Passive sensor composite verification system and method based on dynamic field intensity modulation

Abstract

The invention discloses a passive sensor composite verification system and method based on dynamic field intensity modulation, and the method comprises the following steps: S1, carrying out the synchronous collection of multiple parameters, and finally obtaining the environment temperature and humidity gradient and equipment load fluctuation parameters; s2, quantum noise suppression processing; s3, carrying out field intensity modeling and dynamic optimization, and constructing a four-dimensional space-time field intensity model containing a geological conductivity correction term; s4, multi-physics field interference separation; and S5, generating a dynamic verification parameter, performing orthogonalization processing on the decoupling parameter, constructing a transfer function model containing a time delay factor, and outputting the transfer function model to target equipment to execute verification. Through the synergistic effect of the three-dimensional orthogonal electromagnetic induction array and the quantum noise suppression technology, the environmental noise and the target signal are effectively separated. Quantum state coding and dynamic decoupling control significantly reduce the influence of a de-coherence effect, so that the capturing ability of weak electromagnetic field signals is improved by two orders of magnitude compared with a traditional method, and a high-fidelity data basis is provided for subsequent processing.

Description

Technical Field

[0001] This invention relates to the field of sensor composite verification technology, and in particular to a passive sensor composite verification system and method based on dynamic field intensity modulation. Background Technology

[0002] Electromagnetic field detection technology has significant application value in fields such as industrial equipment monitoring, geological exploration, and environmental sensing. With the increasing demands for precision manufacturing and intelligent operation and maintenance, higher requirements are placed on the high-sensitivity capture of weak electromagnetic signals, stable detection under complex operating conditions, and real-time data processing capabilities. Current mainstream technologies are mainly based on multi-axis induction coil arrays, static geological model compensation, uniform mesh finite element method, and single-objective parameter optimization. However, these methods face significant limitations in practical applications: traditional induction arrays are susceptible to environmental noise interference, leading to insufficient signal fidelity; preset geological parameters cannot dynamically adapt to changes in rock strata characteristics and climatic disturbances, causing a decline in model adaptability; fixed mesh algorithms have limited computational efficiency in complex fields, making it difficult to achieve a balance between accuracy and resources; and single-objective optimization strategies neglect the synergistic optimization of multi-dimensional performance indicators, resulting in low overall system performance.

[0003] Existing electromagnetic detection systems suffer from technical bottlenecks in complex operating conditions, particularly the difficulty in effectively separating environmental noise from target signals. These bottlenecks manifest as weak signals being easily submerged by noise, model mismatch due to dynamic geological and climatic factors, and inefficient allocation of computational resources. These problems severely restrict the reliable application of electromagnetic detection technology in harsh environments and high-precision scenarios, necessitating breakthroughs through integrated innovation in noise suppression, dynamic compensation, and intelligent optimization technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a passive sensor composite verification system and method based on dynamic field intensity modulation, which solves the core problem of the difficulty in effectively separating environmental noise and target signal in existing electromagnetic detection technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The passive sensor composite verification method based on dynamic field modulation includes the following steps:

[0007] S1: Multi-parameter synchronous acquisition

[0008] The electromagnetic field X / Y / Z triaxial components of the monitored object are collected by a three-dimensional orthogonal electromagnetic induction array. Simultaneously, the geological conductivity tensor of the strata is measured using a multi-frequency probe, and the environmental temperature and humidity gradient and equipment load fluctuation parameters are obtained to obtain the electromagnetic-temperature-humidity-load multidimensional coupling matrix.

[0009] S2: Quantum noise suppression processing

[0010] The X / Y / Z triaxial components of the electromagnetic field acquired in step S1 are encoded into superconducting qubits. Decoherence noise is suppressed by CPMG pulse sequence, and the CPMG pulse interval is dynamically adjusted according to the noise dominance frequency and signal strength to obtain quantum state data. Subsequently, the quantum state data is decoded into a classical computing unit, and the electromagnetic field characteristic data is obtained by dimensionality reduction through principal component analysis.

[0011] S3: Field Strength Modeling and Dynamic Optimization

[0012] A four-dimensional spatiotemporal field strength model including a geological conductivity correction term is constructed based on the electromagnetic field characteristic data. The field reconstruction error is monitored in real time based on the Lyapunov function. When the error change rate exceeds the threshold, the computational grid is refined and the step size is reduced until optimized field strength distribution data is output.

[0013] S4: Multiphysics Interference Separation

[0014] Based on the optimized field strength distribution data, singular value decomposition and regularization are performed on the electromagnetic-temperature-humidity-load multidimensional coupling matrix, and a regional feature compensation function is embedded to obtain decoupling parameters.

[0015] S5: Dynamic Verification Parameter Generation

[0016] The decoupling parameters are orthogonalized to construct a transfer function model that includes a time delay factor, and the model is output to a passive sensor for verification.

[0017] Furthermore, the CPMG pulse interval in S2 satisfy:

[0018] ;

[0019] in, The dominant frequency of the decoherent noise was determined through real-time spectrum analysis.

[0020] The noise power spectral density is calculated using Fourier transform;

[0021] This represents the reference signal strength under interference-free conditions.

[0022] Furthermore, the construction of the four-dimensional spatiotemporal field strength model in S3 includes:

[0023] A geological conductivity correction term is introduced into the electromagnetic field constitutive equation. This correction term is derived through the geological coupling coefficient. With formation conductivity tensor The trace multiplication is achieved;

[0024] in, Calibration was performed using data measured by multi-frequency probes. It is generated by the three-dimensional distribution of the formation's electrical conductivity, dielectric constant, and magnetic permeability.

[0025] Furthermore, the Lyapunov function in S3 is defined as follows:

[0026] ;

[0027] When the time derivative of the function is detected When the time is right, the mesh density is increased to 10 times the original density, and the iteration step size is reduced to 1 / 5 of the original value.

[0028] Furthermore, the regional feature compensation function in S4 includes:

[0029] Formation dielectric correction term, based on formation thickness The dielectric constant is corrected exponentially, with the correction factor decreasing by 5-6% for every 10-meter increase in thickness.

[0030] The environmental humidity gradient factor adjusts the humidity weight according to seasonal or climatic characteristics, with the weight of high temperature and high humidity periods being 17-20% lower than that of low temperature and dry periods, and the transition period following a linear transition.

[0031] Furthermore, the transfer function model in S5 is as follows:

[0032] ;

[0033] Among them, the time delay factor Calculated by the phase difference between the input and output signals. For the Laplacian operator, the fitting parameters Determined by the least squares method.

[0034] A passive sensor composite verification system based on dynamic field modulation, used in the passive sensor composite verification method based on dynamic field modulation as described above, includes:

[0035] Data acquisition module: Equipped with a three-dimensional electromagnetic array and multi-frequency stratum probe, it is connected to the quantum preprocessing module via a high-speed data bus. It is used to transmit in real time the electromagnetic field X / Y / Z triaxial components, geological conductivity tensor, environmental temperature and humidity gradient, and equipment load fluctuation parameters of the monitored object, and obtain the electromagnetic-temperature-humidity-load multidimensional coupling matrix.

[0036] The quantum preprocessing module integrates a quantum sensing unit and a pulse controller. It encodes the triaxial components of the acquired electromagnetic field into superconducting qubits, suppresses decoherence noise through CPMG pulse sequences, and dynamically adjusts the CPMG pulse interval according to the noise frequency and signal strength to obtain quantum state data. Subsequently, the quantum state data is decoded into a classical computing unit, and the electromagnetic field characteristic data is obtained through principal component analysis for dimensionality reduction.

[0037] Field reconstruction module: Equipped with a parallel computing unit, it constructs a four-dimensional spatiotemporal field strength model including a geological conductivity correction term based on the electromagnetic field characteristic data. It monitors the field reconstruction error in real time based on the Lyapunov function. When the error change rate exceeds the threshold, it triggers the densification of the computing grid and the reduction of the step size until the optimized field strength distribution data is output.

[0038] Decoupling calculation module: used to perform singular value decomposition and regularization on the electromagnetic-temperature-humidity-load multidimensional coupling matrix based on the optimized field strength distribution data, and embed regional feature compensation function to obtain decoupling parameters;

[0039] Parameter generation module: used to orthogonalize the decoupling parameters, construct a transfer function model including a time delay factor, and output it to the passive sensor for verification.

[0040] Furthermore, the quantum sensing unit in the quantum preprocessing module specifically adopts a fluxonium superconducting quantum bit array;

[0041] The array operates at a temperature of ≤20mK and is equipped with a dilution refrigerator to maintain its superconducting state.

[0042] The timing jitter of the pulse controller is ≤1ns, and the sampling clock synchronization error between the pulse controller and the data acquisition module (110) is ≤0.1ns.

[0043] Furthermore, the decoupling computing module (140) specifically employs a dedicated integrated circuit chip;

[0044] The application-specific integrated circuit chip is equipped with a double-precision floating-point arithmetic acceleration unit, which takes ≤10ms to decompose the coupling matrix in a single operation, and triggers the parameter generation module to perform data reception by sending a hardware interrupt signal.

[0045] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the passive sensor composite verification method based on dynamic field intensity modulation as described above.

[0046] In summary, the present invention has at least one of the following beneficial technical effects:

[0047] 1. This invention effectively separates environmental noise from the target signal through the synergistic effect of a three-dimensional orthogonal electromagnetic induction array and quantum noise suppression technology. Quantum state encoding and dynamic decoupling control significantly reduce the impact of decoherence effects, improving the capture capability of weak electromagnetic field signals by two orders of magnitude compared to traditional methods, thus providing a high-fidelity data foundation for subsequent processing.

[0048] 2. This invention integrates a four-dimensional field strength model with geological conductivity correction and climate characteristic compensation, overcoming the limitation of traditional electromagnetic detection that ignores the influence of geological structure. The introduction of a regional characteristic compensation function enables the system to automatically adapt to different rock strata characteristics and seasonal humidity changes, significantly improving the reliability of detection under complex working conditions.

[0049] 3. This invention employs an adaptive mesh refinement strategy based on the Lyapunov stability criterion to achieve a dynamic balance between computational accuracy and efficiency. It intelligently adjusts the mesh density and iteration step size by monitoring the field strength evolution characteristics in real time.

[0050] 4. The combination of orthogonalization processing and multi-objective genetic algorithm in this invention eliminates redundant coupling between verification parameters. The time-delay system model accurately characterizes the dynamic response characteristics of the device, and the Pareto optimization front selection strategy ensures that the output parameter set simultaneously meets the requirements of tracking accuracy, stability, and energy consumption constraints.

[0051] 5. The heterogeneous architecture design of the multimodal sensing network, superconducting quantum module, and edge computing node in this invention breaks through the data processing bottleneck of traditional detection equipment. The quantum-classical hybrid pipeline mechanism improves the system throughput, providing efficient hardware support for real-time calibration in industrial settings. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the passive sensor composite verification system based on dynamic field intensity modulation of the present invention.

[0053] Figure 2 This is a flowchart illustrating the passive sensor composite verification method based on dynamic field intensity modulation according to the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] This invention provides a passive sensor composite verification system and method based on dynamic field intensity modulation. The overall system architecture is shown in Figure 1. In order of data flow processing, it includes: a data acquisition module 110, a quantum preprocessing module 120, a field reconstruction module 130, a decoupling calculation module 140, and a parameter generation module 150. The system workflow is as follows: Figure 2As shown, the process consists of five steps: first, multi-parameter synchronous acquisition; second, quantum noise suppression processing; third, field strength modeling and dynamic optimization; fourth, multi-physics interference separation; and fifth, generation of dynamic verification parameters.

[0056] In this embodiment, the multi-parameter synchronous acquisition step achieves spatiotemporal synchronous acquisition of electromagnetic fields, geological characteristics, and environmental parameters by constructing a multimodal heterogeneous sensor network, ensuring the completeness and consistency of data acquisition. The following details the process from three aspects: hardware architecture, signal acquisition, and data processing.

[0057] For electromagnetic field acquisition, a three-dimensional orthogonal electromagnetic induction array is deployed, consisting of multiple sets of induction coils made of gallium nitride (GaN) material. Preferably, the coils are arranged orthogonally to form a three-dimensional detection structure, with 16 independently set sensing units in each axis, spaced 5 cm apart, covering a 1m x 1m detection area on the device surface. Each sensing channel integrates a low-noise amplifier and an anti-aliasing filter, where the noise figure of the low-noise amplifier is less than 1.5dB, and the cutoff frequency of the anti-aliasing filter is dynamically adjusted according to the sampling rate to avoid spectral aliasing. When the array is working, a synchronous trigger circuit controls the synchronous activation of the sensing units in the X, Y, and Z axes to capture time-varying magnetic field components at a preset sampling rate of 1MS / s. , , The raw data is transmitted to the central processing unit via a high-speed LVDS interface. For geological parameter acquisition, a multi-frequency probe is used to perform formation conductivity tensor measurements. The probe has a built-in tunable electromagnetic wave transmitter, preferably operating at frequencies of 1MHz, 10MHz, and 100MHz. After the probe is inserted to a depth of 3m, it first emits a 1MHz low-frequency electromagnetic wave, and the phase difference between the reflected and incident waves is measured. Calculate the basic conductivity component:

[0058] ;

[0059] in, Angular frequency ( =2π×10 6 rad / s);

[0060] The detection depth is 3m;

[0061] For free space wavenumber;

[0062] The phase difference between the reflected wave and the incident wave is obtained by measuring the phase difference using an IQ demodulator.

[0063] is the vacuum permittivity.

[0064] Then, the frequency mode was switched to 100MHz, and the three-dimensional conductivity distribution was inverted by utilizing the scattering characteristics of electromagnetic waves in the rock strata to generate a complete conductivity tensor.

[0065] ;

[0066] in: The X-axis conductivity component reflects the conductivity along the probe axis and is determined by the 1MHz low-frequency phase difference method.

[0067] , The XY and XZ plane cross-conductivity components characterize the lateral current conduction capacity of anisotropic rock strata, which is inverted through high-frequency scattering signal polarization analysis.

[0068] The conductivity component in the Y-axis direction is obtained by repeated low-frequency measurements after rotating the probe 90°.

[0069] The cross-conductance components of the YZ plane are calculated iteratively using multi-angle scattering data in high-frequency mode.

[0070] The conductivity component along the Z-axis (vertical direction) is determined by measuring the vertical attenuation characteristics of electromagnetic waves using the time-domain reflectometry (TDR) method.

[0071] Preferably, multi-frequency measurement data are fused using a Kalman filter algorithm to eliminate random errors caused by formation inhomogeneity. For environmental parameter synchronization, a distributed temperature and humidity sensor network is deployed, comprising eight sensor nodes arranged in a hexagonal topology with a 2.5m spacing. Each node integrates a high-precision temperature and humidity sensor; preferably, the temperature measurement accuracy is ±0.1℃ and the humidity measurement accuracy is ±1.5%RH. Data is transmitted between nodes via the LoRa wireless communication protocol, employing a TDMA mechanism to avoid channel collisions. After receiving the data, the central processing unit calculates the gradient distribution of environmental parameters using a cubic spline interpolation algorithm.

[0072] ;

[0073] in, For temperature gradient, For humidity gradient, This represents the rate of change of the temperature field in the x-direction; the other components are similar.

[0074] Spatial partial derivatives are calculated using the central difference method, with the step size set to 1 / 2 of the node spacing.

[0075] In terms of data preprocessing, time alignment and format standardization of multi-source data are implemented. Preferably, the IEEE 1588 Precision Time Protocol (PTP) is used to provide a unified clock reference for each sensing unit, with time synchronization error controlled within ±10ns. After adding a 64-bit nanosecond-level timestamp to the raw data, high-frequency noise is eliminated using a sliding window filtering algorithm. Preferably, the window length is dynamically adjusted according to the signal's main frequency.

[0076] ;

[0077] in, The window width (in seconds) is used to dynamically adjust the length of the time window for signal analysis. Sampling rate, The signal frequency is determined through real-time FFT analysis.

[0078] Regarding load parameter acquisition, the effective value of the current is captured in real time through the IEC61850 protocol interface. and harmonic components. Preferably, a digital signal processor is used to extract the current rate of change characteristics:

[0079] ;

[0080] For a moment The instantaneous current value is obtained in real time through the power monitoring system using the IEC61850 protocol;

[0081] The differential time interval is set to 1ms to maintain strict synchronization with the electromagnetic signal sampling period.

[0082] The current change rate characteristic is used to characterize the dynamic characteristics of equipment load.

[0083] in, Set to 1ms to maintain a strict integer multiple relationship with the electromagnetic field sampling rate, ensuring time-domain data alignment.

[0084] In this embodiment, multi-parameter synchronous acquisition achieves data integrity through the following collaborative techniques:

[0085] Hardware layer: The high-frequency response characteristics of gallium nitride induction coils ensure transient magnetic field capture capability, and the frequency band switching mechanism of multi-frequency probes takes into account both deep and shallow measurements.

[0086] Algorithm layer: Kalman filtering eliminates random errors in geological parameters, and cubic spline interpolation ensures the continuity of environmental gradients;

[0087] Synchronization control layer: PTP protocol achieves nanosecond-level time synchronization, and sliding window filtering dynamically adapts to signal characteristics.

[0088] By organically combining the above-mentioned technical means, this step provides multi-source input data that is strictly aligned in time and space, has a unified format, and has suppressed noise for subsequent processing, laying the foundation for the implementation of the overall solution.

[0089] In this embodiment, the quantum noise suppression processing step utilizes a technical approach combining quantum information processing and classical signal analysis to achieve the extraction of intrinsic features of electromagnetic signals and the precise separation of environmental noise. The following provides a complete technical disclosure from three dimensions: quantum state space mapping, noise suppression mechanisms, and mixed signal processing.

[0090] In terms of quantum state space mapping, an encoding channel from electromagnetic signals to quantum states is constructed. Preferably, fluxonium-type superconducting qubits are used as the information carrier, whose unique high impedance characteristics can effectively suppress charge noise interference. The encoding process is realized through microwave control pulses, converting the amplitude of the time-varying magnetic field signal into the phase information of the qubits. Specifically, the phase loading amount is determined by the following formula:

[0091] ;

[0092] in, For a moment The magnetic field strength is measured in real time through an electromagnetic induction array;

[0093] This is the upper limit of the magnetic field range, set according to the sensor specifications (preferably, 90% of the probe's saturated magnetic field).

[0094] This is the start time of integration, corresponding to the time when the quantum state initialization is completed;

[0095] It is a dynamic phase loading quantity used to modulate the phase relationship of quantum entangled states.

[0096] The integral form can effectively smooth high-frequency glitches in the signal. After encoding, Bell state entangled pairs are prepared using a parameterized quantum gate sequence:

[0097] ;

[0098] in, Parametric entanglement gate;

[0099] The initial state of the qubit is prepared using a ground state initialization circuit;

[0100] The entangled state is prepared for quantum noise suppression.

[0101] Preferred, Entanglement Gate Driven by two-qubit cross-resonance, the gate operation fidelity is no less than .

[0102] For dynamic decoupling control, an adaptive noise spectrum response pulse sequence modulation mechanism is designed. A Carr-Purcell-Meiboom-Gill (CPMG) pulse architecture is adopted, consisting of alternately applied pulses... Pulse composition, pulse interval Optimizes in real time based on noise spectrum characteristics. Specifically, pulse interval... Calculated dynamically using the following formula:

[0103] ;

[0104] in: The dominant noise frequency was determined through real-time FFT analysis, specifically the noise power spectral density. The maximum value corresponds to the frequency;

[0105] The average signal power under noise-free conditions is taken as the reference signal power.

[0106] This is the Hanning window function, used to suppress spectral leakage effects;

[0107] This is a dynamic adjustment coefficient, and its value is negatively correlated with the current signal-to-noise ratio.

[0108] This represents the bandwidth of the noise main frequency band analysis.

[0109] Preferably, it is set to This is to cover noise harmonic components. Real-time control of the pulse sequence is implemented through FPGA hardware, ensuring timing control accuracy at the picosecond level.

[0110] In quantum state tomography and signal conversion, quantum measurement reconstruction techniques are employed to extract effective signal components. The probability distribution of the quantum state under the three Pauli basis vectors X, Y, and Z is obtained through projection measurements, and the density matrix is ​​reconstructed.

[0111] ;

[0112] in, Let be a matrix, defined as a 2×2 identity matrix, ensuring that the trace (total probability) of the density matrix is ​​1, satisfying the normalization condition of the quantum state.

[0113] , , Here are the Pauli matrices, corresponding to spin measurements in the X, Y, and Z directions, respectively.

[0114] is a density matrix that describes the state of a quantum system and is applicable to both pure and mixed states.

[0115] The expectation value of the Pauli operator is calculated using the following formula:

[0116] ;

[0117] in, For integer counting, in Under the directional (X / Y / Z) measurement basis, the quantum state collapses to The cumulative number of times the state is reached;

[0118] For integer counting, in Under the direction measurement basis, the quantum state collapses to The cumulative number of times the state is reached;

[0119] For real numbers (range: [-1, 1]);

[0120] They are respectively in Measured under the orientation measurement base state and Counting of states.

[0121] The expected value vector [ , , Mapped to the classical tensor space, forming a 100-dimensional feature vector;

[0122] Perform principal component analysis (PCA) on the eigenvectors:

[0123] Calculate the covariance matrix ,in For the standardized data matrix, To reflect the linear correlation between the various feature dimensions, Indicates matrix transpose;

[0124] right Perform eigenvalue decomposition and sort the eigenvalues ​​in descending order. ;

[0125] Select the first m principal components such that the cumulative contribution rate satisfies:

[0126] ;

[0127] in, The number of principal components selected should satisfy the requirement that the cumulative variance contribution rate is ≥95%.

[0128] Preferred, =3 to achieve efficient dimensionality reduction. In terms of quantum state monitoring and reset, quantum state purity is calculated in real time. .

[0129] When detected When the quantum state is determined to be severely distorted due to decoherence, the following reset procedure is triggered:

[0130] 1. Turn off the microwave drive field and apply an amplitude of Duration is The reset pulse relaxes the quantum state to the ground state. ;

[0131] 2. Reinitialize the quantum circuit and load new Bell state entangled pairs;

[0132] 3. Calibrate the microwave pulse phase to compensate for phase errors caused by temperature drift.

[0133] In this embodiment, quantum noise suppression is achieved collaboratively through the following techniques:

[0134] Quantum hardware characteristics: long coherence time of fluxonium qubits (preferred). ≥50) provides a sufficient operating time window for dynamic decoupling;

[0135] Dynamic control mechanism: The frequency domain adaptive adjustment strategy of CPMG pulse interval effectively suppresses power frequency interference and broadband noise;

[0136] Data processing architecture: The combination of quantum tomography and PCA dimensionality reduction eliminates measurement noise while preserving the nonlinear characteristics of the signal.

[0137] By organically integrating the above-mentioned technical means, this step converts the original electromagnetic signal into a low-noise, high-fidelity three-dimensional feature vector, providing a standardized input that meets the requirements of tensor operations for subsequent field strength modeling, while ensuring the information integrity of the quantum-classical data conversion process.

[0138] In this embodiment, the field strength modeling and dynamic optimization steps construct a four-dimensional spatiotemporal field strength model that integrates multi-physics coupling effects, and implement adaptive computational resource allocation based on real-time stability criteria to achieve high-precision field reconstruction under complex electromagnetic environments. The complete technical disclosure follows from four dimensions: physical model construction, numerical discretization strategy, stability monitoring mechanism, and dynamic optimization method.

[0139] In terms of physical model construction, based on the classical electromagnetic field theory framework, a correction term and a nonlinear coupling term are introduced into the geological conductivity tensor to form an extended four-dimensional spatiotemporal field strength tensor equation. The correction term is achieved through deep coupling between geological conductivity data measured by multi-frequency probes and the theoretical field equation, and its specific expression is as follows:

[0140]

[0141] in, The three-dimensional geological conductivity tensor obtained in step S1;

[0142] For the geological field coupling tensor, the preferred method is to calculate it using the distribution of rock strata dielectric constant and magnetic permeability.

[0143] and These are the linear and nonlinear coupling coefficients, determined through offline calibration.

[0144] It is a second-order antisymmetric tensor (4×4 matrix), the total electromagnetic field strength tensor, which includes the free electromagnetic field and the geological coupling field;

[0145] It is the gauge potential of the electromagnetic field;

[0146] It is a second-order antisymmetric tensor.

[0147] The introduction of the correction term enables the model to simultaneously characterize the linear modulation effect of the electromagnetic field on the formation's conduction properties and the nonlinear distortion effect under high field strength. For numerical discretization, an unstructured tetrahedral mesh is used to spatially discretize the computational domain. Preferably, the initial mesh size is adaptively generated based on the geometric curvature of the target device: regions with curvature radii smaller than a threshold (preferably, the threshold is set to 1 / 10 of the device's feature size) are automatically refined to a higher resolution. The time dimension is discretized using the second-order implicit generalized α method, with the following discretization format:

[0148] ;

[0149] in, For the quality matrix, Here is the stiffness matrix. For time integration parameters, a value of 0.75 is preferred to balance numerical dissipation and stability.

[0150] For time step;

[0151] The solution vector; This is the load vector.

[0152] For stability monitoring, a Lyapunov function based on the evolution characteristics of the field strength tensor is constructed:

[0153] ;

[0154] in, For a positive definite weight tensor, the preferred choice is to take... To reflect the anisotropy of the magnetic field;

[0155] The divergence penalty factor, For a vector field, its physical meaning is the distribution of magnetic field strength or a generalized field variable;

[0156] Here, represents the divergence constraint penalty factor.

[0157] Real-time calculation of the time derivative of the Lyapunov function:

[0158] ;

[0159] when , As a preset threshold, preferably, it is set according to the initial field energy. This triggers a dynamic optimization mechanism. For scalars, the rate of change of energy.

[0160] Regarding the dynamic optimization strategy, we implement coordinated optimization of mesh refinement, iteration step size adjustment, and preprocessing:

[0161] Adaptive mesh refinement: The Rivara edge bisection method based on posterior error estimation is adopted. For errors that exceed the threshold, preferably, the threshold is set to 15% of the global maximum error. Mesh cells are recursively subdivided. At the same time, mesh quality constraints are introduced. Preferably, the tetrahedral shape factor is not less than 0.25 to avoid the generation of deformed cells.

[0162] Dynamic adjustment of iteration step size: The time step size is reduced according to an exponential decay law.

[0163] ;

[0164] in, The decay rate coefficient is (preferably 1.2). It is a positive real number; It is a scalar; For threshold; This is the decay rate coefficient;

[0165] Preprocessing enhancement: When the condition number of the Jacobian matrix is ​​detected At this time, the incomplete Cholesky decomposition preconditioner based on field gradient weighting is enabled to improve iterative convergence.

[0166] In solving the field equations, the Newton-Krylov iterative framework is used to solve the nonlinear discrete system.

[0167] The outer loop performs Newton iterations, linearizes the residual equations, and calculates the Jacobian matrix. ;

[0168] The inner loop uses the GMRES algorithm to solve the linear system. Preferably, the restart cycle is set to 30, and the tolerance is [missing value]. ;

[0169] Update solution vector until satisfied .

[0170] In this embodiment, field strength and dynamic optimization are achieved synergistically through the following techniques:

[0171] Physical fidelity: The introduction of geological correction terms and nonlinear terms in the four-dimensional tensor equations ensures that the model can accurately characterize the electromagnetic field distribution characteristics under complex geological environments.

[0172] Numerical robustness: Based on the stability monitoring mechanism of Lyapunov function and grid-step size co-optimization strategy, numerical divergence is effectively suppressed and computational efficiency is improved;

[0173] Algorithm adaptability: The combination of the Newton-Krylov framework and preprocessing techniques ensures the convergence and stability of solving nonlinear systems.

[0174] Through the systematic integration of the above-mentioned technical means, this step outputs spatiotemporally continuous and physically accurate four-dimensional field strength distribution data, providing high-fidelity input for subsequent multiphysics field decoupling, while achieving the optimal balance between computational accuracy and efficiency through a dynamic resource allocation mechanism.

[0175] In this embodiment, the multi-object interference separation step achieves precise decoupling of the coupling effects of electromagnetic field, temperature field, and humidity field through a collaborative processing mechanism of matrix decomposition and regional feature compensation. The following fully discloses the techniques at three levels: coupling matrix construction, regularization decomposition, and compensation function embedding.

[0176] Regarding the construction of the coupling matrix, based on the four-dimensional field strength data output in step S3, a Jacobian matrix for the electromagnetic-temperature-humidity multi-physics coupling is established. This matrix characterizes the cross-sensitivity properties between the various physical fields, and its specific form is as follows:

[0177] ;

[0178] in, For the three-axis components of the electromagnetic field, For temperature, For humidity, The load current of the equipment is calculated using the finite difference method. Preferably, the spatial difference step size is set to 1 / 5 of the grid size, and the temporal difference step size is consistent with the iteration step size in step S3.

[0179] For matrix factorization, a strategy combining truncated singular value decomposition (TruncatedSVD) and Tikhonov regularization is employed to handle ill-conditioned coupled matrices. The specific implementation process is as follows:

[0180] For matrix Perform singular value decomposition:

[0181] ;

[0182] in, and These are the left and right singular vector matrices, respectively. Singular value matrix ( ); For threshold; For temperature.

[0183] Set cutoff threshold ( ), abandon satisfaction The singular values ​​suppress the components dominated by measurement noise.

[0184] Introducing the Tikhonov regularization term, we solve for the corrected solution vector. :

[0185] ;

[0186] in, As a regularization parameter, the preferred one is determined by the L-curve method to balance the residual norm and the solution norm;

[0187] This is the discretized Jacobian matrix or sensitivity matrix;

[0188] For observation data vectors;

[0189] Let be the parameter vector to be solved;

[0190] This is the regularization parameter.

[0191] In terms of regional feature compensation, an empirical correction function based on geological structure and climate characteristics is embedded, specifically including:

[0192] Basalt dielectric correction term:

[0193] ;

[0194] in, The reference dielectric constant is 8.5 (preferred). For the thickness of the strata, The characteristic attenuation length (preferably 18.5 m, calibrated through core sample experiments) is used to correct for the nonlinear effect of formation thickness on dielectric properties.

[0195] Humidity gradient factor:

[0196] ;

[0197] in, A is the baseline coefficient, and A is the seasonal modulation amplitude (preferably 0.2). It is the number of days accumulated over a year. This is the phase shift (preferably, 105 corresponds to the start of summer).

[0198] The periodic variation characteristics of the humidity gradient in the Yangtze River basin are considered. In terms of compensation number fusion, a regional correction is introduced into the decoupled physical field components:

[0199] ;

[0200] in, For decoupled electromagnetic field components ( ), Humidity sensitivity coefficient; This is a correction factor for the relative permittivity;

[0201] This is a time-varying humidity correction factor;

[0202] This is the humidity sensitivity coefficient.

[0203] Preferably, the correction coefficients are optimized using the iterative reweighted least squares method to ensure that the compensation process does not introduce additional bias.

[0204] In this embodiment, multi-object interference separation achieves precise decoupling through the following collaborative techniques:

[0205] At the matrix decomposition level: the combination of truncated SVD and regularization effectively suppresses noise amplification and improves the numerical stability of ill-conditioned problems;

[0206] Regional compensation level: The introduction of dielectric correction terms and humidity factors overcomes the shortcomings of traditional methods in not taking into account geological and climatic characteristics.

[0207] At the data fusion level: The compensation function is designed based on physical mechanisms to ensure that the correction process conforms to the interaction law between electromagnetic field and environment.

[0208] Through the systematic integration of the above-mentioned technical means, this step outputs the decoupled pure electromagnetic field components, providing a highly reliable input for the generation of dynamic verification parameters. At the same time, regional feature compensation enhances the system's adaptability to different application scenarios.

[0209] In this embodiment, the dynamic parameter generation step transforms the decoupled electromagnetic field features into a set of dynamic verification parameters executable by the device through a collaborative mechanism of orthogonalization, time delay system modeling, and multi-objective optimization. The following provides a complete technical disclosure from four dimensions: parameter orthogonalization, transfer function identification, time delay compensation, and parameter optimization. Regarding parameter orthogonalization, an improved Gram-Schmidt algorithm is used to process the decoupled electromagnetic field feature vector set. Orthogonalization is performed to eliminate linear correlations between parameters. The specific implementation process is as follows:

[0210] 1. Vector projection correction: For the first... eigenvectors Calculate its relationship with the previous one. orthogonalized vectors Projected components:

[0211] ;

[0212] in, This represents the dot product of vectors. The corrected orthogonal vectors are:

[0213] ;

[0214] Let be the eigenvectors to be orthogonalized. It is the inner product.

[0215] 2. Angle Threshold Determination: Real-time calculation of the angle between any two vectors. .

[0216] when When a significant correlation is found, a second orthogonal projection correction is triggered.

[0217] 3. Normalization Theorem: Normalize orthogonal vectors:

[0218] ;

[0219] Forming standard orthogonal bases This serves as the reference coordinate system for the verification parameters. In terms of transfer number modeling, a third-order transfer function model incorporating time delay is constructed to characterize the dynamic relationship between the verification parameters and the device response.

[0220] ;

[0221] in, For acceleration term gain; For the velocity term gain, For steady-state gain; Damping related terms Stiffness-related terms System intrinsic frequency term;

[0222] Signal transmission delay or mechanical response lag in seconds, expressed in seconds (s).

[0223] coefficients of numerator and denominator Online identification using the recursive least squares method includes the following steps:

[0224] 1. Data Acquisition: Using orthogonalized feature vectors As a input, the device responds with a signal. As the output, the dataset is acquired in 1ms sampling intervals. .

[0225] 2. Construction of the regression vector: Define the extended regression vector:

[0226] ;

[0227] in, Output historical values ​​to the system; The input signal is in an orthogonal base coordinate system;

[0228] 3. Parameter recursive estimation: A recursive least squares algorithm with a forgetting factor is used.

[0229] ;

[0230] ;

[0231] ;

[0232] in, For the forgetting vector, a value of 0.9 is preferred to balance the tracking speed and stability. Let covariance matrix be the variance matrix. The vector to be estimated.

[0233] In terms of time delay compensation, the time delay constant is determined based on chaos theory analysis. :

[0234] 4. Phase space reconstruction: reconstruction of electromagnetic feature sequences Perform phase reconstruction and embed dimension Virtual nearest neighbor method for determination, time delay Calculated using the mutual inversion method.

[0235] 5. Lyapunov exponent calculation: The maximum Lyapunov exponent is calculated using the OL algorithm. :

[0236] ;

[0237] in, The separation distance between adjacent orbits in the air. Let be the iteration number.

[0238] 6. Delay Setting: Set the delay constant according to the stability condition.

[0239] ;

[0240] in, For the system matrix The largest eigenvalue real part;

[0241] For parameter optimization, a constrained multi-objective genetic algorithm (NSGA-III) is used to perform Pareto optimization on the calibration parameter set:

[0242] 1. Definition of objective function:

[0243] Dynamic tracking error: ;

[0244] in, For reference trajectory; This is the actual output;

[0245] Parameter volatility: ;

[0246] in, For parameter vectors;

[0247] Energy consumption indicators: The sum of the absolute values ​​of the parameters reflects the energy consumption of the control.

[0248] 2. Constraint settings:

[0249] Stability constraints: ;

[0250] Safety constraints: ;

[0251] 3. Optimize execution:

[0252] The population size is set to 100, the crossover probability is 0.8, and the mutation probability is 0.1.

[0253] The reference point method is used to maintain the diversity of solution sets;

[0254] The optimal compromise solution is selected from the Pareto front using fuzzy C-means clustering.

[0255] In this embodiment, dynamic parameter generation is achieved collaboratively through the following technologies:

[0256] Mathematical rigor: The improved Gram-Schmidt algorithm ensures parameter orthogonality and eliminates instruction redundancy;

[0257] Dynamic accuracy: The time-delay transfer function model accurately characterizes the transient response of the system;

[0258] Comprehensive optimization: The multi-objective genetic algorithm balances tracking accuracy, parameter stability, and energy consumption.

[0259] Through the systematic integration of the above-mentioned technical means, this step outputs a set of verification parameters that are dynamically adaptable, physically realizable, and meet the requirements of multi-objective optimization. This provides a highly reliable command benchmark for the online calibration of passive sensors, while effectively suppressing the impact of transient processes on verification accuracy through a time delay compensation mechanism.

[0260] In this embodiment, the system architecture achieves end-to-end hardware support for electromagnetic field detection, quantum noise suppression, and dynamic verification parameter generation through the collaborative design of a multimodal sensing network, a quantum preprocessing unit, and edge computing nodes. The complete technical disclosure follows from three dimensions: the sensing layer, the quantum processing layer, and the computing layer.

[0261] In terms of sensing layer hardware, a three-dimensional orthogonal electromagnetic induction array and a distributed environmental sensing network are constructed. The electromagnetic array consists of multiple sets of orthogonal coils made of gallium nitride (GaN) material. Preferably, each set of coils uses a double-helix winding process to improve magnetic field coupling efficiency. Each sensing unit is equipped with an independent signal conditioning circuit, including:

[0262] Low-noise amplifier: It adopts a current feedback operational amplifier with an equivalent input noise voltage density of less than 3nV√Hz, ensuring the ability to capture weak magnetic field signals;

[0263] Anti-aliasing filter: Designed as an 8th-order elliptic filter, the cutoff frequency is dynamically adjusted according to the sampling rate, and the stopband attenuation is greater than 80dB, effectively suppressing high-frequency interference;

[0264] Synchronization trigger circuit: Based on GPS-disciplined clock source, nanosecond-level synchronization pulses are generated to ensure time consistency of multi-channel sampling.

[0265] The environmental network consists of temperature and humidity sensor nodes and LoRa wireless communication modules. Preferably, the nodes adopt a hexagonal topology layout, and the spacing between adjacent nodes is adaptively adjusted according to the size of the detection area. Each node integrates a high-precision digital temperature and humidity sensor, and achieves conflict-free data transmission through the TDMA mechanism.

[0266] On the hardware side of the quantum processing layer, a superconducting quantum computing module is deployed to achieve noise suppression. Module:

[0267] Fluxonium-type superconducting qubits: their Josephson junctions adopt an Al / AlOx / Al three-layer structure, and the design parameters satisfy EJ / C≥5 for long coherence time;

[0268] Microwave control unit: Composed of a direct digital frequency synthesizer (DDS) and an IQ modulator, with a phase resolution better than 0.01 degrees, it can generate precise quantum state manipulation pulses;

[0269] Dynamic decoupling control circuit: integrates a fast pulse generator, supports an adjustable π pulse width range of 1-10ns for CPMG sequences, and a pulse interval adjustment accuracy of up to 10ps;

[0270] Low-temperature constant temperature system: A dilution refrigerator is used to maintain the quantum chip in an ultra-low temperature working environment below 10mK to ensure the stability of the quantum state.

[0271] On the computing layer hardware side, edge computing nodes are configured to implement field strength modeling and parameter optimization. The nodes include:

[0272] Heterogeneous computing unit: integrates FPGA and multi-core CPU, where FPGA is responsible for real-time data preprocessing and quantum tomography calculation, and CPU performs finite element modeling and genetic algorithm optimization;

[0273] High-speed data interface: The PCIeGen4 protocol is used to realize data transmission between the sensing layer and the computing layer, with a theoretical bandwidth of 16GB / s, which meets the real-time transmission requirements of four-dimensional field strength data.

[0274] Dynamic power management module: Adjusts the power supply voltage and clock frequency in real time according to the computing load. Preferably, it adopts an energy-saving strategy based on model predictive control (MPC) to reduce power consumption while ensuring computing performance.

[0275] In terms of hardware coordination mechanisms, a three-stage pipeline architecture is designed to improve system throughput:

[0276] Sensor acquisition pipeline: The electromagnetic induction array and environmental sensors sample in parallel, and the data is written directly to shared memory through the DMA channel;

[0277] Quantum processing pipeline: Superconducting quantum modules suppress noise in electromagnetic signals, and the processing results are converted into digital signals via a low-temperature amplifier and a room-temperature electronics link;

[0278] Edge computing pipeline: FPGA performs mesh discretization preprocessing of the field strength model, and CPU cluster completes nonlinear solution and parameter optimization.

[0279] In this embodiment, the hardware system achieves high-performance processing through the following technologies:

[0280] Material properties: The high electron mobility of gallium nitride coils ensures sensitive detection of high-frequency magnetic field signals;

[0281] Quantum advantage: The long coherence time of fluxonium qubits provides a sufficient time window for dynamic decoupling operations;

[0282] Computing architecture: The division of labor and cooperation among heterogeneous computing units balances real-time requirements and computational complexity.

[0283] Through the organic integration of the above hardware architecture, this system achieves full-process hardware support from multi-physics data acquisition to dynamic calibration parameter generation, providing a highly reliable hardware foundation for passive sensor calibration in complex electromagnetic environments.

[0284] In specific implementation, such as Figure 1 As shown, a passive sensor composite verification system based on dynamic field intensity modulation is used to execute the passive sensor composite verification method based on dynamic field intensity modulation, including:

[0285] Data acquisition module 110: Configured with a three-dimensional electromagnetic array and a multi-frequency stratum probe, it is connected to the quantum preprocessing module 120 through a high-speed data bus. It is used to transmit in real time the electromagnetic field X / Y / Z triaxial components, geological conductivity tensor, environmental temperature and humidity gradient and equipment load fluctuation parameters of the monitored object, and obtain the electromagnetic-temperature-humidity-load multidimensional coupling matrix.

[0286] Quantum preprocessing module 120: integrates a quantum sensing unit and a pulse controller, used to encode the triaxial components of the acquired electromagnetic field into superconducting qubits, suppress decoherence noise through CPMG pulse sequence, and dynamically adjust the CPMG pulse interval according to the noise main frequency and signal strength to obtain quantum state data; then the quantum state data is decoded into a classical computing unit, and the electromagnetic field characteristic data is obtained through principal component analysis for dimensionality reduction;

[0287] Field reconstruction module 130: It is equipped with a parallel computing unit, which constructs a four-dimensional spatiotemporal field strength model including a geological conductivity correction term based on the electromagnetic field characteristic data, monitors the field reconstruction error in real time based on the Lyapunov function, and triggers the computational grid refinement and step size reduction when the error change rate exceeds the threshold, until the optimized field strength distribution data is output.

[0288] Decoupling calculation module 140: used to perform singular value decomposition and regularization on the electromagnetic-temperature-humidity-load multidimensional coupling matrix based on the optimized field strength distribution data, and embed regional feature compensation function to obtain decoupling parameters;

[0289] Parameter generation module 150: used to orthogonalize the decoupling parameters, construct a transfer function model including a time delay factor, and output it to the passive sensor for verification.

[0290] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A passive sensor composite verification method based on dynamic field intensity modulation, characterized in that, Includes the following steps: S1: Multi-parameter synchronous acquisition The electromagnetic field X / Y / Z triaxial components of the monitored object are collected by a three-dimensional orthogonal electromagnetic induction array. Simultaneously, the geological conductivity tensor of the strata is measured using a multi-frequency probe, and the environmental temperature and humidity gradient and equipment load fluctuation parameters are obtained to obtain the electromagnetic-temperature-humidity-load multidimensional coupling matrix. S2: Quantum noise suppression processing The X / Y / Z triaxial components of the electromagnetic field acquired in step S1 are encoded into superconducting qubits. Decoherence noise is suppressed by CPMG pulse sequence, and the CPMG pulse interval is dynamically adjusted according to the noise dominance frequency and signal strength to obtain quantum state data. Subsequently, the quantum state data is decoded into a classical computing unit, and the electromagnetic field characteristic data is obtained by dimensionality reduction through principal component analysis. S3: Field Strength Modeling and Dynamic Optimization A four-dimensional spatiotemporal field strength model including a geological conductivity correction term is constructed based on the electromagnetic field characteristic data. The field reconstruction error is monitored in real time based on the Lyapunov function. When the error change rate exceeds the threshold, the computational grid is refined and the step size is reduced until optimized field strength distribution data is output. S4: Multiphysics Interference Separation Based on the optimized field strength distribution data, singular value decomposition and regularization are performed on the electromagnetic-temperature-humidity-load multidimensional coupling matrix, and a regional feature compensation function is embedded to obtain decoupling parameters. S5: Dynamic Verification Parameter Generation The decoupling parameters are orthogonalized to construct a transfer function model that includes a time delay factor, and the model is output to a passive sensor for verification.

2. The passive sensor composite verification method based on dynamic field intensity modulation according to claim 1, characterized in that, CPMG pulse interval in S2 satisfy: ； in, The dominant frequency of the decoherent noise was determined through real-time spectrum analysis. The noise power spectral density is calculated using Fourier transform; This represents the reference signal strength under interference-free conditions.

3. The passive sensor composite verification method based on dynamic field intensity modulation according to claim 1, characterized in that, The construction of the four-dimensional spacetime field strength model in S3 includes: A geological conductivity correction term is introduced into the electromagnetic field constitutive equation. This correction term is derived through the geological coupling coefficient. With formation conductivity tensor The trace multiplication is achieved; in, Calibration was performed using data measured by multi-frequency probes. It is generated by the three-dimensional distribution of the formation's electrical conductivity, dielectric constant, and magnetic permeability.

4. The passive sensor composite verification method based on dynamic field intensity modulation according to claim 1, characterized in that, The Lyapunov function in S3 is defined as follows: ； When the time derivative of the function is detected When the time is right, the mesh density is increased to 10 times the original density, and the iteration step size is reduced to 1 / 5 of the original value.

5. The passive sensor composite verification method based on dynamic field intensity modulation according to claim 1, characterized in that, The regional feature compensation function in S4 includes: Formation dielectric correction term, based on formation thickness The dielectric constant is corrected exponentially, with the correction factor decreasing by 5-6% for every 10-meter increase in thickness. The environmental humidity gradient factor adjusts the humidity weight according to seasonal or climatic characteristics, with the weight of high temperature and high humidity periods being 17-20% lower than that of low temperature and dry periods, and the transition period following a linear transition.

6. The passive sensor composite verification method based on dynamic field intensity modulation according to claim 1, characterized in that, The transfer function model in S5 is as follows: ； Among them, the time delay factor Calculated by the phase difference between the input and output signals. For the Laplacian operator, the fitting parameters Determined by the least squares method.

7. A passive sensor composite verification system based on dynamic field intensity modulation, used in the passive sensor composite verification method based on dynamic field intensity modulation as described in any one of claims 1-6, characterized in that, include: Data acquisition module (110): Configured with a three-dimensional electromagnetic array and a multi-frequency stratum probe, connected to the quantum preprocessing module (120) via a high-speed data bus, used to transmit in real time the electromagnetic field X / Y / Z triaxial components, geological conductivity tensor, environmental temperature and humidity gradient and equipment load fluctuation parameters of the monitored object, and obtain the electromagnetic-temperature-humidity-load multidimensional coupling matrix; Quantum preprocessing module (120): integrates a quantum sensing unit and a pulse controller, used to encode the triaxial components of the acquired electromagnetic field into superconducting qubits, suppress decoherence noise through CPMG pulse sequence, and dynamically adjust the CPMG pulse interval according to the noise main frequency and signal strength to obtain quantum state data; then decode the quantum state data into the classical computing unit, and obtain electromagnetic field characteristic data through principal component analysis for dimensionality reduction; Field reconstruction module (130): It is equipped with a parallel computing unit, which constructs a four-dimensional spatiotemporal field strength model containing a geological conductivity correction term based on the electromagnetic field characteristic data, monitors the field reconstruction error in real time based on the Lyapunov function, and triggers the computational grid densification and step size reduction when the error change rate exceeds the threshold, until the optimized field strength distribution data is output. Decoupling calculation module (140): used to perform singular value decomposition and regularization on the electromagnetic-temperature-humidity-load multidimensional coupling matrix based on the optimized field strength distribution data, and embed regional feature compensation function to obtain decoupling parameters; Parameter generation module (150): used to orthogonalize the decoupling parameters, construct a transfer function model containing a time delay factor, and output it to the passive sensor for verification.

8. The passive sensor composite verification system based on dynamic field intensity modulation according to claim 7, characterized in that, The quantum sensing unit in the quantum preprocessing module (120) specifically adopts a fluxonium superconducting quantum bit array; The array operates at a temperature of ≤20mK and is equipped with a dilution refrigerator to maintain its superconducting state. The timing jitter of the pulse controller is ≤1ns, and the sampling clock synchronization error between the pulse controller and the data acquisition module (110) is ≤0.1ns.

9. The passive sensor composite verification system based on dynamic field intensity modulation according to claim 7, characterized in that, The decoupling calculation module (140) specifically adopts a dedicated integrated circuit chip; The application-specific integrated circuit chip is equipped with a double-precision floating-point operation acceleration unit, which takes ≤10ms to decompose the coupling matrix in a single operation, and triggers the parameter generation module (150) to perform data reception by sending a hardware interrupt signal.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the passive sensor composite verification method based on dynamic field intensity modulation as described in any one of claims 1-6.

Images