Nondestructive electromagnetic detection and life evaluation method for blast furnace slag chute grid weld state

Abstract

The present application belongs to the technical field of metallurgical equipment, and provides a non-destructive electromagnetic detection and service life evaluation method for the grid weld state of a blast furnace slag flushing channel, comprising: performing segmented electromagnetic scanning on the grid weld along the spatial trajectory in the slag flushing channel to generate weld response mapping data; performing regional division and energy distribution processing on the weld response mapping data to establish a regional response weight field; based on the regional response weight field, performing signal suppression processing on the low signal-to-noise ratio section to form a defect-sensitive trigger section; introducing a local deterioration evolution mapping mechanism in the defect-sensitive trigger section, correlating the electromagnetic response attenuation trend with the weld material fatigue accumulation process in stages, outputting a partitioned damage evolution parameter set, and according to the partitioned damage evolution parameter set, performing cross-domain coupling deduction on the deterioration propagation relationship between different weld sections to output a service life attenuation evaluation result.

Description

Technical Field

[0001] This invention belongs to the field of metallurgical equipment technology, specifically a non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid. Background Technology

[0002] In the INBA slag flushing system of a blast furnace, an alloy grid structure is usually installed in the slag flushing trough to perform primary interception of high-temperature slag, so as to prevent large slag particles from entering the granulation tower and causing blockage or overflow. The grid structure is generally fixed in the support position inside the slag flushing trough by welding. Under the long-term high-temperature scouring, thermal cycle stress and slag particle abrasion, fatigue cracks and local detachment damage are likely to occur in the weld area.

[0003] In existing technologies, the inspection of grid weld conditions often employs electromagnetic non-destructive testing (EMT) to identify surface and near-surface defects, combined with empirical models to assess structural lifespan. However, in conditions where the slag flushing tank is confined and the weld surface is prone to slag and oxide layer adhesion, the electromagnetic probe is susceptible to unstable contact or orientation shifts during weld scanning, leading to fluctuations in the local detection signal. Under these conditions, the weld toe area is prone to early micro-cracks due to geometric abrupt changes and stress concentration. Furthermore, under the influence of slag coverage and signal disturbances, the relevant weak defect features are not clearly visible in the detection signal, resulting in unstable identification. Furthermore, existing life assessment methods are typically modeled based on the assumption of overall structure or uniform damage, which does not adequately consider the local differences in different areas of the weld. When local damage has not yet extended to the overall structural level, the assessment results may deviate from reflecting the local deterioration state. In summary, existing technologies for electromagnetic detection and life assessment of slag-filled grid welds are affected by factors such as space constraints, surface residue coverage, and unstable probe posture, leading to unstable identification of weak defect signals. At the same time, under the assessment model based on the assumption of overall or uniform damage, it is difficult to accurately reflect the local deterioration state, thus resulting in overall deviations and instabilities in the detection and life assessment results.

[0004] Therefore, this invention provides a non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: The weld seam of the grid inside the slag tank is subjected to segmented electromagnetic scanning along the spatial trajectory to obtain the probe attitude offset, weld seam curvature and surface adhering medium distribution state, construct multi-source disturbance parameters, and reconstruct the original induction signal in time sequence to generate weld seam response mapping data. The weld response mapping data is divided into regions and energy is distributed to separate the response characteristics of the weld toe area, weld center area and heat-affected zone. The regional response weight field is established by combining the characteristics of slag coverage and signal attenuation. Based on the regional response weight field, signal suppression processing is performed on the low signal-to-noise ratio section, and frequency domain offset tracking is performed on the micro-amplitude magnetic flux disturbance in continuous scanning. The offset peak value is mapped to the geometric change point of the weld to form a defect-sensitive triggering section. A local degradation evolution mapping mechanism is introduced in the defect-sensitive triggering section to link the electromagnetic response attenuation trend with the fatigue accumulation process of the weld material in stages, and to perform nonlinear cumulative conversion of local damage based on the response degradation rate, outputting a partitioned damage evolution parameter set. Based on the zonal damage evolution parameter set, the deterioration propagation relationship between different weld sections is extrapolated across domains. Combined with the effects of thermal cycling and impact loading, the remaining load-bearing capacity of the structure is evaluated, and the life decay assessment results are output.

[0007] As a further aspect of the present invention: The process of generating weld response mapping data is as follows: Based on the three-dimensional structural drawings of the grid and the weld numbering rules, a spatial scanning path and time index vector set are constructed, and the electromagnetic scanning probe is controlled to perform traversal scanning and acquisition along the spatial scanning path and time index vector set, and the probe attitude angle and scanning speed information of each scanning node are acquired simultaneously. The original induction signals were timestamped and the probe attitude offset was calculated in combination with the standard scanning posture. At the same time, the weld curvature and surface attached medium distribution characteristics were integrated to construct multi-source disturbance parameters. The multi-source disturbance parameters are aligned and quantized on the time axis to construct a multi-dimensional disturbance matrix, and the original inductive signal is reconstructed by inverse disturbance correction based on the multi-dimensional disturbance matrix. The reconstructed signal is interpolated and normalized to generate continuous electromagnetic response mapping data that corresponds one-to-one with the spatial coordinates of the weld.

[0008] The process of separating the response characteristics of the weld toe zone, weld center zone, and heat-affected zone is as follows: Based on the signal gradient and phase reversal features in the weld response mapping data, the geometric boundary line of the weld is extracted, and the scanning area is divided into a central strip area, a transition zone area and a base area according to the geometric boundary line. Using the central zonal region as the energy benchmark, the relative energy decay characteristics of the transition zone and the base zone are calculated, and a mapping relationship between region type and energy decay characteristics is established in combination with calibration data. Based on the mapping relationship, the weld response mapping data is classified into regions, and each sampling point is divided into the weld toe area, the weld center area, or the heat-affected zone. The membership weighting is applied to the points with ambiguous boundaries. Energy redistribution processing is performed on the response data in each region to enhance the response weight of the weld center area and weld toe area, reduce the background energy ratio of the heat-affected zone, and complete the separation of regional response characteristics.

[0009] The process of establishing a regional response weight field is as follows: The response characteristics of the separated weld toe area, weld center area and heat-affected zone were measured. The signal amplitude attenuation and phase hysteresis caused by slag coverage in each area were measured to establish a regionalized slag attenuation coefficient. The residual attenuation coefficient is correlated with the thickness of the surface-attached dielectric layer in the corresponding region to generate a regional attenuation transfer function with the dielectric layer thickness as the independent variable and the signal attenuation rate as the dependent variable. By introducing the relationship between the skin depth and the operating frequency of electromagnetic detection, frequency response correction is performed on the regional attenuation transfer function, and the attenuation differences under different frequency components are normalized to the same evaluation baseline. Based on the modified regional attenuation transfer function, the signal reliability in each region is evaluated. A regional response weight field is constructed by assigning high weights to regions with low signal attenuation rates and thin residue coverage, and low weights to regions with severe attenuation.

[0010] The process of frequency domain offset tracking of small magnetic flux disturbances in continuous scanning is as follows: Using a regional response weight field, the weld response mapping data is weighted and filtered to identify and mark spatial continuous segments with weight values ​​below a preset confidence threshold. These spatial continuous segments are identified as low signal-to-noise ratio segments, and the signal amplitude within the low signal-to-noise ratio segments is compressed and suppressed according to the weight ratio. Short-time Fourier transform is performed on the suppressed global signal to construct a joint time and frequency distribution spectrum. The continuous background magnetic flux baseline is extracted from the spectrum, and the small-amplitude magnetic flux disturbance component that deviates from the background magnetic flux baseline is extracted. For the micro-amplitude magnetic flux disturbance component, a sliding window tracking of the peak frequency is performed along the time axis on the joint time and frequency distribution spectrum, and the offset trajectory of the frequency peak with time or scanning position is recorded to form a frequency domain offset characteristic curve. The frequency domain offset characteristic curve is differentiated to calculate the offset rate. Slowly varying low-frequency common-mode interference caused by ambient temperature drift and power grid fluctuations is filtered out, and the high-frequency offset abrupt component characterizing the discontinuity of the microstructure of the weld material is output.

[0011] The process of forming a defect-sensitive triggering section is as follows: Peak detection is performed on the high-frequency offset abrupt components in the frequency domain offset tracking results to extract the peak amplitude, peak frequency, and position index on the weld space trajectory. Based on the three-dimensional geometric contour data of the weld, the curvature features are calculated to identify the weld toe angle, weld reinforcement abrupt change and undercut location, and the spatial distribution of geometric abrupt change points is constructed. The peak location index is spatially matched with the spatial distribution of geometric change points, and a valid mapping point is determined when the spatial distance is less than a preset threshold. Spatial clustering analysis is performed on the effective mapping points to divide the spatially continuous and densely mapped regions into defect-sensitive triggering segments.

[0012] The process of linking the electromagnetic response decay trend with the fatigue accumulation process of weld materials in stages is as follows: Electromagnetic response characteristics under multiple detection cycles are extracted in the defect-sensitive triggering section, including response amplitude and phase information, and a time series response decay curve is constructed. Based on the fatigue damage theory of weld materials, a fatigue cumulative damage evolution rule is established, and the damage variable is used to describe the damage development process of weld under thermal cycling and impact loading. A piecewise fitting analysis was performed on the response decay curve to extract the inflection point of the decay rate change, and the response decay process was divided into multiple degradation stages based on the inflection point. The characteristics of response decay rate changes in each deterioration stage are statistically analyzed, and the damage growth characteristics of the corresponding stage are determined by combining the fatigue cumulative damage evolution law, so as to obtain the fatigue damage increment corresponding to each stage.

[0013] The process of outputting the partitioned damage evolution parameter set is as follows: Based on the characteristics of response decay rate changes and corresponding fatigue damage increments in each degradation stage, the response degradation rate at each spatial location within the defect-sensitive triggering section is determined. A nonlinear damage accumulation rule considering load sequence and overload hysteresis effect is introduced to correct and accumulate the fatigue damage increment corresponding to each detection cycle, so as to obtain the accumulated damage state at each spatial location. The cumulative damage state within the defect-sensitive triggering section is spatially continuous to form the damage distribution result of the triggering section; Based on the damage distribution results, damage characteristic parameters such as maximum damage value, average damage value, damage propagation direction, and damage area proportion are extracted and combined with corresponding spatial location and regional category information to generate a regionalized damage evolution parameter set.

[0014] The process of cross-domain coupled deduction of the degradation propagation relationship between different weld sections is as follows: Damage characteristic parameters and spatial location information corresponding to each defect-sensitive triggering segment are extracted from the partitioned damage evolution parameter set, and each defect-sensitive triggering segment is determined as an independent degradation unit. By combining the connection relationship and load transfer path of the grid structure, the mutual influence relationship between each deteriorated unit is determined, and the damage transfer correlation between sections is established; Based on the damage development status of each deteriorated unit and the damage transmission correlation between segments, the damage expansion trend between adjacent deteriorated units is calculated. Based on the damage propagation trend, the direction of deterioration propagation, the extent of expansion, and the degree of mutual influence between different weld sections are deduced, forming cross-domain coupled deduction results.

[0015] The process of outputting lifetime degradation assessment results is as follows: The thermal cycle parameters and impact load parameters corresponding to the historical operation of the blast furnace slag flushing trough are obtained to form load action data that reflects the actual operating conditions. By combining the cross-domain coupling simulation results and load data, the damage development state and load-bearing capacity change trend of each weld section are analyzed to determine the current remaining load-bearing capacity of the structure. Based on the changing trend of remaining load-bearing capacity and the state of damage accumulation, predict the remaining operating period of the structure before it reaches the preset failure condition; The predicted remaining operating cycle is compared with the design service life to calculate the degree of service life decay. Combined with the damage distribution characteristics and risk level of each weld section, a service life decay assessment result is generated.

[0016] The beneficial effects of this invention are as follows: By performing segmented electromagnetic scanning on the weld seam of the blast furnace slag flushing grid and reconstructing the original induction signal by incorporating multi-source disturbance factors such as probe attitude deviation, weld curvature, and surface adhering medium, the accuracy and stability of weld electromagnetic detection results under complex working conditions were improved. By dividing the weld response data into regions and allocating energy, a regional response weight field was established, effectively separating the response characteristics of the weld toe area, weld center area, and heat-affected zone, enhancing the identification capability of defect signals in key areas. By performing frequency domain offset tracking of micro-amplitude magnetic flux disturbances and constructing defect-sensitive triggering sections based on weld geometric abrupt changes, potential damage-prone areas can be located more accurately, improving the ability to detect early-stage minor defects. Furthermore, by linking the electromagnetic response decay trend with the fatigue accumulation process of the weld material... By using phased correlation and combining response degradation rate to perform nonlinear cumulative analysis of local damage, a quantitative transformation from electromagnetic detection results to the actual damage state of the weld is achieved, improving the consistency between damage assessment results and actual service conditions. On this basis, by cross-domain coupled extrapolation of the deterioration propagation relationship between different weld sections, and comprehensively considering the effects of thermal cycling and impact loads, the remaining load-bearing capacity and life decay trend of the structure are evaluated. This not only reflects the current weld condition but also predicts the damage propagation direction and future failure risk, thus providing a reliable basis for maintenance decisions, risk warnings, and life management of blast furnace slag flushing grid welds. This helps reduce operational risks such as sudden weld detachment, grid failure, and blockage of the granulation system, and improves the safety, stability, and economy of blast furnace slag flushing system operation. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the steps of the non-destructive electromagnetic detection and life assessment method for the weld condition of the blast furnace slag flushing grid as described in Embodiment 1 of the present invention. Figure 2 This is a flowchart of the non-destructive electromagnetic detection and life assessment system for the weld condition of the blast furnace slag flushing grid as described in Embodiment 2 of the present invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0020] Example 1: Please refer to Figure 1 As shown in the embodiment of the present invention, the non-destructive electromagnetic testing and life assessment method for the weld condition of the blast furnace slag flushing grid includes the following steps: Step S10: Perform segmented electromagnetic scanning on the grid weld seam in the slag flushing tank along the spatial trajectory to obtain the probe attitude offset, weld seam curvature and surface attached medium distribution state, construct multi-source disturbance parameters, and perform time-series reconstruction on the original induction signal to generate weld seam response mapping data. In step S10, the process of generating weld response mapping data is as follows: Based on the 3D structural drawings of the grid and the weld numbering rules, a spatial scanning path and time index vector set are constructed. The electromagnetic scanning probe is controlled to perform traversal scanning and acquisition along the spatial scanning path and time index vector set, and the probe attitude angle and scanning speed information of each scanning node are acquired simultaneously. The 3D structural drawings of the blast furnace slag flushing grid are retrieved, and a weld spatial coordinate system is established based on the transverse support beam, longitudinal support beam and weld connection position of the grid. Each weld is uniquely identified according to the weld numbering rules. A continuous scanning trajectory is generated with the weld centerline as the reference, and the scanning trajectory is discretized into multiple scanning nodes according to the preset sampling interval to form a spatial scanning path. At the same time, a corresponding time index vector set is established according to the scanning start time, sampling frequency and node sequence. The electromagnetic scanning probe installed on the track moving mechanism or the end of the robotic arm is controlled to move node by node along the spatial scanning path. Electromagnetic induction signal acquisition is completed at each scanning node, and the probe pitch angle, roll angle and yaw angle are recorded in real time through the built-in inertial measurement unit. The scanning speed information is recorded through the displacement encoder, thereby obtaining the scanning status data corresponding to the spatial position. The original sensing signals are timestamped and the probe attitude offset is calculated in combination with the standard scanning posture. At the same time, the weld curvature and surface adhering medium distribution characteristics are integrated to construct multi-source disturbance parameters. The original sensing signals acquired by each scanning node are timestamped according to a unified system clock to establish a correspondence between the signals of each node and their corresponding spatial positions. The probe attitude angle recorded during the scanning process is compared with the preset standard scanning posture to calculate the pitch deviation, roll deviation and yaw deviation to obtain the probe attitude offset. The weld surface contour data is further obtained using a laser contour scanning device. The weld curvature change characteristics are calculated based on the contour curve. The coverage and thickness distribution information of adhering media such as residue, scale and oxide layer on the weld surface are obtained using a visual inspection device or a medium thickness detection device. Finally, the probe attitude offset, weld curvature change characteristics and adhering medium distribution characteristics are collected in a unified data format to form multi-source disturbance parameters. Multi-source disturbance parameters are aligned and quantized on the time axis to construct a multi-dimensional disturbance matrix. The original inductive signal is then reconstructed by inverse disturbance correction based on the multi-dimensional disturbance matrix. The multi-source disturbance parameters are synchronized according to the timestamp to ensure that the various disturbance parameters are consistent with the original inductive signal of the corresponding scanning node in time. The attitude offset, weld curvature and the distribution parameters of the attached medium are normalized and assigned corresponding disturbance weights to construct a multi-dimensional disturbance matrix containing spatial position dimension, time dimension and disturbance type dimension. Then, based on the disturbance influence law obtained by pre-calibration, the amplitude change, phase shift and frequency drift of the original inductive signal are compensated and corrected to eliminate the detection error caused by probe attitude change, weld geometry difference and attached medium. The inverse disturbance correction reconstruction of the original inductive signal is realized to obtain a correction signal that reflects the true electromagnetic response state of the weld. The reconstructed signal is interpolated and normalized to generate continuous electromagnetic response mapping data corresponding one-to-one with the weld space coordinates. The discrete scanning node signal after inverse perturbation correction is spatially interpolated, and data points at unsampled locations are supplemented according to the electromagnetic response change trend between adjacent nodes, so that a continuously distributed response curve is formed over the entire length of the weld. The response amplitude, phase and characteristic frequency parameters corresponding to each location are normalized to eliminate the influence of structural size differences between different weld sections. Finally, the processed electromagnetic response parameters are mapped to the weld space coordinate system to establish a continuously distributed electromagnetic response field in the three-dimensional space of the weld, forming weld response mapping data corresponding one-to-one with the weld space coordinates, providing basic data support for response feature separation, defect identification and life assessment.

[0021] It is understandable that the significance of step S10 is as follows: by performing segmented electromagnetic scanning of the grid weld seam in the slag trough along the spatial trajectory, and simultaneously acquiring the probe attitude offset, weld curvature, and surface adhesion medium distribution, multi-source disturbance parameters are constructed to reconstruct the original induction signal in a time sequence. This can effectively compensate for signal distortion caused by factors such as probe attitude changes, weld geometry differences, and surface residue adhesion during the detection process, improve the accuracy of the correspondence between electromagnetic detection data and the actual state of the weld seam, and thus obtain weld response mapping data with spatial positioning capabilities, providing a reliable data foundation for subsequent defect identification and life assessment.

[0022] Step S20: Perform regional division and energy allocation processing on the weld response mapping data, separate the response characteristics of the weld toe area, weld center area and heat-affected zone, and establish a regional response weight field by combining the characteristics of slag coverage and signal attenuation. In step S20, the process of separating the response characteristics of the weld toe area, the weld center area, and the heat-affected zone is as follows: Based on the signal gradient and phase reversal features in the weld response mapping data, the geometric boundary line of the weld is extracted, and the scanning area is divided into a central strip region, a transition zone region, and a base region according to the geometric boundary line. The weld response mapping data is retrieved, and the gradient of the electromagnetic response amplitude corresponding to each spatial position is calculated to obtain the signal change rate distribution along the transverse and longitudinal directions of the weld. At the same time, the phase information of the response signal is extracted, and the location region where the phase changes from positive to negative is identified. The signal gradient abrupt change position and the phase reversal position are superimposed and analyzed. Since there are differences in material structure, structural thickness, and magnetic permeability distribution in the weld center region, weld toe region, and base material region, obvious boundary change zones will be formed in the electromagnetic response field. Based on the identified boundary change zones, the geometric boundary line of the weld is constructed, and the central strip region is formed on both sides of the weld center line as the reference. The transition zone region is formed on both sides of the central strip region, and the base region is formed outside the transition zone region, thus completing the preliminary functional zoning of the scanning area. Using the central strip region as the energy benchmark, the relative energy attenuation characteristics of the transition zone and the base region are calculated, and a mapping relationship between region type and energy attenuation characteristics is established in conjunction with calibration data. Using the average electromagnetic response energy of each sampling point in the central strip region as the reference benchmark energy, the attenuation ratio of each sampling point in the transition zone and the base region relative to the reference benchmark energy is calculated to obtain the energy attenuation characteristic distribution of different regions. Calibration tests are conducted using pre-made standard weld specimens to obtain the typical energy attenuation ranges corresponding to the weld toe region, weld center region, and heat-affected zone, and a correspondence between region type and energy attenuation characteristics is established. For example, the weld center region usually exhibits high energy response characteristics, the weld toe region exhibits medium attenuation characteristics, and the heat-affected zone exhibits high attenuation characteristics. The above calibration results form the basis for region identification and provide a judgment standard for region classification. Based on the mapping relationship, the weld response mapping data is classified into regions, and each sampling point is divided into the weld toe region, weld center region, or heat-affected zone. Membership weights are applied to points with ambiguous boundaries. For each sampling point in the weld response mapping data, the corresponding energy attenuation value, signal gradient value, and phase change characteristics are extracted and matched with the pre-established region type mapping relationship. When the characteristics of the sampling point meet the criteria for the weld center region, it is classified into the weld center region; when it meets the criteria for the weld toe region, it is classified into the weld toe region; and when it meets the criteria for the heat-affected zone, it is classified into the heat-affected zone. For ambiguous boundary points located at the junction of two regions and whose characteristic values ​​meet multiple region criteria, the corresponding membership weights are calculated based on their proximity to the feature centers of each region and allocated to the corresponding regions according to the weight ratio. This avoids the problem of discontinuous region division caused by abrupt boundary changes and improves the stability and accuracy of the region classification results. Energy redistribution processing is performed on the response data in each region to strengthen the response weight of the weld center area and weld toe area, reduce the background energy proportion of the heat-affected zone, and complete the separation of regional response characteristics. The total response energy, average response intensity, and signal fluctuation characteristics in the weld center area, weld toe area, and heat-affected zone are statistically analyzed separately. Different weight coefficients are set for each region according to the probability of defect occurrence. Since the weld toe area and weld center area are high-risk areas for crack initiation, weld detachment, and fatigue damage, the response weight of the corresponding areas is increased to increase the proportion of their abnormal signals in the overall response field. The heat-affected zone mainly serves as a background reference, so its energy weight is appropriately reduced to reduce the interference of background noise on the subsequent analysis results. After the weight adjustment, the response energy distribution of each region is recalculated to form regional response data that highlights the characteristics of defect-sensitive areas. This achieves effective separation of the response characteristics of the weld toe area, weld center area, and heat-affected zone, providing a foundation for establishing a regional response weight field and defect identification analysis.

[0023] In step S20, the process of establishing the regional response weight field is as follows: The response characteristics of the separated weld toe area, weld center area, and heat-affected zone were analyzed. The signal amplitude attenuation and phase lag caused by residue coverage in each area were measured to establish a regionalized residue attenuation coefficient. The response characteristic data of the weld toe area, weld center area, and heat-affected zone were retrieved, and the residue coverage status of the surface of each area was obtained using a visual inspection device, laser thickness measuring device, or ultrasonic thickness measuring device. The same type of residue-free area was selected as a reference area. The electromagnetic response amplitude of the residue-covered area was compared with the response amplitude of the reference area to calculate the signal amplitude attenuation ratio. At the same time, the phase change of the response signal in the corresponding area was extracted, and the phase lag caused by residue coverage was calculated. For multiple sampling points in the same area, the average value and dispersion of their amplitude attenuation ratio and phase lag were statistically analyzed and fused according to preset weights to obtain a regionalized residue attenuation coefficient characterizing the degree of influence of residue coverage. This quantifies the degree of influence of attached residue in different areas on the electromagnetic detection signal. The residual attenuation coefficient is correlated with the thickness of the surface-attached dielectric layer in the corresponding region to generate a regional attenuation transfer function with the dielectric layer thickness as the independent variable and the signal attenuation rate as the dependent variable. The thickness of the surface-attached dielectric layer at each sampling point is measured using a laser thickness gauge or structured light scanning device, and the measured dielectric layer thickness is correlated with the residual attenuation coefficient at the corresponding location. Data sample sets are established according to the weld toe area, weld center area, and heat-affected zone. The relationship between the dielectric layer thickness and the signal attenuation rate is established by least squares fitting, polynomial fitting, or exponential fitting. For each type of region, a regional attenuation transfer function that reflects the influence of the change in the thickness of the attached dielectric layer on the electromagnetic response attenuation is obtained, thereby realizing the prediction and characterization of the signal attenuation degree under different coverage conditions. By introducing the relationship between the skin depth and operating frequency of electromagnetic detection, frequency response correction is applied to the regional attenuation transfer function, normalizing the attenuation differences under different frequency components to the same evaluation baseline. Based on the operating frequency range of the electromagnetic detection equipment and the conductivity and permeability parameters of the weld material, the skin depth of the electromagnetic field in the weld material under different frequency conditions is calculated. Since the detection depth and attenuation characteristics are different for different frequencies, the correspondence between skin depth and frequency is used to perform frequency compensation correction on the aforementioned regional attenuation transfer function. Specifically, the signal attenuation rate corresponding to each frequency component is corrected and converted, and the attenuation results obtained under different detection frequencies are uniformly converted to the equivalent attenuation value under the preset reference frequency condition, so that the data obtained under different detection conditions have a unified comparison basis. Based on the modified regional attenuation transfer function, the signal reliability of each region is evaluated. A regional response weight field is constructed by assigning high weights to regions with low signal attenuation rates and thin residue coverage, and low weights to regions with severe attenuation. The dielectric layer thickness, modified signal attenuation rate, and phase hysteresis corresponding to each sampling point are input into the signal reliability evaluation module to comprehensively score the detection reliability of each sampling point. Regions with thin dielectric layers, low signal attenuation rates, and stable phases are deemed to have high detection reliability and are assigned higher response weights. Regions with thick dielectric coverage, significant attenuation, or large phase fluctuations are assigned lower response weights. The weight values ​​of all sampling points are mapped to the weld response mapping data according to the weld spatial coordinates. A continuously distributed weight field is generated through spatial interpolation, forming a regional response weight field covering the weld toe area, weld center area, and heat-affected zone. This response weight field reflects the reliability of detection results at different locations, providing a weight basis for low signal-to-noise ratio segment identification, magnetic flux disturbance analysis, and defect-sensitive area location.

[0024] It is understandable that the significance of step S20 is as follows: by dividing and allocating energy to the weld response mapping data, and extracting the response features of the weld toe area, weld center area and heat-affected zone respectively, and establishing a regional response weight field by combining the slag coverage and signal attenuation features, it is possible to effectively distinguish the degree of influence of different regions on electromagnetic signals, highlight the response features of defect-sensitive areas, reduce the risk of misjudgment caused by background interference and regional differences, thereby improving the ability to identify abnormal responses in key parts of the weld and the reliability of the detection results.

[0025] Step S30: Based on the regional response weight field, perform signal suppression processing on the low signal-to-noise ratio section, and perform frequency domain offset tracking on the micro-amplitude magnetic flux disturbance in continuous scanning. Map the offset peak value with the geometric change point of the weld to form a defect-sensitive triggering section. In step S30, the process of frequency domain offset tracking of the micro-amplitude magnetic flux disturbance during continuous scanning is as follows: By utilizing a regional response weight field, weighted filtering is applied to the weld response mapping data. Spatially continuous segments with weight values ​​below a preset confidence threshold are identified and marked. These segments are then classified as low signal-to-noise ratio (SNR) segments, and the signal amplitude within these segments is compressed and suppressed according to a weighted ratio. The regional response weight field is retrieved, and the weight values ​​corresponding to each spatial location are mapped to the weld response mapping data. Weighted filtering is then used to process the response signal, where higher weight values ​​retain more original response information, while lower weight values ​​reduce their impact on the overall signal. Further analysis of the weight distribution across multiple consecutive sampling points is conducted. When the weight values ​​of multiple adjacent sampling points are continuously below the preset confidence threshold, the region is marked as a low SNR segment. For low SNR segments, the signal amplitude is proportionally compressed according to the corresponding weight value, for example, by attenuating the response amplitude according to a weight coefficient. This weakens the abnormal response impact caused by residue coverage, surface contamination, and environmental noise, improving the stability and reliability of the frequency domain analysis results. Short-time Fourier transform is performed on the suppressed global signal to construct a joint time and frequency distribution spectrum. The continuous background magnetic flux baseline is extracted from the spectrum, and the small-amplitude magnetic flux disturbance component deviating from the background magnetic flux baseline is extracted. The global response signal of the weld after low signal-to-noise ratio section suppression is segmented according to a preset time window, and short-time Fourier transform is used to perform spectrum analysis on the signal in each time window to obtain the frequency energy distribution results corresponding to different time positions. Then, a joint time and frequency distribution spectrum is constructed, and the main frequency band with the highest energy proportion and continuous and stable change trend in each time window is statistically analyzed. The continuous frequency trajectory formed by the main frequency band is defined as the background magnetic flux baseline. The deviation between the actual spectrum and the background magnetic flux baseline is further calculated, and the frequency deviation, amplitude anomaly, and local energy surge are extracted and defined as the small-amplitude magnetic flux disturbance component. Thus, the disturbance information reflecting the local abnormal state of the material is separated from the overall background response. For micro-amplitude magnetic flux disturbance components, a sliding window tracking of the peak frequency is performed along the time axis on the joint time and frequency distribution spectrum. The offset trajectory of the frequency peak with time or scanning position is recorded to form a frequency domain offset characteristic curve. The sliding time window of preset length moves sequentially along the time axis, and the spectral peak search is performed on the micro-amplitude magnetic flux disturbance component in each window to determine the position of the main peak frequency at the corresponding time. The peak frequency corresponding to each window is recorded in time order or scanning spatial position order, and the peak frequency changes in consecutive windows are connected to form a frequency peak change trajectory. When there are microcracks, local weld failure, abnormal structure or stress concentration in the weld, the corresponding area will cause local permeability changes, which will cause the peak frequency to shift. By continuously tracking this shift process, a frequency domain offset characteristic curve reflecting the local state change law of the weld can be obtained, providing a basis for the identification of defect-sensitive areas. The frequency domain offset characteristic curve is differentiated to calculate the offset rate, and the slowly varying low-frequency common-mode interference caused by ambient temperature drift and power grid fluctuations is filtered out. The high-frequency offset abrupt component characterizing the discontinuity of the weld material's microstructure is output. First-order difference or derivative calculation is performed on the frequency domain offset characteristic curve to obtain the offset rate curve of frequency offset changing with time, so as to enhance the abrupt characteristics of frequency change. At the same time, ambient temperature data, power supply voltage fluctuation data and equipment operating status data are collected and detected to establish an environmental interference reference sequence. High-pass filtering, adaptive filtering or common-mode suppression algorithms are used to remove low-frequency drift components caused by slow changes in ambient temperature, power grid frequency fluctuations and changes in equipment operating status. Only the locally abnormal frequency components with faster change rate are retained. Finally, the high-frequency offset abrupt component is obtained. This component can characterize the micro-damage characteristics such as abrupt changes in magnetic permeability, microcrack initiation, local weld detachment and material discontinuity in the weld, providing basic data for offset peak location and defect-sensitive triggering section construction.

[0026] In step S30, the process of forming the defect-sensitive triggering section is as follows: Peak detection is performed on the high-frequency offset mutation components in the frequency domain offset tracking results to extract the peak amplitude, peak frequency, and position index on the weld space trajectory. The high-frequency offset mutation component data is retrieved, and a frequency domain offset feature sequence is established according to the scanning time sequence. The feature sequence is detected by a peak search algorithm to identify abnormal peak points with frequency offset exceeding a preset threshold and local maxima features. For each peak point, the corresponding peak amplitude, peak frequency, and acquisition time information are recorded. Combined with the spatial trajectory coordinate information saved during the scanning process, the peak occurrence position is mapped to the corresponding scanning node in the weld space trajectory to form an abnormal feature dataset containing peak feature parameters and spatial position index, thereby providing basic data for defect location correlation analysis. Based on the three-dimensional geometric contour data of the weld, the curvature characteristics are calculated to identify weld toe corners, weld reinforcement height abrupt changes, and undercut locations, and to construct the spatial distribution of geometric abrupt change points. Three-dimensional geometric contour data of the weld surface are acquired using laser contour scanning devices, structured light measurement devices, or three-dimensional vision inspection devices, and a weld spatial contour curve is established. The curvature of the contour curve is calculated, and the curvature changes at each location are statistically analyzed. When the curvature value exceeds a preset threshold or the rate of curvature change shows a significant abrupt change, the corresponding location is identified as a geometric anomaly feature point. Further, combined with the weld structural characteristics, the identification results are classified to determine the locations of weld toe corners, weld reinforcement height abrupt changes, and undercut locations, and the corresponding spatial coordinates are recorded. Finally, all geometric anomaly feature points are summarized according to their spatial locations to form spatial distribution data of weld geometric abrupt change points, providing a geometric reference basis for spatial correlation analysis. The peak position index is matched with the spatial distribution of geometric mutation points. When the spatial distance is less than a preset threshold, it is determined as a valid mapping point. Based on the peak position index recorded in the abnormal feature dataset, the corresponding spatial coordinate information is extracted and the distance is calculated one by one with each geometric anomaly feature point in the spatial distribution of geometric mutation points. The distance calculation can be implemented by Euclidean distance or arc length distance along the weld trajectory. Then, a spatial proximity matching threshold is set. When the distance between the peak position and any geometric mutation point is less than the preset threshold, it is considered that there is a spatial correlation between the two and the corresponding position is determined as a valid mapping point. When the distance is greater than the threshold, it is judged as a random disturbance or non-structural abnormal signal and is removed. Through this process, electromagnetic response anomalies can be correlated and verified with weld geometric structure anomalies, improving the reliability and accuracy of defect identification results. Spatial clustering analysis is performed on effective mapping points to divide spatially continuous and densely mapped regions into defect-sensitive triggering segments. All effective mapping points are sorted according to the spatial coordinates of the weld, and the spatial spacing and distribution density between adjacent effective mapping points are statistically analyzed. When multiple effective mapping points are spatially continuous and the number of mapping points per unit length exceeds a preset density threshold, they are grouped into the same abnormal clustering region. Density clustering or distance clustering methods are used to group the effective mapping points, identify the boundaries of regions with highly concentrated mapping relationships, and determine the start and end positions of the corresponding segments. For the formed abnormal clustering regions, the region length, abnormal point density, and peak intensity distribution are further recorded, and the clustering regions that meet the triggering conditions are defined as defect-sensitive triggering segments. In this way, discrete abnormal responses can be transformed into key detection areas with engineering significance, providing target segments for degradation evolution analysis and life assessment.

[0027] It is understandable that the significance of step S30 is as follows: by performing signal suppression processing on low signal-to-noise ratio sections based on regional response weight field, and performing frequency domain offset tracking on micro-amplitude magnetic flux disturbances generated during continuous scanning, the offset peak value is correlated with the geometric change point of the weld, which can extract feature information related to structural anomalies from complex background signals, achieve accurate positioning of potential damage areas, form defect-sensitive triggering sections, and thus improve the ability to detect hidden defects such as early microcracks, weld debonding tendency and local damage.

[0028] Step S40: Introduce a local degradation evolution mapping mechanism in the defect-sensitive triggering section, link the electromagnetic response attenuation trend with the fatigue accumulation process of the weld material in stages, and perform nonlinear cumulative calculation of local damage based on the response degradation rate to output a partitioned damage evolution parameter set. In step S40, the process of linking the electromagnetic response attenuation trend with the fatigue accumulation process of the weld material in stages is as follows: Electromagnetic response characteristics, including response amplitude and phase information, are extracted from the defect-sensitive triggering zone under multiple detection cycles to construct a time-series response decay curve. A long-term tracking file is established for the defect-sensitive triggering zone. During the normal operation of the blast furnace, electromagnetic detection is repeatedly performed on the same weld position according to the preset detection cycle. Each detection records the response amplitude, phase angle, and detection time information of the corresponding spatial position and performs one-to-one matching according to the spatial position. The first detection result is used as the baseline state, and the response change of each subsequent detection cycle relative to the baseline state is calculated. The response amplitude decay and phase shift are arranged in chronological order to form a time-series response data reflecting the weld state change process. Moving average processing is further used to eliminate the influence of random fluctuations. Finally, a continuous response decay curve is constructed to characterize the trend of weld state change with service time in the defect-sensitive triggering zone. Based on the fatigue damage theory of weld materials, fatigue cumulative damage evolution rules are established, and damage variables are used to describe the damage development process of welds under thermal cycling and impact loading. According to the type of metal material used in the weld of the blast furnace slag flushing grid, fatigue test data, thermal cycling test data, and impact loading test data are collected. The damage change laws corresponding to the crack initiation, crack propagation, and failure stages under different stress levels are statistically analyzed. Based on the development characteristics of material damage gradually accumulating with the increase of cycle number, a weld damage stage division rule is established, dividing the weld state into damage initiation stage, stable development stage, and rapid failure stage. Damage variables are used to represent the damage degree of each stage. The larger the value of the damage variable, the higher the degree of internal defect propagation of the weld. This forms the basis for reflecting the fatigue accumulation process of the weld and provides a reference standard for the correlation analysis between response changes and damage growth. Piecewise fitting analysis was performed on the response decay curve to extract the inflection point of decay rate change, and the response decay process was divided into multiple deterioration stages based on the inflection point. Trend analysis was performed on the constructed response decay curve, and the slope of response change in different time intervals was calculated by piecewise linear fitting or piecewise polynomial fitting. When the slope of change in adjacent time intervals changed significantly, the corresponding position was identified as the inflection point of decay rate change. The entire response decay process was divided into multiple continuous stages according to the position of each inflection point. Each stage corresponds to different response change characteristics. For example, the stage of slow response change corresponds to the early damage stage, the stage of continuous response decline corresponds to the damage development stage, and the stage of rapid response decay corresponds to the damage acceleration expansion stage. Through the above processing, the stage division of the response decay process was realized, laying the foundation for fatigue damage increment calculation. The characteristics of response decay rate changes in each degradation stage are statistically analyzed, and the damage growth characteristics of the corresponding stage are determined in conjunction with the fatigue cumulative damage evolution law, thus obtaining the fatigue damage increment corresponding to each stage. The average decay rate, maximum decay rate, and decay rate change amplitude in each degradation stage are statistically analyzed, and the statistical results are compared with the damage stage division rules established above. When the response decay rate of a certain stage continues to increase, it is determined that the damage growth rate of that stage is accelerating. When the response decay rate remains stable, it is determined that the damage is in a stable development state. Based on the correlation between the response change characteristics of each stage and the corresponding damage stage, the amount of new fatigue damage in each degradation stage is calculated, and the fatigue damage increment corresponding to each stage is associated with the spatial location and recorded. Finally, the fatigue damage growth results of the defect-sensitive triggering section under different service stages are obtained, providing basic data for nonlinear damage accumulation calculation and the construction of a regionalized damage evolution parameter set.

[0029] In step S40, the process of outputting the partitioned damage evolution parameter set is as follows: Based on the characteristics of response decay rate changes and corresponding fatigue damage increments in each deterioration stage, the response degradation rate at each spatial location within the defect-sensitive triggering zone is determined. Fatigue damage increment data for each deterioration stage are retrieved, and the response decay rate changes at the corresponding spatial locations in each detection cycle are extracted. Using the weld spatial coordinates as an index, the response decay rates at the same location under different detection cycles are correlated, and the change amplitude of response characteristics within a unit detection cycle is calculated. For regions where the response amplitude continuously decreases and the phase change continuously increases, the degree of degradation is determined to be high; for regions with small changes, the degree of degradation is determined to be low. Combined with the fatigue damage increments of the corresponding stages, the response change rate at each spatial location is quantitatively calculated to obtain the response degradation rate reflecting the rate of deterioration of the local state of the weld. The correspondence between the response degradation rate and spatial location is established, providing basic data for damage accumulation analysis. A nonlinear damage accumulation rule considering load sequence and overload hysteresis effect is introduced to correct and accumulate the fatigue damage increment corresponding to each detection cycle, thereby obtaining the cumulative damage state at each spatial location. Based on the number of thermal cycles, temperature fluctuation amplitude, and slag impact load changes recorded during the actual operation of the blast furnace slag flushing trough, the load history information corresponding to each detection cycle is analyzed. For cases where high loads act first and then low loads act, as well as cases with short-term overload conditions, a correction coefficient is introduced to adjust the fatigue damage increment of the corresponding cycle, so that the damage growth result can reflect the load sequence effect and the residual effect of overload. Then, the corrected fatigue damage increment is accumulated cycle by cycle according to the detection cycle sequence, and the damage state formed in the previous period is superimposed with the newly added damage in the current cycle to obtain the cumulative damage state corresponding to each spatial location, thereby realizing the continuous characterization of the weld damage development process. The cumulative damage state within the defect-sensitive triggering zone is spatially continuous to form the damage distribution result of the triggering zone. The cumulative damage state corresponding to each sampling point is mapped to the weld space coordinate system, and a damage distribution network is established according to the spatial proximity relationship. For locations between sampling points that are not directly detected, inverse distance weighted interpolation, spline interpolation, or local weighted regression are used for continuous compensation calculation to transform discrete damage data into continuous damage distribution results. The continuous damage distribution is smoothed to eliminate the local abrupt changes caused by individual outliers, and finally, a damage distribution result covering the entire defect-sensitive triggering zone is formed. This processing can intuitively reflect the expansion and concentration of damage in the spatial range, providing a basis for feature parameter extraction. Based on the damage distribution results, damage characteristic parameters such as maximum damage value, average damage value, damage propagation direction, and damage area proportion are extracted and correlated with corresponding spatial location and region category information to generate a partitioned damage evolution parameter set. Statistical analysis is performed on the generated damage distribution results to extract the maximum damage value corresponding to the location with the highest damage severity. Simultaneously, the average damage value within the entire defect-sensitive triggering section is calculated. The damage propagation trend is analyzed based on the damage gradient change direction to determine the main direction of damage propagation from high-value areas to low-value areas. The area proportion of regions with damage exceeding a preset threshold in the entire triggering section is statistically analyzed to obtain the damage area proportion parameter. Furthermore, the above damage characteristic parameters are correlated with corresponding spatial coordinate information, the category of weld toe area, weld center area, or heat-affected zone, and encoded and stored according to a unified data format. Finally, a partitioned damage evolution parameter set is generated, providing a basis for deducing degradation propagation relationships and assessing lifespan decay.

[0030] It is understandable that the significance of step S40 is as follows: by establishing a phased correlation between the electromagnetic response decay trend and the fatigue accumulation process of the weld material in the defect-sensitive triggering section, and by performing nonlinear cumulative analysis on local damage based on the response degradation rate, the electromagnetic detection results can be correlated with the internal damage development state of the weld, realizing the conversion from surface response characteristics to actual damage degree, and thus obtaining a partitioned damage evolution parameter set that can reflect the damage development level of different regions, providing a quantitative basis for subsequent deterioration expansion analysis.

[0031] Step S50: Based on the partitioned damage evolution parameter set, perform cross-domain coupled deduction of the deterioration propagation relationship between different weld sections, and combine the effects of thermal cycling and impact load to evaluate the remaining load-bearing capacity of the structure and output the life decay assessment results. In step S50, the process of cross-domain coupling deduction of the degradation propagation relationship between different weld sections is as follows: Damage characteristic parameters and spatial location information corresponding to each defect-sensitive triggering segment are extracted from the partitioned damage evolution parameter set, and each defect-sensitive triggering segment is identified as an independent degradation unit. The partitioned damage evolution parameter set is retrieved, and each defect-sensitive triggering segment is classified and managed according to the weld spatial coordinates. For each defect-sensitive triggering segment, the corresponding maximum damage value, average damage value, damage propagation direction, damage area ratio, and the area category information of the weld toe area, weld center area, or heat-affected zone are extracted. At the same time, combined with the spatial coordinate range recorded during the detection process, the specific location boundary of each triggering segment in the grid structure is determined. Each defect-sensitive triggering segment is taken as an independent analysis object, and a corresponding degradation state file is established for it, which is defined as an independent degradation unit. This allows damage propagation analysis to be carried out with degradation units as the basic object, thereby realizing the independent characterization of the damage evolution state of different weld segments. By combining the connection relationships and load transfer paths of the grid structure, the mutual influence relationships between each deteriorated unit are determined, and the damage transfer correlation between sections is established. Based on the structural layout diagram and welding connection relationships of the blast furnace slag flushing grid, a structural connection network between each weld section is established, and the actual transfer paths of slag flushing impact load, thermal stress, and structural constraint force in the grid are analyzed. For deteriorated units with direct welding connections or common stress paths, it is determined that they have a basis for damage transfer. Subsequently, based on the spatial distance, connection stiffness, load transfer direction, and stress concentration between deteriorated units, the strength level of mutual influence is calculated, and corresponding damage transfer correlation relationships are established. Deteriorated units with high stress path overlap and close distance are assigned higher correlation weights, while deteriorated units with weak stress connections are assigned lower correlation weights, thus forming an inter-section damage transfer correlation reflecting the interaction relationships between each weld section. Based on the damage development status of each deteriorated unit and the damage transmission correlation between segments, the damage expansion trend between adjacent deteriorated units is calculated. Combining the current damage level, damage expansion direction, and damage growth rate of each deteriorated unit, the possibility of damage propagation between adjacent deteriorated units is analyzed. When a certain deteriorated unit is in a rapid damage growth stage and its expansion direction points to adjacent deteriorated units, it is considered to have a high risk of cross-segment expansion. Furthermore, based on the aforementioned established damage transmission correlation, the damage propagation capability is quantitatively calculated. The damage growth rate, consistency of expansion direction, and correlation weight are used as evaluation criteria to calculate the trend strength of damage expansion from the current deteriorated unit to adjacent deteriorated units, providing basic data for deterioration propagation inference. Based on the damage propagation trend, the direction of deterioration propagation, the extent of expansion, and the degree of mutual influence between different weld sections are extrapolated to form a cross-domain coupled extrapolation result. On the basis of the damage propagation trend analysis results of each deteriorated unit, the damage propagation process during the future service stage is gradually extrapolated according to the load transfer direction and structural connection relationship. For deteriorated units with strong expansion trends, it is predicted which adjacent weld sections the damage may preferentially expand to, and the corresponding expansion range and affected area are calculated. At the same time, combined with the interaction relationship between multiple deteriorated units, the possible superposition effect, coupling effect, and convergence effect between different damage sources are analyzed to determine the key damage propagation path. Finally, a cross-domain coupled extrapolation result containing the deterioration propagation direction, expected expansion range, degree of influence, and key risk expansion path is formed, providing a basis for the assessment of the structural remaining load-bearing capacity and life decay analysis.

[0032] In step S50, the process of outputting the lifetime degradation assessment result is as follows: The thermal cycle parameters and impact load parameters corresponding to the historical operation of the blast furnace slag flushing trough are obtained to form load action data reflecting the actual operating conditions. Long-term operation records of the slag flushing trough are retrieved from the blast furnace production control system, equipment monitoring system, and historical operation and maintenance database to obtain temperature change data, start-up and shutdown records, slag flushing flow data, and equipment vibration data during operation. Among them, the highest temperature, lowest temperature, temperature difference change amplitude, and heating and cooling rate in each operating cycle are statistically analyzed based on the temperature monitoring data to form thermal cycle parameters. The peak value, average load level, and number of impact loads are extracted based on the slag flushing flow change, slag block impact frequency, and vibration monitoring results to form impact load parameters. The thermal cycle parameters and impact load parameters are uniformly organized in chronological order and a corresponding time correlation is established to form load action data reflecting the actual operating conditions of the slag flushing trough, providing a real operating condition basis for load-bearing capacity analysis. By combining the cross-domain coupled simulation results and load data, the damage development status and load-bearing capacity change trend of each weld section are analyzed to determine the current remaining load-bearing capacity of the structure. The cross-domain coupled simulation results are retrieved to obtain the current damage status, damage propagation direction and future propagation trend of each weld section. Combined with the load data, the stress state change of each weld section under the combined action of thermal cycle stress and impact load is analyzed. For weld sections with high damage and located on the main load transmission path, the local load-bearing capacity attenuation is analyzed. For weld sections with low damage, the contribution of each section to the overall structural load-bearing capacity is analyzed. Based on the damage level, stress state and structural connection relationship of each weld section, the overall load-bearing capacity of the grid is comprehensively calculated to determine the remaining load-bearing capacity that the current structure can withstand under continued service conditions, thereby obtaining the current safety status evaluation result of the structure. Based on the changing trend of remaining load-bearing capacity and the state of damage accumulation, the remaining operating cycle before the structure reaches the preset failure condition is predicted; according to the current remaining load-bearing capacity and the damage growth rate of each weld section, the damage development process of the subsequent operating stage is established. The preset failure condition can be set as the remaining load-bearing capacity decreasing to below a predetermined proportion of the design load-bearing capacity, or the cumulative damage of the key weld section reaching a specified limit value. Combined with the thermal cycling frequency and impact load frequency under actual working conditions, the damage accumulation process during future service is calculated cycle by cycle, and the changes in the structural load-bearing capacity are continuously updated. When the prediction result reaches the preset failure condition, the corresponding number of operating cycles, thermal cycles, and impacts are recorded and used as the remaining operating cycle of the structure, thereby realizing the prediction and analysis of the future service capacity of the grid weld. The predicted remaining operating cycle is compared with the designed service life to calculate the degree of life decay. Combined with the damage distribution characteristics and risk levels of each weld section, a life decay assessment result is generated. The predicted remaining operating cycle is then compared with the grid's designed service life parameters to calculate the proportion of currently consumed life to the designed life and the proportion of remaining usable life, thus obtaining the degree of life decay. Based on the damage distribution results of each weld section, different areas are classified into risk levels. Weld sections with high damage levels, rapid expansion, and located in critical stress positions are classified as high-risk areas, while sections with lower damage levels and slow expansion are classified as low-risk areas. The remaining structural load-bearing capacity, remaining operating cycle, life decay ratio, high-risk weld locations, and risk level information are summarized to form a life decay assessment result. Corresponding maintenance recommendations, key monitoring areas, and early warning information are output to provide a basis for maintenance decisions regarding the blast furnace slag flushing grid welds.

[0033] Understandably, the significance of step S50 lies in the following: by performing cross-domain coupled deduction of the deterioration propagation relationship between different weld sections based on a partitioned damage evolution parameter set, and by comprehensively evaluating the remaining load-bearing capacity of the structure in conjunction with the effects of thermal cycling and impact loads, it is possible to analyze the development trend of local damage propagating to the surrounding area, predict the decay law of structural performance with service time, and thus obtain the life decay assessment results. This enables early prediction of the remaining service life and failure risk of the grid weld, providing technical support for maintenance planning, risk warning, and equipment life cycle management.

[0034] Example 2: Please refer to Figure 2 As shown in the embodiment of the present invention, the non-destructive electromagnetic detection and life assessment system for the weld condition of the blast furnace slag flushing grid includes the following modules: Multi-source disturbance time-series reconstruction module: Performs segmented electromagnetic scanning of the grid weld seam in the slag flushing tank along the spatial trajectory, obtains probe attitude offset, weld seam curvature and surface attached medium distribution, constructs multi-source disturbance parameters, and performs time-series reconstruction of the original induction signal to generate weld seam response mapping data. Regional response weighting module: Performs regional division and energy allocation processing on weld response mapping data, separates the response characteristics of the weld toe area, weld center area and heat-affected zone, and establishes a regional response weighting field by combining slag coverage and signal attenuation characteristics. Frequency domain offset defect mapping module: Based on the regional response weight field, it performs signal suppression processing on low signal-to-noise ratio sections and performs frequency domain offset tracking on micro-amplitude magnetic flux disturbances in continuous scanning. It maps the offset peak value with the geometric change point of the weld to form a defect-sensitive triggering section. Local degradation evolution mapping module: Introduces a local degradation evolution mapping mechanism in the defect-sensitive triggering section, links the electromagnetic response decay trend with the fatigue accumulation process of the weld material in stages, and performs nonlinear cumulative calculation of local damage based on the response degradation rate, outputting a partitioned damage evolution parameter set. Cross-domain coupled life extrapolation module: Based on the partitioned damage evolution parameter set, it performs cross-domain coupled extrapolation of the deterioration expansion relationship between different weld sections, and combines the effects of thermal cycling and impact loads to evaluate the remaining load-bearing capacity of the structure and output life decay assessment results.

[0035] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A non-destructive electromagnetic testing and life assessment method for the weld condition of blast furnace slag flushing grid, characterized in that: Includes the following steps: The weld seam of the grid inside the slag tank is subjected to segmented electromagnetic scanning along the spatial trajectory to obtain the probe attitude offset, weld seam curvature and surface adhering medium distribution state, construct multi-source disturbance parameters, and reconstruct the original induction signal in time sequence to generate weld seam response mapping data. The weld response mapping data is divided into regions and energy is distributed to separate the response characteristics of the weld toe area, weld center area and heat-affected zone. The regional response weight field is established by combining the characteristics of slag coverage and signal attenuation. Based on the regional response weight field, signal suppression processing is performed on the low signal-to-noise ratio section, and frequency domain offset tracking is performed on the micro-amplitude magnetic flux disturbance in continuous scanning. The offset peak value is mapped to the geometric change point of the weld to form a defect-sensitive triggering section. A local degradation evolution mapping mechanism is introduced in the defect-sensitive triggering section to link the electromagnetic response attenuation trend with the fatigue accumulation process of the weld material in stages, and to perform nonlinear cumulative conversion of local damage based on the response degradation rate, outputting a partitioned damage evolution parameter set. Based on the zonal damage evolution parameter set, the deterioration propagation relationship between different weld sections is deduced through cross-domain coupling. Combined with the effects of thermal cycling and impact loading, the remaining load-bearing capacity of the structure is evaluated, and the life decay assessment results are output.

2. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of generating weld response mapping data is as follows: Based on the three-dimensional structural drawings of the grid and the weld numbering rules, a spatial scanning path and time index vector set are constructed, and the electromagnetic scanning probe is controlled to perform traversal scanning and acquisition along the spatial scanning path and time index vector set, and the probe attitude angle and scanning speed information of each scanning node are acquired simultaneously. The original induction signals were timestamped and the probe attitude offset was calculated in combination with the standard scanning posture. At the same time, the weld curvature and surface attached medium distribution characteristics were integrated to construct multi-source disturbance parameters. The time axis is aligned and quantized for the multi-source disturbance parameters to construct a multi-dimensional disturbance matrix, and the original inductive signal is reconstructed by inverse disturbance correction based on the multi-dimensional disturbance matrix. The reconstructed signal is interpolated and normalized to generate continuous electromagnetic response mapping data that corresponds one-to-one with the spatial coordinates of the weld.

3. The non-destructive electromagnetic testing and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of separating the response characteristics of the weld toe zone, weld center zone, and heat-affected zone is as follows: Based on the signal gradient and phase reversal features in the weld response mapping data, the geometric boundary line of the weld is extracted, and the scanning area is divided into a central strip area, a transition zone area and a base area according to the geometric boundary line. Using the central zonal region as the energy benchmark, the relative energy decay characteristics of the transition zone and the base zone are calculated, and a mapping relationship between region type and energy decay characteristics is established in combination with calibration data. Based on the mapping relationship, the weld response mapping data is classified into regions, and each sampling point is divided into the weld toe area, the weld center area, or the heat-affected zone. The membership weighting is applied to the points with blurred boundaries. Energy redistribution processing is performed on the response data in each region to enhance the response weight of the weld center area and weld toe area, reduce the background energy ratio of the heat-affected zone, and complete the separation of regional response characteristics.

4. The non-destructive electromagnetic testing and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of establishing a regional response weight field is as follows: The response characteristics of the separated weld toe area, weld center area and heat-affected zone were measured. The signal amplitude attenuation and phase hysteresis caused by slag coverage in each area were measured to establish a regionalized slag attenuation coefficient. The residual attenuation coefficient is correlated with the thickness of the surface-attached dielectric layer in the corresponding region to generate a regional attenuation transfer function with the dielectric layer thickness as the independent variable and the signal attenuation rate as the dependent variable. By introducing the relationship between the skin depth and the operating frequency of electromagnetic detection, frequency response correction is performed on the regional attenuation transfer function, and the attenuation differences under different frequency components are normalized to the same evaluation baseline. Based on the modified regional attenuation transfer function, the signal reliability in each region is evaluated. A regional response weight field is constructed by assigning high weights to regions with low signal attenuation rates and thin residue coverage, and low weights to regions with severe attenuation.

5. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of frequency domain offset tracking of micro-amplitude magnetic flux disturbances in continuous scanning is as follows: Using a regional response weight field, the weld response mapping data is weighted and filtered to identify and mark spatial continuous segments with weight values ​​below a preset confidence threshold. These spatial continuous segments are identified as low signal-to-noise ratio segments, and the signal amplitude within the low signal-to-noise ratio segments is compressed and suppressed according to the weight ratio. Short-time Fourier transform is performed on the suppressed global signal to construct a joint time and frequency distribution spectrum. The continuous background magnetic flux baseline is extracted from the spectrum, and the small-amplitude magnetic flux disturbance component that deviates from the background magnetic flux baseline is extracted. For the micro-amplitude magnetic flux disturbance component, a sliding window tracking of the peak frequency is performed along the time axis on the joint time and frequency distribution spectrum, and the offset trajectory of the frequency peak with time or scanning position is recorded to form a frequency domain offset characteristic curve. The frequency domain offset characteristic curve is differentiated to calculate the offset rate. Slowly varying low-frequency common-mode interference caused by ambient temperature drift and power grid fluctuations is filtered out, and the high-frequency offset abrupt component characterizing the discontinuity of the microstructure of the weld material is output.

6. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of forming a defect-sensitive triggering section is as follows: Peak detection is performed on the high-frequency offset abrupt components in the frequency domain offset tracking results to extract the peak amplitude, peak frequency, and position index on the weld space trajectory. Based on the three-dimensional geometric contour data of the weld, the curvature features are calculated to identify the weld toe angle, weld reinforcement abrupt change and undercut location, and the spatial distribution of geometric abrupt change points is constructed. The peak location index is spatially matched with the spatial distribution of geometric change points, and a valid mapping point is determined when the spatial distance is less than a preset threshold. Spatial clustering analysis is performed on the effective mapping points to divide the spatially continuous and densely mapped regions into defect-sensitive triggering segments.

7. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of linking the electromagnetic response decay trend with the fatigue accumulation process of weld materials in stages is as follows: Electromagnetic response characteristics under multiple detection cycles are extracted in the defect-sensitive triggering section, including response amplitude and phase information, and a time series response decay curve is constructed. Based on the fatigue damage theory of weld materials, a fatigue cumulative damage evolution rule is established to describe the damage development process of welds under thermal cycling and impact loading using damage variables. A piecewise fitting analysis was performed on the response decay curve to extract the inflection point of the decay rate change, and the response decay process was divided into multiple degradation stages based on the inflection point. The characteristics of response decay rate changes in each deterioration stage are statistically analyzed, and the damage growth characteristics of the corresponding stage are determined by combining the fatigue cumulative damage evolution law, so as to obtain the fatigue damage increment corresponding to each stage.

8. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of outputting the partitioned damage evolution parameter set is as follows: Based on the characteristics of response decay rate changes and corresponding fatigue damage increments in each degradation stage, the response degradation rate at each spatial location within the defect-sensitive triggering section is determined. A nonlinear damage accumulation rule considering load order and overload hysteresis effect is introduced to correct and accumulate the fatigue damage increment corresponding to each detection cycle, so as to obtain the accumulated damage state at each spatial location. The cumulative damage state within the defect-sensitive triggering section is spatially continuous to form the damage distribution result of the triggering section; Based on the damage distribution results, damage characteristic parameters such as maximum damage value, average damage value, damage propagation direction, and damage area proportion are extracted and combined with corresponding spatial location and regional category information to generate a regionalized damage evolution parameter set.

9. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of cross-domain coupled deduction of the degradation propagation relationship between different weld sections is as follows: Damage characteristic parameters and spatial location information corresponding to each defect-sensitive triggering segment are extracted from the partitioned damage evolution parameter set, and each defect-sensitive triggering segment is determined as an independent degradation unit. By combining the connection relationship and load transfer path of the grid structure, the mutual influence relationship between each deteriorated unit is determined, and the damage transfer correlation between sections is established; Based on the damage development status of each deteriorated unit and the damage transmission correlation between segments, the damage expansion trend between adjacent deteriorated units is calculated. Based on the damage propagation trend, the direction of deterioration propagation, the extent of expansion, and the degree of mutual influence between different weld sections are deduced, forming cross-domain coupled deduction results.

10. The non-destructive electromagnetic detection and life assessment method for the weld condition of blast furnace slag flushing grid according to claim 1, characterized in that, The process of outputting lifetime degradation assessment results is as follows: The thermal cycle parameters and impact load parameters corresponding to the historical operation of the blast furnace slag flushing trough are obtained to form load action data that reflects the actual operating conditions. By combining the cross-domain coupling simulation results and load data, the damage development state and load-bearing capacity change trend of each weld section are analyzed to determine the current remaining load-bearing capacity of the structure. Based on the changing trend of remaining load-bearing capacity and the state of damage accumulation, predict the remaining operating period of the structure before it reaches the preset failure condition; The predicted remaining operating cycle is compared with the design service life to calculate the degree of service life decay. Combined with the damage distribution characteristics and risk level of each weld section, a service life decay assessment result is generated.

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