Degaussing current control device and method based on real-time magnetic field measurement

Abstract

The invention relates to a degaussing current control device and method based on real-time magnetic field measurement. The degaussing current control device comprises a magnetic sensor sensing unit, a magnetic field generation unit, a magnetic field control unit, a display control unit and the like. In an electron microscope laboratory, a three-component fluxgate sensor is used for collecting stray magnetic field signals, the magnetic field control unit calculates the stray magnetic field signals and then controls the magnetic field generation unit to generate a reverse magnetic field, and the magnetic field stability and the imaging quality are improved. The high-precision magnetic sensor captures stray magnetic field signals in real time and cooperates with a rapid tracking algorithm of the magnetic field control unit to accurately calculate and control the magnetic field generation unit to generate a reverse magnetic field and effectively counteract the stray magnetic field, so that the stability of the magnetic field around the electron microscope is greatly improved, the imaging quality is remarkably improved, and the problem of image distortion is solved. The definition and the resolution ratio are improved, and more accurate and clearer microscopic images are provided for scientific research work.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of spraying technology, in particular to a demagnetizing current control device and method based on real-time magnetic field measurement. BACKGROUND

[0002] In the field of high-end manufacturing, scientific research experiments and other fields, nuclear magnetic resonance imaging equipment, quantum computing devices and other precision instruments have very high requirements for the magnetic field environment, and the environmental magnetic field fluctuation needs to be strictly controlled at a very low level, but the magnetic field interference generated by power grid harmonics, motor operation and other factors generally exists. In the current demagnetizing current control technology, the traditional PID control is difficult to adjust, and the dynamic response is slow. The feedforward compensation algorithm based on fixed model is difficult to cope with unknown interference. The single control algorithm lacks coordination and fault tolerance mechanism, and the existing equipment is low in intelligence, cannot meet the real-time processing and predictive maintenance requirements, and urgently needs a high-precision, intelligent and reliable demagnetizing current control scheme.

[0003] Based on this, a demagnetizing current control device and method based on real-time magnetic field measurement are provided, which can eliminate the disadvantages of existing devices. SUMMARY

[0004] The purpose of the present application is to provide a demagnetizing current control device and method based on real-time magnetic field measurement, which solves the problem of inconvenience in the prior art.

[0005] To achieve the above purpose, the present application provides the following technical scheme: A demagnetizing current control device based on real-time magnetic field measurement, comprising: A real-time magnetic field sensing module: a three-dimensional orthogonal fluxgate sensor array is used to collect spatial magnetic field data in real time, and after processing by a signal conditioning circuit, the analog signal is converted into a digital signal and transmitted to an edge computing control unit; a built-in temperature compensation unit ensures the measurement accuracy in the whole temperature range; A three-dimensional orthogonal fluxgate sensor array, with a sampling rate of 1kHz, a measurement range of ±100μT, a resolution of 10nT, and a built-in temperature compensation unit (temperature drift ≤200ppm / ℃); a signal conditioning circuit, including an anti-aliasing filter (cutoff frequency 5kHz), a programmable amplifier (gain range 1-1000 times) and an analog-to-digital converter (precision 16 bits); An edge computing control unit: receiving data from the magnetic field sensing module, running a multi-algorithm collaborative control logic, generating optimal demagnetizing current instructions, and issuing them to the demagnetizing current driving module; at the same time, responsible for system state estimation, fault diagnosis and data storage; Heterogeneous multi-core processor (CPU+GPU+DSP), computing power ≥200TOPS, integrated: model predictive control (MPC) module, rolling optimization period ≤20ms; deep reinforcement learning (DRL) module, using Actor-Critic architecture, policy network update frequency 10Hz; extended Kalman filter (EKF) module, data fusion delay ≤1ms; Demagnetization current driving module: according to the instruction of the edge computing unit, the demagnetization current is accurately output to the compensation coil, a magnetic field opposite to the interference magnetic field is formed, and dynamic magnetic field offset is realized; meanwhile, the current accuracy is ensured through closed-loop feedback; Four-phase H-bridge power amplifier, output current range -15A+15A, ripple ≤1%; closed-loop current feedback loop, using LEM Hall current sensor (bandwidth DC100kHz, accuracy ±0.1%); PWM modulation unit, switch frequency 20kHz~100kHz adjustable; Multi-mode cooperative control logic: automatically switch control mode according to magnetic field error size, dynamically balance between fast response and accurate regulation, and realize full-range high-precision magnetic field compensation; Fast response mode: when the magnetic field error is greater than 5mGauss, the MPC algorithm is started, and the target function is: Wherein: is the prediction time domain, is the control time domain, is the current weight coefficient; adaptive optimization mode: when the magnetic field error is less than or equal to 5mGauss, switch to DRL algorithm, and the reward function is: .

[0006] On the basis of the above technical scheme, the application also provides the following optional technical schemes: The demagnetization current control method based on real-time magnetic field measurement has the characteristics that it comprises the following steps: step one: multi-dimensional magnetic field data acquisition: synchronously acquire X / Y / Z axis magnetic field components through a three-dimensional sensor array , after anti-aliasing filtering, carry out 16-bit analog-to-digital conversion; calculate the magnetic field vector module value and gradient . Data fusion and state estimation: update the system state by using an extended Kalman filter algorithm: Wherein: is the state estimation value, , F is the state transition matrix, B is the control input matrix, u is the current control amount, P is the error covariance matrix, Q is the process noise covariance, is the Kalman gain, is the observation matrix, is the sensor measurement value, and R is the measurement noise covariance; Step two: Intelligent control decision: automatically switch control mode according to magnetic field error: if mGauss, enable MPC algorithm; if mGauss, output current adjustment amount through DRL algorithm, Actor network structure is input layer (9 dimensions) → hidden layer 1 (128 neurons, ReLU) → hidden layer 2 (64 neurons, ReLU) → output layer (3 dimensions, tanh); Step three: execution and feedback: output the magnetic field elimination current through the PWM driving power amplifier, monitor the current feedback in real time and adjust the control parameters; update the DRL experience pool every 100ms, use the priority experience replay (PER) mechanism, and the sample priority .

[0007] In an optional solution: the real-time magnetic field sensing module further includes a self-calibration unit, and the calibration process is: injecting a standard magnetic field μT, calculating the X-axis sensitivity ; injecting Y-axis and Z-axis standard magnetic fields in turn, calculating and ; storing the sensitivity matrix , for real-time measurement value correction.

[0008] In an optional solution: the magnetic field elimination current driving module supports multi-coil collaborative control, and the magnetic field vector decomposition is realized through matrix operation: Wherein: is the current of each coil, K is the coil magnetic field coupling matrix, is the inverse matrix of the coupling matrix.

[0009] In an optional solution: the Critic network of the DRL algorithm adopts a double network structure (Q1 and Q2), and the loss function is: Wherein: is the discount factor, is the target network update coefficient.

[0010] In an optional solution: the method further includes a fault tolerance mechanism: when the deviation of any sensor data is greater than 5%, automatically switch to the redundant sensor channel; when the current output exceeds 120% of the rated value, trigger the hardware overcurrent protection (response time ≤10μs), and record the fault code.

[0011] In an optional solution, the edge computing control unit further comprises: an edge data storage and analysis system: a non-volatile memory (capacity > 32 GB), storing raw magnetic field data (sampling rate > 1 kHz, storage > 7 days), control parameter history (storage > 30 days), fault code (> 1000); edge side data analysis: sliding window statistics (window length 500 ms), spectral analysis (frequency resolution < 1 Hz), abnormal detection threshold dynamic adjustment formula: wherein mGauss, is the standard deviation of the magnetic field in the sliding window.

[0012] In an optional solution, the edge computing control unit further comprises: an industrial-grade secure communication protocol stack: supporting TLS 1.3 encryption (ECDHE-P-256, AES-256-GCM), user authentication based on X.509 certificate (validity period < 90 days), authority divided into three levels (administrator / operator / read-only user); data transmission guarantee: heartbeat packet interval < 5 seconds, SHA-256 integrity check, breakpoint resume (supporting > 1MB data block).

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: Significantly improve the stability of the magnetic field: by capturing the stray magnetic field signal in real time with high-precision magnetic sensors, and cooperating with the fast tracking algorithm of the magnetic field control unit, the reverse magnetic field generated by the magnetic field generation unit can be accurately calculated and controlled, effectively offsetting the stray magnetic field, greatly improving the stability of the magnetic field around the electron microscope, significantly improving the imaging quality, solving the image distortion problem, improving the clarity and resolution, and providing more accurate and clear microscopic images for scientific research. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a structural diagram of the present application.

[0015] Figure 2 is a logic diagram of the method of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0017] The application of the independent magnetic field coil frame set outside the electron microscope is used in an electron microscope laboratory to improve the magnetic field environment around the electron microscope. The system comprises: a magnetic sensor sensing unit: a three-component fluxgate sensor is selected, with a sampling rate of 1 kHz, a measurement range of ±100 muT, a resolution of 10 nT, and a built-in temperature compensation unit that can control the temperature drift within 200 ppm / C. The sensor is installed at key positions around the electron microscope to capture the stray magnetic field signal comprehensively. A magnetic field generating unit: an independent magnetic field coil frame set outside the electron microscope is used, which meets the standard of low magnetic material. The coil is optimally designed to efficiently generate a magnetic field opposite in direction and equal in size to the stray magnetic field. A magnetic field control unit: as the core of the system, it receives the magnetic field signal from the magnetic sensor sensing unit, calculates and generates the corresponding current signal in real time through the pre-written fast tracking algorithm, to control the magnetic field output of the magnetic field generating unit and maintain the stability of the working space magnetic field. A display and control unit: it displays the curve data of the magnetic field in real time, which is convenient for the experimental personnel to intuitively evaluate the quality of the magnetic field environment. At the same time, a preset magnetic field limit is set, and when the magnetic field exceeds the limit, an out-of-limit alarm is sent in time. Working process and calculation content: the three-component fluxgate sensor senses the stray magnetic field in real time and converts the signal into an electric signal, which is sent to the magnetic field control unit. Assuming that the magnetic field strength collected by the sensor at a certain moment is μ , μ , μ , the magnetic field control unit calculates the control current according to the received signal using the fast tracking algorithm. First, calculate the current magnetic field vector μ . Set the target magnetic field strength as μ , then the magnetic field error is μ . Given the coil coefficient of the magnetic field generating unit as μ , the required control current in each axis is calculated according to the magnetic field error: , , .

[0018] The current signal is transmitted to the magnetic field generating unit. The magnetic field generating unit generates a magnetic field opposite in direction and equal in size to the stray magnetic field according to the current signal, and the two magnetic fields superimpose in the working area of the electron microscope to cancel the stray magnetic field. Actual test shows that after using the system, the magnetic field stability around the electron microscope is greatly improved, the imaging quality is significantly improved, the image distortion problem is effectively solved, and the clarity and resolution are obviously improved.

[0019] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A demagnetizing current control device based on real-time magnetic field measurement, characterized in that, include: Real-time magnetic field sensing module: Real-time acquisition of spatial magnetic field data through a three-dimensional orthogonal fluxgate sensor array. After processing by the signal conditioning circuit, the analog signal is converted into a digital signal and transmitted to the edge computing control unit; the built-in temperature compensation unit ensures measurement accuracy across the entire temperature range. Edge computing control unit: Receives data from the magnetic field sensing module, runs multi-algorithm collaborative control logic, generates the optimal demagnetizing current command, and sends it to the demagnetizing current drive module; it is also responsible for system state estimation, fault diagnosis, and data storage. Demagnetizing current drive module: According to the instructions of the edge computing unit, it accurately outputs the demagnetizing current to the compensation coil to form a magnetic field opposite to the interference magnetic field, thereby achieving dynamic magnetic field cancellation; at the same time, it ensures the current accuracy through closed-loop feedback. Multi-mode collaborative control logic: Automatically switches control modes according to the magnitude of magnetic field error, dynamically balancing rapid response and precise adjustment to achieve high-precision magnetic field compensation across the entire range; Fast Response Mode: When the magnetic field error > 5 mGauss, the MPC algorithm is activated, with the objective function as follows: in: To predict the time domain, To control the time domain, This is the current weighting coefficient; Adaptive optimization mode: When the magnetic field error is ≤5mGauss, switch to the DRL algorithm, with the reward function as follows: .

2. A demagnetizing current control method based on real-time magnetic field measurement, characterized in that, Includes the following steps: Step 1: Multidimensional magnetic field data acquisition: Simultaneously acquire X / Y / Z axis magnetic field components using a three-dimensional sensor array. After anti-aliasing filtering, a 16-bit analog-to-digital conversion is performed; the magnitude of the magnetic field vector is calculated. and gradient Data fusion and state estimation: Updating the system state using the extended Kalman filter algorithm. in: This is the state estimate. F is the state transition matrix, B is the control input matrix, u is the current control quantity, P is the error covariance matrix, and Q is the process noise covariance. For Kalman gain, For the observation matrix, R represents the sensor measurement value, and R represents the measurement noise covariance. Step Two: Intelligent Control Decision: Automatically switch control modes based on magnetic field error: If mGauss, enable the MPC algorithm; if mGauss outputs the current regulation amount through the DRL algorithm. The Actor network structure is: input layer (9-dimensional) → hidden layer 1 (128 neurons, ReLU) → hidden layer 2 (64 neurons, ReLU) → output layer (3-dimensional, tanh). Step 3: Execution and Feedback: The power amplifier is driven by PWM to output demagnetizing current, and the current feedback is monitored in real time to adjust the control parameters; the DRL experience pool is updated every 100ms, using a Priority Experience Playback (PER) mechanism with sample priority. .

3. The demagnetizing current control method according to claim 1, characterized in that, The real-time magnetic field sensing module further includes a self-calibration unit, and the calibration process is as follows: injecting a standard magnetic field. μT, calculate X-axis sensitivity Inject standard magnetic fields along the Y and Z axes sequentially, and calculate... and Storage sensitivity matrix It is used for real-time measurement correction.

4. The demagnetizing current control method according to claim 1, characterized in that, The demagnetizing current drive module supports multi-coil coordinated control and achieves magnetic field vector decomposition through matrix operations. in: Let K be the current in each coil, and K be the magnetic field coupling matrix of the coil. It is the inverse of the coupling matrix.

5. The demagnetizing current control method according to claim 1, characterized in that, The Critic network of the DRL algorithm adopts a dual-network structure (Q1 and Q2), and the loss function is: in: As a discount factor, The target network update coefficients.

6. The demagnetizing current control method according to claim 1, characterized in that, The method further includes a fault tolerance mechanism: when any sensor data deviation is greater than 5%, it automatically switches to a redundant sensor channel; when the current output exceeds 120% of the rated value, it triggers hardware overcurrent protection and records the fault code.

7. The demagnetizing current control method according to claim 1, characterized in that, The edge computing control unit further includes: an edge data storage and analysis system: a non-volatile memory for storing raw magnetic field data, historical control parameters, and fault codes; and edge-side data analysis: sliding window statistics, spectrum analysis, and a dynamic adjustment formula for anomaly detection threshold. in mGauss, This represents the standard deviation of the magnetic field within the sliding window.

8. The demagnetizing current control method according to claim 1, characterized in that, The edge computing control unit further includes: an industrial-grade secure communication protocol stack and data transmission assurance.

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