Sensor-based automatic lead seal crack defect detection method and system

Abstract

The embodiment of the invention provides a sensor-based lead seal crack defect automatic detection method and system, and relates to the technical field of defect detection, and the method comprises the steps: employing a multi-frequency eddy current sensor array, and collecting the amplitude and phase information of an eddy current signal in a lead seal; processing the amplitude and phase information to eliminate the influence of the relative motion of the multi-frequency eddy current sensor array and the lead seal on eddy current signal acquisition, and obtaining calibration amplitude and calibration phase information; and determining the type of the lead sealing crack defect according to the calibration amplitude, the calibration phase information and a preset crack and corrosion block mass judgment rule. According to the invention, the accuracy, reliability and applicability of lead seal defect detection can be improved.

Description

Technical Field

[0001] This application relates to the field of defect detection technology, and in particular to an automatic detection method and system for lead seal crack defects based on sensors. Background Technology

[0002] Lead seals are a crucial means of tamper protection in the public utility sector, making integrity testing paramount. Traditional automated testing methods primarily rely on surface optical sensors to identify cracks. However, in real-world outdoor inspection environments, lead seals themselves may experience minor vibrations, and the instability of handheld testing equipment can easily lead to blurred surface images. More importantly, the flexible nature of lead seal materials means that cracks may exist in closed, subsurface, or internal forms, defects that surface optical methods cannot effectively detect. Therefore, existing technologies struggle to comprehensively and reliably detect the true defects in lead seals under complex dynamic environments, especially those hidden defects not visible on the surface, posing potential risks to product quality and safety.

[0003] When inspecting lead seals that have been in service for many years for crack detection in the field, existing automatic detection methods based on single-surface optical sensors face severe challenges. The core technical difficulty lies in how to accurately identify and distinguish between genuine cracks—closed, subsurface, or internally originating due to the plasticity of the lead seal material—and false feature signals generated by motion ambiguity, geometric distortion, or non-defect surface features (such as imprints or grain boundaries)—under the complex dynamic environment where the lead seal itself experiences minute vibrations or deformations, and the detection equipment is handheld, leading to instability in the relative position and attitude between the sensor and the lead seal. Since these genuine crack features may not be fully exposed on the surface, or may even be completely hidden inside the lead seal, and existing surface optical sensors cannot penetrate the material for detection, while the dynamic environment and operational instability severely interfere with the accurate capture of surface features, existing methods struggle to establish a reliable identification method. This results in numerous false alarms and missed detections, especially for internal defects, failing to meet the comprehensiveness and reliability requirements of actual inspection work. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a sensor-based automatic detection method and system for lead seal crack defects, aiming to solve the problem that existing technologies struggle to comprehensively and reliably detect the true defects of lead seals in complex dynamic environments, especially those hidden defects not exposed on the surface, and that in complex dynamic environments where the lead seal experiences minor vibrations or deformations, and the detection equipment is handheld, leading to unstable relative positions and attitudes between the sensor and the lead seal, it is difficult to accurately identify and distinguish between true cracks and false feature signals.

[0005] In a first aspect, embodiments of this application provide a sensor-based automatic detection method for lead seal crack defects, including: A multi-frequency eddy current sensor array is used to collect the amplitude and phase information of the eddy current signal inside the lead seal; The amplitude and phase information are processed to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the acquisition of eddy current signals, so as to obtain the calibration amplitude and calibration phase information. When the calibration amplitude and calibration phase information are consistent with the signal pattern of the preset crack judgment rule, the lead seal crack defect is determined to be a crack. When the calibration amplitude and calibration phase information are consistent with the signal pattern of the preset corrosion block determination rule, the lead seal crack defect is determined to be a corrosion block. When the calibration amplitude and calibration phase information are inconsistent with the signal modes of the preset crack judgment rules and the preset corrosion block judgment rules, the lead seal crack defect is determined to be an overlap of crack and corrosion block.

[0006] Furthermore, after determining that the lead seal crack defect is an overlap of crack and corrosion mass when the calibration amplitude, calibration phase information, and signal modes of the preset crack determination rules and preset corrosion mass determination rules are inconsistent, the method further includes: Based on the calibration amplitude and calibration phase information, the instantaneous rate of change, symmetry of change pattern, and multi-frequency transient correlation of eddy current signal are extracted. Based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation, the region is divided into cracks and corrosion clumps.

[0007] More specifically, in some implementation schemes, the steps for identifying cracks and corrosion clumps based on instantaneous rate of change, symmetry of change morphology, and multi-frequency transient correlation include: Based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation, abnormal regions containing composite defects are identified. The signal of the corroded agglomerates is extracted from the eddy current signal in the abnormal region to obtain the residual signal after extraction. The residual signal is analyzed a second time to identify the crack.

[0008] Building upon the above, this application further proposes that, following the steps of determining the cracks and corrosion clumps based on the instantaneous rate of change, the symmetry of the change morphology, and the multi-frequency transient correlation zone, the method further includes: Based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation, the spatial proximity relationship between cracks and corrosion masses is obtained. Based on spatial proximity, a correction strategy is selected, which includes at least one of the following: When spatial proximity indicates that a crack penetrates a corrosion mass, the depth and length of the crack are corrected based on the depth and width of the crack penetrating the corrosion mass. When spatial proximity indicates that a crack is propagating along the edge of a corrosion mass, the crack width and propagation direction are corrected based on the contact area between the crack and the edge of the corrosion mass and the signal interaction area.

[0009] Preferably, the step of selecting a correction strategy based on spatial proximity includes: The relative motion between the multi-frequency eddy current sensor array and the lead seal is sensed in real time by a laser rangefinder. Based on the relative motion between the multi-frequency eddy current sensor array and the lead seal, the judgment results of the spatial proximity relationship between cracks and corrosion clumps within multiple consecutive time windows are accumulated to obtain the cumulative judgment result; Based on the accumulated judgment results, a correction strategy is dynamically selected.

[0010] In some preferred embodiments, the step of dynamically selecting a correction strategy based on accumulated judgment results includes: A consistency assessment is performed on the spatial proximity relationship judgments within different time windows in the cumulative judgment results; Based on the consistency assessment results, identify areas where the judgment results are inconsistent; In regions where the judgment results are inconsistent, a refined judgment result is obtained by refining the spatial proximity relationship based on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal. Based on the refined judgment results, a correction strategy is selected.

[0011] Specifically, in regions where the judgment results are inconsistent, the steps for refining the spatial proximity judgment based on the instantaneous rate of change of the eddy current signal, the symmetry of the eddy current signal's change pattern, and the multi-frequency transient correlation of the eddy current signal include: In regions where the judgment results are inconsistent, high-resolution spatial sampling is performed on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain a refined feature map of the defect region. Adaptive threshold segmentation is performed on the refined feature map. The segmentation threshold is dynamically adjusted according to the local signal intensity and gradient changes to accurately identify the defect boundary and obtain the preliminary outline of the defect. Morphological processing is performed on the initial outline of the defect, including erosion, dilation, opening and closing operations, to smooth the outline, remove isolated noise points and connect fracture areas, thereby obtaining the refined geometric boundary of the defect. Based on the refined geometric boundary, calculate the geometric parameters of the defect region, including area, perimeter, aspect ratio, and roundness. Based on geometric parameters, combined with the instantaneous rate of change, symmetry of change pattern, and multi-frequency transient correlation of the eddy current signal in the defect area, a refined judgment of spatial proximity is made.

[0012] As a technological improvement, the steps following the dynamic selection of a correction strategy based on accumulated judgment results include: Calculate the variance or dispersion of instantaneous rate of change, symmetry of change pattern, and multi-frequency transient correlation; Assess the morphological irregularity of lead seal cracks based on variance or dispersion; Adjust the correction parameters of the correction strategy according to the irregularity of the shape; The cracks were corrected based on the adjusted correction parameters.

[0013] As a further improvement, the steps following the dynamic selection of the correction strategy based on the accumulated judgment results include: Obtain material property information for different material regions inside the lead seal; Based on material property information, the emission parameters of the eddy current signal are dynamically adjusted to optimize the eddy current penetration characteristics at different depths and in different regions. A hierarchical analysis was conducted on the instantaneous rate of change, the symmetry of the change pattern of the adjusted eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain the irregularity characteristics of defect morphology in different material regions. Based on the irregularity characteristics of defect morphology in different material regions and the material property information of different material regions, a weighted evaluation of the overall defect morphology irregularity is performed to obtain a weighted evaluation result. Based on the weighted evaluation results, the correction parameters of the correction strategy are adjusted.

[0014] Secondly, this application also discloses a sensor-based automatic detection system for lead seal crack defects, the system comprising: The eddy current signal acquisition module is used to acquire the amplitude and phase information of the eddy current signal inside the lead seal using a multi-frequency eddy current sensor array. The calibration module is used to process amplitude and phase information to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the acquisition of eddy current signals, and to obtain calibration amplitude and calibration phase information. The defect identification module is used to determine that the lead seal crack defect is a crack when the calibration amplitude and calibration phase information are consistent with the signal pattern of the preset crack judgment rule; to determine that the lead seal crack defect is a corrosion mass when the calibration amplitude and calibration phase information are consistent with the signal pattern of the preset corrosion mass judgment rule; and to determine that the lead seal crack defect is an overlap of crack and corrosion mass when the calibration amplitude and calibration phase information are inconsistent with the signal patterns of both the preset crack judgment rule and the preset corrosion mass judgment rule.

[0015] The technical solution according to the embodiments of this application has at least the following beneficial effects: This application discloses an automatic detection method for lead seal crack defects based on sensors. By employing a multi-frequency eddy current sensor array, it can penetrate the surface of the lead seal and collect the amplitude and phase information of the eddy current signal inside it, thereby effectively solving the technical problem that existing surface optical sensors cannot detect closed, subsurface, or internally initiated cracks. Furthermore, this application processes the collected amplitude and phase information to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the eddy current signal acquisition, obtaining calibrated amplitude and phase information. This step effectively overcomes the motion blur and geometric distortion problems caused by the slight vibration and deformation of the lead seal, as well as the instability of the handheld detection device, significantly improving the accuracy and stability of the signal. More importantly, by comparing the calibrated signal with the signal patterns of preset crack judgment rules and corrosion agglomeration judgment rules, this application can accurately distinguish between cracks, corrosion agglomerations, and composite defects where cracks and corrosion agglomerations overlap, avoiding false alarms and missed alarms caused by pseudo-feature signals generated by non-defect surface features in traditional methods. In summary, the method of this application can comprehensively and reliably detect the true defects of lead seals, especially those hidden defects that are not exposed on the surface. This greatly improves the accuracy, reliability, and applicability of lead seal defect detection, meets the requirements of comprehensiveness and reliability in actual inspection work, and effectively solves many challenges existing in the prior art.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0018] Figure 1 A schematic flowchart illustrating a sensor-based automatic detection method for lead seal crack defects provided in one embodiment of this application; Figure 2 This is a schematic diagram of a sensor-based automatic detection system for lead seal crack defects provided in one embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are described, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0021] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] Based on the above, this application proposes a sensor-based automatic detection method and system for lead seal crack defects. See also... Figure 1 , Figure 1 This is a schematic flowchart of a sensor-based automatic detection method for lead seal crack defects provided in one embodiment of this application.

[0023] This application provides an automatic detection method for lead seal crack defects based on sensors, including but not limited to steps S110 to S150, which are described in detail below.

[0024] Step S110: Use a multi-frequency eddy current sensor array to collect the amplitude and phase information of the eddy current signal inside the lead seal; Step S120: Process the amplitude and phase information to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the acquisition of eddy current signals, and obtain the calibration amplitude and calibration phase information. Step S130: When the calibration amplitude and calibration phase information are consistent with the signal mode of the preset crack judgment rule, the lead seal crack defect is determined to be a crack. Step S140: When the calibration amplitude and calibration phase information are consistent with the signal mode of the preset corrosion block determination rule, the lead seal crack defect is determined to be a corrosion block. Step S150: When the calibration amplitude and calibration phase information are inconsistent with the signal modes of the preset crack judgment rule and the preset corrosion block judgment rule, the lead seal crack defect is determined to be an overlap of crack and corrosion block.

[0025] Lead seals are a type of sealing device used to prevent tampering or unauthorized opening, commonly found in public utilities such as water meters and electricity meters. Their material is typically flexible, making them prone to internal defects.

[0026] A multi-frequency eddy current sensor array refers to an array composed of multiple eddy current sensors, each capable of operating at a different frequency. Eddy current detection is a non-contact detection method that detects internal defects in conductive materials by inducing eddy currents in the form of current. The use of multiple frequencies can provide information on defects of varying depths and types, while the array configuration can improve detection efficiency and coverage.

[0027] The amplitude and phase information of the eddy current signal are the basic outputs of eddy current testing. The amplitude information is usually related to the size and depth of the defect, while the phase information is related to the type and location of the defect, as well as the electrical conductivity, magnetic permeability and other properties of the material.

[0028] Crack identification rules and corrosion agglomeration identification rules are pre-established signal pattern libraries used to identify different types of defects. These rules are typically based on a large amount of experimental data and expert experience, and are trained and optimized using machine learning or pattern recognition algorithms.

[0029] The calibration amplitude and calibration phase information refer to the eddy current signal amplitude and phase data after processing to eliminate the influence of the relative motion between the sensor and the lead seal. These data more accurately reflect the true defect state inside the lead seal.

[0030] First, a multi-frequency eddy current sensor array is used to acquire the amplitude and phase information of the eddy current signal inside the lead seal. Specifically, the multi-frequency eddy current sensor array can be configured to simultaneously or sequentially emit electromagnetic waves at multiple preset frequencies and receive the eddy current signals generated inside the lead seal. For example, the sensor array can contain three independent sensor units operating at frequencies of 100kHz, 500kHz, and 1MHz respectively to obtain eddy current responses at different penetration depths. These sensor units can be integrated into a single probe, which scans the surface of the lead seal. During the acquisition process, there may be relative movement between the sensor array and the lead seal, such as due to hand operation or minor vibrations of the lead seal itself.

[0031] Subsequently, the acquired amplitude and phase information is processed to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the eddy current signal acquisition, obtaining calibrated amplitude and phase information. For example, signal filtering techniques, such as Kalman filtering or wavelet denoising, can be used to smooth the signal and remove random noise caused by motion. Furthermore, motion compensation algorithms, such as those based on data from an inertial measurement unit (IMU) or laser rangefinder, can be used to estimate the relative displacement and attitude changes between the sensor and the lead seal in real time, and correct the eddy current signal based on these changes. For instance, when the distance between the sensor and the lead seal changes, the amplitude of the eddy current signal will change accordingly; in this case, the amplitude can be normalized based on the distance change.

[0032] After obtaining the calibration amplitude and calibration phase information, the system compares this information with preset defect determination rules. Specifically, when the calibration amplitude and calibration phase information match the signal pattern of the preset crack determination rules, the lead seal crack defect is determined to be a crack. For example, cracks typically exhibit specific sharp changes or local anomalies in the phase diagram of eddy current signals, while they may appear as a signal decrease in the amplitude diagram. The preset crack determination rules may include threshold ranges, rates of change, or pattern recognition models for these characteristics.

[0033] Similarly, when the calibration amplitude and calibration phase information match the signal pattern of the preset corrosion agglomeration determination rule, the lead seal crack defect is identified as a corrosion agglomeration. Corrosion agglomerations typically cause localized changes in the material's electrical conductivity, which may manifest in eddy current signals as a gradual drift in amplitude or phase or a broad anomalous region. The preset corrosion agglomeration determination rule can include threshold ranges, trends, or pattern recognition models for these characteristics.

[0034] Finally, when the calibration amplitude and calibration phase information are inconsistent with the signal patterns of the preset crack determination rules and the preset corrosion agglomeration determination rules, the lead seal crack defect is determined to be an overlap of crack and corrosion agglomeration. This situation indicates that the defect may have composite properties, that is, it simultaneously exhibits the characteristics of both cracks and corrosion agglomerations, but a single rule cannot completely match it. For example, the signal may simultaneously show the sharp changes of cracks and the gradual drift of corrosion agglomerations, or the signal pattern may be in between, making it impossible to clearly classify it as a single defect.

[0035] This application fundamentally solves the problem of traditional optical inspection's inability to detect internal defects by introducing a multi-frequency eddy current sensor array. Eddy current detection technology can penetrate lead seal material and directly sense changes in the electromagnetic field caused by internal defects, thereby effectively identifying cracks and corrosion clumps hidden beneath the surface. For example, when a closed crack exists inside the lead seal, traditional optical sensors cannot capture any surface features, while the multi-frequency eddy current sensor array can detect the impact of the crack on the eddy current path by measuring the penetration depth of electromagnetic waves of different frequencies, and reveal specific abnormal patterns in the amplitude and phase information.

[0036] Furthermore, this application pays special attention to the dynamic nature of the inspection environment and the instability of operation. By processing the amplitude and phase information of the acquired eddy current signals to eliminate the influence of relative motion between the multi-frequency eddy current sensor array and the lead seal, this application significantly improves the robustness and accuracy of the inspection. For example, when the handheld inspection device experiences slight shaking, traditional optical images may become blurred, making it impossible to identify defects. However, this application calibrates the eddy current signals using a motion compensation algorithm, effectively removing noise and errors introduced by relative motion, ensuring that the calibrated amplitude and phase information truly reflect the defect state inside the lead seal.

[0037] Furthermore, this application can distinguish between different types of defects, including cracks, corrosion clumps, and overlaps of both. This refined defect identification capability is unmatched by existing technologies. For example, when both micro-cracks and localized corrosion clumps exist simultaneously inside a lead seal, traditional methods may only vaguely report the anomaly, while this application can accurately determine, based on calibrated eddy current signal patterns, whether it is a crack, a corrosion clump, or a composite defect of both overlapping. This ability to differentiate between them is of significant guiding importance for subsequent defect assessment and maintenance strategy development.

[0038] In summary, this application overcomes the limitations of existing technologies in detecting hidden defects inside lead seals under complex dynamic environments by employing a multi-frequency eddy current sensor array, performing signal calibration processing, and realizing multi-type defect identification. It significantly improves the comprehensiveness, accuracy, and reliability of the detection, providing a more advanced and effective solution for the quality control and safety assurance of lead seals.

[0039] In some embodiments of this application, when the calibration amplitude, the calibration phase information and the signal patterns of the preset crack judgment rule and the preset corrosion block judgment rule are inconsistent, the step of determining that the lead seal crack defect is an overlap of crack and corrosion block includes: extracting the instantaneous rate of change, the symmetry of the change pattern and the multi-frequency transient correlation of the eddy current signal according to the calibration amplitude and the calibration phase information. Based on the above instantaneous rate of change, the above symmetry of change morphology, and the above multi-frequency transient correlation zone, cracks and corrosion clumps are observed.

[0040] Specifically, the instantaneous rate of change of an eddy current signal refers to the speed at which the amplitude or phase of the eddy current signal changes over a very short time or spatial scale, reflecting the steepness of the defect boundary and the local gradient of the signal response. For example, cracks typically cause sharp local changes in the eddy current signal, while corrosion agglomerates may cause more gradual or diffuse changes. The symmetry of the change pattern can be understood as the degree of symmetry of the geometric shape or response mode of the eddy current signal in the defect region. Due to its irregular geometry and anisotropic propagation characteristics, cracks often result in an asymmetrical response mode of the eddy current signal; while certain types of corrosion agglomerates may exhibit relatively more symmetrical or uniform signal characteristics. In practical applications, multi-frequency transient correlation specifically refers to the temporal or spatial relationship and dynamic evolution of the amplitude and phase information of the eddy current signal at different eddy current frequencies. Eddy current fields of different frequencies have different penetration depths and sensitivities to defects. By analyzing the transient correlation between multi-frequency signals, the depth information, geometric structure, and material property changes of defects can be revealed, thereby more effectively separating and identifying overlapping crack and corrosion agglomerate signals.

[0041] This application's solution, through in-depth analysis of the instantaneous rate of change, symmetry of variation morphology, and multi-frequency transient correlation of eddy current signals, effectively solves the problem of difficulty in finely distinguishing between cracks and corrosion masses in lead seal defects where they overlap. The fundamental differences between cracks and corrosion masses in their physical properties, geometric morphology, and response mechanisms to eddy current fields allow for the effective extraction and utilization of these characteristics. Specifically, the instantaneous rate of change captures the local signal abrupt changes caused by cracks, while corrosion masses tend to produce smoother signal transitions; the symmetry of variation morphology utilizes the fact that cracks typically have irregular and asymmetrical geometric features, while corrosion masses may exhibit more uniform or symmetrical morphologies; and the multi-frequency transient correlation, by analyzing the differences in penetration depth and sensitivity of different frequency eddy current signals to defects, achieves layered analysis of overlapping signals, thereby separating the individual characteristics of cracks and corrosion masses from complex composite signals and achieving accurate differentiation between them.

[0042] In some embodiments of this application, the steps of identifying cracks and corrosion clumps based on instantaneous rate of change, symmetry of change morphology, and multi-frequency transient correlation include: Based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation, anomaly regions containing composite defects are identified; The signal of the corroded agglomerate is stripped from the eddy current signal in the abnormal region to obtain the residual signal after stripping. The residual signal is then analyzed a second time to identify the crack.

[0043] Specifically, identifying anomalous regions containing complex defects involves utilizing the instantaneous rate of change, symmetry of change patterns, and multi-frequency transient correlations of eddy current signals. This is achieved through pattern recognition or machine learning algorithms to analyze the acquired calibration amplitude and phase information, thereby determining areas where abnormal signal fluctuations or characteristic patterns match known complex defects (i.e., overlapping cracks and corrosion agglomerates). The aim is to initially locate potential complex defect regions, providing a target range for subsequent refined analysis.

[0044] The process of separating the corrosion agglomerate signal from the eddy current signal in the abnormal region to obtain the residual signal can be understood as follows: since the eddy current signal of the corrosion agglomerate typically exhibits relatively smooth and broad characteristics, while the crack signal shows sharp and localized variations, signal processing techniques, such as wavelet transform, independent component analysis (ICA), or nonnegative matrix factorization (NMF), can be used to separate the signal components of the corrosion agglomerate from the overall eddy current signal of the composite defect region. The residual signal after separation mainly contains the characteristic information of the crack, and its purpose is to eliminate the masking and interference of the corrosion agglomerate on the crack signal, making the crack characteristics more prominent.

[0045] In practical applications, secondary analysis is performed on the residual signal to identify cracks. Specifically, this involves analyzing the residual signal obtained after removing the corrosion agglomerate signal. Crack-specific signal pattern recognition algorithms, such as those based on edge detection, morphological processing, or deep learning models, are then applied to refine the analysis of the residual signal. By analyzing features such as the instantaneous rate of change, local gradient, and phase transition of the residual signal, the presence, location, orientation, and approximate size of the crack can be accurately identified. The aim is to improve the sensitivity and accuracy of crack detection after effectively suppressing the corrosion agglomerate signal.

[0046] This application's solution first identifies anomalous regions containing composite defects, focusing the analysis on the areas most likely to have overlapping defects, thus avoiding blind and time-consuming high-precision analysis of the entire lead seal. Subsequently, by extracting the signals from the eddy current signals of these anomalous regions, the interference and masking effect of corrosion agglomerates on crack signals is effectively eliminated, allowing previously obscured crack features to be clearly revealed. It is precisely because the corrosion agglomerate signals are effectively suppressed that secondary analysis of the residual signals after extraction can more accurately and sensitively capture the crack-specific signal patterns, thereby achieving precise crack identification and solving the problem of misjudgment or omission in traditional methods when dealing with overlapping defects.

[0047] In some embodiments of this application, the step of dividing the crack and corrosion agglomerate based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation includes: obtaining the spatial proximity relationship between the crack and the corrosion agglomerate based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation; selecting a correction strategy based on the spatial proximity relationship, the correction strategy including at least one of the following: when the spatial proximity relationship indicates that the crack penetrates the corrosion agglomerate, correcting the depth and length of the crack based on the depth and width of the crack penetrating the corrosion agglomerate; when the spatial proximity relationship indicates that the crack extends along the edge of the corrosion agglomerate, correcting the width and extension direction of the crack based on the contact area and signal interaction area between the crack and the edge of the corrosion agglomerate.

[0048] Specifically, obtaining the spatial proximity relationship between cracks and corrosion clumps refers to determining the relative position, distance, and possible contact or overlap patterns of cracks and corrosion clumps within the lead seal by analyzing the spatial distribution characteristics and mutual influence patterns of the instantaneous rate of change, symmetry of change morphology, and multi-frequency transient correlation of eddy current signals. For example, when the eddy current signal exhibits typical characteristics of a crack in a certain region, while exhibiting characteristics of a corrosion clump in its immediate vicinity, and a specific transition pattern exists between the two signals, a spatial proximity relationship can be inferred between them.

[0049] The selection of correction strategies refers to dynamically choosing one or more targeted correction methods based on the obtained spatial proximity relationship between the crack and the corrosion agglomerate to improve the measurement accuracy of defect characteristic parameters. The correction strategies include at least two specific cases: The first case is when the spatial proximity relationship indicates that the crack penetrates the corrosion agglomerate, meaning the crack may extend inside or through the corrosion agglomerate. In this case, the eddy current signal is affected by both the crack and the corrosion agglomerate. To accurately assess the true depth and length of the crack, the crack depth and length need to be corrected based on the depth and width information of the crack penetrating the corrosion agglomerate. For example, the presence of the corrosion agglomerate may cause attenuation or distortion of the eddy current signal, causing the directly measured crack depth and length to deviate from the actual values. In this case, compensation is needed by establishing a model or empirical formula. The second case is when the spatial proximity relationship indicates that the crack extends along the edge of the corrosion agglomerate, meaning the crack may grow along the boundary region of the corrosion agglomerate. In this case, the contact area between the crack and the edge of the corrosion agglomerate and the signal interaction area significantly affect the eddy current signal. To accurately assess the crack width and propagation direction, these parameters need to be corrected based on this interactive information. For example, the signal from the edge of a corrosion mass may overlap with the crack signal, leading to an overestimation or underestimation of the crack width, or an incorrect determination of its propagation direction. In such cases, corrections need to be made using signal separation or feature reconstruction techniques.

[0050] This application's solution, based on the differentiation of cracks and corrosion agglomerates, further analyzes the instantaneous rate of change, symmetry of change morphology, and multi-frequency transient correlation of eddy current signals to obtain the spatial proximity relationship between cracks and corrosion agglomerates. It is precisely because these characteristics reflect the local geometry of defects, changes in material properties, and the penetration and diffusion characteristics of the eddy current field at different frequencies that the system can identify whether cracks and corrosion agglomerates penetrate each other, extend along the edges, or are merely adjacent. Based on this, corresponding correction strategies are dynamically selected for different spatial proximity relationships. For example, for the case where a crack penetrates a corrosion agglomerate, the depth and length of the crack are corrected by considering the penetration depth and width; for the case where a crack extends along the edge of a corrosion agglomerate, the width and direction of the crack are corrected by considering the contact area and signal interaction region. This correction mechanism based on spatial proximity can effectively compensate for the influence of defect interactions on the eddy current signal, thereby avoiding the misjudgment and measurement errors that may occur when traditional methods deal with complex composite defects.

[0051] In some embodiments of this application, the step of selecting a correction strategy based on spatial proximity includes: The relative motion between the multi-frequency eddy current sensor array and the lead seal is sensed in real time by a laser rangefinder. Based on the relative motion between the multi-frequency eddy current sensor array and the lead seal, the judgment results of the spatial proximity relationship between the crack and the corrosion mass within multiple consecutive time windows are accumulated to obtain the cumulative judgment result; Based on the accumulated judgment results, a correction strategy is dynamically selected.

[0052] Specifically, real-time sensing of the relative motion between a multi-frequency eddy current sensor array and a lead seal using a laser rangefinder refers to acquiring precise distance and relative positional changes between the sensor array and the lead seal surface using a laser rangefinder. The laser rangefinder provides high-precision distance measurement, aiming to provide accurate spatial references for subsequent eddy current signal processing and defect localization, thereby eliminating or reducing positional errors caused by relative motion. In practical applications, the laser rangefinder can be integrated into the inspection equipment, working synchronously with the multi-frequency eddy current sensor array to output distance data in real time.

[0053] In this system, based on the relative motion between the multi-frequency eddy current sensor array and the lead seal, the system accumulates the judgment results on the spatial proximity relationship between cracks and corrosion clusters over multiple consecutive time windows to obtain a cumulative judgment result. This can be understood as the system not determining spatial proximity based solely on the judgment result at a single instant or a single scan point during the detection process, but rather integrating the judgment results from different time points or different scan areas. For example, along a continuous scan path, the system periodically judges the spatial proximity relationship between cracks and corrosion clusters and accumulates these judgment results over multiple consecutive time windows. This accumulation can be a simple count, a weighted average, or a more complex statistical analysis, with the aim of improving the confidence and stability of the judgment through the fusion of information from multiple points or time periods, effectively suppressing the influence of instantaneous noise and random errors.

[0054] Furthermore, dynamically selecting a correction strategy based on accumulated judgment results means that after obtaining more reliable spatial proximity relationship judgment results that have been accumulated and verified, the system can flexibly adjust the adopted correction strategy according to these results. For example, if the accumulated judgment result clearly indicates that the crack penetrates the corrosion mass, then a correction strategy for penetration-type defects is selected; if it indicates that the crack extends along the edge of the corrosion mass, then a correction strategy for edge-extending defects is selected. The purpose of this dynamic selection mechanism is to ensure that the selected correction strategy is highly matched with the actual defect morphology, thereby achieving more accurate defect parameter correction.

[0055] This application's solution, by introducing a laser rangefinder, enables real-time and precise sensing of the relative motion between a multi-frequency eddy current sensor array and the lead seal, providing a foundation for accurate spatial positioning and calibration of the eddy current signal. Based on this, by accumulating the judgment results of the spatial proximity relationship between cracks and corrosion clusters within multiple consecutive time windows, the randomness and uncertainty that may exist in a single instantaneous judgment are effectively overcome. It is precisely because of this multi-time-window cumulative judgment mechanism that the assessment of spatial proximity relationships becomes more stable and reliable, thereby enabling the dynamic selection of the most suitable correction strategy for the current defect morphology based on more accurate cumulative judgment results, avoiding correction deviations caused by misjudgments.

[0056] In some embodiments of this application, the step of dynamically selecting a correction strategy based on cumulative judgment results includes: A consistency assessment is performed on the spatial proximity relationship judgments within different time windows in the cumulative judgment results; Based on the consistency assessment results, identify areas where the judgment results are inconsistent; In the region where the judgment results are inconsistent, a refined judgment result is obtained by refining the spatial proximity relationship based on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal. Based on the refined judgment results, a correction strategy is selected.

[0057] Specifically, assessing the consistency of spatial proximity judgments across different time windows in the cumulative judgment results involves using statistical methods or pattern recognition algorithms to analyze whether there are significant differences or conflicts in the judgment results regarding the spatial proximity of cracks and corrosion clumps obtained at different time points or along different scanning paths. For example, the voting rate, confidence interval, or entropy value of the judgment results within different time windows can be calculated to quantify their level of consistency. The aim is to identify regions that exhibit unstable or ambiguous judgments over time.

[0058] The process of identifying areas with inconsistent judgments based on the consistency assessment results can be understood as marking the corresponding spatial regions as areas of inconsistency when the consistency assessment result is below a preset threshold. These regions are typically characterized by complex defects, severe signal interference, or local deviations in the sensor scanning path. The aim is to concentrate subsequent refined analysis resources on the areas most in need of further confirmation, thereby improving processing efficiency.

[0059] In practical applications, in areas where judgments are inconsistent, a more refined assessment of spatial proximity is achieved based on the instantaneous rate of change, the symmetry of eddy current signal variations, and the multi-frequency transient correlations of the eddy current signals. This involves utilizing these deeper-level eddy current signal characteristics to re-evaluate or confirm the spatial proximity of defects. For example, the instantaneous rate of change can reveal the sharpness of defect boundaries and the local gradient of the signal; the symmetry of the variation pattern can help distinguish the geometric features of regular cracks from irregular corrosion masses; and the multi-frequency transient correlations can provide defect information at different depths and material layers. By comprehensively analyzing these characteristics, it is possible to more accurately determine whether a crack penetrates a corrosion mass, propagates along its edge, or overlaps with a corrosion mass. The aim is to obtain more reliable information on defect spatial relationships in ambiguous regions.

[0060] Therefore, selecting a correction strategy based on the refined judgment results means determining the most suitable correction strategy for the current defect situation based on the more accurate spatial proximity relationship obtained after refined judgment. For example, if the refined judgment result clearly indicates that the crack penetrates the corrosion mass, then a correction strategy for the depth and width of the penetrating crack is selected; if it indicates that the crack extends along the edge of the corrosion mass, then a correction strategy for the width and direction of the edge-extending crack is selected. The purpose is to ensure that the selected correction strategy is highly matched with the actual spatial relationship of the defect, thereby improving the accuracy of the correction.

[0061] This application's solution, by introducing a consistency evaluation mechanism for accumulated judgment results, effectively identifies regions where judgment results are unstable due to measurement uncertainty, environmental interference, or defect complexity. It is precisely this identification that allows the system to concentrate limited computational resources and more advanced analytical methods on these critical inconsistency regions. By utilizing the instantaneous rate of change, symmetry of variation patterns, and multi-frequency transient correlations of eddy current signals for refined judgment in these regions, defect characteristics can be analyzed from a deeper and more comprehensive perspective. The instantaneous rate of change helps capture subtle changes in defect boundaries, symmetry of variation patterns distinguishes the geometric features of different types of defects, and multi-frequency transient correlations provide information on the defect's response at different depths. The comprehensive analysis of these refined features makes the judgment of the spatial proximity relationship between cracks and corrosion clusters more accurate and reliable, thereby avoiding bias in the selection of correction strategies due to initial judgment inconsistencies.

[0062] In some embodiments of this application, for regions where the judgment results are inconsistent, the step of refining the spatial proximity relationship based on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal includes: In the region where the judgment results are inconsistent, high-resolution spatial sampling is performed on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain a refined feature map of the defect region. The refined feature map is subjected to adaptive threshold segmentation. The segmentation threshold is dynamically adjusted according to the local signal intensity and gradient changes to accurately identify the defect boundary and obtain the preliminary outline of the defect. The initial contour of the defect is subjected to morphological processing, including erosion, dilation, opening and closing operations, to smooth the contour, remove isolated noise points and connect fracture areas, thereby obtaining the refined geometric boundary of the defect. Based on the refined geometric boundary, the geometric parameters of the defect region are calculated, including area, perimeter, aspect ratio, and roundness. Based on the geometric parameters, and combined with the instantaneous rate of change, symmetry of change pattern, and multi-frequency transient correlation of the eddy current signal in the defect area, the spatial proximity relationship is refined.

[0063] Specifically, high-resolution spatial sampling refers to acquiring eddy current signal data with a smaller step size or higher sampling density in areas where judgment results are inconsistent, thereby obtaining denser data points on instantaneous change rate, symmetry of change morphology, and multi-frequency transient correlation. Its purpose is to capture minute signal changes within the defect area, forming a refined feature map that more meticulously reflects the defect characteristics, providing a foundation for subsequent accurate analysis. Adaptive threshold segmentation can be understood as an image processing technique that does not use a single global threshold but dynamically adjusts the segmentation threshold based on the signal characteristics of local areas (such as local signal intensity and gradient changes). Its purpose is to overcome the limitations of traditional fixed threshold segmentation in handling complex backgrounds or uneven lighting (in this case, uneven eddy current signal intensity), thereby more accurately separating the defect area from the refined feature map and obtaining the preliminary outline of the defect. In practical applications, morphological processing is a series of nonlinear operations based on image shape. Erosion operations shrink the target region in an image, removing small noise points; dilation operations expand the target region, connecting broken areas; opening operations involve erosion followed by dilation, smoothing contours and removing small protrusions; closing operations involve dilation followed by erosion, smoothing contours and filling small holes. The aim is to optimize the initial contour, eliminate noise interference, and make the defect boundaries clearer, more continuous, and more accurate, ultimately obtaining the refined geometric boundary of the defect. Specifically, after obtaining the refined geometric boundary of the defect, image processing algorithms can be used to calculate the geometric properties of the region enclosed by this boundary. Area reflects the size of the defect; perimeter reflects the boundary length of the defect; aspect ratio reflects the shape extensibility of the defect; roundness reflects the degree to which the defect's shape approximates a circle. The purpose is to quantify the physical morphological characteristics of the defect, providing objective numerical basis for subsequent refined judgment. Among these, combining geometric parameters with eddy current signal characteristics for judgment refers to comprehensively analyzing the quantified defect geometric information with the original eddy current signal characteristics (instantaneous rate of change, symmetry of change morphology, multi-frequency transient correlation). For example, cracks typically have a small area, a large aspect ratio, and low roundness, and their instantaneous rate of change may exhibit sharp peaks in a specific direction; while corrosion clumps may have a large area, a low aspect ratio, and high roundness, and their signal changes may be more gradual or diffuse. The aim is to more accurately distinguish different types of defects and determine their spatial proximity relationships, such as penetration, edge extension, or overlap, through the fusion of multi-dimensional information.

[0064] This application's solution addresses the problem of traditional methods' difficulty in accurately identifying complex defect boundaries and determining spatial proximity relationships in regions with inconsistent judgment results by introducing a series of refined processing steps, including high-resolution spatial sampling, adaptive threshold segmentation, morphological processing, and geometric parameter calculation. Specifically, high-resolution spatial sampling can capture more subtle signal changes within the defect region, providing a data foundation for subsequent refined analysis. Adaptive threshold segmentation dynamically adjusts the segmentation strategy based on local signal characteristics, effectively addressing uneven eddy current signal intensity and complex backgrounds, thereby accurately outlining the initial contour of the defect. Subsequently, morphological processing further optimizes the defect contour, smooths the boundaries, removes noise, and connects fracture regions through operations such as erosion, dilation, opening, and closing operations, ultimately obtaining highly accurate refined geometric boundaries of the defect. Based on these refined geometric boundaries, key geometric parameters such as the defect's area, perimeter, aspect ratio, and roundness can be calculated, providing a quantitative description of the defect's morphology. Finally, by comprehensively analyzing these geometric parameters with the instantaneous rate of change, symmetry of change morphology, and multi-frequency transient correlation of the eddy current signal in the defect region, the judgment of the spatial proximity relationship between cracks and corrosion masses no longer relies solely on the macroscopic characteristics of the signal, but incorporates the microscopic geometric morphology of the defect, thereby significantly improving the accuracy and reliability of the judgment.

[0065] In some embodiments of this application described above, the steps following the cumulative judgment result and dynamic selection of the correction strategy include: Calculate the variance or dispersion of the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation; The morphological irregularity of the lead seal crack is assessed based on the variance or the dispersion. Based on the morphological irregularity, adjust the correction parameters of the correction strategy; The crack is corrected according to the adjusted correction parameters.

[0066] Specifically, calculating the variance or dispersion of instantaneous rate of change, morphological symmetry, and multi-frequency transient correlation refers to statistically analyzing these characteristics of eddy current signals acquired within the defect region. The instantaneous rate of change reflects the local gradient of the eddy current signal in space or time, and its variance or dispersion quantifies the sharpness or irregularity of the crack edge. Morphological symmetry describes the symmetry of the eddy current signal waveform, and its variance or dispersion indicates the symmetry deviation of the crack geometry. Multi-frequency transient correlation reveals the correlation between eddy current signals of different frequencies in the transient response, and its variance or dispersion reflects the complexity of the crack at different depths or material layers. By calculating these statistics, a quantitative description of the irregularity of the lead seal crack morphology can be obtained.

[0067] Furthermore, assessing the morphological irregularity of lead seal cracks based on variance or dispersion can be understood as comparing the calculated variance or dispersion values ​​with a preset threshold, or feeding them as input into a pre-trained classification model. For example, when the variance of the instantaneous rate of change exceeds a certain threshold, it may indicate that the crack has complex edges with serrated or branching patterns; when the dispersion of the symmetry of the change pattern is high, it may indicate that the crack geometry deviates from a simple linear or elliptical shape. The aim is to transform quantitative statistical characteristics into a qualitative or semi-quantitative assessment of the degree of crack morphological irregularity.

[0068] In practical applications, adjusting the correction parameters of the correction strategy based on morphological irregularity specifically refers to dynamically adjusting the specific parameters in the correction strategy previously selected based on spatial proximity, according to the evaluation results. For example, if the evaluation results show that the crack morphology is highly irregular, an additional correction factor can be added when correcting the crack depth and length to compensate for signal attenuation or underestimation of geometric dimensions caused by irregularity; or, for width correction, the weight of the average width calculation can be adjusted according to the degree of irregularity to better reflect the actual crack opening. The aim is to make the correction process more refined to adapt to crack morphologies of varying complexity.

[0069] This application's solution quantifies the morphological irregularity of lead seal cracks by introducing variance or dispersion calculations of eddy current signal characteristics. Since the irregularity of crack morphology is a key factor affecting the determination of its true size and shape, and traditional correction strategies based on spatial proximity may not adequately consider these subtle morphological differences, this solution enables a deeper understanding of the complex geometric characteristics of cracks through statistical analysis of core features such as instantaneous rate of change, symmetry of the change morphology, and multi-frequency transient correlations. Based on this, the correction parameters of the correction strategy are dynamically adjusted according to the assessed morphological irregularity, thereby ensuring that the correction process more accurately adapts to the actual crack morphology and avoids correction deviations caused by morphological complexity.

[0070] Through the above technical solution, this application can significantly improve the accuracy and robustness of lead seal crack defect detection. Especially when dealing with highly irregular or complex cracks, this solution can quantify their irregularity and adaptively adjust correction parameters accordingly, thereby achieving more precise correction of key geometric parameters such as crack depth, length, width, and propagation direction. This not only compensates for the shortcomings of traditional correction strategies in handling complex defect morphologies but also effectively reduces the false positive and false negative rates, providing more reliable data support for lead seal quality assessment.

[0071] In some embodiments of this application described above, the steps following the dynamic selection of a correction strategy based on the accumulated judgment results include: Obtain material property information for different material regions inside the lead seal; Based on the material property information, the emission parameters of the eddy current signal are dynamically adjusted to optimize the eddy current penetration characteristics at different depths and in different regions. A hierarchical analysis was performed on the instantaneous rate of change of the adjusted eddy current signal, the symmetry of the change pattern of the adjusted eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain the irregularity characteristics of the defect morphology in different material regions. Based on the irregularity characteristics of defect morphology in the different material regions and the material property information of the different material regions, a weighted evaluation of the overall defect morphology irregularity is performed to obtain a weighted evaluation result. Based on the weighted evaluation results, the correction parameters of the correction strategy are adjusted.

[0072] Specifically, obtaining material property information for different material regions inside the lead seal refers to determining whether different alloy compositions, impurity distributions, or layered structures exist inside the lead seal through prior material analysis, spectral detection, X-ray diffraction, or other non-contact material identification techniques. This material property information can include physical parameters such as electrical conductivity, magnetic permeability, and density, and its purpose is to provide basic data for subsequent adjustments to eddy current signal transmission parameters.

[0073] The dynamic adjustment of eddy current signal transmission parameters based on the material property information to optimize eddy current penetration characteristics at different depths and in different regions can be understood as adjusting the excitation frequency, current intensity, or pulse width of the multi-frequency eddy current sensor array in real time, taking into account the conductivity and permeability characteristics of different material regions. For example, for regions with high conductivity, the frequency can be appropriately reduced to increase the penetration depth; for regions with varying permeability, the excitation current may need to be adjusted to optimize the magnetic field distribution. The aim is to ensure that the eddy current signal can effectively penetrate different material layers and produce a sensitive response to defects at different depths and in different regions.

[0074] In practical applications, a hierarchical analysis is performed on the instantaneous rate of change, the symmetry of the morphology of the adjusted eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain the irregular characteristics of defect morphology in different material regions. This means that after acquiring the eddy current signal with optimized penetration characteristics, for the different material regions identified inside the lead seal, the eddy current signal characteristics (instantaneous rate of change, symmetry of morphology, and multi-frequency transient correlation) in these regions are analyzed independently or locally. For example, wavelet transform, Fourier analysis, or machine learning algorithms can be used to extract and quantify the signal features caused by defects in each material region, thereby obtaining the unique irregular characteristics of defect morphology in that region. The purpose is to avoid mutual interference of signal features from different material regions and improve the accuracy of defect feature extraction.

[0075] Furthermore, based on the irregularity characteristics of defect morphology in the different material regions and the material property information of these regions, a weighted evaluation of the overall defect morphology irregularity is performed. This weighted evaluation result means that, considering the potentially different degrees of influence of different material regions on the overall structural integrity of the lead seal, and the differences in the sensitivity of different materials to eddy current signal responses, the irregularity characteristics of defect morphology in each material region are weighted. For example, different weights can be assigned to the defect characteristics of different material regions based on the material's strength, toughness, or its criticality in the lead seal structure. The purpose is to comprehensively consider the impact of material heterogeneity on defect evaluation, forming a more comprehensive and realistic overall defect morphology irregularity evaluation.

[0076] Therefore, adjusting the correction parameters of the correction strategy based on the weighted evaluation results means refining the specific parameters of the correction strategy previously selected based on spatial proximity (e.g., depth and width correction when a crack penetrates a corrosion mass, or width and direction correction when it extends along an edge) based on the aforementioned weighted evaluation results. For example, if the weighted evaluation results show high irregularity of defects in a critical material region, it may be necessary to significantly correct the crack depth or width to ensure the conservatism and safety of the evaluation. The aim is to enable the correction strategy to better adapt to the complex material structure and defect morphology inside the lead seal, thereby improving the accuracy and reliability of defect evaluation and correction.

[0077] This application's solution addresses the inaccurate defect assessment issues that may arise when dealing with lead seals of heterogeneous materials by incorporating consideration of the internal material properties. Specifically, firstly, material property information for different material regions is acquired, providing a data foundation for subsequent signal optimization. Secondly, based on this material property information, the emission parameters of the eddy current signal are dynamically adjusted to ensure that the eddy current field achieves optimal penetration depth and sensitivity for different material regions, thereby obtaining more realistic and effective defect response signals. Furthermore, layered analysis of the adjusted eddy current signal avoids mutual interference between signals from different material regions, allowing the irregularity of defect morphology in each region to be extracted independently and accurately. Finally, by combining the irregularity of defect morphology in different material regions with their material property information for weighted evaluation, this solution comprehensively considers the impact of material heterogeneity on defect assessment, resulting in a more comprehensive and realistic overall defect morphology irregularity assessment. It is precisely this refined handling of material heterogeneity that allows for more precise adjustment of the correction parameters of the correction strategy, significantly improving the accuracy of defect detection and correction for complex lead seals.

[0078] See Figure 2 , Figure 2This is a schematic diagram of a sensor-based automatic detection system for lead seal crack defects provided in one embodiment of this application. Specific embodiments of this application also disclose a sensor-based automatic detection system for lead seal crack defects. The sensor-based automatic detection system for lead seal crack defects 200 includes: Eddy current signal acquisition module 210 is used to acquire the amplitude and phase information of eddy current signals inside the lead seal using a multi-frequency eddy current sensor array. The calibration module 220 is used to process the amplitude and phase information to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the acquisition of the eddy current signal, and to obtain calibration amplitude and calibration phase information. The defect identification module 230 is used to determine that the lead seal crack defect is a crack when the calibration amplitude and the calibration phase information are consistent with the signal pattern of the preset crack judgment rule; to determine that the lead seal crack defect is a corrosion mass when the calibration amplitude and the calibration phase information are consistent with the signal pattern of the preset corrosion mass judgment rule; and to determine that the lead seal crack defect is an overlap of crack and corrosion mass when the calibration amplitude and the calibration phase information are inconsistent with the signal patterns of both the preset crack judgment rule and the preset corrosion mass judgment rule.

[0079] Specifically, the eddy current signal acquisition module can consist of multiple independent eddy current probes, each configured to operate at a specific frequency. For example, a set of fixed-frequency probes or adjustable-frequency probes can be used. These probes are connected to a signal generator and a data acquisition unit via cables. In a preferred embodiment, the eddy current signal acquisition module can be an integrated probe containing multiple coils and corresponding drive circuits, acquiring signals using time-division multiplexing or frequency-division multiplexing. In another implementation, the module can consist of multiple independent probes mounted on a mechanical scanning device, achieving a comprehensive scan of the lead seal surface through mechanical movement.

[0080] The calibration module can be a standalone digital signal processor (DSP) or microcontroller unit, running specific algorithms internally. For example, algorithms based on empirical models or pre-trained neural networks can be used to compensate for motion errors. In one implementation, the calibration module can receive data from external motion sensors (such as accelerometers or gyroscopes) and correct the eddy current signal in real time based on this data. In another implementation, the calibration module can employ adaptive filtering techniques based on signal characteristics, such as identifying and removing motion noise by analyzing the statistical properties of the signal, without requiring external motion sensors.

[0081] The defect identification module can be a high-performance computing unit, such as an industrial PC or embedded system, on which pattern recognition or machine learning algorithms are deployed. For example, algorithms such as Support Vector Machines (SVM), decision trees, or Convolutional Neural Networks (CNN) can be used to classify signal patterns. In one implementation, the defect identification module can pre-store a series of standard signal pattern templates and determine defects through template matching. In another implementation, the module can use feature extraction and clustering analysis to map the features of the calibration signal into a multi-dimensional space and distinguish different defect types based on their distribution in the space.

[0082] In summary, the detection system of this application, through its unique modular architecture and advanced signal processing and recognition mechanism, significantly improves the comprehensiveness, accuracy, and automation level of lead seal defect detection, providing a more advanced and effective technical means for lead seal security in the public utility sector. The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A sensor-based automatic detection method for lead seal crack defects, characterized in that, Includes the following steps: A multi-frequency eddy current sensor array is used to collect the amplitude and phase information of the eddy current signal inside the lead seal; The amplitude and phase information are processed to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the acquisition of the eddy current signal, so as to obtain the calibration amplitude and calibration phase information. When the calibration amplitude and the calibration phase information are consistent with the signal pattern of the preset crack judgment rule, the lead seal crack defect is determined to be a crack. When the calibration amplitude and the calibration phase information are consistent with the signal pattern of the preset corrosion agglomeration determination rule, the lead seal crack defect is determined to be a corrosion agglomeration. When the calibration amplitude, the calibration phase information, and the signal patterns of the preset crack determination rules and the preset corrosion agglomeration determination rules are inconsistent, the lead seal crack defect is determined to be an overlap of crack and corrosion agglomeration.

2. The method according to claim 1, characterized in that, When the calibration amplitude and calibration phase information are inconsistent with the signal modes of the preset crack determination rule and the preset corrosion agglomeration determination rule, the step of determining that the lead seal crack defect is an overlap of crack and corrosion agglomeration includes: Based on the calibration amplitude and calibration phase information, the instantaneous rate of change, symmetry of change pattern, and multi-frequency transient correlation of the eddy current signal are extracted. Based on the instantaneous rate of change, the symmetry of the change morphology, and the multi-frequency transient correlation zone, cracks and corrosion clumps are observed.

3. The method according to claim 2, characterized in that, The step of identifying cracks and corrosion clumps in the region based on the instantaneous rate of change, the symmetry of the change morphology, and the multi-frequency transient correlation includes: Based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation, anomaly regions containing composite defects are identified; The signal of the corroded agglomerate is stripped from the eddy current signal in the abnormal region to obtain the residual signal after stripping. The residual signal is then analyzed a second time to identify the crack.

4. The method according to claim 2, characterized in that, The step following the step of determining the instantaneous rate of change, the symmetry of the change morphology, and the multi-frequency transient correlation zone splitting and corrosion agglomeration includes: Based on the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation, the spatial proximity relationship between cracks and corrosion masses is obtained; Based on the spatial proximity relationship, a correction strategy is selected, and the correction strategy includes at least one of the following: When the spatial proximity relationship indicates that the crack penetrates the corrosion mass, the depth and length of the crack are corrected according to the depth and width of the crack penetrating the corrosion mass; When the spatial proximity relationship indicates that the crack extends along the edge of the corrosion mass, the width and extension direction of the crack are corrected based on the contact area between the crack and the edge of the corrosion mass and the signal interaction area.

5. The method according to claim 4, characterized in that, The step of selecting a correction strategy based on the spatial proximity relationship includes: The relative motion between the multi-frequency eddy current sensor array and the lead seal is sensed in real time by a laser rangefinder. Based on the relative motion between the multi-frequency eddy current sensor array and the lead seal, the judgment results of the spatial proximity relationship between the crack and the corrosion mass within multiple consecutive time windows are accumulated to obtain the cumulative judgment result; Based on the accumulated judgment results, a correction strategy is dynamically selected.

6. The method according to claim 5, characterized in that, The step of dynamically selecting a correction strategy based on the accumulated judgment results includes: A consistency assessment is performed on the spatial proximity relationship judgments within different time windows in the cumulative judgment results; Based on the consistency assessment results, identify areas where the judgment results are inconsistent; In the region where the judgment results are inconsistent, a refined judgment result is obtained by refining the spatial proximity relationship based on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal. Based on the refined judgment results, a correction strategy is selected.

7. The method according to claim 6, characterized in that, The step of refining the spatial proximity judgment in the region where the judgment results are inconsistent, based on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal, includes: In the region where the judgment results are inconsistent, high-resolution spatial sampling is performed on the instantaneous rate of change of the eddy current signal, the symmetry of the change pattern of the eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain a refined feature map of the defect region. The refined feature map is subjected to adaptive threshold segmentation. The segmentation threshold is dynamically adjusted according to the local signal intensity and gradient changes to accurately identify the defect boundary and obtain the preliminary outline of the defect. The initial contour of the defect is subjected to morphological processing, including erosion, dilation, opening and closing operations, to smooth the contour, remove isolated noise points and connect fracture areas, thereby obtaining the refined geometric boundary of the defect. Based on the refined geometric boundary, the geometric parameters of the defect region are calculated, including area, perimeter, aspect ratio, and roundness. Based on the geometric parameters, and combined with the instantaneous rate of change, symmetry of change pattern, and multi-frequency transient correlation of the eddy current signal in the defect area, the spatial proximity relationship is refined.

8. The method according to claim 5, characterized in that, Based on the accumulated judgment results, the steps following the dynamic selection of the correction strategy include: Calculate the variance or dispersion of the instantaneous rate of change, the symmetry of the change pattern, and the multi-frequency transient correlation; The morphological irregularity of the lead seal crack is assessed based on the variance or the dispersion. Based on the morphological irregularity, adjust the correction parameters of the correction strategy; The crack is corrected according to the adjusted correction parameters.

9. The method according to claim 5, characterized in that, The steps following the dynamic selection of the correction strategy based on the accumulated judgment results include: Obtain material property information for different material regions inside the lead seal; Based on the material property information, the emission parameters of the eddy current signal are dynamically adjusted to optimize the eddy current penetration characteristics at different depths and in different regions. A hierarchical analysis was performed on the instantaneous rate of change of the adjusted eddy current signal, the symmetry of the change pattern of the adjusted eddy current signal, and the multi-frequency transient correlation of the eddy current signal to obtain the irregularity characteristics of the defect morphology in different material regions. Based on the irregularity characteristics of defect morphology in the different material regions and the material property information of the different material regions, a weighted evaluation of the overall defect morphology irregularity is performed to obtain a weighted evaluation result. Based on the weighted evaluation results, the correction parameters of the correction strategy are adjusted.

10. A sensor-based automatic detection system for lead seal crack defects, characterized in that, The system includes: The eddy current signal acquisition module is used to acquire the amplitude and phase information of the eddy current signal inside the lead seal using a multi-frequency eddy current sensor array. The calibration module is used to process the amplitude and phase information to eliminate the influence of the relative motion between the multi-frequency eddy current sensor array and the lead seal on the acquisition of the eddy current signal, and to obtain calibration amplitude and calibration phase information. The defect identification module is used to determine that the lead seal crack defect is a crack when the calibration amplitude and the calibration phase information are consistent with the signal pattern of the preset crack judgment rule; to determine that the lead seal crack defect is a corrosion mass when the calibration amplitude and the calibration phase information are consistent with the signal pattern of the preset corrosion mass judgment rule; and to determine that the lead seal crack defect is an overlap of crack and corrosion mass when the calibration amplitude and the calibration phase information are inconsistent with the signal patterns of both the preset crack judgment rule and the preset corrosion mass judgment rule.

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