An array magnetic field excitation target tomography system and method

Abstract

The application relates to the technical field of magnetic heterogeneous target detection imaging, and discloses an array magnetic field excitation target tomography system and method. The system comprises an excitation signal module, an induction signal module, a signal processing module, an intelligent control module and a data storage and imaging module. The excitation signal module generates a time-varying excitation magnetic field through a central excitation coil to form a gradient distribution with an exponentially decaying intensity along the radial distance; the induction signal module synchronously detects target response magnetic field signals by using eight circumferentially uniformly distributed induction coils; the signal processing module introduces a time-varying correction factor to compensate for electromagnetic crosstalk among the coils, separates different depth components through Fourier-Bessel transformation, and constructs a target function to invert the three-dimensional distribution of the conductivity and magnetic permeability of the target body. The application simplifies the hardware structure, reduces the cost, effectively suppresses signal distortion, improves the imaging resolution and anti-interference ability, and realizes high-precision tomographic imaging of the electromagnetic properties and morphological structure of the target.

Description

Technical Field

[0001] This invention relates to the field of magnetic anomaly detection imaging technology, and in particular to a target tomography imaging system and method excited by an array magnetic field. Background Technology

[0002] In key areas such as national defense and security, mineral resource development, and engineering quality inspection, the demand for three-dimensional morphological identification and electromagnetic parameter analysis of magnetically anomalies such as ferromagnetic and paramagnetic targets within enclosed structures is becoming increasingly urgent. This demand is directly related to the accurate identification of targets (such as the location of concealed equipment in the defense field), the efficient exploration of resources (such as the determination of ore body boundaries in the mineral field), and the safety assessment of engineering projects (such as the detection of steel corrosion inside building structures), and is one of the core demands driving technological upgrades in related fields.

[0003] However, traditional magnetic anomaly detection technologies have significant limitations: existing technologies are mostly based on a single magnetic field sensor or a simple coil array, which can only achieve rough spatial positioning of the target and cannot further analyze the target's morphological characteristics (such as size, shape, and spatial distribution), nor can they distinguish the target's magnetic properties (such as differences in conductivity and permeability). This technical bottleneck of "only being able to locate, not image" makes it unable to meet the needs of high-precision detection scenarios. For example, it is difficult to accurately delineate the boundaries of ore bodies in mineral exploration, and it is impossible to accurately identify the morphology of defects inside closed structures in engineering inspection, which seriously restricts the technological development and application effectiveness in related fields.

[0004] To overcome the aforementioned technical limitations, magnetic anomaly tomography has emerged. The core value of this technology lies in its ability to accurately acquire magnetic field distribution data within the detection space, combined with specialized signal processing and inversion algorithms, to reconstruct the three-dimensional morphology and magnetic properties of hidden magnetic anomalies, ultimately achieving a crucial leap from "coarse localization" to "refined imaging" in target detection. However, existing magnetic anomaly tomography technologies still have shortcomings. For example, the use of multi-layer coil structures leads to high hardware complexity and expensive manufacturing costs; the lack of effective compensation for electromagnetic crosstalk between coils during signal processing causes signal distortion; and the inversion algorithm lacks depth constraints, resulting in low imaging resolution. Therefore, there is an urgent need for a simplified, signal-fidelity-preserving, and precisely imaging array magnetic field-excited target tomography scheme to meet practical application requirements. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide an array magnetic field-excited target tomography system and method, which aims to overcome the shortcomings of existing magnetic anomaly tomography techniques, such as complex hardware structure, large signal crosstalk interference, and low imaging resolution.

[0006] To achieve the aforementioned objectives, the technical solution adopted is as follows: A target tomographic imaging system based on array magnetic field excitation includes an excitation signal module, a sensing signal module, a signal processing module, an intelligent control module, and a data storage and imaging module.

[0007] The excitation signal module includes a signal generator, a power amplifier, and a central excitation coil. The signal generator is used to provide an AC voltage signal. The power amplifier is used to amplify the AC voltage signal and input it into the circuit where the central excitation coil is located, so that a changing current is generated in the central excitation coil, thereby generating a time-varying excitation magnetic field. The excitation magnetic field forms a gradient distribution in the detection space with an intensity that decreases exponentially with radial distance. The sensing signal module includes eight induction coils. The eight induction coils are coplanar with the central excitation coil and are evenly distributed in a circle. The magnetic field lines generated by the central excitation coil are perpendicular to the plane of the induction coils and enter the eight induction coils respectively. The induction coils are used to receive the response magnetic field signal generated by the target under the action of the excitation magnetic field and transmit the response magnetic field signal to the signal processing module. The signal processing module is used to acquire the response magnetic field signal, analyze the voltage and current characteristic parameters of the response magnetic field signal, compare the differences in characteristic parameters acquired by each induction coil to construct a mapping relationship, perform Fourier-Bessel transform on the response magnetic field signal to separate frequency domain components at different depths, introduce a time-varying correction factor to compensate for electromagnetic crosstalk between coils to construct a composite magnetic field characteristic model, use the vector method to discretize the target area and solve the induced current density distribution, and solve the electromagnetic field equation in a finite grid to invert the conductivity and permeability of the target body. The intelligent control module is used to set the excitation current sequence, control the excitation signal module to drive the central excitation coil according to the excitation current sequence, and simultaneously control the sensing signal module to collect signals synchronously and the signal processing module to perform signal analysis and processing. The data storage and imaging module consists of a data acquisition card, a data processing unit, and an image display unit. The data acquisition card is used to store various types of data generated by the excitation signal module, the induction signal module, and the signal processing module. The data processing unit is used to reconstruct the target magnetic field distribution based on the inverted conductivity and permeability data. The image display unit is used to output a three-dimensional tomographic image of the target.

[0008] As a further improvement of the present invention, the excitation current sequence is a multi-level current with intensity ranging from weak to strong, and the central excitation coil generates excitation magnetic fields of different intensities under the drive of the excitation current sequence, so as to achieve differentiated detection of the shallow to deep layers of the detection area.

[0009] 2. As a further improvement of the present invention, the time-varying correction factor is denoted as... The signal processing module calculates the amplitude change by comparing the airfield reference signal collected by the induction coil when there is no target with the target signal collected when there is a target. and phase change ,in, The composite magnetic field containing the target response is collected by the nth induction coil. This represents the reference magnetic field in the air field collected by the nth induction coil when there is no target. Let n be the magnetic field phase acquired by the nth induction coil when there is a target. The phase of the magnetic field of the nth induction coil is the reference phase of the targetless spatiotemporal field, combined with the time-varying correction factor. The coil crosstalk is compensated to obtain the compensated composite magnetic field. The composite magnetic field The expression is: ,in, For the m-th stage excitation current, The excitation magnetic field generated by the central excitation coil under the m-th level excitation current. To the angular frequency of the excitation signal, This represents the crosstalk field amplitude at the nth induction coil, extracted from the air field reference signal.

[0010] 3. As a further improvement of the present invention, the signal processing module processes the compensated composite magnetic field. When performing a Fourier-Bessel transform, the spatially distributed induced signal is decomposed into a combination of basis functions with different wavenumbers, each wavenumber component corresponding to a different depth region within the target body; simultaneously, phase changes are incorporated. and amplitude change Construct depth estimation parameters, which include the depth estimation parameters corresponding to the nth induction coil. and confidence weight Their expressions are as follows: ; Where c is the electromagnetic wave velocity in the medium. Let be the frequency of the excitation signal corresponding to the m-th stage excitation current. It is the maximum value among the absolute values ​​of the amplitude changes of the eight induction coils.

[0011] 4. As a further improvement of the present invention, the signal processing module is constructed with conductivity and permeability Joint objective function for variables The joint objective function is minimized through an iterative optimization algorithm to invert the three-dimensional conductivity and permeability distribution of the target body. The expression for the joint objective function is: ,in, For a forward model operator based on Maxwell's equations, The regularization parameter selected through cross-validation. For conductivity gradient, For the permeability gradient, As a confidence-weighted factor, As the confidence level weight, This is a depth estimate for the nth induction coil calculated based on the centroid method of conductivity distribution. The depth estimation parameter is the parameter corresponding to the nth induction coil.

[0012] A target tomographic imaging method excited by an array magnetic field, implemented based on any of the array magnetic field excited target tomographic imaging systems described above, includes the following steps: S1. The intelligent control module sets the excitation current sequence from weak to strong. When there is no target in the detection area, the control excitation signal module drives the central excitation coil sequentially according to the excitation current sequence, and records the original induced signals of each induction coil. Establish airfield reference data and output airfield reference signal set. And record the amplitude of each current level. and phase ; S2. Place the object to be tested into the detection area, and the intelligent control module controls the excitation signal module to follow the current sequence again. The central excitation coil is driven, and the eight induction coils of the induction signal module synchronously acquire composite magnetic field signals containing the target response. Establish target signal dataset And record the amplitude of each current level. With phase ; S3. The signal processing module processes the target signal dataset. Compared with airfield baseline data Compare and calculate the amplitude change. Phase change Introducing time-varying correction factors To compensate for electromagnetic crosstalk between coils, a compensated composite magnetic field is obtained. for: ; in, The excitation magnetic field generated by the central excitation coil under the m-th level excitation current. To the angular frequency of the excitation signal, This represents the crosstalk field amplitude at the nth induction coil, extracted from the air field reference signal.

[0013] S4. Signal processing module for the compensated composite magnetic field Perform Fourier-Bessel transform to separate components of different depths, and combine the phase difference. and amplitude attenuation The depth estimation parameters are obtained as follows: ; in, Here, c represents the depth estimation parameter corresponding to the nth induction coil, and c is the electromagnetic wave velocity in the medium. As confidence weights, the output signal set ; S5. The signal processing module discretizes the detection area into finite grid cells and combines them with the compensated composite magnetic field. With depth parameter set Establish based on conductivity and permeability Objective function for variables: ; in, For a forward model operator based on Maxwell's equations, The regularization parameter selected through cross-validation. For conductivity gradient, For the permeability gradient, As a confidence-weighted factor, As the confidence level weight, This is a depth estimate for the nth induction coil calculated based on the centroid method of conductivity distribution. The depth estimation parameters are for the nth induction coil.

[0014] The conductivity is continuously optimized through an iterative optimization algorithm. and permeability When the objective function The minimum point obtained when converging to the minimum value. The corresponding dielectric distribution is the desired three-dimensional conductivity and permeability distribution.

[0015] S6. Data storage and imaging module will store the original dataset. Target signal dataset Compensating magnetic field and inversion results Store the data and output a 3D distribution map in the imaging module.

[0016] As a further improvement of the present invention, the iterative optimization algorithm in step S5 includes, but is not limited to, gradient descent, conjugate gradient, and Newton's method. During the iteration process, the conductivity is updated by calculating the current value of the objective function and the gradient. and permeability The parameters are adjusted until the difference between two adjacent iterations of the objective function is less than a preset threshold, at which point the objective function is considered to have converged.

[0017] The beneficial effects of this invention are: 1. This invention employs a single-plane nine-coil layout, consisting of a central excitation coil and eight evenly distributed ring-shaped induction coils. Compared to the complex arrangement of traditional multi-layer coils, this scheme significantly simplifies hardware design and wiring, reducing system size and manufacturing costs.

[0018] 2. By introducing a time-varying correction factor to compare the empty field reference data with the target measurement data, electromagnetic coupling and crosstalk between different coils can be effectively compensated, ensuring the independence and authenticity of the induced signal and reducing artifacts and distortion.

[0019] 3. By performing Fourier-Bessel transform on the compensated composite magnetic field signal and constructing depth estimation parameters and confidence weights by combining phase difference and amplitude attenuation, the effective separation of different depth components of the target body can be achieved, thereby improving the longitudinal resolution of the system.

[0020] 4. This invention constructs a joint objective function, introducing confidence weights and centroid estimation as depth constraints on top of the traditional regularization term. This can suppress excessive oscillations while ensuring data fitting accuracy, thereby improving the stability and accuracy of three-dimensional conductivity and permeability inversion.

[0021] 5. Through comprehensive optimization of simplified structure, enhanced signal processing, and improved inversion algorithm, this invention achieves high-precision reconstruction of the three-dimensional conductivity and magnetic permeability distribution of the target area while maintaining hardware simplicity, resulting in higher imaging resolution and anti-interference capability. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the system workflow of the present invention; Figure 2 This is a flowchart of the iterative optimization algorithm of the present invention; Figure 3 This is a schematic diagram of the central excitation coil and eight uniformly distributed ring-shaped induction coils of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] like Figure 1-3 As shown, a target tomography system based on array magnetic field excitation includes an excitation signal module, a sensing signal module, a signal processing module, an intelligent control module, and a data storage and imaging module.

[0026] The excitation signal module includes a signal generator, a power amplifier, and a central excitation coil. The signal generator is used to provide an AC voltage signal. The power amplifier is used to amplify the AC voltage signal and input it into the circuit where the central excitation coil is located, so that a changing current is generated in the central excitation coil, thereby generating a time-varying excitation magnetic field. The excitation magnetic field forms a gradient distribution in the detection space with an intensity that decreases exponentially with radial distance. The sensing signal module includes eight induction coils. The eight induction coils are coplanar with the central excitation coil and are evenly distributed in a circle. The magnetic field lines generated by the central excitation coil are perpendicular to the plane of the induction coils and enter the eight induction coils respectively. The induction coils are used to receive the response magnetic field signal generated by the target under the action of the excitation magnetic field and transmit the response magnetic field signal to the signal processing module. The signal processing module is used to acquire the response magnetic field signal, analyze the voltage and current characteristic parameters of the response magnetic field signal, compare the differences in characteristic parameters acquired by each induction coil to construct a mapping relationship, perform Fourier-Bessel transform on the response magnetic field signal to separate frequency domain components at different depths, introduce a time-varying correction factor to compensate for electromagnetic crosstalk between coils to construct a composite magnetic field characteristic model, use the vector method to discretize the target area and solve the induced current density distribution, and solve the electromagnetic field equation in a finite grid to invert the conductivity and permeability of the target body. The intelligent control module is used to set the excitation current sequence, control the excitation signal module to drive the central excitation coil according to the excitation current sequence, and simultaneously control the sensing signal module to collect signals synchronously and the signal processing module to perform signal analysis and processing. The data storage and imaging module consists of a data acquisition card, a data processing unit, and an image display unit. The data acquisition card is used to store various types of data generated by the excitation signal module, the induction signal module, and the signal processing module. The data processing unit is used to reconstruct the target magnetic field distribution based on the inverted conductivity and permeability data. The image display unit is used to output a three-dimensional tomographic image of the target.

[0027] The excitation current sequence is a multi-level current with increasing intensity. The central excitation coil generates excitation magnetic fields of different intensities under the drive of the excitation current sequence, so as to achieve differentiated detection of the shallow to deep layers of the detection area.

[0028] 5. The time-varying correction factor is denoted as The signal processing module calculates the amplitude change by comparing the airfield reference signal collected by the induction coil when there is no target with the target signal collected when there is a target. and phase change ,in, The composite magnetic field containing the target response is collected by the nth induction coil. This represents the reference magnetic field in the air field collected by the nth induction coil when there is no target. Let n be the magnetic field phase acquired by the nth induction coil when there is a target. The phase of the magnetic field of the nth induction coil is the reference phase of the targetless spatiotemporal field, combined with the time-varying correction factor. The coil crosstalk is compensated to obtain the compensated composite magnetic field. The composite magnetic field The expression is: ,in, For the m-th stage excitation current, The excitation magnetic field generated by the central excitation coil under the m-th level excitation current. To the angular frequency of the excitation signal, This represents the crosstalk field amplitude at the nth induction coil, extracted from the air field reference signal.

[0029] 6. The signal processing module processes the compensated composite magnetic field. When performing a Fourier-Bessel transform, the spatially distributed induced signal is decomposed into a combination of basis functions with different wavenumbers, each wavenumber component corresponding to a different depth region within the target body; simultaneously, phase changes are incorporated. and amplitude change Construct depth estimation parameters, which include the depth estimation parameters corresponding to the nth induction coil. and confidence weight Their expressions are as follows: ; Where c is the electromagnetic wave velocity in the medium. Let be the frequency of the excitation signal corresponding to the m-th stage excitation current. It is the maximum value among the absolute values ​​of the amplitude changes of the eight induction coils.

[0030] 7. The signal processing module is constructed based on conductivity. and permeability Joint objective function for variables The joint objective function is minimized through an iterative optimization algorithm to invert the three-dimensional conductivity and permeability distribution of the target body. The expression for the joint objective function is: ,in, For a forward model operator based on Maxwell's equations, The regularization parameter selected through cross-validation. For conductivity gradient, For the permeability gradient, As a confidence-weighted factor, As the confidence level weight, This is a depth estimate for the nth induction coil calculated based on the centroid method of conductivity distribution. The depth estimation parameters are for the nth induction coil.

[0031] A target tomographic imaging method excited by an array magnetic field, implemented based on any of the array magnetic field excited target tomographic imaging systems described above, includes the following steps: S1. The intelligent control module sets the excitation current sequence from weak to strong. When there is no target in the detection area, the control excitation signal module drives the central excitation coil sequentially according to the excitation current sequence, and records the original induced signals of each induction coil. Establish airfield reference data and output airfield reference signal set. And record the amplitude of each current level. and phase ; S2. Place the object to be tested into the detection area, and the intelligent control module controls the excitation signal module to follow the current sequence again. The central excitation coil is driven, and the eight induction coils of the induction signal module synchronously acquire composite magnetic field signals containing the target response. Establish target signal dataset And record the amplitude of each current level. With phase ; S3. The signal processing module processes the target signal dataset. Compared with airfield baseline data Compare and calculate the amplitude change. Phase change Introducing time-varying correction factors To compensate for electromagnetic crosstalk between coils, a compensated composite magnetic field is obtained. for: ; in, The excitation magnetic field generated by the central excitation coil under the m-th level excitation current. To the angular frequency of the excitation signal, This represents the crosstalk field amplitude at the nth induction coil, extracted from the air field reference signal.

[0032] S4. Signal processing module for the compensated composite magnetic field Perform Fourier-Bessel transform to separate components of different depths, and combine the phase difference. and amplitude attenuation The depth estimation parameters are obtained as follows: ; in, Here, c represents the depth estimation parameter corresponding to the nth induction coil, and c is the electromagnetic wave velocity in the medium. As confidence weights, the output signal set ; S5. The signal processing module discretizes the detection area into finite grid cells and combines them with the compensated composite magnetic field. With depth parameter set Establish based on conductivity and permeability Objective function for variables: ; in, For a forward model operator based on Maxwell's equations, The regularization parameter selected through cross-validation. For conductivity gradient, For the permeability gradient, As a confidence-weighted factor, As the confidence level weight, This is a depth estimate for the nth induction coil calculated based on the centroid method of conductivity distribution. The depth estimation parameters are for the nth induction coil.

[0033] The conductivity is continuously optimized through an iterative optimization algorithm. and permeability When the objective function The minimum point obtained when converging to the minimum value. The corresponding dielectric distribution is the desired three-dimensional conductivity and permeability distribution.

[0034] S6. Data storage and imaging module will store the original dataset. Target signal dataset Compensating magnetic field and inversion results Store the data and output a 3D distribution map in the imaging module.

[0035] The iterative optimization algorithms in step S5 include, but are not limited to, gradient descent, conjugate gradient, and Newton's method. During the iteration process, the conductivity is updated by calculating the current value of the objective function and the gradient. and permeability The parameters are adjusted until the difference between two adjacent iterations of the objective function is less than a preset threshold, at which point the objective function is considered to have converged.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, component splitting or combination, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A target tomographic imaging system based on array magnetic field excitation, characterized in that: It includes an excitation signal module, a sensing signal module, a signal processing module, an intelligent control module, and a data storage and imaging module; The excitation signal module includes a signal generator, a power amplifier, and a central excitation coil. The signal generator is used to provide an AC voltage signal. The power amplifier is used to amplify the AC voltage signal and input it into the circuit where the central excitation coil is located, so that a changing current is generated in the central excitation coil, thereby generating a time-varying excitation magnetic field. The excitation magnetic field forms a gradient distribution in the detection space with an intensity that decreases exponentially with radial distance. The sensing signal module includes eight induction coils. The eight induction coils are coplanar with the central excitation coil and are evenly distributed in a circle. The magnetic field lines generated by the central excitation coil are perpendicular to the plane of the induction coils and enter the eight induction coils respectively. The induction coils are used to receive the response magnetic field signal generated by the target under the action of the excitation magnetic field and transmit the response magnetic field signal to the signal processing module. The signal processing module is used to acquire the response magnetic field signal, analyze the voltage and current characteristic parameters of the response magnetic field signal, compare the differences in characteristic parameters acquired by each induction coil to construct a mapping relationship, perform Fourier-Bessel transform on the response magnetic field signal to separate frequency domain components at different depths, introduce a time-varying correction factor to compensate for electromagnetic crosstalk between coils to construct a composite magnetic field characteristic model, use the vector method to discretize the target area and solve the induced current density distribution, and solve the electromagnetic field equation in a finite grid to invert the conductivity and permeability of the target body. The intelligent control module is used to set the excitation current sequence, control the excitation signal module to drive the central excitation coil according to the excitation current sequence, and simultaneously control the sensing signal module to collect signals synchronously and the signal processing module to perform signal analysis and processing. The data storage and imaging module consists of a data acquisition card, a data processing unit, and an image display unit. The data acquisition card is used to store various types of data generated by the excitation signal module, the induction signal module, and the signal processing module. The data processing unit is used to reconstruct the target magnetic field distribution based on the inverted conductivity and permeability data. The image display unit is used to output a three-dimensional tomographic image of the target. The time-varying correction factor is denoted as The signal processing module calculates the amplitude change by comparing the airfield reference signal collected by the induction coil when there is no target with the target signal collected when there is a target. and phase change ,in, The composite magnetic field containing the target response is collected by the nth induction coil. This represents the reference magnetic field in the air field collected by the nth induction coil when there is no target. Let n be the magnetic field phase acquired by the nth induction coil when there is a target. The phase of the magnetic field of the nth induction coil is the reference phase of the targetless spatiotemporal field, combined with the time-varying correction factor. The coil crosstalk is compensated to obtain the compensated composite magnetic field. The composite magnetic field The expression is: ,in, For the m-th stage excitation current, The excitation magnetic field generated by the central excitation coil under the m-th level excitation current. To the angular frequency of the excitation signal, This represents the crosstalk field amplitude at the nth induction coil extracted from the air field reference signal. The signal processing module processes the compensated composite magnetic field. When performing a Fourier-Bessel transform, the spatially distributed induced signal is decomposed into a combination of basis functions with different wavenumbers, each wavenumber component corresponding to a different depth region within the target body; simultaneously, phase changes are incorporated. and amplitude change Construct depth estimation parameters, which include the depth estimation parameters corresponding to the nth induction coil. and confidence weight Their expressions are as follows: ； Where c is the electromagnetic wave velocity in the medium. Let be the frequency of the excitation signal corresponding to the m-th stage excitation current. It is the maximum value among the absolute values ​​of the amplitude changes of the eight induction coils; The signal processing module is constructed based on conductivity. and permeability Joint objective function for variables The joint objective function is minimized through an iterative optimization algorithm to invert the three-dimensional conductivity and permeability distribution of the target body. The expression for the joint objective function is: ,in, For a forward model operator based on Maxwell's equations, The regularization parameter selected through cross-validation. For conductivity gradient, For the permeability gradient, As a confidence-weighted factor, As the confidence level weight, This is a depth estimate for the nth induction coil calculated based on the centroid method of conductivity distribution. The depth estimation parameters are for the nth induction coil.

2. The target tomographic imaging system based on array magnetic field excitation according to claim 1, characterized in that: The excitation current sequence is a multi-level current with increasing intensity. The central excitation coil generates excitation magnetic fields of different intensities under the drive of the excitation current sequence, so as to achieve differentiated detection of the shallow to deep layers of the detection area.

3. A target tomographic imaging method excited by an array magnetic field, characterized in that, The target tomographic imaging system based on the array magnetic field excitation of any one of claims 1-2 is implemented by including the following steps: S1. The intelligent control module sets the excitation current sequence from weak to strong. When there is no target in the detection area, the control excitation signal module drives the central excitation coil sequentially according to the excitation current sequence, and records the original induced signals of each induction coil. Establish airfield reference data and output airfield reference signal set. And record the amplitude of each current level. and phase ; S2. Place the object to be tested into the detection area, and the intelligent control module controls the excitation signal module to follow the current sequence again. The central excitation coil is driven, and the eight induction coils of the induction signal module synchronously acquire composite magnetic field signals containing the target response. Establish target signal dataset And record the amplitude of each current level. With phase ; S3. The signal processing module processes the target signal dataset. Compared with airfield baseline data Compare and calculate the amplitude change. Phase change Introducing time-varying correction factors To compensate for electromagnetic crosstalk between coils, a compensated composite magnetic field is obtained. for: ； in, The excitation magnetic field generated by the central excitation coil under the m-th level excitation current. To the angular frequency of the excitation signal, This represents the crosstalk field amplitude at the nth induction coil extracted from the air field reference signal. S4. Signal processing module for the compensated composite magnetic field Perform Fourier-Bessel transform to separate components of different depths, and combine the phase difference. and amplitude attenuation The depth estimation parameters are obtained as follows: ； in, Here, c represents the depth estimation parameter corresponding to the nth induction coil, and c is the electromagnetic wave velocity in the medium. As confidence weights, the output signal set ; S5. The signal processing module discretizes the detection area into finite grid cells and combines them with the compensated composite magnetic field. With depth parameter set Establish based on conductivity and permeability Objective function for variables: ； in, For a forward model operator based on Maxwell's equations, The regularization parameter selected through cross-validation. For conductivity gradient, For the permeability gradient, As a confidence-weighted factor, As the confidence level weight, This is a depth estimate for the nth induction coil calculated based on the centroid method of conductivity distribution. These are the depth estimation parameters corresponding to the nth induction coil; Continuously optimize through iterative optimization algorithms and When the objective function The minimum point obtained when converging to the minimum value. The corresponding dielectric distribution is the desired three-dimensional conductivity and permeability distribution; S6. Data storage and imaging module will store the original dataset. Target signal dataset Compensating magnetic field and inversion results Store the data and output a 3D distribution map in the imaging module.

4. The target tomographic imaging method excited by array magnetic field according to claim 3, characterized in that: The iterative optimization algorithms in step S5 include gradient descent, conjugate gradient, and Newton's method. During the iteration process, the conductivity is updated by calculating the current value of the objective function and the gradient. and permeability The parameters are adjusted until the difference between two adjacent iterations of the objective function is less than a preset threshold, at which point the objective function is considered to have converged.

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