A self-regulating cryogenic superconducting magnetic gradient readout system and its parameter setting method

Abstract

This application discloses an autonomously controlled low-temperature superconducting magnetic gradient readout system and parameter setting method, belonging to the field of geophysical exploration. Before magnetic gradient measurement, the method autonomously sets the SQUID to the optimal operating point based on an adaptive successive optimization algorithm. During measurement, a differential mutation detection algorithm is used to determine in real time whether the system has lost lock and autonomously resets it. If continuous loss of lock occurs, the method autonomously selects the parameter combination of the integrating capacitor and feedback resistor to reduce system sensitivity and relock the measurement. This application solves the problems of tediousness, time consumption, and insufficient accuracy in manually adjusting the optimal operating point of multi-channel readout systems. Simultaneously, by autonomously reducing system sensitivity, it solves the problem of continuous system loss of lock caused by bumps or large magnetic field change rates during dynamic measurements on mobile platforms such as vehicle-mounted, marine, and aerial vehicles. This enables autonomously controlled magnetic gradient measurement under unattended conditions, improving work efficiency and the reliability of magnetic gradient measurement.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration, and more specifically, to an autonomously controlled low-temperature superconducting magnetic gradient readout system and its parameter setting method. Background Technology

[0002] The Superconducting Quantum Interference Device (SQUID), as one of the most sensitive magnetic sensors known to date, boasts a sensitivity on the order of fT. Magnetic gradient tensor exploration based on SQUID sensors offers advantages such as being unaffected by normal geomagnetic fields and their diurnal variations, enabling the acquisition of high-precision Earth's magnetic field tensor parameter data. This provides rich information about magnetic anomalies, effectively improving the detection resolution and location accuracy of magnetic anomalies, reducing ambiguity in inversion, and demonstrating significant advantages in magnetic anomaly interpretation. Superconducting magnetic gradient measurement systems have the capability to depict large-scale, regional geological structures, providing technical support for the detection of subsurface targets and attracting widespread attention from geophysicists.

[0003] Before performing magnetic gradient measurements, the low-temperature superconducting magnetic gradient readout system needs to set the bias current and bias voltage parameters, and set the SQUID to its optimal operating point. The parameter setting time increases proportionally with the number of channels. For the 9-channel direct-reading low-noise readout circuit of this system, setting the parameters to the optimal operating point is a complex and tedious task.

[0004] In low-temperature superconducting magnetic gradient readout systems, the stability of the SQUID's operating point is crucial for magnetic field measurement. Ideally, a readout system based on the zero-flux locking principle ensures that the SQUID remains stable at its optimal operating point during operation. However, during dynamic measurements on mobile platforms such as vehicle-mounted towed, marine-towed, and aircraft-flying systems, turbulence is a significant concern, and the magnetic field's rate of change is substantial. This can lead to continuous loss of lock, which cannot be manually adjusted in real time, resulting in ineffective magnetic gradient data and impacting work efficiency. In such cases, a simple self-reset function is insufficient for experimental conditions. It is necessary to select appropriate combinations of integrating capacitor and feedback resistor parameters to reduce system sensitivity and achieve relocking measurement, returning the system to normal operating mode.

[0005] Currently, there is no autonomously controllable cryogenic superconducting magnetic gradient readout system for unattended mobile platforms. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, this application aims to provide an autonomously controllable low-temperature superconducting magnetic gradient readout system and parameter setting method. This system solves the problems of optimal operating point setting before magnetic gradient measurement in SQUID (Superconducting Quantum Interferometer) multi-channel readout systems and continuous loss of lock-up during dynamic measurement in the face of unattended conditions, thus providing technical support for improving the efficiency and accuracy of magnetic exploration.

[0007] To achieve the above objectives, this application adopts the following technical solution:

[0008] An autonomously controlled cryogenic superconducting magnetic gradient readout system according to an embodiment of this application includes:

[0009] The main control circuit continuously monitors the modulation signal output by the direct-reading low-noise readout circuit, and sets the bias current and bias voltage parameters of the SQUID according to the modulation signal to keep the SQUID at its optimal operating point. Based on the magnetic gradient data, it determines in real time whether the system has lost lock. If a loss of lock occurs, the corresponding channel in the direct-reading low-noise readout circuit is reset. If a series of loss of lock occurs, the sensitivity of the system is switched before resetting.

[0010] Furthermore, the main control circuit includes an FPGA main controller and a DAC module, with the input terminal of the DAC module connected to the FPGA main controller;

[0011] The direct-reading low-noise readout circuit includes, in sequence, a preamplifier circuit, an integrator circuit, a voltage follower circuit, and a differential amplifier circuit. The differential amplifier circuit outputs magnetic gradient data to the acquisition card. The integrator capacitor bank includes multiple parallel integrator capacitors with different parameters. The integrator capacitor bank is connected to the output of the integrator circuit through a second multiplexer. The other end of the integrator capacitor bank is connected to the input of the integrator circuit through a selection switch.

[0012] The feedback resistor group includes multiple parallel feedback resistors with different parameters, and is connected to the output of the integrator circuit through the first multiplexer. The other end of the feedback resistor group is connected to SQUID. The integrator capacitor and feedback resistor with different parameter combinations are used to adjust the low, medium and high sensitivity of the system.

[0013] A first switch is provided between the output of the first multiplexer and the output of the integrating circuit. The first switch includes a common terminal, an upper terminal and a lower terminal. When the common terminal of the first switch is connected to the lower terminal, the output of the DAC module is connected to one side terminal of the first multiplexer through the first switch.

[0014] An NPN transistor is connected in parallel across the integrating capacitor and the second multiplexer.

[0015] The control terminals of the NPN transistor, the first multiplexer, and the second multiplexer are connected to the FPGA main controller.

[0016] A parameter setting method according to an embodiment of this application, used in the aforementioned autonomously controlled cryogenic superconducting magnetic gradient readout system, includes:

[0017] Based on the adaptive successive optimization algorithm, the peak-to-peak value of the modulation signal is detected cyclically. Based on the comparison between the current peak-to-peak value and the previous peak-to-peak value, the bias current parameter and bias voltage parameter are set to make SQUID operate at the optimal point.

[0018] Based on the differential mutation detection algorithm, the first-order differential mutation measure of adjacent samples of magnetic gradient data is obtained. The system is judged in real time whether it has lost the lock based on the first-order differential mutation measure. If it loses the lock, the corresponding channel is automatically reset.

[0019] If the system fails to lock after three consecutive resets, it is considered a continuous loss of lock. The system will then autonomously select a combination of parameters for the integrating capacitor and the feedback resistor to reduce the system's sensitivity and relock the measurement.

[0020] Furthermore, the adaptive successive optimization algorithm specifically includes:

[0021] Set the bias current parameters to make SQUID operate in the flux modulation region;

[0022] Loop calculation of SQUID The peak-to-peak value of the characteristic curve is compared with the peak-to-peak value of the previous cycle. If the value is smaller, the bias current step size is halved. If the bias current step size is already less than the limit value, the bias current parameter is determined to be optimal. Otherwise, the bias current parameter is adjusted according to the bias current step size, and the cycle is re-entered until the optimal bias current parameter of SQUID is found.

[0023] Set the bias voltage parameter to 0 at the SQUID operating point so that the signal magnitude for integration by the integrator circuit is close to 0.

[0024] When the SQUID's bias current parameter is optimal, the sum of the positive half-axis maximum and negative half-axis minimum of the modulation signal is calculated and denoted as sum. When the absolute value of sum is greater than the bias voltage step size and sum is greater than 0, the bias voltage parameter is decreased by the bias voltage step size. When sum is less than 0, the bias voltage parameter is increased by the bias voltage step size. When the absolute value of sum is less than the bias voltage step size, the bias voltage step size is halved. If the bias voltage step size is already less than the limit value at this time, the SQUID is determined to have the optimal bias voltage parameter, and the SQUID is at its optimal operating point. Otherwise, the loop calculation is restarted according to the sign of the sum value until the optimal operating point of the SQUID is found.

[0025] Furthermore, the differential mutation detection algorithm specifically includes:

[0026] Calculate the first-order difference abrupt change measure of adjacent samples in magnetic gradient data;

[0027] The current absolute value of the first-order differential mutation metric is normalized to the sum of the historical cumulative absolute values ​​of the first-order differential mutation metric, highlighting the relative position of the current change in the historical changes.

[0028] Set a mutation threshold and perform first-order differential mutation metric filtering. When the filtered first-order differential mutation metric has a non-zero value, it is determined to be an abnormal lockout signal.

[0029] Furthermore, the corresponding channel automatically resets, including,

[0030] The main control circuit triggers the base of the NPN transistor and short-circuits the two ends of the integrating capacitor to reset it. After the integrating capacitor finishes discharging, the system switches back to the locked measurement mode and continues to convert magnetic flux changes and output voltage.

[0031] Furthermore, the system autonomously selects the parameter combination of the integrating capacitor and the feedback resistor to reduce the system sensitivity and achieve re-locking of the measurement, including: the main control circuit controls the first multiplexer and the second multiplexer to switch to the integrating capacitor and feedback resistor with the sensitivity required by SQUID.

[0032] The resistance parameter of the feedback resistor is calculated based on the required sensitivity of SQUID, and the calculation formula is as follows: , For output voltage, For equivalent magnetic flux, For feedback resistor, The mutual inductance coefficient between the SQUID's internal feedback coil and the pickup loop, and the capacitance parameters of the paired integrating capacitors are set by modeling and calculating the system.

[0033] Furthermore, the capacitance parameters of the paired integrating capacitors are set through system modeling and calculation, including:

[0034] A small-signal linear model is established at the optimal operating point of SQUID, and the output voltage of SQUID is linearized by small signal. The output voltage is obtained by proportional transformation of the equivalent magnetic flux by the magnetic flux-voltage conversion slope and superimposed with the background output noise. The equivalent magnetic flux is composed of external input magnetic flux and feedback magnetic flux.

[0035] A direct-reading low-noise readout circuit model is established. The preamplifier circuit is equivalent to a constant gain G. The dynamic characteristics of the integrator circuit are determined by the adjustment resistor and the integrator capacitor. The frequency response of the feedback current to the voltage excitation is determined by the feedback resistor, the resistance of the SQUID internal feedback coil, and the inductance of the SQUID internal feedback coil.

[0036] Modeling yields the open-loop transfer function Solve , Given a unity-gain frequency, the formula for calculating the integrating capacitor is: , To adjust the resistor, Here, G is the feedback resistor, and G is the constant gain. The flux-to-voltage conversion slope, For frequency domain variables, The internal feedback coil resistance of the SQUID This is the inductance of the SQUID's internal feedback coil.

[0037] Based on the above technical solution, the advantages of this application compared with the prior art are as follows:

[0038] Before the magnetic gradient system measurement, the method of this application adopts an adaptive successive optimization algorithm to autonomously adjust the SQUID to the optimal operating point. During the measurement process, the differential mutation detection algorithm is used to determine the loss of lock in real time and autonomously reset. If continuous loss of lock occurs, the method autonomously selects the combination of integration capacitor and feedback resistor parameters to reduce the system sensitivity and achieve relock measurement.

[0039] The optimal operating point autonomous setting method based on the adaptive successive optimization algorithm in this application not only solves the tedious and time-consuming problem of manual adjustment of the optimal operating point caused by the excessive number of channels in the direct-reading low-noise readout circuit, but also achieves greater modulation depth and more precise control by continuously halving the bias current step size to approach the optimal bias current parameter. Simultaneously, by autonomously reducing system sensitivity, it solves the problem of continuous system lock-out caused by bumps or large magnetic field change rates during dynamic measurements on mobile platforms such as vehicles, oceans, and aircraft. This enables autonomously controlled magnetic gradient measurement under unattended conditions, improving work efficiency and the reliability of magnetic gradient measurement. Attached Figure Description

[0040] Figure 1 A structural block diagram of a self-regulating cryogenic superconducting magnetic gradient readout system provided in this application embodiment;

[0041] Figure 2 A flowchart illustrating a parameter setting method for an autonomously controlled cryogenic superconducting magnetic gradient readout system provided in this application embodiment;

[0042] Figure 3 The SQUID provided in the embodiments of this application Characteristic curve diagram;

[0043] Figure 4 A flowchart for setting the optimal operating point based on the adaptive successive optimization algorithm provided in this application embodiment;

[0044] Figure 5 A flowchart of continuous lock loss determination and system sensitivity setting based on differential mutation detection algorithm provided in an embodiment of this application;

[0045] 1. First-stage amplifier circuit; 2. Second-stage amplifier circuit; 3. Integrator circuit; 4. Voltage follower circuit; 5. Differential amplifier circuit; 6. NPN transistor; 7. Selector switch; 8. First switch; 9. Preamplifier circuit; 81. First multiplexer; 82. Second multiplexer. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] like Figure 1 As shown in the embodiment of this application, an autonomously controlled low-temperature superconducting magnetic gradient readout system includes: a direct-reading low-noise readout circuit and a main control circuit.

[0048] The main control circuit continuously monitors the modulation signal output by the direct-reading low-noise readout circuit, sets the bias current and bias voltage parameters of the SQUID to keep the SQUID at its optimal operating point, and determines in real time whether the system has lost lock based on the magnetic gradient data. If a lock loss occurs, the corresponding channel in the direct-reading low-noise readout circuit is reset. If a series of lock losses occur, the sensitivity of the system is switched before resetting.

[0049] The direct-read low-noise readout circuit is connected to the SQUID, and the main control circuit is connected to both the SQUID and the direct-read low-noise readout circuit.

[0050] The direct-read low-noise readout circuit includes feedback resistor groups for adjusting different parameters of the system's low, medium, and high sensitivity. , and ) and paired integrating capacitor banks ( , and Specifically, it includes a preamplifier circuit 9 composed of a first-stage amplifier circuit 1 and a second-stage amplifier circuit 2, an integrating circuit 3, a voltage follower circuit 4, a differential amplifier circuit 5, a first multiplexer 81, and a second multiplexer 82.

[0051] From the direction of the data flow, the first-stage amplifier circuit 1, the second-stage amplifier circuit 2, the integrating circuit 3, the voltage follower circuit 4, and the differential amplifier circuit 5 are connected in sequence. The data is output to the data acquisition card through the differential amplifier circuit 5, and the feedback resistor group ( , and This includes multiple parallel feedback resistors with different parameters. The feedback resistors are connected to the output of the integrating circuit 3 via the first multiplexer 81, and the other end is connected to SQUID; the integrating capacitor bank ( , and The setup includes setting multiple parallel integrating capacitors with different parameters. The integrating capacitors are connected to the output of the integrating circuit 3 through the second multiplexer 82, and the other end is connected to the input of the integrating circuit 3 through the selection switch 7.

[0052] Set a debugging resistor R1, one end of which can be connected to the selector switch 7, and the other end connected to one end of the second multiplexer 82.

[0053] A first switch 8 is provided between the output terminal of the first multiplexer 81 and the integrator 3.

[0054] The preamplifier circuit 9 is a composite amplifier composed of a first-stage amplifier circuit 1 and a second-stage amplifier circuit 2, used to improve the amplification factor, bandwidth and dynamic range of the circuit.

[0055] The first switch 8 includes a common terminal, an upper terminal, and a lower terminal. In debug mode, the common terminal of the first switch 8 is connected to the lower terminal. One output terminal of the DAC module is connected to one side terminal of the first multiplexer 81 via one terminal (lower terminal) of the first switch 8, and then through the feedback resistor ( , or When connected to SQUID, a debugging signal is input. Selector switch 7 is connected to debugging resistor R1, and the integrating capacitor is not connected. Integrating circuit 3 only serves to amplify the signal. Debugging resistor R1 acts as a feedback loop and sets the closed-loop gain of the amplifier. In lockout mode, the common terminal of the first switch 8 is connected to the upper end to form a feedback loop. At this time, a set of parameters for the integrating capacitor and feedback resistor are set to keep the system locked at zero flux under the current sensitivity.

[0056] During dynamic measurement, the SQUID output signal is amplified by the preamplifier circuit 9, the integrator circuit 3 accumulates the integrated signal deviation and forms a feedback magnetic flux with the feedback loop, the voltage follower circuit 4 realizes impedance transformation and front-end isolation, and the differential amplifier circuit 5 converts the signal into differential output.

[0057] The main control circuit includes an FPGA main controller and a DAC module. The input terminal of the DAC module is connected to the FPGA main controller. The control terminals of the first multiplexer 81 and the second multiplexer 82 are connected to the FPGA main controller to realize the autonomous selection of the parameter combination of the integrating capacitor and the feedback resistor, reduce the system sensitivity and realize the re-locking measurement. The DAC module refers to the digital-to-analog converter.

[0058] An NPN transistor 6 is connected in parallel across the integrating capacitor and the second multiplexer 82. The control terminal of the NPN transistor 6 is connected to the FPGA master controller to enable autonomous reset after the system loses lock.

[0059] When SQUID is in debug mode, the FPGA master controller controls the DAC module, and the generated debug signal is fed into the direct-read low-noise readout circuit. After passing through the feedback resistor, it is fed into SQUID, and then the bias current and bias voltage parameters are set. At this time, the common terminal of the first switch 8 is connected to the lower terminal, the selector switch 7 is connected to the debug resistor R1, the integrating capacitor is not connected, and the integrating circuit 3 only plays the role of amplification.

[0060] The parameter setting method is integrated into the main control circuit. The parameter setting method includes the optimal operating point setting based on the adaptive successive optimization algorithm, the continuous loss-of-lock judgment based on the differential mutation detection algorithm, and the system sensitivity setting method, so as to realize the autonomous control of the low-temperature superconducting magnetic gradient readout system.

[0061] like Figure 2 As shown, this embodiment provides a parameter setting method for an autonomously controlled cryogenic superconducting magnetic gradient readout system, including:

[0062] Based on the adaptive successive optimization algorithm, the peak-to-peak value of the modulation signal is detected cyclically. Based on the comparison between the current peak-to-peak value and the previous peak-to-peak value, the bias current parameter and bias voltage parameter are set to make SQUID operate at the optimal point.

[0063] Based on the differential mutation detection algorithm, the first-order differential mutation measure of adjacent samples of magnetic gradient data is obtained. The system is judged in real time whether it has lost the lock based on the first-order differential mutation measure. If it loses the lock, the corresponding channel is automatically reset.

[0064] If the system fails to lock after three consecutive resets, it is considered a continuous loss of lock. The system then autonomously selects a combination of parameters for the integrating capacitor and the feedback resistor to reduce system sensitivity and relock the measurement.

[0065] like Figure 3 As shown, when the SQUID is at its optimal operating point W, it can maintain zero flux lockout, meaning that changes in magnetic flux will cause changes in the output voltage, causing the operating point W to move along... Figure 3 In The characteristic curve shifts vertically. The external magnetic flux change in the environment is represented by ∆V, which is the voltage value output after linear integration by the integrator circuit. This voltage value passes through the feedback resistor and couples with the feedback coil inside the SQUID, generating a reverse magnetic flux change. to counteract changes in external magnetic flux This achieves a stable balance of magnetic flux, i.e., zero flux locking.

[0066] At its optimal operating point W, the SQUID exhibits a linear relationship between output voltage and magnetic flux change, thus ensuring the system's acquisition of magnetic gradient data.

[0067] First, set the SQUID to debug mode. The FPGA master controller controls the DAC module, and the generated debug signal is fed into the direct-read low-noise readout circuit. After passing through the feedback resistor, it is fed into the SQUID. Then, the bias current parameter and bias voltage parameter are set.

[0068] like Figure 4 As shown, specifically, the adaptive successive optimization algorithm includes:

[0069] Setting the bias current parameters causes the SQUID to operate in the flux modulation region. Since the bias current parameters are related to the SQUID's... The peak-to-peak value of the characteristic curve satisfies a single extremum function relationship. When the peak-to-peak value is at its maximum, SQUID is at its optimal operating point. During this process, SQUID is set to debug mode.

[0070] First, parameter initialization is performed. The parameters include bias current and bias voltage. The initial value of the bias current parameter is set to the minimum critical current of the superconducting state, and the initial value of the bias voltage parameter is set to 0V.

[0071] The FPGA master controller controls the DAC module to generate a bias current, which is fed into the SQUID to cyclically calculate the SQUID's... The peak-to-peak value of the characteristic curve is denoted as . Compare the current peak value with the peak value of the previous cycle; the judgment formula is: If the value decreases, the bias current step size will be adjusted. Halve the current setting and check if the bias current step size is less than the limit value. If the bias current step size is already less than the limit value, then the bias current parameter is considered optimal. Otherwise, adjust the bias current parameter according to the bias current step size. , For the first The bias current parameters of the next step. For the first The bias current parameters of the next step. For the first The bias current step size is adjusted, and the loop is re-entered until the optimal bias current parameter of SQUID is found.

[0072] Setting the bias voltage parameter sets the voltage at the SQUID operating point to 0, making the signal magnitude integrated by the integrator circuit 3 close to 0, ensuring that the SQUID is stably locked at the optimal operating point and linearizing the output voltage signal.

[0073] When the SQUID's bias current parameter is optimal, the FPGA controller controls the DAC module to generate a bias voltage, which is applied to the preamplifier circuit 9. The sum of the positive half-axis maximum and negative half-axis minimum values ​​of the modulated signal is calculated and denoted as sum. This is then used to determine... , The bias voltage step size is defined as follows: when the absolute value of sum is greater than or equal to the bias voltage step size, and if sum is greater than 0, the bias voltage parameter decreases by the bias voltage step size; when sum is less than 0, the bias voltage parameter increases by the bias voltage step size. At this point, SQUID is at its optimal operating point; when the absolute value of sum is less than the bias voltage step size, i.e. If the bias voltage step size is halved, and the bias voltage step size is already less than the limit value, then SQUID is determined to be the optimal bias voltage parameter and SQUID is at its best operating point. Otherwise, depending on the sign of the sum value, the loop calculation is restarted until the best operating point of SQUID is found.

[0074] After setting the parameters to bring the SQUID to its optimal operating point, the system is adjusted to lock mode for magnetic field measurement. During normal measurement, the output signal changes relatively smoothly, but when lockout occurs, the output signal waveform will deviate from the full range. In lock mode, the common terminal of the first switch 8 is connected to the upper end to form a feedback loop. In this mode, a set of parameters for the integrating capacitor and feedback resistor are set to keep the system locked at zero magnetic flux at the current sensitivity.

[0075] In one embodiment, such as Figure 5 As shown, the differential mutation detection algorithm specifically includes:

[0076] Set a mutation threshold;

[0077] The formula for measuring the first-order difference abrupt change between adjacent samples in magnetic gradient data is as follows: ,in, It is a first-order differential mutation measure. Number of discrete points Magnetic gradient data, Number of discrete points Magnetic gradient data, This represents the total number of samples. The intensity of local changes is reflected by calculating the first-order difference abrupt change measure between adjacent samples.

[0078] The current absolute value of the first-order differential mutation metric is normalized to the sum of the historical cumulative absolute values ​​of the first-order differential mutation metric, highlighting the relative position of the current change within historical changes. The formula is as follows: abs represents absolute value; This is the absolute value of the differential mutation measure after normalization.

[0079] A mutation threshold is used to perform first-order differential mutation metric filtering. The first-order differential mutation metric after filtering the magnetic gradient data is judged to be 0. When the first-order differential mutation metric after filtering has a non-zero value, it is judged as an abnormal lock-out signal.

[0080] By setting a mutation threshold, when no lock loss occurs during magnetic gradient measurement, the differential mutation metric is below the threshold. When a lock loss occurs, the differential mutation metric should be above the threshold. First-order differential mutation metric filtering is performed, where the differential mutation metric below the threshold is always 0. When the first-order differential mutation metric after magnetic gradient data filtering has a non-zero value, it is an abnormal lock loss signal.

[0081] If the first-order differential mutation metric is not zero after magnetic gradient data filtering, it is an abnormal unlocking signal. Reset the system and determine if the number of consecutive resets is greater than 3. If so, a continuous unlocking has occurred. Switch the system sensitivity and then reset the system.

[0082] See Figure 5 As shown, in one embodiment, the corresponding channel autonomously resets, including:

[0083] In the main control circuit, the FPGA main controller triggers the base of NPN transistor 6 to short-circuit the two ends of the integrating capacitor for reset. After the integrating capacitor finishes discharging, the readout system switches back to the locked mode and continues to convert magnetic flux change and output voltage. It then determines whether the locking is successful. If the locking is successful, the process ends. If the locking is unsuccessful, it returns to the step of autonomously selecting the parameter combination of the integrating capacitor and feedback resistor.

[0084] When the system experiences a series of lock-outs, it can autonomously select the parameter combination of the integrating capacitor and the feedback resistor to reduce the system sensitivity and relock the measurement.

[0085] Specifically, by autonomously selecting the parameter combination of the integrating capacitor and the feedback resistor, the system sensitivity is reduced to achieve re-locking of the measurement.

[0086] The formula for calculating system sensitivity is: , For output voltage, For equivalent magnetic flux, For feedback resistor, For the mutual inductance between the feedback coil and the SQUID;

[0087] The main control circuit controls the parameter combination of the feedback resistor and integrating capacitor that control the first multiplexer 81 and the second multiplexer 82 to switch to the sensitivity required for SQUID.

[0088] The system sensitivity is directly proportional to the feedback resistor parameters. The resistance parameters of the feedback resistor are set according to the system sensitivity, and the capacitance parameters of the paired integrating capacitor are set by modeling and calculating the system.

[0089] Specifically, the integrating capacitor primarily determines the loop bandwidth and stability. After determining the feedback resistor parameters, the parameters of the paired integrating capacitor are set through system modeling and calculation, including:

[0090] First, a small-signal linear model of SQUID is established at the optimal operating point. The characteristics are linearized to small signals, i.e. , This indicates the output voltage of the SQUID. The equivalent magnetic flux acting on the squid. SQUID flux-to-voltage conversion slope, This represents the SQUID's inherent output noise level. The equivalent magnetic flux consists of two parts: , It is the feedback coil current. It is the mutual inductance coefficient between the SQUID's internal feedback coil and the pickup loop;

[0091] Establish a direct-read low-noise readout circuit model, assuming the preamplifier circuit 9 has a constant gain G, and the integrator circuit 3 is modeled. , The output of integrator circuit 3, To adjust the resistor, For integrating capacitors. Model the feedback loop. , For feedback resistor, The internal feedback coil resistance of the SQUID For the SQUID's internal feedback coil inductance, For feedback current;

[0092] Modeling yields the open-loop transfer function and at unity gain frequency As a target bandwidth constraint, solve The formula for calculating the integrating capacitance is obtained as follows: ,but , , and The resistance parameters of the feedback resistors for low sensitivity, medium sensitivity, and high sensitivity are respectively. , and The capacitance parameters of the integrating capacitors for low sensitivity, medium sensitivity, and high sensitivity are given, and the capacitance parameters of the integrating capacitor are calculated by substituting them into the calculation.

[0093] In one embodiment, the resistance parameter of the feedback resistor in the low-sensitivity circuit is set to 1kΩ. The capacitance parameter of the integrating capacitor is set to 100nF; the resistance parameter of the feedback resistor in the intermediate sensitivity is set to 10kΩ. The capacitance parameter of the integrating capacitor is set to 10nF; the resistance parameter of the feedback resistor in the high-sensitivity circuit is set to 100kΩ. The capacitance parameter of the integrating capacitor is set to 1nF.

[0094] This application not only solves the problem of the tedious and time-consuming process of manually setting the optimal operating point due to the excessive number of channels in the direct-reading low-noise readout circuit, but also improves the modulation curve by continuously halving the bias current step size to approach the optimal bias current parameter, resulting in a larger peak-to-peak value, a greater modulation depth, and a more precise modulation effect.

[0095] Meanwhile, the sensitivity autonomous switching method of this application in the face of continuous loss of lock problem solves the problem of continuous loss of lock when dynamic measurement of mobile platforms such as vehicle towing, marine towing, and air flight occurs due to turbulence and large magnetic field change rate, and the inability to intervene manually in real time, thus improving work efficiency and the accuracy of magnetic gradient measurement.

[0096] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A self-regulating low-temperature superconducting magnetic gradient readout system, characterized in that, include: The main control circuit continuously monitors the modulation signal output by the direct-reading low-noise readout circuit, and sets the bias current and bias voltage parameters of the SQUID according to the modulation signal to keep the SQUID at its optimal operating point. Based on the magnetic gradient data, it determines in real time whether the system has lost lock. If a lock loss occurs, the corresponding channel in the direct-reading low-noise readout circuit is reset. If consecutive lock losses occur, the sensitivity of the system is switched before resetting. The main control circuit includes an FPGA main controller and a DAC module, with the input terminal of the DAC module connected to the FPGA main controller; The direct-reading low-noise readout circuit includes, in sequence, a preamplifier circuit, an integrator circuit, a voltage follower circuit, and a differential amplifier circuit. The differential amplifier circuit outputs magnetic gradient data to the acquisition card. The integrator capacitor bank includes multiple parallel integrator capacitors with different parameters. The integrator capacitor bank is connected to the output of the integrator circuit through a second multiplexer. The other end of the integrator capacitor bank is connected to the input of the integrator circuit through a selection switch. The feedback resistor group includes multiple parallel feedback resistors with different parameters, and is connected to the output of the integrator circuit through the first multiplexer. The other end of the feedback resistor group is connected to SQUID. The integrator capacitor and feedback resistor with different parameter combinations are used to adjust the low, medium and high sensitivity of the system. A first switch is provided between the output of the first multiplexer and the output of the integrating circuit. The first switch includes a common terminal, an upper terminal and a lower terminal. When the common terminal of the first switch is connected to the lower terminal, the output of the DAC module is connected to one side terminal of the first multiplexer through the first switch. An NPN transistor is connected in parallel across the integrating capacitor and the second multiplexer. The control terminals of the NPN transistor, the first multiplexer, and the second multiplexer are connected to the FPGA main controller.

2. A parameter setting method for the autonomously controlled low-temperature superconducting magnetic gradient readout system as described in claim 1, characterized in that, include: Based on the adaptive successive optimization algorithm, the peak-to-peak value of the modulation signal is detected cyclically. Based on the comparison between the current peak-to-peak value and the previous peak-to-peak value, the bias current parameter and bias voltage parameter are set to make SQUID operate at the optimal point. Based on the differential mutation detection algorithm, the first-order differential mutation measure of adjacent samples of magnetic gradient data is obtained. The system is judged in real time whether it has lost the lock based on the first-order differential mutation measure. If it loses the lock, the corresponding channel is automatically reset. If the system fails to lock after three consecutive resets, it is considered a continuous loss of lock. The system will then autonomously select a combination of parameters for the integrating capacitor and the feedback resistor to reduce the system's sensitivity and relock the measurement.

3. The parameter setting method according to claim 2, characterized in that, The adaptive successive optimization algorithm specifically includes: Set the bias current parameters to make SQUID operate in the flux modulation region; Loop calculation of SQUID The peak-to-peak value of the characteristic curve is compared with the peak-to-peak value of the previous cycle. If the value is smaller, the bias current step size is halved. If the bias current step size is already less than the limit value, the bias current parameter is determined to be optimal. Otherwise, the bias current parameter is adjusted according to the bias current step size, and the cycle is re-entered until the optimal bias current parameter of SQUID is found. Set the bias voltage parameter to 0 at the SQUID operating point so that the signal magnitude for integration by the integrator circuit is close to 0. When the SQUID's bias current parameter is optimal, the sum of the positive half-axis maximum and negative half-axis minimum of the modulation signal is calculated and denoted as sum. When the absolute value of sum is greater than the bias voltage step size and sum is greater than 0, the bias voltage parameter is decreased by the bias voltage step size. When sum is less than 0, the bias voltage parameter is increased by the bias voltage step size. When the absolute value of sum is less than the bias voltage step size, the bias voltage step size is halved. If the bias voltage step size is already less than the limit value at this time, the SQUID is determined to have the optimal bias voltage parameter, and the SQUID is at its optimal operating point. Otherwise, the loop calculation is restarted according to the sign of the sum value until the optimal operating point of the SQUID is found.

4. The parameter setting method according to claim 2, characterized in that, The differential mutation detection algorithm specifically includes: Calculate the first-order difference abrupt change measure of adjacent samples in magnetic gradient data; The current absolute value of the first-order differential mutation metric is normalized to the sum of the historical cumulative absolute values ​​of the first-order differential mutation metric, highlighting the relative position of the current change in the historical changes. Set a mutation threshold and perform first-order differential mutation metric filtering. When the filtered first-order differential mutation metric has a non-zero value, it is determined to be an abnormal lockout signal.

5. The parameter setting method according to claim 2, characterized in that, Corresponding channel self-reset, including, The main control circuit triggers the base of the NPN transistor and short-circuits the two ends of the integrating capacitor to reset it. After the integrating capacitor finishes discharging, the system switches back to the locked measurement mode and continues to convert magnetic flux changes and output voltage.

6. The parameter setting method according to claim 2, characterized in that, The system autonomously selects the parameter combination of the integrating capacitor and the feedback resistor to reduce the system sensitivity and achieve re-locking of the measurement. This includes: the main control circuit controlling the first multiplexer and the second multiplexer to switch to the integrating capacitor and feedback resistor with the sensitivity required by SQUID. The resistance parameter of the feedback resistor is calculated based on the required sensitivity of SQUID, and the calculation formula is as follows: , For output voltage, For equivalent magnetic flux, For feedback resistor, The mutual inductance coefficient between the SQUID's internal feedback coil and the pickup loop, and the capacitance parameters of the paired integrating capacitors are set by modeling and calculating the system.

7. The parameter setting method according to claim 6, characterized in that, The capacitance parameters of the paired integrating capacitors are set through system modeling and calculation, including: A small-signal linear model is established at the optimal operating point of SQUID, and the output voltage of SQUID is linearized by small signal. The output voltage is obtained by proportional transformation of the equivalent magnetic flux by the magnetic flux-voltage conversion slope and superimposed with the background output noise. The equivalent magnetic flux is composed of external input magnetic flux and feedback magnetic flux. A direct-reading low-noise readout circuit model is established. The preamplifier circuit is equivalent to a constant gain G. The dynamic characteristics of the integrator circuit are determined by the adjustment resistor and the integrator capacitor. The frequency response of the feedback current to the voltage excitation is determined by the feedback resistor, the resistance of the SQUID internal feedback coil, and the inductance of the SQUID internal feedback coil. Modeling yields the open-loop transfer function Solve , Given a unity-gain frequency, the formula for calculating the integrating capacitor is: , To adjust the resistor, Here, G is the feedback resistor, and G is the constant gain. The flux-to-voltage conversion slope, For frequency domain variables, The internal feedback coil resistance of the SQUID This is the inductance of the SQUID's internal feedback coil.

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