots of relative error between the measured impedance resistance and the theoretical value before and after correction in Examples 2 and 3 obtained by the method of the present invention are shown, where (7a) is Example 2 and (7b) is Example 3;

[0037] Figure 8 Box plot of the capacitance value after correction in Example 3 obtained by using the method of the present invention versus frequency. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment proposes a method for improving measurement performance through adaptive identification of circuit distributed parameters, including the following steps:

[0041] S1. The measurement circuit obtains two amplitudes Z1 and Z2 by performing impedance measurements on two precision resistors R1 and R2 with known resistance values ​​on the measurement channel k at a set excitation frequency.

[0042] Impedance measurement value Z of channel k LIt is obtained based on the ratio of the measured voltage to the excitation current, and the measured impedance Z is... L It can be expressed as:

[0043]

[0044] Where L is the relay path inductance, ω is the angular frequency of the excitation signal, C1 is the parasitic capacitance on the line connecting the multiplexer output to the relay input, C2 is the parasitic capacitance of the cable connecting the data acquisition system and the electrode array, and Z... LX This is the actual impedance of the object being measured.

[0045] When the objects being measured are resistors R1 and R2, the corresponding impedance measurements are represented as Z1 and Z2. When the object being measured is a precision resistor R1, the actual impedance Z of the object being measured is... L1 =R1, when the object being measured is a precision resistor R2, then the actual impedance Z of the object being measured is... L2 =R2.

[0046] S2. Based on the amplitude Z1, a simplified circuit model is used to obtain the value of the distributed capacitance C0, where C0 is the parasitic capacitance between the input pins of the multiplexer switch of adjacent channels.

[0047] Figure 4 This is an equivalent model of the measurement circuit containing distributed circuit parameters established when measuring impedance using the method of the present invention, wherein R S For current sampling resistors, by measuring R S The voltage value on the circuit is then used to obtain the excitation current value I. S R SWi and R SWj L represents the on-resistance of the multiplexer switches on the selected measurement channels i and j, respectively. i and L j The relay conducting inductances on channels i and j are represented by their respective impedances Z. LX Equivalent to a resistor R X With C X In a parallel structure, if R1 is small enough, the impedance Z after R1 and C2 are connected in parallel is... LX_C2 satisfy:

[0048]

[0049] Where ω = 2πf, f = 1MHz, and R1 < 50Ω, then (ωR1C2) 2 <9.87×10 -4 <<1, therefore |Z LX_C2 |≈R1.

[0050] At this time, the voltage V is measured. i and V jThe corresponding measured impedance Z′ L It can be simplified to:

[0051]

[0052] The value of L, obtained from the impedance analyzer, is 1.6 pH. Therefore, 2ω... 2 LC1<1.6×10 -2 << 1 and (ωR1C1) 2 <9.87×10 -4 <<1, therefore |Z′ L |≈|R1+2jωL|.

[0053] The above steps are manifested as follows: Figure 4 The equivalent model of the measurement circuit shown is simplified to Figure 5 The circuit equivalent model is shown.

[0054] When the value of R1 is small enough to meet the requirements of circuit simplification, then Z′ L The parasitic capacitance C0 between the input pin of the multiplexer and the adjacent channel satisfies the following formula:

[0055]

[0056] Among them, R SW Z is the on-resistance of the multiplexer switch. C0 Let I be the impedance of C0. ij Z is the actual current passing through the object being measured. t To measure the actual voltage difference V based on the channel measurement ij =|V i -V j |and the measured sampling current|I S The obtained measured impedance amplitude, when the measured object is R1, Z t =Z1. R SW L and Z are inherent properties of the device, and their values ​​are fixed, therefore they can be determined through Z. t With Z′ L Based on the relationship, the value of C0 can be obtained.

[0057] At this time, according to Figure 5 The equivalent circuit can be obtained from the following system of equations. C0 The value of C0 is obtained by taking the value of C0:

[0058]

[0059] S3. The values ​​of distributed capacitances C1 and C2 are obtained using a global probabilistic search algorithm. There are many methods for global probabilistic search algorithms; this embodiment uses simulated annealing to estimate the distributed capacitance values.

[0060] refer to Figure 2 As shown, in this embodiment, step S3 specifically includes:

[0061] S301. Initialize the problem parameters, set the initial solution S = (C1, C2), and input the measured value Z2 of the precision resistor R2 at 1MHz. Calculate the objective function value of the initial solution, where the objective function is:

[0062] minimize E(S) = ||Z t_s (C1, C2)|-|Z2|| 2

[0063] Z t_s The simulation value calculated by substituting the solution S into the circuit model represents the impedance value obtained by dividing the voltage difference directly measured by the measuring circuit by the excitation current value.

[0064] S302. Generate a new solution S' by subjecting the solution S to a small random perturbation, and calculate the objective function value E(S) of the new solution;

[0065] S303. If the objective function E(S') of the new solution is less than that of the current solution E(S), then the new solution S' is accepted.

[0066] S304. If E(S') is greater than the current solution E(S), then the acceptance probability exp((E(S)-E(S')) / T) is used to determine whether to accept the new solution, where T is the current temperature. If the random number between (0, 1) is less than the acceptance probability, then the new solution is accepted; otherwise, the new solution is not accepted.

[0067] S305. Reduce the temperature, T = α * T, and continue iterating until the iteration is complete. Output the optimized solution S = (C1, C2).

[0068] S4. Obtain a measurement circuit model based on distributed circuit parameters, including distributed capacitances C0, C1, and C2 obtained by adaptive identification in steps S2 and S3, as well as the device's inherent properties R. SW And L.

[0069] S5. Correct the measurement impedance. Based on the measurement circuit model, correct the directly obtained measurement impedance Z. L The resistance value R is obtained by using a global optimization algorithm. t and capacitance value C t This method can correct impedance values ​​directly measured from the target object at different frequencies, especially at excitation frequencies from 500kHz to 1MHz. Many global optimization algorithms exist; this embodiment employs a differential evolution algorithm to correct the measured impedance.

[0070] refer to Figure 3 As shown, in this embodiment, step S5 specifically includes:

[0071] S501. Initialize algorithm parameters and input the measured impedance Z. L The measured value Z at the current frequency f L [f], whose value is the measured voltage difference V at the excitation frequency f. ij [f] and the measured excitation current value I S The ratio of [f];

[0072] S502, Randomly generate a population {xi=[R E C E ]};

[0073] S503. Evaluate fitness by calculating the fitness of each individual in the population, i.e., the objective function value is:

[0074]

[0075] Where n is the number of individuals in the population, Z t_s (xi) represents the simulation value calculated in the circuit model for different individuals;

[0076] S504, Mutation operation: For each individual xi in the population, select three different individuals X. a X b X c Generate mutation vector V i :

[0077] V i =X a +F×(X b -X c )

[0078] Where F is the variation factor;

[0079] S505, Crossover operation, for each individual xi and its mutation vector V i Perform crossover to generate test vector U i For the j-th component:

[0080]

[0081] Where j rand It is a randomly selected index that guarantees at least one component comes from the mutation vector, and CR is the crossover probability;

[0082] S506. Selection operation: Compare test vector U i Based on the fitness of the target vector xi, select individuals with better fitness to enter the next generation, continue iterating until the termination condition is met, and output the individual with the best fitness in the current population as the optimal solution.

[0083] The corrected impedance value is separated into resistance value R. Eand capacitance value C E The corrected impedance Z is obtained according to the following formula. E The real and imaginary parts:

[0084]

[0085] As can be seen from the above implementation scheme, this invention measures the impedance values ​​of different precision resistors R1 and R2 at an excitation frequency of 1MHz. Combining the impedance analysis instrument and the measured impedance value of R1, the capacitance C0 is obtained. Then, based on the measured impedance value of R2, the capacitances C1 and C2 are obtained through a global probability search algorithm, thereby identifying the distributed parameter model of the measurement circuit. Based on this circuit model, a global optimization algorithm is used to correct the different measured impedances at different frequencies, resulting in the resistors R that constitute the measured impedance. E and capacitor C E .

[0086] The method of this invention flexibly combines impedance measurement and a global probability search algorithm to adaptively adjust the distributed circuit parameters, maintaining high measurement accuracy and stability in changing environments. Simultaneously, it can analyze the actual impedance value from the impedance measurement of the tested object to eliminate the influence of distributed circuit parameters, ensuring the accuracy of the measured impedance value after correction, especially in the excitation frequency range of 500kHz to 1MHz.

[0087] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] Example 2:

[0089] Example 2 is a preferred embodiment of Example 1, which is used to illustrate the present invention in more detail.

[0090] The impedance to be measured, Z L Composed of a 499Ω precision resistor and a 0pF precision capacitor connected in parallel, the amplitude of its measured impedance {|Z} is measured at different excitation frequencies. L(k)|,k=1,2,3}, where k is the frequency label, corresponding to 500kHz, 800kHz and 1MHz respectively.

[0091] according to Figure 1 and Figure 2 The steps shown yield the distributed circuit parameters: R SW =126Ω, L=1.6μH, C0=16pF, C1=37pF, C2=59.7pF.

[0092] refer to Figure 6 As shown, Figures (a), (b), and (c) correspond to bar graphs of the relative residuals between the simulated and measured values ​​of the equivalent circuit model obtained by the method of this invention when R2 = 499Ω, at 500kHz, 800kHz, and 1MHz, respectively. The relative residual distributions are all less than 0.5%, and most of them are distributed to the left of the average value, which means that the equivalent circuit model has a high goodness of fit.

[0093] according to Figure 3 The steps shown are for {|Z L The resistance value {|R} is obtained by correcting the resistance using a global optimization algorithm. E (k)|,k=1,2,3} and the capacity {|C E (k)|,k=1,2,3}, thus obtaining Figure 7 (a) Box plot of relative error between the resistance portion of the measured impedance and the theoretical value before and after calibration. It can be clearly seen that the overall relative error after calibration is significantly reduced compared with that before calibration, especially at 1MHz, the relative error is reduced from 5.8% before calibration to less than 0.5%.

[0094] Example 3:

[0095] Example 3 is a preferred embodiment of Example 1, which is used to illustrate the present invention in more detail.

[0096] The impedance to be measured, Z L Composed of a 499Ω precision resistor and a 56pF precision capacitor connected in parallel, the amplitude of its measured impedance {|Z} is measured at different excitation frequencies. L (k)|,k=1,2,3}, where k is the frequency label, corresponding to 500kHz, 800kHz and 1MHz respectively.

[0097] according to Figure 3 The steps shown are for {|Z L The resistance value {|R} is obtained by correcting the resistance using a global optimization algorithm. E (k)|,k=1,2,3} and the capacity {|C E (k)|,k=1,2,3}, thus obtaining Figure 7(b) The box plot of the relative error between the measured resistance portion and the theoretical value of the impedance before and after calibration clearly shows that the relative error of the resistance portion after calibration is significantly reduced compared with that before calibration, especially at 1MHz, the relative error is reduced from 12.5% ​​before calibration to less than 0.5%. Figure 8 The corrected capacitance value shows that the corrected capacitance gradually approaches 56pF as the frequency increases.

Claims

1. A measurement impedance correction method based on adaptive identification of circuit distributed parameters, characterized in that, Includes the following steps: The measurement circuit obtains two amplitudes Z1 and Z2 by performing impedance measurements on two precision resistors R1 and R2 with known resistance values ​​at a set excitation frequency. Based on amplitude Z1, a simplified circuit model is used to obtain the value of distributed capacitance C0, and based on amplitude Z2, a global probability search algorithm is used to obtain the values ​​of distributed capacitance C1 and C2. A measurement circuit model corresponding to the measurement circuit is constructed based on the distributed capacitances C0, C1, and C2. Based on the measurement circuit model, the directly obtained measurement impedance is corrected using a global optimization algorithm. The global probability search algorithm is the simulated annealing algorithm; When using the simulated annealing algorithm to estimate the values ​​of distributed capacitances C1 and C2, the objective function expression is as follows: in, Based on solution The calculated simulation value of the circuit model represents the impedance value obtained by dividing the voltage difference directly measured by the excitation current value by the excitation current value. The global optimization algorithm is the differential evolution algorithm; The correction process using the differential evolution algorithm includes: Initialize the algorithm parameters, randomly generate a population, and decompose the calibrated measured impedance into resistance and capacitance values, using the calibrated resistance and capacitance values ​​as individuals in the population; Calculate the fitness of each individual in the population; Mutation, crossover, and selection operations are performed on individuals in the population to select individuals with better fitness to enter the next generation. This process continues until the termination condition is met. The individual with the best fitness in the current population is output as the optimal solution, and the corrected measurement impedance is obtained based on the optimal solution.

2. The measurement impedance correction method based on adaptive identification of circuit distributed parameters according to claim 1, characterized in that, The resistance of the precision resistor R1 is less than 50 Ω.

3. The measurement impedance correction method based on adaptive identification of circuit distributed parameters according to claim 1, characterized in that, The set excitation frequency is 1MHz.

4. The measurement impedance correction method based on adaptive identification of circuit distributed parameters according to claim 1, characterized in that, The value of the distributed capacitance C0 is obtained based on the amplitude Z1 and the amplitude of the measured impedance obtained from the channel measured voltage difference and the sampling current.

5. A computer-readable storage medium, characterized in that, Includes one or more programs executable by one or more processors of an electronic device, the one or more programs including instructions for performing the measurement impedance correction method based on adaptive identification of circuit distributed parameters as described in any one of claims 1-4.

6. An electronic device, characterized in that, It includes one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the measurement impedance correction method based on adaptive identification of circuit distributed parameters as described in any one of claims 1-4.

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