A multi-channel eddy current sensor array and its data processing method

Abstract

The present invention belongs to the technical field of eddy current sensor detection, and discloses a multi-channel eddy current sensor array and a data processing method thereof. The method includes: dividing a detection area into N detection sub-areas according to a preset method, and allocating M multi-channel eddy current sensors to each detection sub-area; extracting features from the sensor signal data sets collected by the multi-channel eddy current sensors corresponding to the N detection sub-areas to obtain sensor signal feature data corresponding to the N detection sub-areas; constructing regions for the N detection sub-areas based on the sensor signal feature data corresponding to the N detection sub-areas to obtain R regions to be precisely detected; based on the sensor signal feature data corresponding to the N detection sub-areas, intelligently and adaptively allocating resources and parameter settings for the R regions to be precisely detected. The present invention dynamically sets the number of sub-area divisions, the number of sensors, and the sampling strategy according to the actual detection requirements of the region, meeting the increasingly complex requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of eddy current sensor detection, and more specifically, to a multi-channel eddy current sensor array and a data processing method thereof. Background Art

[0002] With the continuous improvement of the requirements for structural integrity assessment and defect detection accuracy in fields such as high-end manufacturing, aerospace, and energy equipment, the traditional single-channel eddy current detection method has been difficult to meet the rapid detection needs for large-area workpieces and complex-shaped structures. In the industry, a multi-channel eddy current sensor array structure has gradually emerged, which uses multiple channels to simultaneously collect electromagnetic response signals at multiple positions, improving the detection efficiency while enhancing the ability to capture local abnormal responses.

[0003] However, in practical engineering applications, with the expansion of the array scale and the complexity of the detection target structure, the existing technology still faces bottleneck problems. There is an obvious conflict between the arrangement density of the sensor array and the detection resolution: although the increase in the array size can cover a larger area, the resolution of a single channel decreases accordingly, and crosstalk may occur between channels, affecting the measurement stability; if high resolution is maintained, the probe arrangement density surges, resulting in high system manufacturing difficulty, high signal acquisition bandwidth, and a sharp increase in data processing pressure. In addition, in the multi-region simultaneous detection task, most of the existing solutions adopt a fixed partitioning and static resource allocation strategy, and fail to dynamically set the number of sub-region partitions, the number of sensors, and the sampling strategy according to the actual detection needs of the region, resulting in the inability to meet the increasingly complex requirements.

[0004] Therefore, how to construct a multi-channel eddy current sensor array system that supports heterogeneous region partitioning and intelligent resource scheduling on the basis of ensuring detection accuracy and response integrity, and realize full-process data-driven partition construction and resource allocation has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above object, the present invention provides the following technical solutions: A data processing method for a multi-channel eddy current sensor array, including:

[0006] Dividing the detection area into N detection sub-regions according to a preset method, and allocating M multi-channel eddy current sensors to each detection sub-region;

[0007] Performing feature extraction on the sensor signal data set collected by the multi-channel eddy current sensors corresponding to the N detection sub-regions to obtain sensor signal feature data corresponding to the N detection sub-regions;

[0008] Performing region construction on the N detection sub-regions based on the sensor signal feature data corresponding to the N detection sub-regions to obtain R regions to be precisely inspected;

[0009] Based on the sensor signal feature data corresponding to N detection sub-regions, resources and parameter settings are intelligently and adaptively allocated for R regions to be precisely inspected.

[0010] Furthermore, the method for intelligently and adaptively allocating resources and parameter settings for R regions to be precisely inspected includes:

[0011] S600: Let the initial value of r be 1, and the value range of r is from 1 to R;

[0012] S601: Denote the number of detection sub-regions in the r-th region to be precisely inspected as ; input the sensor signal feature data corresponding to detection sub-regions into the region demand assessment model respectively to obtain the corresponding region demand levels; the region demand levels include low demand, medium demand, and high demand;

[0013] S602: Construct the corresponding resource allocation feature data for each detection sub-region and the corresponding region demand level in the r-th region to be precisely inspected, and construct a resource allocation feature data set;

[0014] S603: Input the resource allocation feature data set of the r-th region to be precisely inspected into the resource configuration setting model to obtain a resource configuration pool; the resource configuration pool includes the allocation quantity of multi-channel eddy current sensors, scanning time, sampling frequency, resolution, and compression ratio;

[0015] S604: Allocate resources and set parameters for the r-th region to be precisely inspected according to the corresponding resource configuration pool;

[0016] S605: Let r = r + 1. If r is less than or equal to R, continue to execute S601 to S604; if r is greater than R, complete the intelligent and adaptive allocation of resources and parameter settings for R regions to be precisely inspected, and end the current process.

[0017] Furthermore, the method for obtaining the sensor signal feature data corresponding to N detection sub-regions includes:

[0018] S100: Let the initial value of n be 1, and the value range of n is from 1 to N;

[0019] S101: Obtain H groups of sensor signal data of the n-th detection sub-region according to the spatial arrangement order of the probe units of the multi-channel eddy current sensor, the real part matrix of impedance, the imaginary part matrix of impedance, the amplitude matrix of impedance, and the phase matrix; map the real part of impedance in the H groups of sensor signal data into the real part matrix of impedance; map the imaginary part of impedance in the H groups of sensor signal data into the imaginary part matrix of impedance; map the amplitude of impedance in the H groups of sensor signal data into the amplitude matrix of impedance; map the phase in the H groups of sensor signal data into the phase matrix;

[0020] S102: Analyze and process the phase based on the phase matrix to obtain the phase characteristic data of the nth detection sub-region; analyze and process the real part of the impedance based on the real part matrix of the impedance to obtain the real part characteristic data of the impedance of the nth detection sub-region; analyze and process the imaginary part and the magnitude of the impedance based on the imaginary part matrix and the magnitude matrix of the impedance to obtain the fusion characteristic data;

[0021] S103: Construct the sensor signal characteristic data of the nth detection sub-region from the phase characteristic data, the real part characteristic data of the impedance, and the fusion characteristic data;

[0022] S104: Let n = n + 1. If n is less than or equal to N, continue to execute S101 to S103; if n is greater than N, obtain the sensor signal characteristic data corresponding to N detection sub-regions, and end the current process.

[0023] Furthermore, the method for obtaining the phase characteristic data includes:

[0024] S200: Let the initial value of h be 1, and the value range of h is from 1 to H; calculate the phase mean value based on the phases in the phase matrix; let the initial value of the phase mutation quantity be 0;

[0025] S201: Obtain the hth phase from the phase matrix, obtain the adjacent phase to the hth phase from the phase matrix, denote it as the adjacent phase, and denote the number of adjacent phases as ;

[0026] Subtract the hth phase from each of the adjacent phases respectively and take the absolute value to obtain phase gradients; if there is a phase gradient greater than or equal to the preset phase gradient threshold among the phase gradients, mark the hth phase as a mutation phase point, and let the phase mutation quantity increase by 1;

[0027] S202: Let h = h + 1. If h is less than or equal to H, continue to execute S201; if h is greater than H, execute S203;

[0028] S203: Denote the number of mutation phase points as TBXW, calculate the phase mutation degree based on the phase mean value and the phases of the TBXW mutation phase points; divide the phase mutation quantity by H to obtain the phase mutation ratio;

[0029] S204: Construct the phase characteristic data from the phase mutation degree and the phase mutation ratio, and end the current process.

[0030] Furthermore, the method for obtaining the real part characteristic data of the impedance includes:

[0031] S300: Set the initial value of h to 1, where the value range of h is from 1 to H; calculate the mean value of the real part of impedance based on the real part of impedance in the real part of impedance matrix; set the initial value of the number of real part of impedance mutations to 0;

[0032] S301: Obtain the h-th real part of impedance from the real part of impedance matrix, obtain the adjacent real part of impedance adjacent to the h-th real part of impedance from the real part of impedance matrix, denote it as the adjacent real part of impedance, and denote the number of adjacent real parts of impedance as ;

[0033] Subtract the h-th real part of impedance from each of the adjacent real parts of impedance and take the absolute value to obtain gradients of the real part of impedance; if there is a gradient of the real part of impedance greater than or equal to the preset threshold of the gradient of the real part of impedance among the gradients of the real part of impedance, mark the h-th real part of impedance as a mutant real part of impedance point, and increment the number of real part of impedance mutations by 1;

[0034] S302: Let h = h + 1. If h is less than or equal to H, continue to execute S301; if h is greater than H, execute S303;

[0035] S303: Denote the number of mutant real part of impedance points as TBSB, calculate the degree of mutation of the real part of impedance based on the mean value of the real part of impedance and the real part of impedance of the TBSB mutant real part of impedance points; divide the number of real part of impedance mutations by H to obtain the mutation ratio of the real part of impedance;

[0036] S304: Construct the degree of mutation of the real part of impedance and the mutation ratio of the real part of impedance into the feature data of the real part of impedance, and end the current process.

[0037] Furthermore, the method for obtaining the fusion feature data includes:

[0038] S400: Let the initial value of h be 1, where the value range of h is from 1 to H; calculate the mean value of the imaginary part of impedance based on the imaginary part of impedance in the imaginary part of impedance matrix; calculate the mean value of the magnitude of impedance based on the magnitude of impedance in the magnitude of impedance matrix;

[0039] S401: Obtain the h-th imaginary part of impedance from the imaginary part of impedance matrix, obtain the h-th magnitude of impedance from the magnitude of impedance matrix, and construct the h-th fusion feature pair from the h-th imaginary part of impedance and the magnitude of impedance; add the fusion feature pair to the set of fusion feature pairs;

[0040] S402: Calculate the impedance imaginary part - magnitude ratio and the combined response amplitude corresponding to the h-th fusion feature pair based on the h-th fusion feature pair;

[0041] S403: Add the impedance imaginary part - magnitude ratio to the set of impedance imaginary part - magnitude ratios; add the combined response amplitude to the set of combined response amplitudes;

[0042] S404: Let h = h + 1. If h is less than or equal to H, continue to execute S401 to S403; if h is greater than H, execute S405;

[0043] S405: Calculate the fusion feature correlation coefficient based on the mean of the imaginary part of impedance, the mean of the magnitude of impedance, the set of imaginary part - magnitude ratio of impedance, and the set of combined response amplitudes;

[0044] S406: Construct the set of imaginary part - magnitude ratio of impedance, the set of combined response amplitudes, and the fusion feature correlation coefficient into fusion feature data, and end the current process.

[0045] Furthermore, the method for obtaining R regions to be precisely inspected includes:

[0046] S500: Let the initial value of n be 1, and the value range of n is from 1 to N; initialize the region construction flag of N detection sub - regions as not constructed; let the initial value of the region - to - be - precisely - inspected count variable R be 1;

[0047] S501: If the region construction flag of the nth detection sub - region is not constructed, execute S502; if the region construction flag of the nth detection sub - region is constructed, execute S505;

[0048] S502: Construct the detection sub - regions adjacent to the nth detection sub - region and with the region construction flag as not constructed into the region to be processed corresponding to the nth detection sub - region; convert the sensor signal feature data of the nth detection sub - region and the corresponding region to be processed into sensor signal feature vectors, and construct them into the Rth set of sensor signal feature vectors;

[0049] S503: Apply a clustering algorithm to classify the Rth set of sensor signal feature vectors; add the detection sub - regions corresponding to the sensor signal feature vectors in the same clustering cluster as the nth detection sub - region in the clustering result to the Rth set to be constructed;

[0050] Judge whether there is a detection sub - region adjacent to the detection sub - regions in the Rth set to be constructed and with the region construction flag as not constructed; if there is, convert the sensor signal feature data of the corresponding detection sub - region into a sensor signal feature vector, and add it to the Rth set of sensor signal feature vectors, and continue to execute S503; if not, execute S504;

[0051] S504: Construct the detection sub - regions in the set to be constructed into the Rth region to be precisely inspected, and let R = R + 1; set the region construction flag of the detection sub - regions in the Rth set to be constructed as constructed;

[0052] S505: Let \(n = n + 1\). If \(n\) is less than or equal to \(N\), then continue to execute S501 to S504; if \(n\) is greater than \(N\), then obtain \(R\) regions to be precisely inspected and end the current process.

[0053] Further, divide the detection region into \(N\) detection sub-regions according to a preset method, and allocate \(M\) multi-channel eddy current sensors to each detection sub-region, including:

[0054] Pre-collect the effective sensing range of the multi-channel eddy current sensors, the total area of the detection region, the material type of the detection region, and the structural type of the detection region; the material type of the detection region includes aluminum, copper, magnesium, titanium, and steel; the structural type of the detection region includes regular plane, curved structure, concave-convex surface, and multi-layer composite structure;

[0055] Numericalize the material type of the detection region to obtain the material type value of the detection region; numericalize the structural type of the detection region to obtain the structural type value of the detection region;

[0056] Input the effective sensing range of the multi-channel eddy current sensors, the total area of the detection region, the material type value of the detection region, and the structural type value of the detection region into the sub-region division parameter setting model to obtain the number of detection sub-regions and the number of multi-channel eddy current sensors. The number of detection sub-regions is \(N\); the number of multi-channel eddy current sensors is \(M\);

[0057] Divide the detection region into \(N\) equal detection sub-regions, and allocate \(M\) multi-channel eddy current sensors to each detection sub-region.

[0058] Further, the training method of the sub-region division parameter setting model includes:

[0059] Pre-construct a sub-region division parameter data set. The sub-region division parameter data set includes HF group sub-region division parameter data and the corresponding number of detection sub-regions and multi-channel eddy current sensors. HF is a positive integer greater than 0. The sub-region division parameter data includes the effective sensing range of the multi-channel eddy current sensors, the total area of the detection region, the material type value of the detection region, and the structural type value of the detection region. Divide the sub-region division parameter data set into a sub-region division parameter data training set and a sub-region division parameter data verification set. The sub-region division parameter data training set is used for parameter learning of the sub-region division parameter setting model, and the sub-region division parameter data verification set is used for real-time evaluation of the generalization ability of the sub-region division parameter setting model;

[0060] During the training process of the sub-region division parameter setting model, the sub-region division parameter setting model adopts a support vector machine model or a decision tree model, converts the sub-region division parameter data into feature vectors as inputs, extracts non-linear features in the data through a hidden layer, and finally generates the probability distributions of the number of detection sub-regions and the number of multi-channel eddy current sensors at the output layer using the softmax activation function, and outputs the number of detection sub-regions and the number of multi-channel eddy current sensors corresponding to the maximum probability as the final prediction result; the training process aims to minimize the cross-entropy loss function, and at the same time introduces an early stopping strategy to monitor the performance of the sub-region division parameter data validation set. When the prediction accuracy rate on the sub-region division parameter data validation set reaches the preset accuracy rate, it is considered that the sub-region division parameter setting model has converged, and the training stops immediately.

[0061] Further, the sensor signal data set includes H groups of sensor signal data, where H = M × B, and B is the number of probe units of the multi-channel eddy current sensor, that is, the number of channels; the sensor signal data includes the real part of impedance, the imaginary part of impedance, the amplitude of impedance, and the phase.

[0062] A multi-channel eddy current sensor array for implementing the data processing method of the multi-channel eddy current sensor array includes:

[0063] A first division module divides the detection area into N detection sub-regions according to a preset method and assigns M multi-channel eddy current sensors to each detection sub-region;

[0064] A first processing module extracts features based on the sensor signal data set collected by the multi-channel eddy current sensors corresponding to the N detection sub-regions to obtain the sensor signal feature data corresponding to the N detection sub-regions;

[0065] A second division module constructs regions for the N detection sub-regions based on the sensor signal feature data corresponding to the N detection sub-regions to obtain R regions to be refined for inspection;

[0066] A resource allocation module intelligently and adaptively allocates resources and parameter settings for the R regions to be refined for inspection based on the sensor signal feature data corresponding to the N detection sub-regions.

[0067] Compared with the prior art, the technical effects and advantages of the multi-channel eddy current sensor array and its data processing method of the present invention are as follows:

[0068] The multi-channel eddy current sensor array and its data processing method provided by the present invention integrate array signal acquisition, detection area construction based on feature driving, resource adaptive allocation, and priority scheduling strategies, and have an overall detection ability that is efficient, flexible, and intelligent.

[0069] By adaptively dividing the detection area according to the material type, structure type, and the effective sensing range of the sensor, multiple detection sub-areas are obtained. Combining the multi-channel sensor data collected from each sub-area, multi-dimensional signal features such as the real part of impedance, the imaginary part of impedance, impedance amplitude, and phase are extracted to construct a regional feature matrix. Based on the regional feature matrix, a clustering algorithm is executed to complete the dynamic construction of sub-areas with similar features, and further aggregated into a to-be-precisely-inspected area with controllable accuracy and reasonable distribution, significantly improving the physical consistency and detection effectiveness of the area division. In the stage of precisely-inspected resource allocation, the system generates a matching resource configuration pool according to the resource allocation feature data set of each to-be-precisely-inspected area, in combination with the resource evaluation model, to achieve intelligent and adaptive resource allocation and parameter setting. That is, the number of sub-area divisions, the number of sensors, and the sampling strategy are dynamically set according to the actual detection requirements of the area, so as to meet the increasingly complex requirements.

[0070] In addition, in the scenario of limited device resources, the present invention further introduces a comprehensive priority scoring mechanism to sort and schedule the to-be-precisely-inspected areas, giving priority to ensuring the detection integrity of the to-be-precisely-inspected areas with high comprehensive priority scores; and dynamically scheduling the occupied multi-channel eddy current sensors through a task release and recycling mechanism to achieve the maximization of resource utilization and the improvement of the overall system performance. Overall, the present invention can significantly improve the eddy current detection accuracy, efficiency, and adaptive ability under multi-region heterogeneous structures without increasing the additional hardware burden, and has good engineering feasibility and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic diagram of the structure of a multi-channel eddy current sensor array according to Embodiment 1 of the present invention;

[0072] Figure 2 It is a flowchart of a data processing method for a multi-channel eddy current sensor array according to Embodiment 2 of the present invention;

[0073] Figure 3 It is a flowchart of a data processing method for a multi-channel eddy current sensor array according to Embodiment 3 of the present invention;

[0074] Figure 4 It is a flowchart of a method for intelligent and adaptive allocation of resources and parameter setting for R to-be-precisely-inspected areas;

[0075] Figure 5 It is a schematic diagram of the software and hardware structure of a multi-channel eddy current sensor array according to Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0076] The following will describe in detail, clearly, and completely the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. It should be particularly noted that the specific embodiments described below are only used to better illustrate and explain the technical solutions of the present invention, aiming to enable those skilled in the art to better understand and implement the present invention, and should not be construed as a limitation on the protection scope of the present invention. Without departing from the spirit and essence of the present invention, those skilled in the art can modify, adjust, or make equivalent replacements according to the content disclosed in the present invention, and these should all be regarded as within the protection scope of the present invention.

[0077] Embodiment 1

[0078] Please refer to Figure 1 As shown, this embodiment discloses a multi-channel eddy current sensor array, including a first partitioning module, a first processing module, a second partitioning module, and a resource allocation module. Each module is connected by wire and / or wirelessly to achieve data transmission.

[0079] The first partitioning module divides the detection area into N detection sub-areas according to a preset method and assigns M multi-channel eddy current sensors to each detection sub-area.

[0080] The method of dividing the detection area into N detection sub-areas and setting M multi-channel eddy current sensors for each detection sub-area includes:

[0081] Pre-collect the effective induction range of the multi-channel eddy current sensor, the total area of the detection area, the material type of the detection area, and the structural type of the detection area; the material type of the detection area includes aluminum, copper, magnesium, titanium, steel, etc.; the structural type of the detection area includes regular plane, curved structure, concave-convex surface, multi-layer composite structure, etc.;

[0082] Numericalize the material type of the detection area to obtain the material type value of the detection area; numericalize the structural type of the detection area to obtain the structural type value of the detection area;

[0083] For example, aluminum can be numericalized to 1, copper to 2, magnesium to 3, titanium to 4, and steel to 5. The regular plane can be numericalized to 11, the curved structure to 12, the concave-convex surface to 13, and the multi-layer composite structure to 14.

[0084] Input the effective induction range of the multi-channel eddy current sensor, the total area of the detection area, the material type value of the detection area, and the structural type value of the detection area into the sub-area partitioning parameter setting model to obtain the number of detection sub-areas and the number of multi-channel eddy current sensors. The number of detection sub-areas is N; the number of multi-channel eddy current sensors is M;

[0085] The detection area is equally divided into N detection sub-areas, and M multi-channel eddy current sensors are allocated to each detection sub-area.

[0086] The training method of the sub-area division parameter setting model includes:

[0087] Pre-construct a sub-area division parameter data set, which includes sub-area division parameter data of the HF group and the number of detection sub-areas and the number of multi-channel eddy current sensors corresponding to the sub-area division parameter data of the HF group. HF is a positive integer greater than 0. The sub-area division parameter data includes the effective induction range of the multi-channel eddy current sensor, the total area of the detection area, the numerical value of the material type of the detection area, and the numerical value of the structure type of the detection area; the sub-area division parameter data set is divided into a sub-area division parameter data training set and a sub-area division parameter data verification set, where the sub-area division parameter data training set is used for parameter learning of the sub-area division parameter setting model, and the sub-area division parameter data verification set is used to evaluate the generalization ability of the sub-area division parameter setting model in real time;

[0088] During the training process of the sub-area division parameter setting model, the sub-area division parameter setting model adopts a support vector machine model or a decision tree model, converts the sub-area division parameter data into a feature vector as input, extracts non-linear features in the data through the hidden layer, and finally generates the probability distribution of the number of detection sub-areas and the number of multi-channel eddy current sensors at the output layer, and outputs the number of detection sub-areas and the number of multi-channel eddy current sensors corresponding to the maximum probability as the final prediction result; the training process aims to minimize the cross-entropy loss function, and at the same time introduces an early stopping strategy to monitor the performance of the sub-area division parameter data verification set. When the prediction accuracy on the sub-area division parameter data verification set reaches the preset accuracy, it is considered that the sub-area division parameter setting model has converged and the training stops immediately.

[0089] The first processing module extracts features from the sensor signal data set collected by the multi-channel eddy current sensors corresponding to the N detection sub-areas to obtain the sensor signal feature data corresponding to the N detection sub-areas. The sensor signal data set includes H groups of sensor signal data, H = M×B, B is the number of probe units of the multi-channel eddy current sensor, that is, the number of channels; the sensor signal data includes the real part of impedance, the imaginary part of impedance, the amplitude of impedance, and the phase; the real part of impedance refers to the resistance component, and the imaginary part of impedance refers to the inductive reactance component.

[0090] It should be noted that for M multi-channel eddy current sensors, each multi-channel eddy current sensor contains B independent-channel probe units. The value of B is determined by the hardware attributes of the multi-channel eddy current sensor itself. The system can obtain M×B groups of sensor signal data in each detection sub-area.

[0091] The method for obtaining the sensor signal characteristic data corresponding to N detection sub-regions includes:

[0092] S100: Let the initial value of n be 1, and the value range of n is from 1 to N;

[0093] S101: Obtain H groups of sensor signal data for the nth detection sub-region according to the spatial arrangement order of the probe units of the multi-channel eddy current sensor, the real part matrix of impedance, the imaginary part matrix of impedance, the amplitude matrix of impedance, and the phase matrix; Map the real part of impedance in the H groups of sensor signal data into the real part matrix of impedance; Map the imaginary part of impedance in the H groups of sensor signal data into the imaginary part matrix of impedance; Map the amplitude of impedance in the H groups of sensor signal data into the amplitude matrix of impedance; Map the phase in the H groups of sensor signal data into the phase matrix;

[0094] S102: Based on the phase matrix, analyze and process the phase to obtain the phase characteristic data of the nth detection sub-region; Based on the real part matrix of impedance, analyze and process the real part of impedance to obtain the real part characteristic data of impedance of the nth detection sub-region; Based on the imaginary part matrix of impedance and the amplitude matrix of impedance, analyze and process the imaginary part of impedance and the amplitude of impedance to obtain the fusion characteristic data;

[0095] S103: Construct the sensor signal characteristic data of the nth detection sub-region from the phase characteristic data, the real part characteristic data of impedance, and the fusion characteristic data;

[0096] S104: Let n = n + 1. If n is less than or equal to N, then continue to execute S101 to S103; If n is greater than N, then obtain the sensor signal characteristic data corresponding to N detection sub-regions, and end the current process.

[0097] The method for obtaining the phase characteristic data includes:

[0098] S200: Let the initial value of h be 1, and the value range of h is from 1 to H; Calculate the phase mean value based on the phases in the phase matrix; Let the initial value of the number of phase mutations be 0;

[0099] The method for obtaining the phase mean value includes:

[0100] ;

[0101] Wherein, is the phase mean value, h is the index variable in the summation formula, is the hth phase in the phase matrix.

[0102] S201: Obtain the hth phase from the phase matrix, obtain the phase adjacent to the hth phase from the phase matrix, denote it as the adjacent phase, and denote the number of adjacent phases as ;

[0103] Subtract the h-th phase from each of the adjacent phases respectively and take the absolute value to obtain phase gradients; if there is a phase gradient greater than or equal to a preset phase gradient threshold among the phase gradients, then mark the h-th phase as a mutation phase point and increment the phase mutation count by 1;

[0104] It should be noted that the phase gradient threshold can be obtained based on the statistical distribution of phase gradients in defect-free samples, and for example, the 95% or 99% quantile is extracted as the threshold.

[0105] S202: Let h = h + 1. If h is less than or equal to H, continue to execute S201; if h is greater than H, execute S203;

[0106] S203: Denote the number of mutation phase points as TBXW, and calculate the phase mutation degree based on the phase mean and the phases of the TBXW mutation phase points; divide the phase mutation count by H to obtain the phase mutation ratio;

[0107] The method for obtaining the phase mutation degree includes:

[0108] ;

[0109] where is the phase mutation degree, is the index variable of the summation formula, is the phase corresponding to the

[0110] S204: Construct phase feature data from the phase mutation degree and the phase mutation ratio, and end the current process.

[0111] It should be noted that the present invention further introduces two feature indicators, the phase mutation degree and the phase mutation ratio, to characterize the regions with drastic changes in local responses in the phase matrix.

[0112] Among them, the phase mutation degree is used to represent the magnitude of the phase difference between a certain channel and its adjacent channels, and can reflect the characteristics of local electromagnetic response delay changes caused by conductivity changes, thickness mutations, or crack propagation; the phase mutation ratio is used to count the proportion of channels exceeding the preset phase gradient threshold, and can reflect the extensiveness and aggregation trend of the defect region. Introducing the phase mutation degree and the phase mutation ratio can further improve the system's recognition ability for concealed defects (such as internal cracks and deep material heterogeneity), and enhance the spatial integrity of regional feature expression and the accuracy of anomaly recognition.

[0113] The method for obtaining the real part of impedance feature data includes:

[0114] S300: Set the initial value of h to 1, and the value range of h is from 1 to H; Calculate the mean value of the real part of impedance based on the real part of impedance in the real part of impedance matrix; Set the initial value of the number of real part of impedance mutations to 0;

[0115] The method for obtaining the mean value of the real part of impedance includes:

[0116] ;

[0117] where, is the mean value of the real part of impedance, h is the index variable in the summation formula, is the h-th real part of impedance in the real part of impedance matrix.

[0118] S301: Obtain the h-th real part of impedance from the real part of impedance matrix, obtain the real part of impedance adjacent to the h-th real part of impedance from the real part of impedance matrix, denote it as the adjacent real part of impedance, and denote the number of adjacent real parts of impedance as ;

[0119] Subtract the h-th real part of impedance from each of the adjacent real parts of impedance and take the absolute value to obtain real part of impedance gradients; If there is a real part of impedance gradient greater than or equal to the preset real part of impedance gradient threshold among the real part of impedance gradients, then mark the h-th real part of impedance as a mutant real part of impedance point, and increment the number of real part of impedance mutations by 1;

[0120] It should be noted that the real part of impedance gradient threshold can be obtained based on the statistical distribution of the real part of impedance gradients in the defect-free samples, and extract, for example, the 95% or 99% quantile as the threshold.

[0121] S302: Let h = h + 1, if h is less than or equal to H, then continue to execute S301; if h is greater than H, then execute S303;

[0122] S303: Denote the number of mutant real part of impedance points as TBSB, calculate the degree of real part of impedance mutation based on the mean value of the real part of impedance and the real part of impedance of the TBSB mutant real part of impedance points; Divide the number of real part of impedance mutations by H to obtain the real part of impedance mutation ratio;

[0123] The method for obtaining the degree of real part of impedance mutation includes:

[0124] ;

[0125] where, is the degree of real part of impedance mutation, is the index variable of the summation formula, is the The real part of the impedance corresponding to the real part points of a mutation impedance.

[0126] S304: Form the real part of the impedance feature data from the degree of mutation of the real part of the impedance and the proportion of mutation of the real part of the impedance, and end the current process.

[0127] It should be noted that in the present invention, by analyzing the response differences between channels in the real part of the impedance matrix, two characteristic indexes, namely the degree of mutation of the real part of the impedance and the proportion of mutation of the real part of the impedance, are constructed to characterize the abnormal local energy dissipation in the detection area.

[0128] Among them, the degree of mutation of the real part of the impedance is used to measure the maximum difference in the real part of the impedance value between a certain channel in the detection area and its adjacent channels, and can effectively reflect the structural mutation positions such as the crack tip and the edge of the corrosion pit; the proportion of mutation of the real part of the impedance statistically counts the proportion of the number of channels that meet the mutation conditions in the detection sub-area, and is used to reflect the overall non-uniformity degree and the spatial distribution density of the abnormal area. Introducing the degree of mutation of the real part of the impedance and the proportion of mutation of the real part of the impedance as features helps to quickly lock the high-risk sub-areas and reasonably schedule the inspection resources, improving the detection accuracy and efficiency.

[0129] The method for obtaining the fused feature data includes:

[0130] S400: Let the initial value of h be 1, and the value range of h is from 1 to H; calculate the mean value of the imaginary part of the impedance based on the imaginary part of the impedance in the imaginary part of the impedance matrix; calculate the mean value of the amplitude of the impedance based on the amplitude of the impedance in the amplitude of the impedance matrix;

[0131] S401: Obtain the h-th imaginary part of the impedance from the imaginary part of the impedance matrix, obtain the h-th amplitude of the impedance from the amplitude of the impedance matrix, and construct the h-th fused feature pair from the h-th imaginary part of the impedance and the amplitude of the impedance; add the fused feature pair to the set of fused feature pairs;

[0132] It should be noted that the channel positions corresponding to the h-th imaginary part of the impedance and the h-th amplitude of the impedance are the same.

[0133] S402: Calculate the impedance imaginary part - amplitude ratio and the combined response amplitude corresponding to the h-th fused feature pair based on the h-th fused feature pair;

[0134] S403: Add the impedance imaginary part - amplitude ratio to the set of impedance imaginary part - amplitude ratios; add the combined response amplitude to the set of combined response amplitudes;

[0135] S404: Let h = h + 1. If h is less than or equal to H, continue to execute S401 to S403; if h is greater than H, execute S405;

[0136] S405: Calculate the correlation coefficient of the fusion features based on the mean value of the imaginary part of the impedance, the mean value of the impedance amplitude, the set of imaginary part - amplitude ratios of the impedance, and the set of combined response amplitudes;

[0137] S406: Construct the set of imaginary part - amplitude ratios of the impedance, the set of combined response amplitudes, and the correlation coefficient of the fusion features into the fusion feature data, and end the current process.

[0138] The method for obtaining the mean value of the imaginary part of the impedance includes:

[0139] ;

[0140] Among them, is the mean value of the imaginary part of the impedance, h is the index variable in the summation formula, is the h - th imaginary part of the impedance in the imaginary part of the impedance matrix.

[0141] The method for obtaining the mean value of the impedance amplitude includes:

[0142] ;

[0143] Among them, is the mean value of the impedance amplitude, h is the index variable in the summation formula, is the h - th impedance amplitude in the impedance amplitude matrix.

[0144] The calculation method of the imaginary part - amplitude ratio of the impedance includes:

[0145] ;

[0146] Among them, is the imaginary part - amplitude ratio corresponding to the h - th fusion feature pair; the imaginary part - amplitude ratio of the impedance is used to reflect the proportion of the inductive reactance component in the overall response under the corresponding channel. An increase in the imaginary part - amplitude ratio of the impedance indicates phenomena such as thickness reduction and enhanced magnetic coupling, and has the significance of defect indication.

[0147] The calculation method of the combined response amplitude includes:

[0148] ;

[0149] Among them, is the h - th combined response amplitude. The combined response amplitude can be regarded as the modulus value of a two - dimensional vector composed of the inductive reactance characteristics and the overall response intensity, and is used to comprehensively reflect the change trend of the intensity and structural characteristics of the corresponding channel in the electromagnetic response. Compared with a single physical quantity, the combined response amplitude has stronger sensitivity and discrimination ability in identifying composite defects, boundary mutations, and material coupling changes, etc.;

[0150] The calculation method of the correlation coefficient of the fusion features includes:

[0151] ;

[0152] Wherein, is the correlation coefficient of the fusion feature. By centering (subtracting the average value) the imaginary part of the impedance and the amplitude of the impedance respectively, the offset interference caused by different amplitude bases between different detection sub-regions can be eliminated; the denominator is normalized by the standard deviation, making it more concerned about the "change trend" rather than the absolute value difference, which is suitable for cross-detection sub-region feature analysis. If the imaginary part of the impedance and the amplitude have a consistent fluctuation trend, it indicates that the response of this region is stable; if the fluctuations of the two are decoupled or the difference is significant, it means that there are structural abnormalities, signal disturbances or defects.

[0153] It should be noted that there are H groups of sensor signal data in the detection sub-region. The corresponding real part matrix of impedance, imaginary part matrix of impedance, amplitude matrix of impedance and phase matrix are obtained through the H groups of sensor signal data. Therefore, when processing the feature data, h is used to maintain consistency.

[0154] Based on the construction of the imaginary part matrix of impedance and the amplitude matrix of impedance, the present invention adopts a combined fusion extraction method to extract the fusion features of the two types of matrices. The imaginary part matrix of impedance represents the distribution of the inductive reactance component in each channel during the detection process, mainly reflecting the changes in material permeability, thickness and excitation coupling degree; the amplitude matrix of impedance reflects the comprehensive response intensity (impedance modulus) of each channel, comprehensively reflecting the conductivity, structural integrity and defect influence. Since there is a strong correlation between the imaginary part of the impedance and the amplitude in the detection physical mechanism, it is suitable for joint analysis to improve the feature interpretation ability. Through the fusion extraction of the imaginary part of the impedance and the amplitude data, the present invention can more comprehensively describe the composite change trend of the material properties and structural features inside the detection region.

[0155] The correlation coefficient of the fusion feature can effectively measure the change consistency between the imaginary part and the amplitude of the impedance in the detection sub-region, and be used as a reference basis for the regional response stability, structural integrity and defect determination. When the correlation coefficient of the fusion feature of the detection sub-region decreases significantly, it indicates that the response characteristics of this detection sub-region are disordered, and there are problems of composite defects, signal disturbances or material discontinuity.

[0156] The second partitioning module constructs regions for the N detection sub-regions based on the sensor signal feature data corresponding to the N detection sub-regions to obtain R regions to be refined;

[0157] The method for obtaining the R regions to be refined includes:

[0158] S500: Let the initial value of n be 1, and the value range of n is from 1 to N; initialize the region construction flag of the N detection sub-regions as not constructed; let the initial value of the counting variable R of the regions to be refined be 1;

[0159] S501: If the region construction flag of the nth detection sub-region is not constructed, then execute S502; if the region construction flag of the nth detection sub-region is already constructed, then execute S505;

[0160] S502: Construct the detection sub-regions adjacent to the nth detection sub-region and with the region construction flag not constructed into the to-be-processed region corresponding to the nth detection sub-region; convert the sensor signal feature data of the nth detection sub-region and the corresponding to-be-processed region into sensor signal feature vectors, and construct them into the Rth sensor signal feature vector set;

[0161] S503: Apply a clustering algorithm to the Rth sensor signal feature vector set for classification processing; add the detection sub-regions corresponding to the sensor signal feature vectors belonging to the same clustering cluster as the nth detection sub-region in the clustering result to the Rth to-be-constructed set; the clustering algorithm includes K-Means, DBSCAN;

[0162] Determine whether there is a detection sub-region adjacent to the detection sub-regions in the Rth to-be-constructed set and with the region construction flag not constructed; if so, convert the sensor signal feature data of the corresponding detection sub-region into a sensor signal feature vector, and add it to the Rth sensor signal feature vector set, and continue to execute S503; if not, then execute S504;

[0163] S504: Construct the detection sub-regions in the to-be-constructed set into the Rth to-be-precisely-inspected region, and let R = R + 1; set the region construction flag of the detection sub-regions in the Rth to-be-constructed set to already constructed;

[0164] S505: Let n = n + 1, if n is less than or equal to N, then continue to execute S501 to S504; if n is greater than N, then obtain R to-be-precisely-inspected regions, and end the current process.

[0165] It should be noted that the Rth sensor signal feature vector set and the Rth to-be-constructed set described in this specification are both used to represent the relevant data set and the detection object set corresponding to the "Rth to-be-precisely-inspected region", and there is a one-to-one correspondence among the three. Among them, the "Rth sensor signal feature vector set" represents the data input set participating in the clustering analysis, the "Rth to-be-constructed set" represents the set of detection sub-regions belonging to the same cluster after the clustering analysis, and finally the "Rth to-be-precisely-inspected region" is constructed. Therefore, the Rth to-be-precisely-inspected region and its corresponding feature vector set and to-be-constructed set maintain consistency and uniqueness during the process execution, and have a clear corresponding relationship.

[0166] By introducing a recursive spatial expansion mechanism, it is ensured that all feature-consistent and spatially adjacent detection sub-regions are included in the same region to be refined, effectively avoiding the phenomenon of region fragmentation caused by boundary jumps or scattered critical points. At the same time, through the combination of adjacent expansion and clustering attribution, an adaptive decision-making and minimal manual intervention in the region construction process are achieved, which is applicable to the data partitioning task of large-scale and high-resolution sensor arrays. The method for constructing the region to be refined not only has higher accuracy and robustness in the response space structure partitioning, but also provides a more targeted and controllable regional input basis for subsequent defect location and resource scheduling.

[0167] The resource allocation module intelligently and adaptively allocates resources and sets parameters for R regions to be refined based on the sensor signal feature data corresponding to N detection sub-regions.

[0168] As Figure 4 shown, the method for intelligently and adaptively allocating resources and setting parameters for R regions to be refined includes:

[0169] S600: Let the initial value of r be 1, and the value range of r is from 1 to R;

[0170] S601: Denote the number of detection sub-regions in the r-th region to be refined as ; Input the sensor signal feature data corresponding to detection sub-regions into the region demand assessment model respectively to obtain the corresponding region demand levels; the region demand levels include low demand, medium demand, and high demand;

[0171] S602: Construct the corresponding resource allocation feature data from each detection sub-region and the corresponding region demand level in the r-th region to be refined, and construct a resource allocation feature data set;

[0172] S603: Input the resource allocation feature data set of the r-th region to be refined into the resource configuration setting model to obtain a resource configuration pool; the resource configuration pool includes the allocation quantity of multi-channel eddy current sensors, scanning time, sampling frequency, resolution, and compression ratio;

[0173] S604: Allocate resources and set parameters for the r-th region to be refined according to the corresponding resource configuration pool;

[0174] S605: Let r = r + 1. If r is less than or equal to R, continue to execute S601 to S604; if r is greater than R, complete the intelligent and adaptive allocation of resources and parameter setting for R regions to be refined, and end the current process.

[0175] It should be noted that a schematic diagram of the software and hardware structure of a multi-channel eddy current sensor array is as Figure 5As shown in the figure, the multi-channel eddy current sensor array has a hardware layer and an algorithm layer. The hardware layer of the multi-channel eddy current sensor array includes a multi-channel array probe, a multi-channel acquisition unit, a precision displacement sensor, and a multi-axis adjustment bracket; the algorithm layer of the multi-channel eddy current sensor array includes a dynamic area division algorithm, a multi-dimensional feature extraction module, an adaptive clustering analysis, and a resource allocation optimization model; Exemplarily, a 16-channel acquisition unit can be used for the multi-channel acquisition unit, and a 6-axis adjustment bracket can be used for the multi-axis adjustment bracket. The area demand level is used to determine the resource guarantee level required for the area to be precisely inspected during the actual detection process. The higher the area demand level, the more significant the structural interference, signal interference enhancement, or defect masking risk in the area to be precisely inspected. Therefore, more sensor channels need to be allocated, the sampling frequency needs to be increased, the scanning time needs to be extended, or a low compression rate (retaining more details) needs to be adopted.

[0176] The resolution is used to control the spatial resolution of the response data collected or generated, the signal reconstruction accuracy, or the clarity level of the subsequent image visualization results during the detection process in the area to be precisely inspected. When performing multi-area detection tasks, it is possible to intelligently control the spatial resolution of data acquisition according to the actual detection requirements of the target area, thereby effectively compressing redundant data while ensuring detection accuracy, and improving the operating efficiency, computing resource utilization rate, and response speed of the overall system.

[0177] By introducing an area demand level evaluation and feature-driven resource evaluation mechanism, this method realizes the refined and intelligent allocation of detection resources based on the feature differences and demand differences of the internal detection sub-areas of each area to be precisely inspected. Compared with the traditional static mean allocation method, this method can dynamically match the number of sensors, scanning strategies, and data processing capabilities according to the complexity of the detection task, effectively improving the adaptability and resource utilization efficiency of the detection system in a multi-area and complex target environment.

[0178] In addition, the multi-channel eddy current sensors in each area to be precisely inspected are constructed into a multi-channel eddy current sensor array. Each multi-channel eddy current sensor array has the physical characteristics of being detachable or spliceable, facilitating free combination according to the workpiece size or detection area. Ordinary density probes can be used for large-scale rapid detection areas, and smaller probes with higher density can be used for key areas requiring high resolution.

[0179] The multi-channel eddy current sensors in the multi-channel eddy current sensor array are assembled on an adjustable support base, and fine adjustment of several degrees of freedom (height / tilt angle, etc.) is achieved through mechanisms such as springs, lead screws, or micro-motors. The support mechanism can automatically adapt to the workpiece surface with different curvatures during the installation stage or the detection process, reducing the gap difference between the probes of the multi-channel eddy current sensors and the workpiece.

[0180] The training method of the area demand evaluation model includes:

[0181] Pre-collect a regional risk assessment data set, where the regional risk assessment data set includes regional risk assessment data of the FX group and the corresponding regional demand levels for the regional risk assessment data of the FX group. FX is a positive integer greater than 0. The regional risk assessment data includes sensor signal feature data; divide the regional risk assessment data set into a training set and a validation set, where the training set is used to train a regional demand assessment model, and the validation set is used to evaluate the generalization performance of the regional demand assessment model;

[0182] During the training process of the regional demand assessment model, minimize the cross-entropy loss function as the optimization objective, use an early stopping strategy to monitor the performance of the validation set, and optimize the model performance by continuously adjusting network parameters; when the prediction accuracy on the validation set reaches the expected accuracy, it is considered that the regional demand assessment model has converged and stop training; the regional demand assessment model is trained based on a Naive Bayes model;

[0183] Convert the regional risk assessment data into a feature vector; the input layer of the regional demand assessment model receives the feature vector, extracts the non-linear relationship in the data through the hidden layer, and finally the output layer of the regional demand assessment model calculates the probability distribution of the regional demand level through the softmax activation function, and outputs the regional demand level corresponding to the maximum probability as the final prediction result.

[0184] The training method of the resource allocation setting model includes:

[0185] Pre-collect a resource allocation setting data set, where the resource allocation setting data set includes resource allocation setting data of the SD group and the corresponding resource allocation pool for the resource allocation setting data of the SD group. SD is a positive integer greater than 0. The resource allocation setting data includes a set of resource allocation feature data; divide the resource allocation setting data set into a training set and a validation set, where the training set is used to train a resource allocation setting model, and the validation set is used to evaluate the generalization performance of the resource allocation setting model;

[0186] During the training process of the resource allocation setting model, minimize the cross-entropy loss function as the optimization objective, use an early stopping strategy to monitor the performance of the validation set, and optimize the model performance by continuously adjusting network parameters; when the prediction accuracy on the validation set reaches the expected accuracy, it is considered that the resource allocation setting model has converged and stop training; the resource allocation setting model is trained using a Random Forest model;

[0187] Convert the resource allocation setting data into a feature vector; the input layer of the resource allocation setting model receives the feature vector, extracts the non-linear relationship in the data through the hidden layer, and finally the output layer of the resource allocation setting model calculates the probability distribution of the resource allocation pool through the softmax activation function, and outputs the resource allocation pool corresponding to the maximum probability as the final prediction result.

[0188] Embodiment 2

[0189] Please refer to Figure 2 As shown in the figure, this embodiment provides a data processing method for a multi-channel eddy current sensor array, including:

[0190] Divide the detection area into N detection sub-areas according to a preset method, and allocate M multi-channel eddy current sensors to each detection sub-area;

[0191] Based on the sensor signal data set collected by the multi-channel eddy current sensors corresponding to the N detection sub-areas, perform feature extraction to obtain the sensor signal feature data corresponding to the N detection sub-areas;

[0192] Based on the sensor signal feature data corresponding to the N detection sub-areas, perform area construction on the N detection sub-areas to obtain R areas to be precisely inspected;

[0193] Based on the sensor signal feature data corresponding to the N detection sub-areas, intelligently and adaptively allocate resources and parameter settings for the R areas to be precisely inspected.

[0194] Embodiment 3

[0195] Please refer to Figure 3 As shown in the figure, this embodiment provides a data processing method for a multi-channel eddy current sensor array, further including:

[0196] When the number of multi-channel eddy current sensor resources is limited, perform priority scheduling and allocation on the multi-channel eddy current sensors in the R areas to be precisely inspected.

[0197] The method for performing priority scheduling and allocation on the multi-channel eddy current sensors in the R areas to be precisely inspected includes:

[0198] Step 1: Input the resource allocation feature data set and the resource allocation pool of the R areas to be precisely inspected into the comprehensive priority evaluation model respectively to obtain the comprehensive priority scores corresponding to the R areas to be precisely inspected; sort the R areas to be precisely inspected in descending order according to the comprehensive priority scores to form an area priority queue; execute Step 2;

[0199] Step 2: Count the total number of available multi-channel eddy current sensors, denoted as the current available quantity; when the regional priority queue is not empty, execute Step 3; when the regional priority queue is empty, indicating that all regions to be precisely inspected have been allocated, then execute Step 5;

[0200] Step 3: Determine whether the current available quantity is greater than or equal to the number of multi-channel eddy current sensors allocated for the region to be precisely inspected corresponding to the head of the regional priority queue; if the condition is met, set the required number of multi-channel eddy current sensors in the resource configuration pool corresponding to the head of the regional priority queue, remove the head of the regional priority queue from the regional priority queue, and execute Step 2; if the condition is not met, then execute Step 4;

[0201] Step 4: The system enters the waiting state; when the multi-channel eddy current sensor resources are released by the regions to be precisely inspected that have completed tasks, execute Step 2;

[0202] Step 5: Complete the priority scheduling and allocation process of multi-channel eddy current sensors for R regions to be precisely inspected, and end the current scheduling process.

[0203] The training method of the comprehensive priority evaluation model includes:

[0204] Pre-collect a comprehensive priority evaluation data set, which includes ZH groups of comprehensive priority evaluation data and the corresponding comprehensive priority scores for the ZH groups of comprehensive priority evaluation data, where ZH is a positive integer greater than 0. The comprehensive priority evaluation data includes a set of resource allocation feature data and a resource configuration pool; divide the comprehensive priority evaluation data set into a training set and a validation set, where the training set is used to train the comprehensive priority evaluation model, and the validation set is used to evaluate the generalization performance of the comprehensive priority evaluation model;

[0205] During the training process of the comprehensive priority evaluation model, minimize the cross-entropy loss function as the optimization goal, use the early stopping strategy to monitor the performance of the validation set, and optimize the model performance by continuously adjusting the network parameters; when the prediction accuracy on the validation set reaches the expected accuracy, it is considered that the comprehensive priority evaluation model has converged, and stop the training; the comprehensive priority evaluation model is trained using a support vector machine model;

[0206] Convert the comprehensive priority evaluation data into feature vectors; the input layer of the comprehensive priority evaluation model receives the feature vectors, extracts the non-linear relationships in the data through the hidden layer, and finally the output layer of the comprehensive priority evaluation model calculates the probability distribution of the comprehensive priority scores through the softmax activation function, and outputs the comprehensive priority score corresponding to the maximum probability as the final prediction result.

[0207] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the said claims.

[0208] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data processing method for a multi-channel eddy current sensor array, characterized in that Including: Dividing the detection area into N detection sub-areas according to a preset method, and allocating M multi-channel eddy current sensors to each detection sub-area; Performing feature extraction based on the sensor signal data sets collected by the multi-channel eddy current sensors corresponding to the N detection sub-areas to obtain the sensor signal feature data corresponding to the N detection sub-areas; The method for obtaining the sensor signal feature data corresponding to the N detection sub-areas includes: S100: Let the initial value of n be 1, and the value range of n is from 1 to N; S101: According to the spatial arrangement order of the probe units of the multi-channel eddy current sensors, the real part matrix of impedance, the imaginary part matrix of impedance, the amplitude matrix of impedance, and the phase matrix, obtain H sets of sensor signal data of the nth detection sub-area; map the real part of impedance in the H sets of sensor signal data into the real part matrix of impedance; map the imaginary part of impedance in the H sets of sensor signal data into the imaginary part matrix of impedance; map the amplitude of impedance in the H sets of sensor signal data into the amplitude matrix of impedance; map the phase in the H sets of sensor signal data into the phase matrix; S102: Based on the phase matrix, analyze and process the phase to obtain the phase feature data of the nth detection sub-area; based on the real part matrix of impedance, analyze and process the real part of impedance to obtain the real part feature data of impedance of the nth detection sub-area; based on the imaginary part matrix of impedance and the amplitude matrix of impedance, analyze and process the imaginary part of impedance and the amplitude of impedance to obtain the fusion feature data; S103: Construct the sensor signal feature data of the nth detection sub-area from the phase feature data, the real part feature data of impedance, and the fusion feature data; S104: Let n = n + 1. If n is less than or equal to N, continue to execute S101 to S103; if n is greater than N, obtain the sensor signal feature data corresponding to the N detection sub-areas, and end the current process; Based on the sensor signal feature data corresponding to the N detection sub-areas, perform regional construction on the N detection sub-areas to obtain R areas to be precisely inspected; Based on the sensor signal feature data corresponding to the N detection sub-areas, perform intelligent adaptive resource allocation and parameter setting for the R areas to be precisely inspected; The method for performing intelligent adaptive resource allocation and parameter setting for the R areas to be precisely inspected includes: S600: Let the initial value of r be 1, and the value range of r is from 1 to R; S601: Denote the number of detection sub-regions in the r-th region to be precisely inspected as ; and input the sensor signal feature data corresponding to detection sub-regions into the regional demand assessment model respectively to obtain the corresponding regional demand levels; the regional demand levels include low demand, medium demand, and high demand; S602: Construct the corresponding resource allocation feature data from each detection sub-area and the corresponding area demand level in the rth area to be precisely inspected, and construct a resource allocation feature data set; S603: Input the resource allocation feature data set of the rth area to be precisely inspected into the resource configuration setting model to obtain a resource configuration pool; The resource configuration pool includes the allocation quantity of multi-channel eddy current sensors, the scanning time, the sampling frequency, the resolution, and the compression ratio; S604: Perform resource allocation and parameter setting for the rth area to be precisely inspected according to the corresponding resource configuration pool; S605: Let r = r + 1. If r is less than or equal to R, continue to execute S601 to S604; if r is greater than R, complete the intelligent adaptive resource allocation and parameter setting for the R areas to be precisely inspected, and end the current process.

2. The data processing method of a multi-channel eddy current sensor array according to claim 1, wherein The method for obtaining the phase feature data includes: S200: Set the initial value of h to 1, where the value range of h is from 1 to H; calculate the phase mean value based on the phases in the phase matrix; set the initial value of the phase mutation count to 0; S201: Obtain the h-th phase from the phase matrix, obtain the phase adjacent to the h-th phase from the phase matrix, denote it as the adjacent phase, and denote the number of adjacent phases as ; The h-th phase is respectively subtracted from adjacent phases, and the absolute value is taken to obtain phase gradients; if there is a phase gradient greater than or equal to a preset phase gradient threshold among the phase gradients, the h-th phase is marked as a mutation phase point, and the phase mutation count is incremented by 1; S202: Let h = h + 1. If h is less than or equal to H, continue to execute S201; if h is greater than H, execute S203; S203: Denote the number of mutant phase points as TBXW, calculate the phase mutation degree based on the phase mean value and the phases of the TBXW mutant phase points; divide the phase mutation count by H to obtain the phase mutation ratio; S204: Construct the phase feature data from the phase mutation degree and the phase mutation ratio, and end the current process.

3. The data processing method of a multi-channel eddy current sensor array according to claim 1, characterized in that The method for obtaining the real part of impedance feature data includes: S300: Set the initial value of h to 1, where the value range of h is from 1 to H; calculate the real part of impedance mean value based on the real parts of impedance in the real part of impedance matrix; set the initial value of the real part of impedance mutation count to 0; S301: Obtain the h-th real part of impedance from the real part matrix of impedance, obtain the real part of impedance adjacent to the h-th real part of impedance from the real part matrix of impedance, denote it as the adjacent real part of impedance, and denote the number of adjacent real parts of impedance as ; The real part of the h-th impedance is respectively subtracted from the real parts of the adjacent impedances, and the absolute value is taken to obtain the gradients of the real parts of the impedances; if there is a gradient of the real part of the impedance greater than or equal to the preset threshold of the gradient of the real part of the impedance among the gradients of the real parts of the impedances, then the real part of the h-th impedance is marked as a mutant point of the real part of the impedance, and the number of mutations of the real part of the impedance is incremented by 1; S302: Let h = h + 1. If h is less than or equal to H, continue to execute S301; if h is greater than H, execute S303; S303: Denote the number of mutant real part of impedance points as TBSB, calculate the real part of impedance mutation degree based on the real part of impedance mean value and the real parts of impedance of the TBSB mutant real part of impedance points; divide the real part of impedance mutation count by H to obtain the real part of impedance mutation ratio; S304: Construct the real part of impedance feature data from the real part of impedance mutation degree and the real part of impedance mutation ratio, and end the current process.

4. A data processing method for a multi-channel eddy current sensor array according to claim 1, characterized in that The method for obtaining the fusion feature data includes: S400: Set the initial value of h to 1, where the value range of h is from 1 to H; calculate the imaginary part of impedance mean value based on the imaginary parts of impedance in the imaginary part of impedance matrix; calculate the magnitude of impedance mean value based on the magnitudes of impedance in the magnitude of impedance matrix; S401: Obtain the h-th imaginary part of impedance from the imaginary part of impedance matrix, obtain the h-th magnitude of impedance from the magnitude of impedance matrix, and construct the h-th fusion feature pair from the h-th imaginary part of impedance and the magnitude of impedance; add the fusion feature pair to the fusion feature pair set; S402: Calculate the imaginary part of impedance - magnitude ratio and the combined response amplitude corresponding to the h-th fusion feature pair based on the h-th fusion feature pair; S403: Add the imaginary part of impedance - magnitude ratio to the imaginary part of impedance - magnitude ratio set; add the combined response amplitude to the combined response amplitude set; S404: Let h = h + 1. If h is less than or equal to H, continue to execute S401 to S403; if h is greater than H, execute S405; S405: Calculate the fusion feature correlation coefficient based on the imaginary part of impedance mean value, the magnitude of impedance mean value, the imaginary part of impedance - magnitude ratio set, and the combined response amplitude set; S406: Construct the fusion feature data from the imaginary part of impedance - magnitude ratio set, the combined response amplitude set, and the fusion feature correlation coefficient, and end the current process.

5. A data processing method for a multi-channel eddy current sensor array according to claim 1, characterized in that The method for obtaining R regions to be precisely inspected includes: S500: Set the initial value of n to 1, where the value range of n is from 1 to N; initialize the region construction flag of the N detection sub-regions as not constructed; set the initial value of the region count variable R for regions to be precisely inspected to 1; S501: If the region construction flag of the nth detection sub-region is not constructed, then execute S502; if the region construction flag of the nth detection sub-region is constructed, then execute S505; S502: Construct the detection sub-regions adjacent to the nth detection sub-region and with the region construction flag not constructed into the to-be-processed region corresponding to the nth detection sub-region; convert the sensor signal feature data of the nth detection sub-region and the corresponding to-be-processed region into sensor signal feature vectors, and construct them into the Rth sensor signal feature vector set; S503: Apply a clustering algorithm to classify the Rth sensor signal feature vector set; add the detection sub-regions corresponding to the sensor signal feature vectors in the same clustering cluster as the nth detection sub-region in the clustering result to the Rth to-be-constructed set; Judge whether there are detection sub-regions adjacent to the detection sub-regions in the Rth to-be-constructed set and with the region construction flag not constructed; if there are, then convert the sensor signal feature data of the corresponding detection sub-regions into sensor signal feature vectors, and add them to the Rth sensor signal feature vector set, and continue to execute S503; if not, then execute S504; S504: Construct the detection sub-regions in the to-be-constructed set into the Rth to-be-precisely-inspected region, and let R = R + 1; set the region construction flag of the detection sub-regions in the Rth to-be-constructed set to constructed; S505: Let n = n + 1, if n is less than or equal to N, then continue to execute S501 to S504; if n is greater than N, then obtain R to-be-precisely-inspected regions, and end the current process.

6. The data processing method of a multi-channel eddy current sensor array according to claim 1, characterized in that, Divide the detection region into N detection sub-regions according to a preset method, and allocate M multi-channel eddy current sensors to each detection sub-region, including: Pre-collect the effective sensing range of the multi-channel eddy current sensors, the total area of the detection region, the material type of the detection region, and the structural type of the detection region; the material type of the detection region includes aluminum, copper, magnesium, titanium, and steel; the structural type of the detection region includes regular plane, curved structure, concave-convex surface, and multi-layer composite structure; Numericalize the material type of the detection region to obtain the material type value of the detection region; numericalize the structural type of the detection region to obtain the structural type value of the detection region; Input the effective sensing range of the multi-channel eddy current sensors, the total area of the detection region, the material type value of the detection region, and the structural type value of the detection region into the sub-region division parameter setting model to obtain the number of detection sub-regions and the number of multi-channel eddy current sensors, where the number of detection sub-regions is N; the number of multi-channel eddy current sensors is M; Divide the detection region equally into N detection sub-regions, and allocate M multi-channel eddy current sensors to each detection sub-region.

7. A data processing method for a multi-channel eddy current sensor array according to claim 6, characterized in that The training method of the sub-region division parameter setting model includes: Pre-build a sub-region division parameter data set, where the sub-region division parameter data set includes HF group sub-region division parameter data, as well as the number of detection sub-regions and the number of multi-channel eddy current sensors corresponding to the HF group sub-region division parameter data. HF is a positive integer greater than 0. The sub-region division parameter data includes the effective sensing range of the multi-channel eddy current sensor, the total area of the detection region, the numerical value of the material type of the detection region, and the numerical value of the structure type of the detection region; divide the sub-region division parameter data set into a sub-region division parameter data training set and a sub-region division parameter data validation set, where the sub-region division parameter data training set is used for parameter learning of the sub-region division parameter setting model, and the sub-region division parameter data validation set is used to evaluate the generalization ability of the sub-region division parameter setting model in real time; During the training process of the sub-region division parameter setting model, the sub-region division parameter setting model uses a support vector machine model or a decision tree model, converts the sub-region division parameter data into a feature vector as input, extracts non-linear features in the data through the hidden layer, and finally generates the probability distribution of the number of detection sub-regions and the number of multi-channel eddy current sensors at the output layer, and outputs the number of detection sub-regions and the number of multi-channel eddy current sensors corresponding to the maximum probability as the final prediction result; the training process aims to minimize the cross-entropy loss function, and at the same time introduces an early stopping strategy to monitor the performance of the sub-region division parameter data validation set. When the prediction accuracy on the sub-region division parameter data validation set reaches the preset accuracy, it is considered that the sub-region division parameter setting model has converged, and the training stops immediately.

8. A data processing method for a multi-channel eddy current sensor array according to claim 1, characterized in that The sensor signal data set includes H groups of sensor signal data, where H = M × B, and B is the number of probe units of the multi-channel eddy current sensor, that is, the number of channels; the sensor signal data includes the real part of the impedance, the imaginary part of the impedance, the amplitude of the impedance, and the phase.

9. A multi-channel eddy current sensor array for implementing the data processing method of the multi-channel eddy current sensor array according to any one of claims 1-8, characterized in that, Including: The first division module divides the detection region into N detection sub-regions according to a preset method, and assigns M multi-channel eddy current sensors to each detection sub-region; The first processing module extracts features from the sensor signal data set collected by the multi-channel eddy current sensors corresponding to the N detection sub-regions to obtain the sensor signal feature data corresponding to the N detection sub-regions; The second division module constructs regions for the N detection sub-regions based on the sensor signal feature data corresponding to the N detection sub-regions to obtain R regions to be precisely inspected; The resource allocation module intelligently and adaptively allocates resources and parameter settings for the R regions to be precisely inspected based on the sensor signal feature data corresponding to the N detection sub-regions.

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