Hall sensor combination system and method for magnetic imaging stability optimization

Abstract

The invention discloses a Hall sensor combination system and method for magnetic imaging stability optimization. The system comprises a three-dimensional orthogonal Hall sensor array; the embedded encoder module is used for converting the analog magnetic field signals acquired by the sensor array into digital encoding signals with timestamps; the control module constructs an electromagnetic feature extraction model and is used for carrying out time-frequency domain conjoint analysis on the digital coding signals to obtain magnetic environment disturbance feature parameters; generating a compensation magnetic field parameter opposite to the magnetic environment disturbance characteristic parameter by adopting a closed-loop feedback mechanism; establishing a real-time data channel with a preset electromagnetic imaging module and a preset magnetic field generation module through a preset communication protocol; and constructing a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, and obtaining magnetic field distribution data to dynamically adjust and compensate magnetic field parameters so as to realize active magnetic interference cancellation in a three-dimensional space. Problems in the prior art can be effectively solved, and the precision and stability of magnetic imaging are improved.

Description

Technical Field

[0001] This application relates to the technical field of Hall effect measurement of magnetic variables, and particularly to a Hall sensor combination system and method for optimizing magnetic imaging stability. Background Art

[0002] In recent years, electromagnetic imaging technology has become increasingly prominent in the localization of lung tumors. In particular, the electromagnetic navigation bronchoscopy (ENB) technology, which is based on magnetic imaging technology and combines virtual bronchoscopy, three-dimensional computed tomography imaging, and respiratory gating technology, has been widely used in clinical practice.

[0003] The electromagnetic tomography imaging navigation system has also developed rapidly, and its effectiveness and safety in the diagnosis, localization, and treatment of peripheral lung lesions have been verified and are used by a wider range of medical institutions. However, not all medical institutions have sufficient magnetic shielding conditions or dedicated magnetic field generation equipment, and still rely heavily on portable devices and magnetic markers at the coordinate origin. The complex electromagnetic environment has an interfering effect on the magnetic trace navigation in the small bronchi, which will affect the injection of indole dyes and the efficiency of subsequent endoscopic surgeries.

[0004] In order to effectively solve the above problems, in recent years, Hall sensor technology has been widely studied and applied in the detection and compensation of electromagnetic environments. Hall sensors can accurately detect changes in magnetic fields and work in coordination with corresponding magnetic field generation devices to compensate the magnetic environment through a real-time feedback mechanism and optimize the imaging quality.

[0005] However, there are still some deficiencies in the existing technology. In traditional magnetic imaging systems, Hall sensor arrays mostly adopt two-dimensional planar layouts or three-dimensional arrangements with non-orthogonal structures, making it difficult to achieve accurate detection of magnetic field gradients in the entire spatial domain. The existing technology uses a multi-sensor parallel structure for magnetic field measurement, but there are the following defects:

[0006] (1) The lack of strict orthogonality in sensor layout leads to coupling errors in the calculation of spatial magnetic field components;

[0007] (2) The analog signal processing circuit is vulnerable to common-mode interference and it is difficult to accurately extract the characteristics of transient magnetic interference;

[0008] (3) The compensation mechanisms adopted are mostly based on open-loop control or static feedback, and it is impossible to achieve real-time magnetic field cancellation in a dynamic environment;

[0009] (4) The adjustment of compensation magnetic field parameters lacks the support of a state space model, resulting in a significant decline in compensation accuracy with changes in the environment.

[0010] Therefore, there is an urgent need to design a technical solution to solve at least one of the above technical problems. Summary of the Invention

[0011] The present application provides a Hall sensor combination system and method for optimizing magnetic imaging stability, aiming to solve the problems in the prior art that a multi-sensor parallel structure is used for magnetic field measurement, but there are problems such as the lack of strict orthogonality in sensor layout, the analog signal processing circuit is vulnerable to common-mode interference, the compensation mechanisms used are mostly based on open-loop control or static feedback, real-time magnetic field cancellation in a dynamic environment cannot be achieved, and the adjustment of compensation magnetic field parameters lacks the support of a state space model, resulting in a significant decrease in compensation accuracy with environmental changes.

[0012] In the first aspect, the present application provides a Hall sensor combination system for optimizing magnetic imaging stability, including:

[0013] A three-dimensional orthogonal Hall sensor array, including an X-axis Hall sensing line, a Y-axis Hall sensing line, and a Z-axis Hall sensing line. The X-axis Hall sensing line, Y-axis Hall sensing line, and Z-axis Hall sensing line are arranged at intervals to form a spatial gradient detection network. The three-dimensional orthogonal Hall sensor array establishes a bidirectional data connection with the control port of a preset magnetic field generation module;

[0014] An embedded encoder module, integrated with an analog-to-digital conversion unit and a signal preprocessing module, for converting the analog magnetic field signals collected by the three-dimensional orthogonal Hall sensor array into digital coded signals with timestamps;

[0015] A control module, which constructs an electromagnetic feature extraction model for performing joint time-frequency domain analysis on the digital coded signals to obtain magnetic environment perturbation characteristic parameters; the control module uses a closed-loop feedback mechanism to generate compensation magnetic field parameters opposite to the magnetic environment perturbation characteristic parameters; the control module establishes a real-time data channel with a preset electromagnetic imaging module and the magnetic field generation module through a preset communication protocol;

[0016] The control module constructs a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, obtains the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjusts the compensation magnetic field parameters. After the compensation magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0017] In some embodiments, it further includes: a common-mode suppression structure, including a metal shielding housing and star-shaped grounding terminals. The metal shielding housing forms a low-impedance loop corresponding to the Hall sensor combination system through a multi-point equipotential connection method.

[0018] In some embodiments, the magnetic environment disturbance characteristic parameters include harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient; the joint time-frequency domain analysis of the digital coding signal to obtain the magnetic environment disturbance characteristic parameters includes: separating the digital coding signal into intrinsic mode functions according to the empirical mode decomposition algorithm to generate multi-scale time-domain components; calculating the instantaneous frequency characteristic spectrum corresponding to the multi-scale time-domain components through Hilbert transform; constructing a frequency-domain feature extraction model based on wavelet packet energy entropy to calculate the entropy change rate corresponding to each preset frequency band within a preset time window of the instantaneous frequency characteristic spectrum; the entropy change rate is used to characterize the energy distribution corresponding to each preset frequency band; inputting the instantaneous frequency characteristic spectrum and the entropy change rate into a pre-trained convolutional neural network model to output the harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient.

[0019] In some embodiments, the generation of the compensation magnetic field parameters opposite to the magnetic environment disturbance characteristic parameters by using a closed-loop feedback mechanism includes: establishing a transfer function matrix including a magnetic dipole inverse model to map the magnetic environment disturbance characteristic parameters into equivalent interference magnetic moments; designing an adaptive observer based on Lyapunov stability theory to estimate the phase lag error corresponding to the compensation magnetic field parameters in real time; using a sliding mode variable structure control algorithm to generate a compensation magnetic moment component with the same amplitude and opposite phase as the equivalent interference magnetic moment according to the phase lag error; performing decoupling control on the compensation magnetic moment component through singular value decomposition to generate the compensation magnetic field parameters.

[0020] In some embodiments, the magnetic environment state space model is a reduced-order observer model; the construction of the magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array includes: constructing a differential algebraic combination with the output signals corresponding to the three-dimensional orthogonal Hall sensor array as state variables; establishing a magnetic flux observation equation including a Lorentz force correction term; constructing a time-varying parameter matrix according to the magnetic flux conservation principle and a preset environmental temperature drift coefficient; performing online parameter identification on the sensitivity matrix of the three-dimensional orthogonal Hall sensor array according to the improved cubature Kalman filter algorithm; constructing a diffeomorphism corresponding to the three-dimensional orthogonal Hall sensor array through Lie derivative, and generating the reduced-order observer model according to the diffeomorphism, the identified sensitivity matrix, the time-varying parameter matrix, the magnetic flux observation equation, and the differential algebraic combination.

[0021] In some embodiments, acquiring the magnetic field distribution data collected by the electromagnetic imaging module to dynamically adjust the compensation magnetic field parameters includes: generating an inverse magnetic gradient tensor model corresponding to the electromagnetic imaging module; calculating the magnetic dipole distribution density of each spatial point corresponding to the magnetic field distribution data in the inverse magnetic gradient tensor model; generating a Pareto front solution set corresponding to the magnetic dipole distribution density according to a topology optimization algorithm for balancing the compensation accuracy and the energy consumption index; generating an inverse compensation strategy corresponding to the Pareto front solution set according to the virtual magnetic charge method for solving the optimal compensation current combination corresponding to the inverse compensation strategy according to the conjugate gradient method; generating a dual-time scale update mechanism according to the Pareto front solution set for finely tuning the current parameters of the compensation magnetic field parameters within a millisecond-level time window and updating the compensation algorithm weight coefficients corresponding to the compensation magnetic field parameters within a second-level time window.

[0022] In some embodiments, after the compensation magnetic field parameters are output by the magnetic field generation module, they are vectorially superimposed with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array to achieve active magnetic interference cancellation in three-dimensional space, including: acquiring the Helmholtz decomposition result of the spatial magnetic field corresponding to the three-dimensional orthogonal Hall sensor array; determining the drive current of the compensation coil corresponding to the compensation magnetic field parameters according to the Helmholtz decomposition result based on a multi-coil cooperative control strategy; generating a current tracking controller including phase-advance compensation according to the drive current for ensuring the time-domain synchronization of the compensation magnetic field output by the magnetic field generation module with the interference magnetic field corresponding to the original positioning magnetic field; calculating the magnetic field superposition residual of each spatial point corresponding to the magnetic field distribution data according to the magnetic vector projection algorithm in the current tracking controller; generating an iterative learning control law for closed-loop correction according to the magnetic field superposition residual; dynamically offsetting and calibrating the sensitivity principal axis direction of the three-dimensional orthogonal Hall sensor array according to the iterative learning control law based on the magnetic anisotropy compensation technology to complete the active magnetic interference cancellation, so as to achieve a magnetic interference suppression ratio of not less than 40 dB for the Hall sensor combined system in the full spatial domain.

[0023] In some embodiments, it further includes: a multi-physical-field temperature compensation unit, including a thermocouple array and a heat flux density sensor, and the thermocouple array and the heat flux density sensor are distributed at preset key nodes of the three-dimensional orthogonal Hall sensor array; the control module constructs a thermo-magnetic coupling model based on finite element analysis, and calculates in real time the magnetic sensitivity drift amount corresponding to the three-dimensional orthogonal Hall sensor array; the control module fuses the temperature monitoring data and the magnetic field distribution data corresponding to the Hall sensor combination system according to an asymmetric Kalman filter to generate a calibration parameter matrix with a thermal compensation coefficient; the control module dynamically adjusts the bias current corresponding to the three-dimensional orthogonal Hall sensor array and adjusts the regulated temperature corresponding to the thermocouple array according to the calibration parameter matrix, so that the magnetic measurement accuracy error of the Hall sensor combination system within the full temperature range is less than 0.5%; the temperature monitoring data is measured by the heat flux density sensor.

[0024] In some embodiments, it further includes: a self-calibration magnetic field generating unit, including an orthogonal Helmholtz coil group and a programmable current source; the control module constructs a calibration signal generation model based on a quantum particle swarm optimization algorithm, and generates discrete frequency test magnetic fields covering the range of the three-dimensional orthogonal Hall sensor array according to the calibration signal generation model; the control module analyzes the response data of the three-dimensional orthogonal Hall sensor array under the test magnetic field by using the singular value decomposition method, and generates a sensitivity deviation matrix according to the response data; the control module updates the compensation magnetic field parameter library based on the nonlinear least squares fitting algorithm according to the sensitivity deviation matrix to form a closed-loop calibration model including frequency-temperature bivariate compensation; the control module controls the self-calibration magnetic field generating unit to perform calibration according to the calibration parameters output by the closed-loop calibration model.

[0025] In a second aspect, the present application provides a Hall sensor combination method for optimizing magnetic imaging stability, which is applied to the control module of the Hall sensor combination system provided in any embodiment of the present application; the method includes:

[0026] Construct an electromagnetic feature extraction model for performing joint time-frequency domain analysis on digital coding signals to obtain magnetic environment disturbance characteristic parameters;

[0027] Adopt a closed-loop feedback mechanism to generate compensation magnetic field parameters opposite to the magnetic environment disturbance characteristic parameters;

[0028] Establish a real-time data channel with a preset electromagnetic imaging module and a magnetic field generating module through a preset communication protocol;

[0029] Construct a magnetic environment state space model corresponding to a three-dimensional orthogonal Hall sensor array, obtain the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjust the compensation magnetic field parameters. After the compensation magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0030] In a third aspect, the present application provides a Hall sensor combination device for optimizing magnetic imaging stability, including:

[0031] A model construction unit for constructing an electromagnetic feature extraction model for performing joint time-frequency domain analysis on digital encoded signals to obtain magnetic environment perturbation characteristic parameters;

[0032] A parameter acquisition unit for generating compensation magnetic field parameters opposite to the magnetic environment perturbation characteristic parameters by using a closed-loop feedback mechanism;

[0033] A data establishment unit for establishing a real-time data channel with a preset electromagnetic imaging module and a magnetic field generation module through a preset communication protocol;

[0034] An interference cancellation unit for constructing a magnetic environment state space model corresponding to a three-dimensional orthogonal Hall sensor array, obtaining the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjusting the compensation magnetic field parameters. After the compensation magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0035] In a fourth aspect, the present application provides a control module, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method provided in any embodiment of the present application.

[0036] In a fifth aspect, the present application provides a computer-readable storage medium storing a computer program. When the computer-readable instructions are executed by a processor, one or more processors are caused to execute the method provided in any embodiment of the present application.

[0037] The present application provides a Hall sensor combination system and method for optimizing magnetic imaging stability, mainly solving problems in the prior art such as the lack of strict orthogonality in the sensor layout of a multi-sensor parallel structure during magnetic field measurement, the susceptibility of analog signal processing circuits to common-mode interference, the inability of the compensation mechanism to achieve real-time magnetic field cancellation in a dynamic environment, and the lack of a state space model support for adjusting compensation magnetic field parameters. The system includes a three-dimensional orthogonal Hall sensor array, an embedded encoder module, and a control module.

[0038] In the provided system, the three-dimensional orthogonal Hall sensor array includes Hall sensing lines along the X-axis, Y-axis, and Z-axis, which are arranged at intervals to form a spatial gradient detection network. A bidirectional data connection is established with the control port of the preset magnetic field generation module.

[0039] The embedded encoder module integrates an analog-to-digital conversion unit and a signal preprocessing module. It converts the analog magnetic field signals collected by the Hall sensor array into digital coded signals with timestamps.

[0040] Control module: Construct an electromagnetic feature extraction model, perform joint time-frequency domain analysis on the digital coded signals to obtain magnetic environment disturbance characteristic parameters. Adopt a closed-loop feedback mechanism to generate compensation magnetic field parameters opposite to the magnetic environment disturbance characteristic parameters. Establish a real-time data channel with the preset electromagnetic imaging module and magnetic field generation module through the preset communication protocol. Construct a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, obtain the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjust the compensation magnetic field parameters.

[0041] The magnetic field generation module outputs compensation magnetic field parameters, which form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0042] The provided system has at least the following beneficial effects:

[0043] Improve measurement accuracy: Through the strictly orthogonal three-dimensional Hall sensor array, improve the accuracy and stability of magnetic field measurement.

[0044] Reduce interference: The embedded encoder module converts analog signals into digital signals, reducing common-mode interference.

[0045] Dynamic compensation: The control module adopts a closed-loop feedback mechanism and a state space model to achieve real-time magnetic field cancellation in a dynamic environment and improve the compensation accuracy.

[0046] Real-time adjustment: Through the real-time data channel, the control module can dynamically adjust the compensation magnetic field parameters to adapt to environmental changes.

[0047] Active cancellation: The compensation magnetic field parameters output by the magnetic field generation module form a vector superposition with the original magnetic field, realizing active magnetic interference cancellation and improving the stability and accuracy of magnetic imaging.

[0048] In summary, the system and method have significant technical advantages and application values in the field of magnetic imaging, can effectively solve the problems in the existing technology, and improve the accuracy and stability of magnetic imaging.

[0049] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. Description of the Drawings

[0050] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0051] Figure 1 is a structural schematic block diagram of a Hall sensor combination system provided by an embodiment of the present application;

[0052] Figure 2 is a step schematic flowchart of a Hall sensor combination method for optimizing magnetic imaging stability provided by an embodiment of the present application;

[0053] Figure 3 is a structural schematic block diagram of a control module provided by an embodiment of the present application.

[0054] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Detailed implementation manners

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0056] The flowchart shown in the drawings is only an example illustration, and does not necessarily include all the contents and operations / steps, nor does it necessarily execute in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may be changed according to the actual situation.

[0057] It should be understood that in order to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the terms "first" and "second" do not limit the quantity and execution order, and the terms "first" and "second" do not necessarily mean different.

[0058] It should be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0059] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0060] The following will, with reference to the accompanying drawings, elaborate on some embodiments of this application. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0061] In recent years, the status of electromagnetic imaging technology in the localization of lung tumors has become increasingly prominent. In particular, the electromagnetic navigation bronchoscopy (ENB) technology, which is based on magnetic imaging technology and combines virtual bronchoscopy, three-dimensional computed tomography imaging, and respiratory gating technology, is a new type of bronchoscopy method that has been widely used in clinical practice.

[0062] The electromagnetic tomography navigation system has also developed rapidly. Its effectiveness and safety in the diagnosis, localization, and treatment of peripheral lung lesions have been verified and it is used by a wider range of medical institutions. However, not all medical institutions have sufficient magnetic shielding conditions or dedicated magnetic field generation equipment, and still rely heavily on portable devices and magnetic markers at the coordinate origin. The complex electromagnetic environment has an interfering effect on the magnetic trace navigation in the small bronchi, which will affect the injection of indole dyes and the efficiency of subsequent endoscopic surgeries.

[0063] In order to effectively solve the above problems, in recent years, Hall sensor technology has been widely studied and applied in the detection and compensation of electromagnetic environments. Hall sensors can accurately detect changes in magnetic fields and work in coordination with corresponding magnetic field generation equipment to compensate for the magnetic environment through a real-time feedback mechanism and optimize the imaging quality.

[0064] However, there are still some deficiencies in the existing technology. In traditional magnetic imaging systems, Hall sensor arrays mostly adopt two-dimensional planar layouts or three-dimensional arrangements with non-orthogonal structures, making it difficult to achieve accurate detection of magnetic field gradients in the entire spatial domain. The existing technology uses a multi-sensor parallel structure for magnetic field measurement, but there are the following defects:

[0065] (1) The lack of strict orthogonality in the sensor layout leads to coupling errors in the calculation of spatial magnetic field components;

[0066] (2) The analog signal processing circuit is vulnerable to common-mode interference and it is difficult to accurately extract the characteristics of transient magnetic interference;

[0067] (3) The compensation mechanisms adopted are mostly based on open-loop control or static feedback, and it is impossible to achieve real-time magnetic field cancellation in a dynamic environment;

[0068] (4) The adjustment of the compensation magnetic field parameters lacks the support of a state space model, resulting in a significant decrease in the compensation accuracy with the change of the environment.

[0069] Therefore, it is urgent to design a technical solution to solve at least one of the above technical problems.

[0070] To solve the above problems, please refer to Figure 1 , this application provides a Hall sensor combination system for optimizing the stability of magnetic imaging, including: a three-dimensional orthogonal Hall sensor array, including an X-axis Hall sensing line, a Y-axis Hall sensing line, and a Z-axis Hall sensing line. The X-axis Hall sensing line, the Y-axis Hall sensing line, and the Z-axis Hall sensing line are arranged at intervals to form a spatial gradient detection network. The three-dimensional orthogonal Hall sensor array establishes a bidirectional data connection with the control port of a preset magnetic field generation module;

[0071] An embedded encoder module, integrated with an analog-to-digital conversion unit and a signal preprocessing module, is used to convert the analog magnetic field signals collected by the three-dimensional orthogonal Hall sensor array into digital coded signals with time stamps;

[0072] A control module, which constructs an electromagnetic feature extraction model to perform joint time-frequency domain analysis on the digital coded signals to obtain magnetic environment disturbance feature parameters; the control module uses a closed-loop feedback mechanism to generate compensation magnetic field parameters opposite to the magnetic environment disturbance feature parameters; the control module establishes a real-time data channel with a preset electromagnetic imaging module and the magnetic field generation module through a preset communication protocol;

[0073] The control module constructs a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, obtains the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjusts the compensation magnetic field parameters. After the compensation magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0074] Specifically, this application provides a Hall sensor combination system for optimizing the stability of magnetic imaging, aiming to solve the deficiencies of the Hall sensor array in magnetic field gradient detection, signal processing, dynamic compensation, etc. in traditional magnetic imaging systems. Through the collaborative work of the three-dimensional orthogonal Hall sensor array, the embedded encoder module, the control module, and the magnetic field generation module, the system realizes high-precision magnetic field detection and dynamic compensation. The following is the detailed technical content of the system:

[0075] The three-dimensional orthogonal Hall sensor array consists of X-axis, Y-axis, and Z-axis Hall sensing lines. The three sensing lines are arranged in a strictly orthogonal manner in space to form a spatial gradient detection network. This layout ensures independent detection of the magnetic field in three dimensions and avoids coupling errors in traditional two-dimensional planar layouts or non-orthogonal three-dimensional arrangements. The three-dimensional orthogonal Hall sensor array is used to collect magnetic field signals in space and transmit the analog signals to the embedded encoder module for processing.

[0076] The embedded encoder module includes an analog-to-digital conversion unit and a signal preprocessing module. The analog-to-digital conversion unit is used to convert the analog magnetic field signals collected by the three-dimensional orthogonal Hall sensor array into digital signals to ensure signal accuracy and anti-interference ability. The signal preprocessing module is used to perform preliminary processing on the digital signals, such as filtering, amplification, etc., to eliminate noise and common-mode interference and extract effective magnetic field information. By adding timestamps to each digital encoded signal, it facilitates subsequent joint time-frequency domain analysis.

[0077] The control module is used to perform joint time-frequency domain analysis on the digital encoded signals by constructing an electromagnetic feature extraction model to extract magnetic environment perturbation characteristic parameters. This model can identify transient magnetic interference and quantify its impact.

[0078] According to the extracted magnetic environment perturbation characteristic parameters, generate compensation magnetic field parameters opposite to them. The closed-loop feedback mechanism ensures the real-time and accurate compensation and avoids the limitations of open-loop control or static feedback. Construct a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array and dynamically adjust the compensation magnetic field parameters. This model can update the compensation strategy in real time according to environmental changes to ensure the stability of compensation accuracy. Establish a real-time data channel with the electromagnetic imaging module and the magnetic field generation module through a preset communication protocol to ensure the coordinated operation of each module.

[0079] The magnetic field generation module outputs the corresponding compensation magnetic field according to the compensation magnetic field parameters generated by the control module. The compensation magnetic field is vectorially superimposed on the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array to achieve active magnetic interference cancellation in three-dimensional space.

[0080] The specific usage method of the provided system can be as follows:

[0081] Start the system and initialize the three-dimensional orthogonal Hall sensor array, the embedded encoder module, the control module, and the magnetic field generation module. Establish communication connections between each module to ensure the real-time and accurate data transmission.

[0082] A three-dimensional orthogonal Hall sensor array collects magnetic field signals in space and transmits the analog signals to an embedded encoder module. The embedded encoder module converts the analog signals into digital encoded signals with timestamps and performs preliminary processing to eliminate noise and common-mode interference.

[0083] The control module performs joint time-frequency domain analysis on the digital encoded signals, extracts the characteristic parameters of magnetic environment disturbances, constructs a magnetic environment state space model, and dynamically adjusts the compensation magnetic field parameters.

[0084] The control module generates corresponding compensation magnetic field parameters according to the extracted characteristic parameters of magnetic environment disturbances and transmits them to the magnetic field generation module. The magnetic field generation module outputs a compensation magnetic field, which is vectorially superimposed on the original positioning magnetic field to achieve active magnetic interference cancellation in three-dimensional space.

[0085] The system monitors the magnetic field changes in real time, and dynamically adjusts the compensation strategy through a closed-loop feedback mechanism and a state space model to ensure the stability of the compensation accuracy.

[0086] The strict orthogonal layout of the three-dimensional orthogonal Hall sensor array ensures independent detection of the magnetic field in three dimensions, avoids the coupling error in the traditional layout, and improves the accuracy of magnetic field detection. The analog-to-digital conversion and signal preprocessing functions of the embedded encoder module effectively eliminate noise and common-mode interference, ensuring the purity and reliability of the signals. The closed-loop feedback mechanism and state space model of the control module achieve real-time magnetic field cancellation in a dynamic environment, avoiding the deficiencies of traditional compensation mechanisms in a dynamic environment. Through the real-time data channel and joint time-frequency domain analysis, the system can quickly respond to magnetic field changes, ensuring the real-time and accurate compensation. The state space model can dynamically adjust the compensation strategy according to environmental changes, ensuring the stability of the compensation accuracy in different environments and improving the environmental adaptability of the system.

[0087] In summary, the Hall sensor combination system provided by this application effectively solves many problems in traditional magnetic imaging systems through technical means such as three-dimensional orthogonal layout, embedded encoding, closed-loop feedback, and state space model, realizes high-precision magnetic field detection and dynamic compensation, and has significant technical advantages and broad application prospects.

[0088] In some embodiments, it further includes: a common-mode suppression structure, which includes a metal shielding housing and a star-shaped grounding terminal, and the metal shielding housing forms a low-impedance loop corresponding to the Hall sensor combination system through a multi-point equipotential connection method.

[0089] The metal shielding enclosure is made of high-conductivity materials (such as copper or aluminum) to ensure good electromagnetic shielding effect. The enclosure is designed as a completely enclosed structure that covers the entire Hall sensor combination system to ensure that external electromagnetic interference cannot penetrate. The enclosure is connected to the grounding terminal of the system through a multi-point equipotential connection method to form a low-impedance loop, ensuring the maximization of the shielding effect. The star-shaped grounding terminal layout ensures that all grounding points are at the same electrical potential, reducing the potential difference in the grounding loop. Each Hall sensor and signal processing module is connected to the star-shaped grounding terminal through a separate grounding wire to avoid the propagation of common-mode interference through the grounding loop.

[0090] By optimizing the connection method between the metal shielding enclosure and the star-shaped grounding terminal, a low-impedance loop is ensured to be formed, effectively suppressing common-mode interference. In practical applications, the effectiveness of the common-mode suppression structure is verified through electromagnetic compatibility tests to ensure its reliability in various environments.

[0091] The combination of the metal shielding enclosure and the star-shaped grounding terminal effectively suppresses external electromagnetic interference and common-mode noise, ensuring the purity of the Hall sensor signal. The formation of the low-impedance loop reduces the potential difference in the grounding loop, improves the electrical stability of the system, and reduces signal distortion. The design of the common-mode suppression structure enables it to work stably in various complex electromagnetic environments, improving the environmental adaptability of the system.

[0092] Among them, it should be noted that the enclosure is connected to the grounding terminal at multiple positions to ensure that the potential of each part of the enclosure is consistent, forming a low-impedance loop. The shielding effect can be verified through electromagnetic compatibility tests (EMC tests) to ensure that the shielding effectiveness meets the design requirements within the target frequency band. It is also necessary to ensure that the grounding wire is as short as possible to reduce the impedance of the grounding loop. Use copper wires or silver-plated wires with low impedance to further reduce the impedance of the grounding loop. A network analyzer can be used to measure the loop impedance to ensure that it is lower than the target value. Optimize the loop design through simulation and experiments to ensure its stability under various working conditions.

[0093] In some embodiments, the magnetic environment disturbance characteristic parameters include harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient; the joint time-frequency domain analysis of the digital coding signal to obtain the magnetic environment disturbance characteristic parameters includes: separating the digital coding signal into intrinsic mode functions according to the empirical mode decomposition algorithm to generate multi-scale time-domain components; calculating the instantaneous frequency characteristic spectrum corresponding to the multi-scale time-domain components through Hilbert transform; constructing a frequency-domain feature extraction model based on wavelet packet energy entropy to calculate the entropy value change rate corresponding to each preset frequency band within a preset time window; the entropy value change rate is used to characterize the energy distribution corresponding to each preset frequency band; inputting the instantaneous frequency characteristic spectrum and the entropy value change rate into a pre-trained convolutional neural network model to output the harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient.

[0094] By performing empirical mode decomposition on the digital coding signal, multiple intrinsic mode functions (IMFs) are generated, and each IMF represents a different scale time-domain component of the signal. Through the decomposition of IMFs, multi-scale analysis of the signal is realized, and features at different time scales are extracted. Hilbert transform is performed on each IMF to calculate its instantaneous frequency characteristic spectrum and obtain the instantaneous frequency information of the signal. Through the instantaneous frequency characteristic spectrum, the changes of the signal at different frequencies are analyzed. The instantaneous frequency characteristic spectrum is divided into multiple preset frequency bands, and the energy distribution of each frequency band is calculated. Through the wavelet packet energy entropy model, the entropy value change rate of each frequency band is calculated to characterize the energy distribution within the frequency band. The instantaneous frequency characteristic spectrum and the entropy value change rate are input into a pre-trained convolutional neural network model to extract the magnetic environment disturbance characteristic parameters. The model outputs the harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient for subsequent compensation control.

[0095] Through empirical mode decomposition and Hilbert transform, multi-scale and instantaneous frequency analysis of the signal is realized, and the accuracy of feature extraction is improved. The comprehensive analysis of the energy distribution within the frequency band by the wavelet packet energy entropy model ensures the comprehensiveness and accuracy of feature extraction. The application of the convolutional neural network model realizes intelligent feature recognition and improves the automation level and processing efficiency of the system.

[0096] Among them, appropriate stopping conditions also need to be set to ensure the accuracy and stability of IMF decomposition. The signal is preprocessed before EMD decomposition to remove high-frequency noise. Appropriate frequency band division methods are selected according to the signal characteristics to ensure the comprehensiveness of feature extraction. The entropy value calculation algorithm is optimized to improve the calculation efficiency and accuracy. An appropriate CNN structure is designed according to the task requirements to improve the accuracy of feature extraction. The robustness of the model is improved through data augmentation techniques.

[0097] A digital encoded signal can be received by an Empirical Mode Decomposition (EMD) module. 8 - 12 Intrinsic Mode Functions (IMFs) are generated through extreme point detection and cubic spline interpolation. The Hilbert transform unit constructs the analytical signal for each IMF component and calculates the instantaneous frequency matrix (sampling rate 1 MHz). The wavelet packet energy entropy model uses the db4 wavelet basis for 5 - layer decomposition, calculates the energy proportion of 32 frequency bands, and calculates the entropy change rate with a 200 - ms window length. The pre - trained CNN model (including 3 convolutional layers and 2 pooling layers) receives the fused time - frequency feature map and outputs a set of parameters including the harmonic distortion degree (0.1% resolution), magnetic field gradient mutation index (0.01 T / m sensitivity), etc.

[0098] In some embodiments, generating the compensation magnetic field parameters opposite to the magnetic environment disturbance characteristic parameters by using a closed - loop feedback mechanism includes: establishing a transfer function matrix including the inverse model of the magnetic dipole, mapping the magnetic environment disturbance characteristic parameters to an equivalent interference magnetic moment; designing an adaptive observer based on the Lyapunov stability theory to estimate the phase - lag error corresponding to the compensation magnetic field parameters in real - time; using a sliding - mode variable - structure control algorithm to generate a compensation magnetic moment component with the same amplitude and opposite phase as the equivalent interference magnetic moment according to the phase - lag error; and performing decoupling control on the compensation magnetic moment component by using the singular - value decomposition method to generate the compensation magnetic field parameters.

[0099] By establishing a transfer function matrix including the inverse model of the magnetic dipole, the magnetic environment disturbance characteristic parameters are mapped to an equivalent interference magnetic moment. Through the transfer function matrix, the accurate mapping of the disturbance characteristics is realized, ensuring the accuracy of the compensation parameters. An adaptive observer is designed based on the Lyapunov stability theory to estimate the phase - lag error corresponding to the compensation magnetic field parameters in real - time. Through the adaptive observer, the dynamic adjustment of the compensation parameters is realized, ensuring the real - time and accuracy of the compensation. The sliding - mode variable - structure control algorithm is used to generate a compensation magnetic moment component with the same amplitude and opposite phase as the equivalent interference magnetic moment according to the phase - lag error. The sliding - mode variable - structure control algorithm ensures the stability and robustness of the compensation process. Decoupling control is performed on the compensation magnetic moment component by using the singular - value decomposition method to generate the compensation magnetic field parameters. The decoupling control ensures the independence and optimization of the compensation parameters, improving the compensation effect.

[0100] The application of the magnetic dipole inverse model and the adaptive observer realizes the high - precision generation of the compensation magnetic field parameters, ensuring the accuracy of the compensation. The sliding - mode variable - structure control algorithm and the singular - value decomposition method realize the real - time dynamic adjustment of the compensation parameters, improving the response speed and stability of the system. The design of the closed - loop feedback mechanism improves the robustness of the system, enabling it to work stably in a complex environment and ensuring the reliability of the compensation effect.

[0101] Among them, it is also necessary to calibrate the transfer function matrix through experimental data to ensure its accuracy. The sliding mode control parameters are optimized through simulation and experiments to ensure the stability and accuracy of the control.

[0102] Among them, the transfer function matrix can be constructed using an equivalent magnetic dipole model (order N = 6); the design of the adaptive observer includes the Lyapunov function V = 0.5e^T Pe (P is a positive definite matrix), and e is the error vector; the sliding mode controller adopts an exponential reaching law: Singular value decomposition is used to orthogonalize the 3×3 magnetic moment matrix, and the decoupling error is <0.5%. k is the sliding mode gain (take 10), and s is the sliding mode surface.

[0103] In some embodiments, the magnetic environment state space model is a reduced-order observer model; the construction of the magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array includes: constructing a differential algebraic combination of the output signals corresponding to the three-dimensional orthogonal Hall sensor array as state variables; establishing a magnetic flux observation equation including a Lorentz force correction term; constructing a time-varying parameter matrix according to the magnetic flux conservation principle and a preset environmental temperature drift coefficient; performing online parameter identification on the sensitivity matrix of the three-dimensional orthogonal Hall sensor array according to an improved cubature Kalman filter algorithm; constructing a diffeomorphism corresponding to the three-dimensional orthogonal Hall sensor array through Lie derivative, and generating the reduced-order observer model according to the diffeomorphism, the identified sensitivity matrix, the time-varying parameter matrix, the magnetic flux observation equation, and the differential algebraic combination.

[0104] By constructing a differential algebraic combination of the output signals of the three-dimensional orthogonal Hall sensor array as state variables, the comprehensiveness and representativeness of the state variables are ensured. Differentiating and algebraically operating on the output signals to extract key state variables for subsequent model construction. Adding a Lorentz force correction term to the magnetic flux observation equation to ensure that the equation can accurately reflect the changes in the magnetic field. Based on the basic principle of the magnetic field and the Lorentz force correction term, an accurate magnetic flux observation equation is established.

[0105] According to the magnetic flux conservation principle, a time-varying parameter matrix is constructed to ensure that the matrix can reflect the changing trend of the magnetic field. Adding an environmental temperature drift coefficient to the time-varying parameter matrix to ensure the adaptability of the matrix in different environments. Performing online parameter identification on the sensitivity matrix of the three-dimensional orthogonal Hall sensor array using an improved cubature Kalman filter algorithm to ensure the accuracy and real-time performance of the identification. Through the cubature Kalman filter algorithm, the sensitivity matrix is optimized to improve the accuracy and stability of the model. Constructing a diffeomorphism corresponding to the three-dimensional orthogonal Hall sensor array through Lie derivative to ensure the accuracy and comprehensiveness of the mapping. According to the diffeomorphism, the identified sensitivity matrix, the time-varying parameter matrix, the magnetic flux observation equation, and the differential algebraic combination, the reduced-order observer model is generated.

[0106] A high-precision magnetic flux observation equation was constructed through differential algebraic combination and Lorentz force correction terms, ensuring the accuracy of the model. The improved cubature Kalman filter algorithm realized the online parameter identification of the sensitivity matrix, ensuring the real-time performance and adaptability of the model. The application of diffeomorphic mapping achieved a comprehensive analysis of state variables, ensuring the comprehensiveness and representativeness of the model.

[0107] Such as the selection of state variables B_x, B_y, and B_z are the magnetic field components of the X-axis Hall sensing line, Y-axis Hall sensing line, and Z-axis Hall sensing line, respectively; the time-varying matrix A(t) includes a compensation term with a temperature coefficient α = 4.2×10^-5 / °C; the cubature Kalman filter adopts a 5th-order spherical radial rule, and the estimated covariance matrix Q = diag(0.01, 0.1, 1), where Q is the process noise covariance matrix; the diffeomorphic mapping constructs the Lie derivative h(x) is the observation function.

[0108] In some embodiments, obtaining the magnetic field distribution data collected by the electromagnetic imaging module to dynamically adjust the compensation magnetic field parameters includes: generating an inverse magnetic gradient tensor model corresponding to the electromagnetic imaging module; calculating the magnetic dipole distribution density of each spatial point corresponding to the magnetic field distribution data in the inverse magnetic gradient tensor model; generating a Pareto front solution set corresponding to the magnetic dipole distribution density according to the topology optimization algorithm for balancing the compensation accuracy and energy consumption index; generating an inverse compensation strategy corresponding to the Pareto front solution set according to the virtual magnetic charge method for solving the optimal compensation current combination corresponding to the inverse compensation strategy according to the conjugate gradient method; generating a dual-time-scale update mechanism according to the Pareto front solution set for fine-tuning the current parameters of the compensation magnetic field parameters within a millisecond-level time window and updating the compensation algorithm weight coefficient corresponding to the compensation magnetic field parameters within a second-level time window.

[0109] Generate the magnetic gradient tensor inversion model corresponding to the electromagnetic imaging module for calculating the magnetic field distribution data. Calculate the magnetic dipole distribution density at each spatial point through the inversion model to ensure the accuracy and comprehensiveness of the calculation. Generate the Pareto front solution set corresponding to the magnetic dipole distribution density according to the topology optimization algorithm for balancing the compensation accuracy and energy consumption index. Through the Pareto front solution set, optimize the compensation accuracy and energy consumption to ensure the efficiency and economy of the system. Generate the reverse compensation strategy corresponding to the Pareto front solution set according to the virtual magnetic charge method for solving the optimal compensation current combination. Solve the optimal compensation current combination corresponding to the reverse compensation strategy through the conjugate gradient method to ensure the accuracy and efficiency of the compensation. Generate a dual-time scale update mechanism according to the Pareto front solution set to fine-tune the current parameters of the compensation magnetic field parameters within a millisecond-level time window and update the compensation algorithm weight coefficients within a second-level time window. Through the dual-time scale update mechanism, realize the dynamic adjustment of the compensation parameters to ensure the real-time and accuracy of the compensation.

[0110] The magnetic gradient tensor inversion model realizes the high-precision calculation of the magnetic field distribution data, ensuring the accuracy and comprehensiveness of the calculation. The application of the topology optimization algorithm and the virtual magnetic charge method realizes the optimization of the compensation strategy, ensuring the efficiency and economy of the compensation. The dual-time scale update mechanism realizes the real-time dynamic adjustment of the compensation parameters, ensuring the real-time and accuracy of the compensation.

[0111] For example, Tikhonov regularization (λ = 0.1) is used for magnetic gradient tensor inversion. The weight coefficients ω1 = 0.7 (accuracy) and ω2 = 0.3 (energy consumption) are set for Pareto optimization. 256 equivalent magnetic sources are arranged for reverse calculation by the virtual magnetic charge method. Dual-time scale mechanism: 100 ms fine-tuning (ΔI ± 5 mA), 10 s weight update.

[0112] In some embodiments, after the compensated magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, achieving active magnetic interference cancellation in three-dimensional space, including: obtaining the Helmholtz decomposition result of the spatial magnetic field corresponding to the three-dimensional orthogonal Hall sensor array; based on the multi-coil cooperative control strategy, determining the drive current of the compensation coil corresponding to the compensated magnetic field parameters according to the Helmholtz decomposition result; generating a current tracking controller including phase-advance compensation according to the drive current, for ensuring the time-domain synchronization of the compensated magnetic field output by the magnetic field generation module and the interference magnetic field corresponding to the original positioning magnetic field; in the current tracking controller, calculating the magnetic field superposition residual of each spatial point corresponding to the magnetic field distribution data according to the magnetic vector projection algorithm; generating an iterative learning control law for closed-loop correction according to the magnetic field superposition residual; based on the magnetic anisotropy compensation technology, dynamically biasing and calibrating the sensitivity principal axis direction of the three-dimensional orthogonal Hall sensor array according to the iterative learning control law, completing the active magnetic interference cancellation, so as to achieve that the magnetic interference suppression ratio of the Hall sensor combined system in the full spatial domain is not less than 40 dB.

[0113] Obtain the Helmholtz decomposition result of the spatial magnetic field corresponding to the three-dimensional orthogonal Hall sensor array to ensure the accuracy and comprehensiveness of the decomposition. Through the Helmholtz decomposition, achieve a comprehensive analysis of the spatial magnetic field to ensure the accuracy of subsequent compensation. Based on the multi-coil cooperative control strategy, determine the drive current of the compensation coil corresponding to the compensated magnetic field parameters according to the Helmholtz decomposition result. Through the multi-coil cooperative control strategy, achieve the cooperative control of the compensation coil to ensure the comprehensiveness and efficiency of the compensation. Generate a current tracking controller including phase-advance compensation to ensure the time-domain synchronization of the compensated magnetic field and the original positioning magnetic field. Through the current tracking controller, achieve the precise control of the compensated magnetic field to ensure the accuracy and real-time performance of the compensation.

[0114] Calculate the magnetic field superposition residual of each spatial point corresponding to the magnetic field distribution data according to the magnetic vector projection algorithm to ensure the accuracy and comprehensiveness of the calculation. Through the magnetic field superposition residual, comprehensively analyze the compensation effect to ensure the accuracy of compensation. Generate an iterative learning control law for closed-loop correction based on the magnetic field superposition residual to ensure the real-time and accuracy of compensation. Through the iterative learning control law, dynamically adjust the compensation parameters to ensure the real-time and accuracy of compensation. Based on the magnetic anisotropy compensation technology, dynamically bias and calibrate the sensitivity principal axis direction of the three-dimensional orthogonal Hall sensor array according to the iterative learning control law to complete active magnetic interference cancellation. Through dynamic bias calibration, ensure that the magnetic interference suppression ratio of the Hall sensor combined system in the full spatial domain is not less than 40 dB. The application of Helmholtz decomposition and multi-coil collaborative control strategy realizes high-precision analysis of the spatial magnetic field and ensures the accuracy of compensation. The application of the current tracking controller and the iterative learning control law realizes real-time dynamic compensation of the compensation magnetic field and ensures the real-time and accuracy of compensation. The application of the magnetic anisotropy compensation technology realizes comprehensive suppression of magnetic interference and ensures that the magnetic interference suppression ratio of the Hall sensor combined system in the full spatial domain is not less than 40 dB.

[0115] For example, Helmholtz decomposition separates the magnetic field into curl / divergence components (threshold 0.05 T).

[0116] Design the phase lead compensator G_c(s) = (0.02s + 1) / (0.005s + 1).

[0117] Calculate the residual of the projection algorithm ‖B_comp + B_env‖ < 0.001 T.

[0118] The dynamic bias calibration frequency is 1 kHz, and the anisotropy compensation rate > 90%.

[0119] In some embodiments, it further includes: a multi-physical field temperature compensation unit, including a thermocouple array and a heat flux density sensor, and the thermocouple array and the heat flux density sensor are distributed at preset key nodes of the three-dimensional orthogonal Hall sensor array; the control module constructs a thermomagnetic coupling model based on finite element analysis to calculate the magnetic sensitivity drift amount corresponding to the three-dimensional orthogonal Hall sensor array in real time; the control module fuses the temperature monitoring data and the magnetic field distribution data corresponding to the Hall sensor combined system according to an asymmetric Kalman filter to generate a calibration parameter matrix with a thermal compensation coefficient; the control module dynamically adjusts the bias current corresponding to the three-dimensional orthogonal Hall sensor array and adjusts the regulated temperature corresponding to the thermocouple array according to the calibration parameter matrix, so that the magnetic measurement accuracy error of the Hall sensor combined system within the full temperature range is less than 0.5%; the temperature monitoring data is measured by the heat flux density sensor.

[0120] A thermocouple array is arranged at preset key nodes of a three-dimensional orthogonal Hall sensor array to monitor temperature changes in real time. A heat flux density sensor is arranged at the key nodes to measure the heat flux density and provide temperature monitoring data. The thermocouple array and the heat flux density sensor transmit the temperature monitoring data to a control module for subsequent thermomagnetic coupling analysis. The control module constructs a thermomagnetic coupling model based on finite element analysis and calculates the magnetic sensitivity drift amount corresponding to the three-dimensional orthogonal Hall sensor array in real time. Through finite element analysis, the thermomagnetic coupling model is optimized to ensure the accuracy and adaptability of the model at different temperatures. The control module fuses the temperature monitoring data and the magnetic field distribution data according to an asymmetric Kalman filter to generate a calibration parameter matrix with a thermal compensation coefficient. Through the asymmetric Kalman filter, the temperature monitoring data and the magnetic field distribution data are optimized to ensure the accuracy and real-time performance of data fusion. The control module dynamically adjusts the bias current corresponding to the three-dimensional orthogonal Hall sensor array according to the calibration parameter matrix to ensure the accuracy of magnetic measurement. The control module adjusts the regulated temperature corresponding to the thermocouple array according to the calibration parameter matrix to ensure the stability of the system within the full temperature range. Through the thermomagnetic coupling model and the asymmetric Kalman filter, it is ensured that the magnetic measurement accuracy error of the Hall sensor combined system within the full temperature range is less than 0.5%.

[0121] The application of the thermocouple array and the heat flux density sensor realizes high-precision monitoring of temperature changes and ensures the accuracy of temperature compensation. The application of the asymmetric Kalman filter realizes the real-time fusion of temperature monitoring data and magnetic field distribution data and ensures the real-time performance and accuracy of data processing. The application of the thermomagnetic coupling model and the calibration parameter matrix realizes the stability control of the system within the full temperature range and ensures high-precision measurement of the system at different temperatures.

[0122] For example, a thermocouple array (16 PT100 sensors) is arranged at the key nodes of the PCB. The finite element model is divided into 2,304 hexahedral elements for thermo-magnetic coupling simulation. The process noise Q = 0.01I and the observation noise R = 0.1I are set for the asymmetric Kalman filter. The variable gain PID parameter range is: Kp = 2 - 8, Ki = 0.1 - 0.5, Kd = 0 - 0.2.

[0123] In some embodiments, it further includes: a self-calibration magnetic field generating unit, including an orthogonal Helmholtz coil group and a programmable current source; the control module constructs a calibration signal generation model based on the quantum particle swarm optimization algorithm, and generates discrete frequency test magnetic fields covering the range of the three-dimensional orthogonal Hall sensor array according to the calibration signal generation model; the control module analyzes the response data of the three-dimensional orthogonal Hall sensor array under the test magnetic field by the singular value decomposition method, and generates a sensitivity deviation matrix according to the response data; the control module updates the compensation magnetic field parameter library based on the nonlinear least squares fitting algorithm according to the sensitivity deviation matrix, and forms a closed-loop calibration model including frequency-temperature bivariate compensation; the control module controls the self-calibration magnetic field generating unit to perform calibration according to the calibration parameters output by the closed-loop calibration model.

[0124] Arrange an orthogonal Helmholtz coil group for generating discrete frequency test magnetic fields covering the range of the three-dimensional orthogonal Hall sensor array. Control the current of the Helmholtz coil group through a programmable current source to ensure the accuracy and stability of the test magnetic field. According to the instructions of the control module, the Helmholtz coil group generates test magnetic fields with different frequencies for calibrating the three-dimensional orthogonal Hall sensor array. The control module constructs a calibration signal generation model based on the quantum particle swarm optimization algorithm for generating discrete frequency test magnetic fields covering the range of the three-dimensional orthogonal Hall sensor array. Through the quantum particle swarm optimization algorithm, optimize the calibration signal generation model to ensure the comprehensiveness and representativeness of the test magnetic field. The control module analyzes the response data of the three-dimensional orthogonal Hall sensor array under the test magnetic field by the singular value decomposition method to generate a sensitivity deviation matrix. Through the singular value decomposition method, optimize the response data to ensure the accuracy and comprehensiveness of the sensitivity deviation matrix. The control module updates the compensation magnetic field parameter library based on the nonlinear least squares fitting algorithm according to the sensitivity deviation matrix to form a closed-loop calibration model including frequency-temperature bivariate compensation. Through the nonlinear least squares fitting algorithm, optimize the closed-loop calibration model to ensure the accuracy and adaptability of the model at different frequencies and temperatures. The control module controls the self-calibration magnetic field generating unit to perform calibration according to the calibration parameters output by the closed-loop calibration model to ensure the calibration accuracy and stability of the system.

[0125] The application of the orthogonal Helmholtz coil group and the programmable current source realizes the high-precision calibration of the three-dimensional orthogonal Hall sensor array, ensuring the accuracy and comprehensiveness of the calibration. The application of the quantum particle swarm optimization algorithm and the nonlinear least squares fitting algorithm realizes the intelligent optimization of the calibration signal generation model and the closed-loop calibration model, ensuring the accuracy and adaptability of the models. The application of the closed-loop calibration model realizes the frequency-temperature bivariate compensation, ensuring the high-precision measurement of the system at different frequencies and temperatures.

[0126] For example, a Helmholtz coil set generates a standard magnetic field of 0 - 100 mT (step 1 mT). The quantum particle swarm optimization sets the population size to 50 and the number of iterations to 100. The singular value decomposition calculates the condition number threshold cond(A) < 1e5. The nonlinear least squares uses the LM algorithm (μ = 0.01).

[0127] Please refer to Figure 2 , Figure 2 FIG. is a schematic flow chart of a Hall sensor combination method for optimizing magnetic imaging stability provided by an embodiment of the present application. The execution device of the method is the control module of the Hall sensor combination system provided by any embodiment of the present application.

[0128] As Figure 2 shown, the provided method includes steps S101 to S104. Among them, the control module can be a handheld terminal, a laptop, a wearable device, or a robot, etc. It is used to implement steps S101 to S104 and their corresponding embodiments.

[0129] Step S101. Construct an electromagnetic feature extraction model for performing joint time-frequency domain analysis on digital encoded signals to obtain magnetic environment disturbance characteristic parameters.

[0130] Specifically, the core of step S101 is to construct an electromagnetic feature extraction model for performing joint time-frequency domain analysis on digital encoded signals to obtain magnetic environment disturbance characteristic parameters. This model extracts key parameters reflecting magnetic environment disturbance, such as harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient, by analyzing the time domain and frequency domain characteristics of the signal.

[0131] The three-dimensional orthogonal Hall sensor array collects magnetic field signals in space and converts them into digital encoded signals. Preprocess the digital encoded signals, including filtering, amplification, and denoising, to eliminate noise and common-mode interference. Use the empirical mode decomposition algorithm (EMD) to separate the digital encoded signals into intrinsic mode functions (IMFs) to generate multi-scale time domain components. Calculate the instantaneous frequency characteristic spectrum of each IMF through Hilbert transform to obtain the instantaneous frequency information of the signal. Construct a frequency domain feature extraction model based on wavelet packet energy entropy, calculate the entropy value change rate of each preset frequency band, and characterize the energy distribution within the frequency band. Input the instantaneous frequency characteristic spectrum and the entropy value change rate into a pre-trained convolutional neural network model to output magnetic environment disturbance characteristic parameters, such as harmonic distortion degree, magnetic field gradient mutation index, and spectrum leakage coefficient.

[0132] Through the joint time-frequency domain analysis, the characteristics of the magnetic environment disturbance can be comprehensively captured, ensuring the accuracy and comprehensiveness of feature extraction. The application of the convolutional neural network model realizes intelligent feature recognition, improving the automation level and processing efficiency of the system. The joint time-frequency domain analysis and feature extraction process have high real-time performance, can quickly respond to magnetic field changes, and ensure the real-time performance of the system

[0133] Step S102. Generate compensation magnetic field parameters opposite to the magnetic environment disturbance characteristic parameters by using a closed-loop feedback mechanism.

[0134] Specifically, the core of step S102 is to use a closed-loop feedback mechanism to generate compensation magnetic field parameters opposite to the magnetic environment disturbance characteristic parameters extracted in step S101 according to the magnetic environment disturbance characteristic parameters. This mechanism realizes the real-time cancellation of the magnetic environment disturbance by dynamically adjusting the compensation magnetic field parameters.

[0135] By establishing a transfer function matrix containing the inverse model of the magnetic dipole, map the magnetic environment disturbance characteristic parameters to the equivalent interference magnetic moment. Design an adaptive observer based on the Lyapunov stability theory to estimate the phase lag error corresponding to the compensation magnetic field parameters in real time. Use a sliding mode variable structure control algorithm to generate a compensation magnetic moment component with the same amplitude and opposite phase as the equivalent interference magnetic moment according to the phase lag error. Perform decoupling control on the compensation magnetic moment component by the singular value decomposition method to generate the compensation magnetic field parameters.

[0136] The application of the magnetic dipole inverse model and the adaptive observer realizes the high-precision generation of the compensation magnetic field parameters, ensuring the accuracy of the compensation. The sliding mode variable structure control algorithm and the singular value decomposition method realize the real-time dynamic adjustment of the compensation parameters, improving the response speed and stability of the system. The design of the closed-loop feedback mechanism improves the robustness of the system, enabling it to work stably in a complex environment and ensuring the reliability of the compensation effect.

[0137] Step S103. Establish a real-time data channel with a preset electromagnetic imaging module and a magnetic field generation module through a preset communication protocol.

[0138] Specifically, the core of step S103 is to establish a real-time data channel with the control module, the electromagnetic imaging module, and the magnetic field generation module through a preset communication protocol to ensure the coordinated work of each module.

[0139] Select a high-speed communication protocol (such as SPI or I2C) to ensure the real-time performance and reliability of data transmission. Through the communication protocol, establish a real-time data channel between the control module, the electromagnetic imaging module, and the magnetic field generation module to ensure the real-time transmission and processing of data. Ensure data synchronization between each module to avoid delays and errors during data transmission.

[0140] The application of high-speed communication protocols ensures the real-time transmission of data and improves the response speed of the system. The establishment of real-time data channels ensures the collaborative work among modules and enhances the overall efficiency and stability of the system. The data synchronization mechanism ensures the consistency of data among modules and avoids errors during data transmission.

[0141] Step S104. Construct a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, obtain the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjust the compensation magnetic field parameters. After being output by the magnetic field generation module, the compensation magnetic field parameters form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0142] Specifically, the core of step S104 is to construct a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, obtain the magnetic field distribution data collected by the electromagnetic imaging module, dynamically adjust the compensation magnetic field parameters, and output the compensation magnetic field through the magnetic field generation module to form a vector superposition with the original positioning magnetic field, realizing active magnetic interference cancellation in three-dimensional space.

[0143] By constructing a differential algebraic combination with the output signals of the three-dimensional orthogonal Hall sensor array as state variables. Establish a magnetic flux observation equation containing a Lorentz force correction term. Construct a time-varying parameter matrix according to the principle of magnetic flux conservation and a preset environmental temperature drift coefficient. Use an improved cubature Kalman filter algorithm for online parameter identification of the sensitivity matrix. Generate a reduced-order observer model through a Lie derivative to construct a diffeomorphism mapping. Obtain the magnetic field distribution data collected by the electromagnetic imaging module for dynamically adjusting the compensation magnetic field parameters. Generate the compensation magnetic field parameters according to the magnetic environment state space model and the magnetic field distribution data. Output the compensation magnetic field through the magnetic field generation module to form a vector superposition with the original positioning magnetic field, realizing active magnetic interference cancellation in three-dimensional space.

[0144] The construction of the magnetic environment state space model realizes high-precision analysis of magnetic field changes and ensures the accuracy of compensation. Dynamically adjusting the compensation magnetic field parameters ensures the real-time and accuracy of compensation, improving the response speed and stability of the system. The vector superposition of the compensation magnetic field and the original positioning magnetic field realizes comprehensive suppression of magnetic interference and ensures high-precision measurement of the system.

[0145] In summary, steps S101 to S104 realize high-precision detection and dynamic compensation of magnetic environment disturbances through technical means such as an electromagnetic feature extraction model, a closed-loop feedback mechanism, a real-time data channel, and a magnetic environment state space model, with significant technical advantages and broad application prospects.

[0146] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the above-described Hall sensor combination method and the specific working processes of each step can refer to the corresponding processes in the embodiments of the Hall sensor combination system for magnetic imaging stability optimization described in the above embodiments, and will not be elaborated here.

[0147] The embodiments of the present application also provide a Hall sensor combination device. This Hall sensor combination device is used to execute the steps of the Hall sensor combination method for magnetic imaging stability optimization shown in the above embodiments. The Hall sensor combination device can be a single server or a server cluster, or the Hall sensor combination device can be a terminal, and the terminal can be a handheld terminal, a laptop computer, a wearable device, or a robot, etc.

[0148] The Hall sensor combination device includes:

[0149] A model construction unit, configured to construct an electromagnetic feature extraction model for performing joint time-frequency domain analysis on digital coded signals to obtain magnetic environment disturbance feature parameters;

[0150] A parameter acquisition unit, configured to generate compensation magnetic field parameters opposite to the magnetic environment disturbance feature parameters by using a closed-loop feedback mechanism;

[0151] A data establishment unit, configured to establish a real-time data channel with a preset electromagnetic imaging module and a magnetic field generation module through a preset communication protocol;

[0152] An interference cancellation unit, configured to construct a magnetic environment state space model corresponding to a three-dimensional orthogonal Hall sensor array, obtain the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjust the compensation magnetic field parameters. After the compensation magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, realizing active magnetic interference cancellation in three-dimensional space.

[0153] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the above-described Hall sensor combination device and the specific working processes of each unit can refer to the corresponding processes in the embodiments of the Hall sensor combination method for magnetic imaging stability optimization described in the above embodiments, and will not be elaborated here.

[0154] The above Hall sensor combination method is implemented in the form of a computer program, and this computer program can run on the above device.

[0155] Please refer to Figure 3 , Figure 3It is a schematic block diagram of the control module provided by an embodiment of the present application. The control module includes a processor, a memory, and a network interface connected through a device bus. Among them, the memory may include a storage medium and an internal memory.

[0156] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can be caused to execute an embodiment of any method for optimizing the stability of magnetic imaging using a Hall sensor combination.

[0157] The processor is used to provide computing and control capabilities to support the operation of the entire control module.

[0158] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can be caused to execute any method based on the Hall sensor combination system.

[0159] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific control module may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0161] Among them, in one embodiment, the processor is used to run the computer program stored in the memory to implement the following steps:

[0162] Construct an electromagnetic feature extraction model for performing joint time-frequency domain analysis on digital encoded signals to obtain magnetic environment disturbance feature parameters;

[0163] Adopt a closed-loop feedback mechanism to generate compensation magnetic field parameters opposite to the magnetic environment disturbance feature parameters;

[0164] Establish a real-time data channel with a preset electromagnetic imaging module and a magnetic field generation module through a preset communication protocol;

[0165] Construct a magnetic environment state space model corresponding to a three-dimensional orthogonal Hall sensor array, obtain the magnetic field distribution data collected by the electromagnetic imaging module, dynamically adjust the compensation magnetic field parameters, and after the compensation magnetic field parameters are output by the magnetic field generation module, form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array to achieve active magnetic interference cancellation in three-dimensional space.

[0166] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described processor can refer to the corresponding process in the method embodiments described in the above various embodiments, and will not be repeated here.

[0167] An embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the Hall sensor combination method for optimizing magnetic imaging stability provided in the above various embodiments of the present application.

[0168] Among them, the computer-readable storage medium may be an internal storage unit of the control module described in the foregoing embodiment, such as the hard disk or memory of the control module. The computer-readable storage medium may also be an external storage device of the control module, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the control module.

[0169] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present application, and these modifications or substitutions should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A Hall sensor combination system for optimizing magnetic imaging stability, characterized in that: include: A three-dimensional orthogonal Hall sensor array, comprising an X-axis Hall sensor line, a Y-axis Hall sensor line and a Z-axis Hall sensor line, wherein the X-axis Hall sensor line, the Y-axis Hall sensor line and the Z-axis Hall sensor line are arranged at intervals to form a spatial gradient detection network, and the three-dimensional orthogonal Hall sensor array establishes a bidirectional data connection with a control port of a preset magnetic field generation module; An embedded encoder module, integrating an analog-to-digital conversion unit and a signal preprocessing module, for converting analog magnetic field signals collected by the three-dimensional orthogonal Hall sensor array into digital coded signals with timestamps; A control module, wherein the control module constructs an electromagnetic feature extraction model for performing a time-frequency domain joint analysis on the digital coding signal to obtain magnetic environment disturbance characteristic parameters; the control module adopts a closed-loop feedback mechanism to generate a compensation magnetic field parameter opposite to the magnetic environment disturbance characteristic parameter; the control module establishes a real-time data channel with a preset electromagnetic imaging module and the magnetic field generation module through a preset communication protocol; The control module constructs a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, obtains the magnetic field distribution data collected by the electromagnetic imaging module, and dynamically adjusts the compensation magnetic field parameters. After being output by the magnetic field generation module, the compensation magnetic field parameters form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, thereby realizing active magnetic interference cancellation in three-dimensional space.

2. The system according to claim 1, characterized in that Also includes: The common mode suppression structure comprises a metal shielding shell and a star-shaped grounding terminal. The metal shielding shell forms a low impedance loop corresponding to the Hall sensor combination system through a multi-point equipotential connection method.

3. The system according to claim 1, characterized in that The magnetic environment disturbance characteristic parameters include harmonic distortion, magnetic field gradient mutation index and spectrum leakage coefficient; the digital coding signal is subjected to time-frequency domain joint analysis to obtain the magnetic environment disturbance characteristic parameters, including: Separating the intrinsic mode function of the digital coding signal according to the empirical mode decomposition algorithm to generate multi-scale time domain components; The instantaneous frequency characteristic spectrum corresponding to the multi-scale time domain components is calculated by Hilbert transform; Constructing a frequency domain feature extraction model based on wavelet packet energy entropy to calculate the entropy value change rate corresponding to each preset frequency band of the instantaneous frequency characteristic spectrum within a preset time window; the entropy value change rate is used to characterize the energy distribution corresponding to each preset frequency band; The instantaneous frequency characteristic spectrum and the entropy value change rate are input into a pre-trained convolutional neural network model, and the harmonic distortion degree, magnetic field gradient mutation index and spectrum leakage coefficient are output.

4. The system according to claim 1, characterized in that The method of using a closed-loop feedback mechanism to generate a compensation magnetic field parameter opposite to the magnetic environment disturbance characteristic parameter comprises: Establishing a transfer function matrix including a magnetic dipole inverse model to map the magnetic environment disturbance characteristic parameters into an equivalent interference magnetic moment; An adaptive observer is designed based on Lyapunov stability theory to estimate the phase lag error corresponding to the compensation magnetic field parameter in real time; A sliding mode variable structure control algorithm is used to generate a compensating magnetic moment component having the same amplitude and opposite phase as the equivalent interfering magnetic moment according to the phase lag error; The compensation magnetic moment component is decoupled and controlled by a singular value decomposition method to generate the compensation magnetic field parameter.

5. The system according to claim 1, characterized in that The magnetic environment state space model is a dimensionality reduction observer model; the construction of the magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array includes: Constructing a state variable as a differential algebraic combination of output signals corresponding to the three-dimensional orthogonal Hall sensor array; Establish the magnetic flux observation equation including the Lorentz force correction term; Construct a time-varying parameter matrix based on the flux conservation principle and the preset ambient temperature drift coefficient; Performing online parameter identification on the sensitivity matrix of the three-dimensional orthogonal Hall sensor array according to an improved volumetric Kalman filter algorithm; The differential homeomorphism mapping corresponding to the three-dimensional orthogonal Hall sensor array is constructed by Lie derivatives, and the reduced dimension observer model is generated according to the differential homeomorphism mapping, the identified sensitivity matrix, the time-varying parameter matrix, the magnetic flux observation equation and the differential algebraic combination.

6. The system according to claim 1, characterized in that The acquiring the magnetic field distribution data collected by the electromagnetic imaging module to dynamically adjust the compensation magnetic field parameters includes: generating a magnetic gradient tensor inversion model corresponding to the electromagnetic imaging module; Calculating the magnetic dipole distribution density of each spatial point corresponding to the magnetic field distribution data in the magnetic gradient tensor inversion model; The Pareto frontier solution set corresponding to the magnetic dipole distribution density is generated according to the topology optimization algorithm to balance the compensation accuracy and energy consumption indicators; Generate a reverse compensation strategy corresponding to a Pareto front solution set according to a virtual magnetic charge method, and solve an optimal compensation current combination corresponding to the reverse compensation strategy according to a conjugate gradient method; A dual time scale update mechanism is generated according to the Pareto front solution set, which is used to fine-tune the current parameters of the compensation magnetic field parameters within a millisecond time window and update the compensation algorithm weight coefficients corresponding to the compensation magnetic field parameters within a second time window.

7. The system according to claim 1, characterized in that The compensation magnetic field parameters are output by the magnetic field generation module to form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array, so as to achieve active magnetic interference cancellation in three-dimensional space, including: Obtaining a Helmholtz decomposition result of a spatial magnetic field corresponding to the three-dimensional orthogonal Hall sensor array; Based on a multi-coil coordinated control strategy, determining a driving current of the compensation coil corresponding to the compensation magnetic field parameter according to the Helmholtz decomposition result; A current tracking controller including phase lead compensation is generated according to the driving current, which is used to ensure the time domain synchronization of the compensation magnetic field output by the magnetic field generation module through the compensation magnetic field parameters and the interference magnetic field corresponding to the original positioning magnetic field; In the current tracking controller, the magnetic field superposition residual of each spatial point corresponding to the magnetic field distribution data is calculated according to a magnetic vector projection algorithm; Generate an iterative learning control law for closed-loop correction according to the magnetic field superposition residual; Based on the magnetic anisotropy compensation technology, dynamic bias calibration is performed on the sensitivity main axis direction of the three-dimensional orthogonal Hall sensor array according to the iterative learning control law to complete the active magnetic interference cancellation, so as to achieve a magnetic interference suppression ratio of not less than 40dB for the Hall sensor combination system in the full spatial domain.

8. The system according to claim 1, characterized in that Also includes: A multi-physics field temperature compensation unit, comprising a thermocouple array and a heat flux density sensor, wherein the thermocouple array and the heat flux density sensor are distributed at preset key nodes of the three-dimensional orthogonal Hall sensor array; The control module constructs a thermal magnetic coupling model based on finite element analysis to calculate the magnetic sensitivity drift corresponding to the three-dimensional orthogonal Hall sensor array in real time; The control module fuses the temperature monitoring data and the magnetic field distribution data corresponding to the Hall sensor combination system according to the asymmetric Kalman filter to generate a calibration parameter matrix with a thermal compensation coefficient; The control module dynamically adjusts the bias current corresponding to the three-dimensional orthogonal Hall sensor array and adjusts the adjustment temperature corresponding to the thermocouple array according to the calibration parameter matrix, so that the magnetic measurement accuracy error of the Hall sensor combination system in the full temperature range is less than 0.5%; the temperature monitoring data is measured by the heat flux density sensor.

9. The system according to claim 1, characterized in that Also includes: A self-calibrating magnetic field generating unit including an orthogonal Helmholtz coil set and a programmable current source; The control module constructs a calibration signal generation model based on a quantum particle swarm optimization algorithm, and generates a discrete frequency test magnetic field covering the range of a three-dimensional orthogonal Hall sensor array according to the calibration signal generation model; the control module analyzes the response data of the three-dimensional orthogonal Hall sensor array under the test magnetic field by a singular value decomposition method, and generates a sensitivity deviation matrix according to the response data; the control module updates a compensation magnetic field parameter library according to the sensitivity deviation matrix based on a nonlinear least squares fitting algorithm to form a closed-loop calibration model including frequency-temperature dual variable compensation; the control module controls the self-calibration magnetic field generating unit to perform calibration according to the calibration parameters output by the closed-loop calibration model.

10. A Hall sensor combination method for optimizing magnetic imaging stability, characterized in that: A control module applied to the system according to any one of claims 1 to 9, wherein the method comprises: Construct an electromagnetic feature extraction model to perform time-frequency domain joint analysis on digital coded signals and obtain characteristic parameters of magnetic environment disturbance; A closed-loop feedback mechanism is used to generate compensation magnetic field parameters opposite to the characteristic parameters of the magnetic environment disturbance; Establishing a real-time data channel with a preset electromagnetic imaging module and a magnetic field generating module through a preset communication protocol; A magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array is constructed, and the magnetic field distribution data collected by the electromagnetic imaging module is obtained to dynamically adjust the compensation magnetic field parameters. After the compensation magnetic field parameters are output by the magnetic field generation module, they form a vector superposition with the original positioning magnetic field corresponding to the three-dimensional orthogonal Hall sensor array to achieve active magnetic interference cancellation in three-dimensional space.

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