A method and system for non-destructive corrosion assessment of a typical grounding body

Abstract

The application discloses a kind of methods and systems for non-destructive corrosion evaluation of typical grounding body, wherein the method comprises: carrying out field calibration scanning on reference grounding body with known health state, obtaining dielectric constant and health reference amplitude;Data acquisition is carried out on the grounding body to be measured using ground penetrating radar system, and original GPR data is obtained;Based on the dielectric constant, data processing and analysis are carried out on the original GPR data, and the spatial coordinates of the grounding body to be measured at each scanning position are located from the original GPR data;Based on the spatial coordinates of the grounding body to be measured, the real reflection amplitude of the grounding body to be measured is extracted;Based on the health reference amplitude and the real reflection amplitude, the corrosion state of the grounding body to be measured is determined.The significant effect is: effectively overcome the detection uncertainty problem caused by the complexity and non-uniformity of soil medium;Realize the quantitative evaluation and accurate positioning of the corrosion state of the grounding body.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of non-destructive testing of underground pipe networks, and in particular to a method, system, device and medium for non-destructively evaluating corrosion of a typical grounding body using ground penetrating radar (GPR). BACKGROUND

[0002] A grounding network is an indispensable safety component in lightning protection infrastructure of buildings and structures. It is composed of grounding bodies (usually flat steel or round steel) buried in the soil, and its main function is to provide a safe and low-impedance discharge channel for lightning current when lightning strikes, and to ensure the equipotential connection inside the facility, thereby ensuring the safety of personnel and equipment.

[0003] The grounding network is operated underground for a long time. With the passage of time, electrochemical corrosion continues to occur in the complex electrolyte environment of the soil, resulting in a decrease in the cross-sectional area of the grounding body, an increase in the resistance of the grounding network, and a deterioration in the voltage equalization effect, which cannot play a good role in lightning current discharge. In severe cases, the conductor may be completely broken locally, causing significant safety hazards. Detection of the grounding network is an important safety measure for lightning protection. The laying of the grounding network is a concealed project, and ground buildings or underground concealments will hinder its detection and maintenance.

[0004] Currently, electrical parameter measurement methods are widely used in lightning protection detection to indirectly infer the integrity of the grounding network, such as testing the grounding resistance to determine whether the grounding network is working normally, or measuring the step voltage to determine whether the grounding body of the grounding network has been broken. In practical applications, indirect detection methods have significant limitations. For example, for a large grounding network with many branches, even if a branch has corroded and broken, if other parallel branches are intact, the total resistance value measured may still be within the acceptable range, creating a false sense of security. The change in step voltage can only find the location of the broken grounding network, but cannot find the severely corroded section that has not been broken. When the above indirect detection methods suspect a problem, the final confirmation of the defect location is done by visual inspection through on-site excavation, which is blind, labor-intensive, and inefficient, and has a negative impact on business operations and residents' lives. Meanwhile, excavation and maintenance of the grounding network of key lightning protection units such as gas stations, oil and gas storage bases, and hazardous chemical warehouses also pose additional safety risks.

[0005] Ground Penetrating Radar (GPR) is a mature non-destructive testing technology. It detects targets by transmitting high-frequency electromagnetic waves and receiving their reflections in the underground medium. It has been widely used in the positioning of underground pipelines, cavities, and other targets. The physical basis of its application in corrosion detection is that the corrosion products (rust) of the grounding body and the water accumulated around it form a "corrosion anomaly zone" that is significantly different from the electromagnetic properties (mainly the dielectric constant) of the intact metal and the surrounding soil, thereby producing unique reflection, attenuation, and scattering effects on the GPR signal. However, when applying GPR technology to the quantitative assessment of the corrosion state of buried grounding bodies, there are still severe challenges at the current technical level:

[0006] Severe interference from complex soil environment: Soil is an extremely complex and non-uniform natural medium. Its composition, compaction, and especially the dramatic changes in water content can significantly affect its dielectric constant. For example, a small area of clay with high water content may have an attenuation effect on the GPR signal comparable to or even stronger than that caused by the corrosion of the grounding body. This makes it difficult to attribute signal attenuation to target corrosion in practical applications, and false judgments are easily made.

[0007] Complexity of data processing and interpretation: In the GPR raw data (profile), the response of the grounding body usually takes the form of a hyperbolic curve, which needs to be processed through complex migration algorithms to focus its energy back to the true spatial location. The effectiveness of this processing is heavily dependent on the accurate estimation of the dielectric constant of the underground medium. Incorrect parameters can lead to image distortion and positioning errors. The entire process highly depends on the professional experience of the analyst for parameter tuning and iterative calibration, with low automation and high interpretation difficulty.

[0008] In summary, the existing grounding grid detection technology system has obvious shortcomings, and although GPR technology has great potential, it still faces technical bottlenecks in solving soil environment interference, establishing reliable evaluation criteria, and achieving process-based and high-precision interpretation. Therefore, there is an urgent need in the industry for a new non-destructive testing technology solution that can overcome the above shortcomings and quickly locate defects in the grounding grid under non-excavation conditions. SUMMARY

[0009] To overcome the shortcomings of the prior art, the purpose of the present application is to provide a method, system, device, and medium for non-destructive corrosion evaluation of typical grounding bodies. By introducing mandatory field calibration and systematic data processing, the combination of signal focusing, target positioning, and high-fidelity amplitude extraction can achieve quantitative assessment and precise positioning of the corrosion state of grounding bodies under non-excavation conditions.

[0010] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0011] The application discloses a method for non-destructive corrosion evaluation of typical grounding bodies.

[0012] Step 1, on-site calibration scanning is performed on a reference grounding body in a known healthy state to obtain a dielectric constant and a healthy reference amplitude;

[0013] Step 2, data acquisition is performed on a to-be-measured grounding body by using a ground penetrating radar system to obtain original GPR data;

[0014] Step 3, data processing and analysis are performed on the original GPR data based on the dielectric constant obtained in step 1, and the spatial coordinates of the to-be-measured grounding body at each scanning position are located from the original GPR data;

[0015] Step 4, real reflection amplitudes of the to-be-measured grounding body are extracted based on the spatial coordinates of the to-be-measured grounding body;

[0016] Step 5, corrosion state determination is performed on the to-be-measured grounding body based on the healthy reference amplitude;

[0017] The process of extracting the real reflection amplitudes of the to-be-measured grounding body based on the spatial coordinates of the to-be-measured grounding body in step 4 specifically includes the following processes:

[0018] Step 4.1, the spatial coordinates of the to-be-measured grounding body are converted into time domain coordinates;

[0019] Step 4.2, initial reflection amplitudes of the to-be-measured grounding body are obtained by bilinear interpolation based on the time domain coordinates of the to-be-measured grounding body;

[0020] Step 4.3, physical correction is performed on the initial reflection amplitudes based on a propagation effect to obtain the real reflection amplitudes of the to-be-measured grounding body;

[0021] The process of performing corrosion state determination on the to-be-measured grounding body based on the healthy reference amplitude and the real reflection amplitude in step 5 specifically includes the following processes:

[0022] Step 5.1, a final data set containing all identified grounding body positions is constructed, and the final data set includes the coordinates, depths and real reflection amplitudes extracted at the positions of the identified to-be-measured grounding body;

[0023] Step 5.2, the corresponding healthy reference amplitudes are found out based on the reference standard that the known grounding body is in a healthy state;

[0024] Step 5.3, all real reflection amplitudes and healthy reference amplitudes are converted into decibels;

[0025] Step 5.4, the relative attenuation amount of each measurement point relative to the healthy reference point is calculated;

[0026] Step 5.5, compare the relative attenuation amount of each measurement point with the preset threshold value, and make a corrosion state judgment according to the comparison result.

[0027] Further, the data acquisition of the grounding body to be measured by the ground penetrating radar system in step 2 specifically includes the following process:

[0028] Step 2.1, according to the detection requirements of the grounding body to be measured, the ground penetrating radar system and the antenna are selected;

[0029] Step 2.2, systematic network layout and geographic registration are performed on the grounding body to be measured;

[0030] Step 2.3, after setting the acquisition parameters of the ground penetrating radar system, data acquisition is performed to obtain the original GPR data.

[0031] Further, the data processing and analysis of the original GPR data in step 3 includes the following process:

[0032] Step 3.1, data preprocessing and signal adjustment are performed on the original GPR data;

[0033] Step 3.2, F-K migration processing is performed on the preprocessed GPR data to focus the reflection signal energy of the grounding body;

[0034] Step 3.3, the spatial coordinates of the grounding body to be measured at each scanning position are automatically identified by combining a machine learning clustering algorithm.

[0035] Further, the F-K migration processing includes the following steps:

[0036] Step A1, calculate the electromagnetic wave speed according to the known dielectric constant ;

[0037] Step A2, convert the preprocessed GPR data in the space-time domain to the frequency-wave number domain to obtain F-K domain data ;

[0038] Step A3, in the F-K domain, perform coordinate mapping according to the dispersion relationship, and perform phase shift for each imaging depth to obtain the mapped F-K domain data;

[0039] Step A4, inverse Fourier transform the mapped F-K domain data to transform the migrated data back to the spatial domain.

[0040] Further, the automatic identification of the spatial coordinates of the grounding body to be measured at each scanning position by combining a machine learning clustering algorithm specifically includes the following steps:

[0041] Step B1, normalize the data amplitude after F-K migration, and set an amplitude threshold to filter out all high-energy data points above the threshold to form a point cloud around the real ground body to be measured;

[0042] Step B2, randomly select K points from the point cloud as initial centroids, where K is the estimated number of ground bodies to be measured;

[0043] Step B3, for each point in the point cloud, calculate its distance from the K centroids and assign it to the cluster where the nearest centroid is located;

[0044] Step B4, recalculate the centroid of each cluster;

[0045] Step B5, repeat steps B3 and B4 until the centroid position no longer changes significantly or reaches a preset number of iterations, obtaining the coordinates of the K centroids, i.e. the spatial coordinates of the ground bodies to be measured.

[0046] Further, the method further comprises the step of generating a corrosion distribution map according to the corrosion state determination result to realize visual output of the corrosion state determination result.

[0047] The second aspect is a non-destructive corrosion evaluation system for typical grounding bodies using ground penetrating radar, which is used to implement the steps of the method of the first aspect, comprising:

[0048] A reference data acquisition module is used to perform on-site calibration scanning on reference grounding bodies with known health status to obtain dielectric constant and health reference amplitude;

[0049] A data acquisition module is used to collect data from the grounding body to be measured using a ground penetrating radar system to obtain raw GPR data;

[0050] A data processing and analysis module is used to process and analyze the raw GPR data based on the obtained dielectric constant to locate the spatial coordinates of the grounding body to be measured at each scanning position from the raw GPR data;

[0051] A true reflection amplitude extraction module is used to extract the true reflection amplitude of the grounding body to be measured based on the spatial coordinates of the grounding body to be measured;

[0052] A corrosion state determination module is used to determine the corrosion state of the grounding body to be measured based on the health reference amplitude and the true reflection amplitude.

[0053] Further, the system further comprises:

[0054] A visualization output module is used to generate a corrosion distribution map according to the corrosion state determination result to realize visual output of the corrosion state determination result.

[0055] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to acquire the code and execute the method of the first aspect.

[0056] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the method of the first aspect.

[0057] The present application has the following remarkable effects:

[0058] Firstly, the present application obtains the dielectric constant for precise migration processing and the healthy reference amplitude for quantitative evaluation by on-site calibration scanning of the reference grounding body with known health condition; then, the present application performs systematic ground penetrating radar (GPR) data acquisition on the grounding body to be measured, performs F-K migration processing on the acquired data based on the calibrated dielectric constant, focuses the reflection signal energy of the grounding body, and automatically identifies the precise spatial coordinates of the grounding body at each scanning position in combination with a machine learning clustering algorithm; further, the present application queries the high-fidelity true reflection amplitude without migration in the preprocessed original data according to the extracted spatial coordinates, and calculates the relative attenuation of the reflection amplitude of each measurement point according to the reference maximum amplitude, i.e. the healthy reference amplitude, obtained by calibration; finally, the present application compares the relative attenuation of each measurement point with a preset corrosion determination threshold, thereby determining whether there is serious corrosion at the position, and generating a corrosion distribution map.

[0059] As can be seen, the present application obtains a reliable evaluation reference by introducing mandatory on-site calibration, effectively overcomes the detection uncertainty problem caused by the complex and non-uniformity of soil medium; further, the present application combines signal focusing, target positioning and high-fidelity amplitude extraction through a systematic data processing procedure, realizes quantitative evaluation and accurate positioning of the corrosion state of the grounding body. The method avoids the indirectness and misjudgment risk of traditional electrical measurement method, and also avoids the destructiveness, blindness and inefficiency of excavation inspection, can find serious corrosion hidden danger before the physical fracture of the grounding body, thereby greatly improving the accuracy, reliability and efficiency of detection, and providing accurate technical support for preventive maintenance and safe operation of the grounding net. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a flowchart of the method of the present application;

[0061] Figure 2 is a flowchart of corrosion state determination of the method of the present application;

[0062] Figure 3 is a structural schematic diagram of the system of the present application;

[0063] Figure 4 is a structural diagram of the computer device of the present application. DETAILED DESCRIPTION

[0064] The specific embodiments of the present application and the working principle will be further described in detail below with reference to the accompanying drawings.

[0065] First of all, it needs to be pointed out that: the grounding grid in the present application specifically refers to a simple lightning protection grounding grid (such as a lightning protection grounding grid specially laid in places such as gas stations, oil depots, and dangerous chemical depots), and the ordinary building grounding grid or structure with foundation steel drainage and the grounding grid of power stations with complex environment are not within the application scope; the typical grounding body refers to a horizontal grounding body of 40mm*4mm flat steel or φ12mm round steel, and other material types and vertical grounding bodies are not within the application scope.

[0066] Secondly, the basic principle and applicability of the present application are analyzed as follows:

[0067] 1. Electrochemical basis of corrosion

[0068] The corrosion of steel is an electrochemical process, and its essence is to convert the metal iron (Fe) into an oxide form that is more stable in thermodynamics, i.e. rust (Fe2O3·xH2O). This process requires three basic elements: anode (area where oxidation reaction occurs), cathode (area where reduction reaction occurs), and electrolyte. In the application scenario of buried grounding body, different areas of the grounding conductor itself can act as anode and cathode, and the surrounding moist soil provides an electrolyte environment for ion flow.

[0069] This electrochemical process mainly includes two core chemical reactions:

[0070] Oxidation of steel (anode reaction): metal iron loses electrons and is converted into ferrous ions.

[0071]

[0072] Reduction of water (cathode reaction): in the presence of oxygen, water and oxygen gain electrons to form hydroxyl ions.

[0073]

[0074] Subsequently, at the interface between the steel and the soil, ferrous ions combine with hydroxyl ions to form hydrated iron oxide, i.e. rust, the formation of corrosion products not only consumes the original metal material, but also weakens the structural integrity and conductivity of the grounding body, and its volume is much larger than that of the original steel. This volume expansion will generate extrusion stress on the surrounding soil or packaging material, further changing the physical environment around the grounding body.

[0075] 2. Interaction of GPR signals and corroded materials

[0076] The core principle of Ground Penetrating Radar (GPR) technology is to detect underground targets by transmitting high-frequency electromagnetic waves and receiving their reflected signals in the underground medium. The propagation speed and attenuation characteristics of electromagnetic waves in different media depend on the electromagnetic properties of the medium, such as dielectric constant, electrical conductivity, etc. When GPR electromagnetic waves encounter an interface with different electromagnetic properties, part of the energy will be reflected back to the ground receiving antenna.

[0077] The corrosion process of the grounding body significantly changes the electromagnetic properties of the microenvironment around it, which constitutes the physical basis of GPR detection. Specifically, the formation of corrosion products (rust) and the accumulation of water during the corrosion process will jointly cause the following two key changes:

[0078] Change in dielectric constant: The dielectric constant of the mixture of corrosion products and water is significantly different from that of the surrounding dry soil or intact metal, forming a clear electromagnetic wave reflection interface.

[0079] Signal attenuation and scattering: Corrosion areas are usually rich in ions and water, forming a local environment with high electrical conductivity and high loss. When GPR signals pass through this area, the energy will be sharply attenuated due to conduction loss. At the same time, the irregular shape of the corrosion products and the micro-cracks in the surrounding medium caused by corrosion will cause scattering of the GPR signal, further weakening the signal energy that can be effectively reflected back to the receiving antenna.

[0080] Therefore, compared with healthy, uncorroded grounding bodies, the GPR reflected signal amplitude generated by severely corroded grounding bodies will be significantly reduced, and the degree of signal amplitude attenuation becomes the core indicator for quantitative evaluation of the corrosion state.

[0081] 3. ASTM D6087 standard as an analysis framework

[0082] In order to standardize and quantitatively evaluate the attenuation of GPR signal amplitude, the present invention draws on the core analysis logic of the ASTM D6087 standard test method, which provides a non-destructive evaluation method to predict whether there is deterioration in the concrete or steel above or at the top steel layer. This method is mainly used to evaluate the corrosion status of steel in concrete bridge decks, but its basic principles can be applied to buried grounding body detection.

[0083] The core of this method is a relative comparison method, that is, the reflected signal amplitude of the grounding body in the test area is compared with that of a known "healthy" (uncorroded or extremely low corrosion) reference grounding body. In order to more effectively represent the huge dynamic range of signal strength, the evaluation is carried out on the logarithmic decibel (dB) scale. The conversion formula of signal amplitude is as follows:

[0084]

[0085] Among them, A dB A is the true reflection amplitude in decibels, and A is the true reflection amplitude value (in data units) in the original GPR data.

[0086] The analysis process is as follows:

[0087] Within the entire testing area, the amplitude of reflected signals from all detectable grounding bodies was first identified and measured.

[0088] Of all the measurements, the point with the highest amplitude was selected as the "healthy" reference point, and its decibel value was denoted as A. max_dB This point is assumed to be the area with the lowest degree of corrosion.

[0089] Calculate the signal attenuation at each measurement point within the test area relative to the reference point: Attenuation (dB) = A dB -A max_dB Because A dB Always less than or equal to A max_dB This attenuation is usually a negative value.

[0090] A diagnostic threshold is set: when the signal attenuation at a location is below -6 to -8 dB, that location is considered to be in a potentially corrosive environment. In this invention, to ensure high reliability in detecting "severe corrosion," a more stringent -8 dB threshold is recommended.

[0091] 4. Applicability Analysis

[0092] Applying reference methods based on the detection of reinforcing steel bars in concrete to the detection of grounding conductors in soil presents even more severe challenges, which profoundly affect every aspect of the invention design.

[0093] First, the fundamental difference in application environment is the biggest challenge. The concrete addressed by the reference method is a relatively homogeneous, controllable man-made material with a relatively stable dielectric constant within a certain range. However, the soil addressed in this invention is an extremely complex natural medium. The dielectric constant of soil is drastically affected by its composition (the ratio of clay, sand, and loam), compaction, and especially its moisture content. For example, the dielectric constant of dry sandy soil (… The value may be between 3 and 4, but can jump to 20-30 when wet. A small area of ​​clay with high water content may have an attenuation effect on the GPR signal comparable to that caused by grounding electrode corrosion. This means that in soil environments, the assumption that signal attenuation can be simply attributed to corrosion becomes unreliable. Therefore, this invention must introduce additional control and calibration steps to distinguish between attenuation caused by soil inhomogeneity and attenuation caused by target corrosion.

[0094] Secondly, the validity of the entire ASTM D6087 method relies entirely on the selection of an accurate and pristine "healthy" reference signal A max In a bridge deck, it is reasonable to assume that some areas of reinforcement are intact. But for a continuous buried grounding system that can be hundreds of meters long, it is impossible to a priori guarantee that any segment is completely "healthy". If the grounding body chosen as the reference point itself has slight corrosion, its A max value will be lower than that of a truly pristine conductor. This will cause the entire decibel scale to be compressed, so that the signal attenuation of some severely corroded areas can not reach the threshold of -8 dB, resulting in a "false negative" misjudgment. The most critical step in this invention is not the GPR scan itself, but the establishment of a verified and reliable "healthy" reference standard on site. This requirement must be explicitly stated as a mandatory prerequisite in this invention.

[0095] Based on the above description, in order to solve the problems existing in the detection of the corrosion state of the typical grounding body, the technical scheme adopted is as follows: Embodiments

[0096] As Figure 1 shown, a method for non-destructive corrosion evaluation of a typical grounding body, the specific steps are as follows:

[0097] Step 1, on-site calibration scanning of a reference grounding body with known healthy state, obtaining dielectric constant for accurate offset processing and healthy reference amplitude for quantitative evaluation;

[0098] Step 2, data acquisition of the grounding body to be measured by using a ground penetrating radar system, obtaining original GPR data;

[0099] Step 3, based on the dielectric constant obtained in step 1, data processing and analysis of the original GPR data, locating the spatial coordinates of the grounding body to be measured at each scanning position from the original GPR data;

[0100] Step 4, based on the spatial coordinates of the grounding body to be measured, extracting the true reflection amplitude of the grounding body to be measured;

[0101] Step 5, based on the healthy reference amplitude, corrosion state determination of the grounding body to be measured;

[0102] Step 6, generating a corrosion distribution map according to the corrosion state determination result, realizing visual output of the corrosion state determination result.

[0103] In the embodiments of the present application, the field calibration is a mandatory and indispensable initial step to provide a reliable anchor point for the entire data processing and analysis process. Before the evaluation, a dedicated GPR field calibration scan must be performed on a target with a known condition in the field. The target with a known condition can be:

[0104] a. New reference body: A brand new grounding body (40mm*4mm flat steel or 12mm round steel) of the same specification as the to-be-tested conductor is buried at the same depth of 80 centimeters near the field. The scan result of the target will be used as the benchmark of the "perfect health" state to determine the optimal dielectric constant and the reference maximum amplitude, i.e., the health reference amplitude Amax.

[0105] b. Excavation verification point: A location on the to-be-tested grounding body is selected for convenient excavation, a small range is excavated, and the corrosion condition is directly visually inspected; then the location is backfilled and scanned, and the GPR response thereof is associated with the actual condition.

[0106] Through this calibration stage, the risks brought by soil uncertainty and instrument settings can be greatly reduced.

[0107] In some optional embodiments, the data acquisition of the to-be-tested grounding body using the ground penetrating radar system in step 2 specifically includes the following process:

[0108] Step 2.1, selecting a ground penetrating radar system and an antenna according to the detection requirements of the to-be-tested grounding body;

[0109] Specifically, for a target body buried at a depth of 80 centimeters, and considering the detection requirements of targets such as flat steel and round steel, the selection of the antenna frequency needs to balance the detection depth and the resolution. Low-frequency antennas have strong penetration ability but low resolution, and high-frequency antennas have high resolution but limited penetration depth. Therefore, the GPR antenna with a center frequency in the range of 250-400 MHz is selected in the present embodiment, which can effectively penetrate to a depth of 80 centimeters under typical soil conditions, and provide sufficient resolution for targets such as 40mm*4mm flat steel and 12mm round steel.

[0110] In addition, in order to minimize electromagnetic interference from objects above the ground (such as operators, vegetation, and air-ground interface), shielded antennas must be used. Non-shielded antennas will receive a large amount of clutter from the air, which will seriously contaminate the effective signal of the underground target and bring great difficulty to subsequent data processing.

[0111] Step 2.2, systematic network layout and geographic registration of the to-be-tested grounding body;

[0112] In practice, a systematic measurement network must be established to achieve a comprehensive assessment of the corrosion status of the grounding electrode. This embodiment uses the concepts of "measurement zone" and "measurement line" to plan the scanning scheme.

[0113] In this embodiment, the projected area of ​​the grounding electrode to be tested on the ground surface and a certain range on both sides are defined as one or more survey "areas". Within the survey area, a series of high-density parallel GPR survey lines are laid out. To ensure that corroded sections can be captured, the spacing between the main survey lines is no more than 25-50 cm. A local Cartesian coordinate system is established on site, and the starting and ending coordinates of each survey line are accurately recorded. Precise geographic registration is the basis for generating an accurate corrosion distribution map.

[0114] Step 2.3: After setting the acquisition parameters of the ground penetrating radar system, data acquisition is performed to obtain the raw GPR data.

[0115] To ensure that the quality of the collected data meets the requirements of subsequent complex processing procedures, the field operator must set the parameters of the GPR system appropriately.

[0116] In this example, the recommended GPR acquisition parameter benchmarks are shown in Table 1:

[0117] Table 1 Recommended GPR Acquisition Parameters

[0118]

[0119] In some alternative implementations, the data processing and analysis of the raw GPR data in step 3 is achieved in the following ways:

[0120] Step 3.1: Perform data preprocessing and signal conditioning on the raw GPR data;

[0121] In practice, the purpose of data preprocessing and signal conditioning is to eliminate noise and enhance the effective signal, preparing for subsequent localization and analysis. The specific processing methods are as follows:

[0122] First, the basic signal model of GPR data can be represented as:

[0123]

[0124] in, This represents the GPR receiving antenna at Voltage amplitude recorded at measuring points over time The change is expressed in mV; For the first road The amplitude of the direct wave (electromagnetic wave that propagates directly from the transmitting antenna to the receiving antenna) in the signal; Source Wavelet, i.e. the basic waveform of GPR transmitting pulse; First i trace True Time Zero, i.e. the exact moment when radar pulse leaves antenna and enters underground medium; Total Number of Effective Reflective Targets (such as rebar, cavity, rock interface, etc.) existing in underground medium; First trace Amplitude of reflected wave from the th target in the signal; First trace Two-way travel time of reflected wave from the th target in the signal; Random noise mixed in during measurement.

[0125] In implementation, the purpose of data preprocessing and signal conditioning is to eliminate noise and enhance effective signals, in preparation for subsequent positioning and analysis. The specific processing means are as follows:

[0126] Time Zero Correction: Time zero is the reference point for depth calculation. Due to instrument delay, antenna coupling differences, etc., there may be systematic deviations in the starting time of each trace signal. This step aims to accurately align the time starting point of all A-scan signals, ensuring the accuracy of depth calculation.

[0127] Specifically: Adopt the "trace-by-trace scanning" time zero correction method to detect the strongest direct wave or surface reflected wave in each trace signal, and define its position as the time zero of the trace.

[0128] Estimate the zero time of the th trace :

[0129]

[0130] Where, is the time window length set for searching direct wave peak value (usually 5%-10% of the total recording duration).

[0131] Perform correction:

[0132]

[0133] Where, is the original signal of the th A-scan before correction; is the signal after completing time zero correction.

[0134] Background removal: This step aims to eliminate horizontal striping coherent noise caused by internal reflections of the antenna, antenna coupling, or air-ground interface direct waves. These noises appear as horizontal stripes in the B-scan image, which can severely mask the real reflection signals from underground targets.

[0135] Specifically: Subtract the average of spatially adjacent traces from the current trace signal using a horizontal moving average filter, thus retaining the local anomaly signal.

[0136] Subtract the average of spatially adjacent traces from the current signal:

[0137]

[0138] where, is the input signal (from the time zero correction step); is the background removed signal; is the input signal is the average of spatially adjacent traces, i.e., the local average background signal.

[0139] In this example, the local average background signal is calculated as:

[0140]

[0141] where, is the window size of the moving average filter, indicating the number of adjacent traces used to calculate the average.

[0142] Gain compensation: When electromagnetic waves propagate underground, energy will be exponentially attenuated due to geometric spreading and medium absorption, resulting in very weak reflection signals from deep targets. Gain compensation aims to compensate for this energy loss, amplify deep signals, and make targets at different depths have comparable amplitude levels.

[0143] Specifically: Apply a gain function to the signal that increases with time (corresponding to depth), and in this example, use the "Spreading and Exponential Compensation (SEC)" gain.

[0144] Apply gain:

[0145]

[0146] where, is the input signal (from the background removal step); is the signal after applying gain. is the SEC gain function:

[0147]

[0148] where, is the exponential gain parameter (typical value: 0.01-0.1), mainly used to compensate for the exponential attenuation caused by medium absorption; is the power gain parameter (typical value: 1-2), mainly used to compensate for the attenuation caused by wavefront geometric diffusion; is the time (proportional to depth).

[0149] Low-frequency drift removal: The application of the gain function will cause low-frequency drift of the signal baseline, which will cause the signal baseline to deviate from zero. This step aims to remove this low-frequency component and restore the signal baseline to near zero mean, which is crucial for subsequent amplitude analysis.

[0150] Specifically: achieve through high-pass filtering, subtract the low-frequency component obtained by moving average filter from the signal.

[0151] Subtract the low-frequency component from the signal:

[0152]

[0153] where, is the input signal (from the gain compensation step); is the final preprocessed signal after removing low-frequency drift.

[0154] Low-frequency component is calculated as:

[0155]

[0156] where, is the number of sampling points corresponding to the half-width of the time-domain moving average window; is the time sampling interval (ns).

[0157] In summary, the complete GPR data preprocessing flow in this example is in order:

[0158] Time zero correction: align the starting time of each channel signal;

[0159] Background removal: eliminate horizontal coherent noise and direct wave;

[0160] Exponential gain: compensate for depth attenuation;

[0161] Low-frequency drift removal: remove low-frequency drift and DC offset;

[0162] The output of each step is used as the input of the next step, forming a cascade processing chain:

[0163]

[0164] The final result is the preprocessed GPR data.

[0165] Step 3.2: Perform FK offset processing on the preprocessed GPR data to focus the reflected signal energy of the grounding electrode;

[0166] FK (Frequency-Wavenumber) Migration: The response of GPR to point or line targets (such as grounded conductors) exhibits a hyperbolic shape on the B-scan profile. This is because as the antenna sweeps across the target, the distance between the antenna and the target first decreases and then increases, causing the round-trip time of the electromagnetic wave to change accordingly. The FK migration algorithm can focus this diffuse hyperbolic energy back to its true underground spatial location, forming a clear bright spot or focal point.

[0167] The variables used in this step are described in Table 2.

[0168] Table 2. Explanation of variables in FK offset processing steps

[0169]

[0170] It should be noted here that the preprocessed data output in step 3.1 With this step The correspondence is as follows:

[0171]

[0172] here, It is according to the Taoist name The preprocessed signal of the index, It is based on spatial coordinates The same dataset indexed, The two representations essentially describe the same preprocessed GPR data, differing only in their indexing method: the former is suitable for channel-by-channel processing, while the latter is suitable for two-dimensional spatial domain processing.

[0173] The implementation steps of the FK offset algorithm are as follows:

[0174] Step A1: Calculate the electromagnetic wave velocity based on the known dielectric constant. :

[0175]

[0176] Step A2, Two-dimensional Fourier Transform: Transform the preprocessed spatiotemporal GPR data Convert to the frequency-wavenumber domain (FK domain) to obtain FK domain data. ;

[0177]

[0178] The specific implementation is to make FFT transform in x direction to obtain , make FFT in t direction to obtain ;

[0179] Step A3, depth migration: in F-K domain, using wave velocity in step A1 , according to dispersion relation , coordinate mapping is performed, and phase shift is performed for each imaging depth :

[0180]

[0181] Step A4, inverse transform imaging: inverse Fourier transform is performed on the mapped F-K domain data, and the migrated data is transformed back to the spatial domain:

[0182]

[0183] The algorithm flow of the above process is:

[0184] Input:

[0185] - : GPR data matrix [N x × N t] after preprocessing

[0186] - : Known dielectric constant

[0187] - : Spatial sampling interval (m)

[0188] - : Time sampling interval (ns)

[0189] - : Maximum imaging depth (m)

[0190] Output:

[0191] - I(x, z): Migrated image [Nx × Nz]

[0192] Algorithm:

[0193] 1. Calculate wave velocity

[0194] 2. Transform to f-k domain

[0195] 3. Build frequency axis:

[0196] 4. Build wave number axis:

[0197] 5. For each depth from 0 to :

[0198] For each :

[0199] # Vertical wavenumber

[0200] if For real numbers:

[0201] # Phase Shift

[0202] else:

[0203] # Evanescent wave attenuation

[0204] # Depth imaging

[0205] 6. Output

[0206] Step 3.3: Automatically identify the spatial coordinates of the grounding body under test at each scanning position using machine learning clustering algorithms.

[0207] After FK offset, the grounding electrode appears as a high-energy focal point in the cross-section. To automatically and accurately extract the position coordinates of these points, the machine learning clustering algorithm described in this embodiment employs the K-Means clustering algorithm to achieve centroid identification. K-Means is an unsupervised learning algorithm whose goal is to divide the dataset into K clusters such that the sum of squared distances between data points within each cluster and their centroid (the cluster mean) is minimized. The specific implementation process of the K-Means clustering algorithm is as follows:

[0208] Step B1, Data Filtering: Normalize the amplitude of the data after FK offset and set an amplitude threshold. Filter out all high-energy data points that are higher than the threshold and use these high-energy data points to form a point cloud around the actual grounding body to be tested.

[0209] Step B2: Initialize the centroid: Randomly select K points from the point cloud as the initial centroids, where K is the estimated number of grounding bodies to be tested;

[0210] Step B3, Cluster Assignment: For each point in the point cloud, calculate its Euclidean distance to the K centroids and assign it to the cluster containing the nearest centroid;

[0211] Step B4, Update Centroid: Recalculate the centroid of each cluster, i.e., take the average of the coordinates of all data points within the cluster;

[0212] Step B5, iteration: repeat step B3 and step B4 until the centroid position no longer changes significantly or reaches a preset number of iterations, obtaining the coordinates (x, z) of the K centroids, i.e. the spatial coordinates of the grounding body to be measured.

[0213] Step 3 described in this embodiment uses signal processing and machine learning algorithms to automatically identify the position of the grounding body to be measured from the preprocessed data.

[0214] In some optional embodiments, the real reflection amplitude of the grounding body to be measured is extracted based on the spatial coordinates of the grounding body to be measured in step 4, and the specific implementation process is as follows:

[0215] Step 4.1, coordinate conversion: convert the spatial coordinates of the grounding body to be measured into time domain coordinates;

[0216] Since the spatial coordinates of the grounding body to be measured ( x , z ) are obtained from the F-K offset image, it is necessary to convert them back to the time domain coordinates ( x , t ):

[0217]

[0218] wherein, z is the depth coordinate (m); t is the time (ns); is the dielectric constant; c is the speed of light in vacuum (0.3 m / ns); x is the horizontal coordinate which remains unchanged.

[0219] Step 4.2, two-dimensional interpolation: based on the time domain coordinates of the grounding body to be measured, the initial reflection amplitude is obtained by bilinear interpolation;

[0220] Since the time domain coordinates ( x , t ) are floating-point numbers, and the GPR data matrix after preprocessing but before F-K offset is a discrete grid (wherein corresponds to the spatial index, and corresponds to the time index), bilinear interpolation is needed to obtain the accurate amplitude.

[0221] For a target point , find the four grid points surrounding it: , , , , wherein:

[0222]

[0223] wherein, is the spatial sampling interval, is the time sampling interval.

[0224] The bilinear interpolation formula is:

[0225]

[0226] wherein, is the target point at which the interpolated amplitude, i.e. the initial reflection amplitude (mV), is to be determined; is the amplitude value (mV) at the grid point (m, n).

[0227] The weight coefficients in the above formula are determined by the distance from the target point to the grid point:

[0228]

[0229] wherein, (i,j) are the time domain coordinates of the grid point (m, n). ,

[0230] It can be understood that in the bilinear interpolation formula m , n are indices in the double summation symbol, not variables. Specifically, m and n represent the indices for traversing the four grid points around the target point:

[0231] m = i to i +1: traverse two adjacent grid points in the spatial ( x ) direction;

[0232] n = j to j +1: traverse two adjacent grid points in the time ( t ) direction;

[0233] Specifically, this double summation will traverse four grid points:

[0234] ( i , j )-lower left corner grid point, ( i +1, j )-lower right corner grid point;

[0235] ( i , j +1)-upper left corner grid point, (​​i +1, j +1) - Top right grid point.

[0236] Therefore, for each grid point ( m , n Each of them has an amplitude value s⁽ 4 [m,n] and a corresponding weight .

[0237] Bilinear interpolation is a weighted average of the amplitude values ​​of these four grid points according to their distance from the target point (x0, t0). The closer the grid point is, the greater its weight and the greater its contribution to the interpolation result.

[0238] In this way, the amplitude value at any floating-point coordinate position can be estimated using data from four known grid points.

[0239] Step 4.3, Physical Compensation: Based on the propagation effect, the interpolated amplitude is physically corrected to obtain the true amplitude of the grounding body under test;

[0240] The geometric diffusion and medium absorption effects during electromagnetic wave propagation are compensated using the following formula:

[0241]

[0242] in, The actual reflection amplitude (mV) obtained after physical compensation; The initial reflection amplitude (mV); z The depth of the grounding electrode (m); It is the geometric diffusion compensation factor; The dielectric attenuation coefficient can be obtained by studying the amplitude attenuation law of healthy grounding bodies at different depths. This is the attenuation compensation factor.

[0243] Therefore, the purpose of this step is to obtain the target amplitude for quantitative analysis without distortion.

[0244] It should be noted that steps 3 and 4 illustrate a multi-stage, systematic data processing workflow designed to accurately locate the grounding conductor from the raw GPR data and extract its true reflection amplitude for corrosion assessment.

[0245] The aforementioned data processing flow is interdependent; any error in parameter selection or procedure at any early stage will be amplified at each stage, ultimately rendering the entire analysis meaningless. Therefore, this embodiment defines the entire processing flow as an expert-driven, iterative calibration loop. Data analysts must be prepared to repeatedly adjust key calibration parameters, especially… , re-run the migration, and visually check the quality of the migration results. Only when the migration effect is confirmed to be optimal, the subsequent clustering and amplitude extraction can be continued.

[0246] In some optional embodiments, the process of determining the corrosion state of the grounding body to be tested based on the health reference amplitude in step 5 is as shown in Figure 2 , and the specific implementation process is as follows:

[0247] Step 5.1, constructing a final data set:

[0248] A final data set containing all identified grounding body positions is constructed, which is a final data table that should at least contain three columns: the coordinates (x, y) of the identified grounding body to be tested, the depth z, and the high-fidelity original amplitude A extracted at the position;

[0249] Step 5.2, determining the health reference amplitude (A max ):

[0250] From the final data set, or according to the reference standard of the known grounding body in the healthy state determined in the field calibration stage, find the corresponding maximum reflection amplitude value as the health reference amplitude Amax, thereby determining the benchmark for the entire relative comparison analysis;

[0251] Step 5.3, converting all amplitudes to decibels (dB):

[0252] Using the standard formula, convert all real reflection amplitude data in the final data table to decibel values A dB :

[0253]

[0254] Step 5.4, calculating the relative attenuation amount:

[0255] For each measurement point, calculate its relative attenuation amount relative to the health reference point . The calculation formula is:

[0256] Relative attenuation amount (dB)

[0257] It should be noted that since is always less than or equal to , the calculated relative attenuation amount will be negative or zero.

[0258] Step 5.5, corrosion state determination:

[0259] The relative attenuation value of each measurement point is compared with a preset threshold value, and the corrosion state is determined according to the comparison result. In this example, the preset threshold value is -8 dB. That is, if the value of the relative attenuation (dB) of a point is <-8 dB, the position is marked as having a high probability of serious corrosion.

[0260] This step realizes automatic identification of potential serious corrosion positions by converting the extracted real reflection amplitude into a relative attenuation (dB) and comparing it with a threshold value, thereby realizing corrosion state determination of the grounding body to be measured.

[0261] In some specific optional embodiments, the corrosion distribution map is generated according to the corrosion state determination result in step 6, realizing visual output of the corrosion state determination result, and the specific implementation process is as follows:

[0262] Spatial interpolation: A suitable spatial interpolation algorithm (such as linear interpolation, Kriging interpolation or spline interpolation) is used to generate a continuous attenuation data grid 20 covering the entire survey area according to the discrete data points, and a two-dimensional contour map is obtained;

[0263] Color coding and visualization: The generated grid data is visualized, different attenuation levels are represented by colors, and a corrosion distribution map is generated. This embodiment adopts a clear and intuitive color mapping scheme, for example: all areas with attenuation exceeding the threshold value of -8 dB are marked with high-contrast and eye-catching colors such as bright red or dark red, so that decision makers can visually identify potential corrosion hot spot areas. The final map should be similar to the corrosion evaluation result map shown in the reference file.

[0264] Through this step, the discrete corrosion state determination evaluation results are converted into a continuous two-dimensional contour map (Contour Map), so that decision makers can intuitively show the spatial distribution of corrosion in the entire survey area.

[0265] As can be seen from the above, the embodiment of the present application proposes a systematic process for detecting serious corrosion of grounding bodies with a buried depth of 80 cm based on ground penetrating radar (GPR) technology, which mainly includes the following stages:

[0266] The first stage: field calibration: this is a mandatory starting step of the project. By scanning the target with known healthy condition, a reliable ground truth is established, which is used to fine-tune the key parameters (such as dielectric constant) in subsequent data processing and set the corrosion evaluation baseline (A max );

[0267] Stage 2: Data Acquisition: Systematically collect GPR data covering the entire area to be tested based on a high-resolution, precisely positioned survey design.

[0268] Stage 3: Data Processing: Perform a multi-step, iterative computational workflow. Specifically corresponding to steps 3 and 4 in this embodiment, the process includes data preprocessing, automatic target location through F-K migration and K-means clustering, and high-fidelity true reflection amplitude extraction.

[0269] Stage 4: Evaluation and Reporting: Convert the extracted amplitudes into relative attenuation (dB) and compare them with a threshold to identify potential severe corrosion locations, and finally generate intuitive corrosion distribution maps and / or detailed technical reports.

[0270] Embodiment 2:

[0271] Referring to the accompanying drawings, Figure 3 , the present embodiment is a non-destructive corrosion evaluation system for typical grounding bodies using ground penetrating radar, which is used to implement the steps of the method as described in Embodiment 1, comprising:

[0272] A reference data acquisition module is used to perform on-site calibration scanning on reference grounding bodies with known health status to obtain dielectric constant and healthy reference amplitudes.

[0273] A data acquisition module is used to collect data on the test grounding body using a ground penetrating radar system to obtain raw GPR data.

[0274] A data processing and analysis module is used to process and analyze the raw GPR data based on the obtained dielectric constant to locate the spatial coordinates of the test grounding body at each scanning position from the raw GPR data.

[0275] A true reflection amplitude extraction module is used to extract the true reflection amplitudes of the test grounding body based on the spatial coordinates of the test grounding body.

[0276] A corrosion state determination module is used to determine the corrosion state of the test grounding body based on the healthy reference amplitudes and the true reflection amplitudes.

[0277] In this example, the system further comprises:

[0278] A visualization output module is used to generate corrosion distribution maps based on the corrosion state determination results to realize the visualization output of the corrosion state determination results.

[0279] Embodiment 3:

[0280] In some embodiments, the reference Figure 4, which is a structural schematic diagram of a computer device for implementing the method for non-destructive corrosion evaluation of a typical grounding body according to some embodiments of the present application. The method for non-destructive corrosion evaluation of a typical grounding body in Embodiment 1 described above can be implemented by the computer device shown in the figure, which includes at least one processor, a communication bus, a memory, and at least one communication interface. Figure 4 The computer device shown in the figure includes at least one processor, a communication bus, a memory, and at least one communication interface.

[0281] The processor can be a general central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0282] The communication bus can be used to transmit information between the above-mentioned components.

[0283] The memory can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory can exist independently and be connected to the processor through the communication bus. The memory can also be integrated with the processor.

[0284] The memory is used to store program code for implementing the scheme of the present application, and the processor 501 is used to control the execution. The processor is used to execute the program code stored in the memory. The program code can include one or more software modules. The method described in Embodiment 1 above can be implemented by the processor and one or more software modules in the program code in the memory.

[0285] The communication interface uses any transceiver-like device to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0286] In a particular implementation, as one embodiment, the computer device can include multiple processors, each of which can be a single-CPU processor or a multi-CPU processor. A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0287] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In a particular implementation, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. Embodiments of the present disclosure do not limit the type of computer device.

[0288] Embodiment 4:

[0289] Embodiments of the present disclosure also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of embodiment 1 described above.

[0290] To sum up, the present application firstly obtains the dielectric constant for precise migration processing and the healthy reference amplitude for quantitative evaluation by on-site calibration scanning of the reference grounding body with known health condition; then performs systematic ground penetrating radar (GPR) data acquisition on the grounding body to be measured, performs F-K migration processing on the collected data based on the calibrated dielectric constant, focuses the reflection signal energy of the grounding body, and automatically identifies the precise spatial coordinates of the grounding body at each scanning position in combination with a machine learning clustering algorithm; further, according to the extracted spatial coordinates, the high-fidelity reflection amplitude without migration in the preprocessed original data is queried, and the relative attenuation of the reflection amplitude of each measurement point is calculated according to the reference maximum amplitude obtained by calibration; finally, the relative attenuation of each measurement point is compared with the preset corrosion determination threshold, to determine whether there is serious corrosion at the position, and a corrosion distribution map is generated.

[0291] As can be seen, the present application not only effectively overcomes the detection uncertainty problem caused by the complex and non-uniformity of soil medium; but also realizes quantitative evaluation and precise positioning of the corrosion state of the grounding body; avoids the indirectness and misjudgment risk of the traditional electrical measurement method, and also avoids the destructiveness, blindness and inefficiency of excavation inspection, can find serious corrosion hidden danger before the grounding body is physically broken, greatly improves the accuracy, reliability and efficiency of detection, and provides accurate technical support for preventive maintenance and safe operation of the grounding net.

[0292] The technical solutions provided by the present application are described in detail above. The principles and implementation modes of the present application are described by applying specific examples, and the above examples are only used to help understand the method of the present application and its core idea. It should be pointed out that, for ordinary skilled persons in the technical field, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. A method for non-destructive corrosion assessment of typical grounding electrodes, characterized in that, Includes the following steps: Step 1: Perform on-site calibration scanning on the reference grounding electrode with known health status to obtain the dielectric constant and health reference amplitude; Step 2: Use a ground penetrating radar system to collect data from the grounding body under test to obtain raw GPR data; Step 3: Based on the dielectric constant obtained in Step 1, perform data processing and analysis on the original GPR data, and locate the spatial coordinates of the grounding body under test at each scanning position from the original GPR data; Step 4: Extract the true reflected amplitude of the grounding body under test based on its spatial coordinates; Step 5: Determine the corrosion status of the grounding electrode under test based on the health reference amplitude and the true reflected amplitude; Step 4, which involves extracting the true reflection amplitude of the grounding electrode based on its spatial coordinates, specifically includes the following process: Step 4.1: Convert the spatial coordinates of the grounding electrode to be tested into time domain coordinates; Step 4.2: Based on the time domain coordinates of the grounding body under test, obtain its initial reflection amplitude through bilinear interpolation; Step 4.3: Physically correct the initial reflection amplitude based on the propagation effect to obtain the true reflection amplitude of the grounding body under test; Step 5, which involves determining the corrosion status of the grounding electrode under test based on the healthy reference amplitude and the true reflected amplitude, specifically includes the following process: Step 5.1: Construct a final dataset containing the locations of all identified grounding bodies. This final dataset includes: the coordinates and depth of the identified grounding bodies to be tested, as well as the actual reflected amplitude extracted at that location. Step 5.2: Based on the known grounding electrode as the reference standard for health status, find its corresponding health reference amplitude; Step 5.3: Convert all real reflected amplitude and healthy reference amplitude data into decibels; Step 5.4: Calculate the relative decay of each measurement point relative to the healthy reference point; Step 5.5: Compare the calculated relative attenuation value of each measurement point with the preset threshold, and determine the corrosion status based on the comparison results.

2. The method for non-destructive corrosion assessment of typical grounding electrodes according to claim 1, characterized in that: Step 2, which involves using a ground-penetrating radar system to collect data from the grounding body under test, specifically includes the following process: Step 2.1: Select the ground-penetrating radar system and antenna according to the detection requirements of the grounding body to be tested; Step 2.2: Systematically set up the measurement network and perform geographical registration for the grounding electrode to be measured; Step 2.3: After setting the acquisition parameters of the ground penetrating radar system, data acquisition is performed to obtain the raw GPR data.

3. The method for non-destructive corrosion assessment of typical grounding electrodes according to claim 1, characterized in that: Step 3, which involves data processing and analysis of the raw GPR data, includes the following steps: Step 3.1: Perform data preprocessing and signal conditioning on the raw GPR data; Step 3.2: Perform FK offset processing on the preprocessed GPR data to focus the reflected signal energy of the grounding electrode; Step 3.3: Automatically identify the spatial coordinates of the grounding body under test at each scanning position using machine learning clustering algorithms.

4. The method for non-destructive corrosion assessment of typical grounding electrodes according to claim 3, characterized in that: The FK offset processing includes the following steps: Step A1: Calculate the electromagnetic wave velocity based on the known dielectric constant. ; Step A2: Convert the preprocessed spatiotemporal domain GPR data into the frequency-wavenumber domain to obtain FK domain data. ; Step A3: In the FK domain, perform coordinate mapping based on the dispersion relation for each imaging depth. Perform a phase shift to obtain the mapped FK domain data; Step A4: Perform an inverse Fourier transform on the mapped FK domain data to transform the offset data back into the spatial domain.

5. The method for non-destructive corrosion assessment of typical grounding electrodes according to claim 3, characterized in that: The automatic identification of the spatial coordinates of the grounding body under test at each scanning position using machine learning clustering algorithms specifically includes the following steps: Step B1: Normalize the amplitude of the data after FK offset, set an amplitude threshold, and filter out all high-energy data points above the threshold to form a point cloud around the actual grounding body location. Step B2: Randomly select K points from the point cloud as initial centroids, where K is the estimated number of grounding bodies to be tested; Step B3: For each point in the point cloud, calculate its distance to the K centroids and assign it to the cluster containing the nearest centroid; Step B4: Recalculate the centroid of each cluster; Step B5: Repeat steps B3 and B4 until the centroid position no longer changes significantly or the preset number of iterations is reached, to obtain the coordinates of K centroids, which are the spatial coordinates of the grounding body to be tested.

6. The method for non-destructive corrosion assessment of typical grounding electrodes according to any one of claims 1-5, characterized in that: It also includes the step of generating a corrosion distribution map based on the corrosion state determination results, thereby enabling the visualization output of the corrosion state determination results.

7. A system for non-destructive corrosion assessment of typical grounding electrodes, for implementing the steps of the method as described in any one of claims 1-6, characterized in that, include: The reference data acquisition module is used to perform on-site calibration scanning of a reference grounding electrode with a known health status to obtain the dielectric constant and health reference amplitude; The data acquisition module is used to acquire data from the ground-penetrating radar system to obtain raw GPR data. The data processing and analysis module is used to process and analyze the raw GPR data based on the obtained dielectric constant, and to locate the spatial coordinates of the grounding body under test at each scanning position from the raw GPR data. The true reflection amplitude extraction module is used to extract the true reflection amplitude of the grounding body under test based on its spatial coordinates. The corrosion status determination module is used to determine the corrosion status of the grounding body under test based on the health reference amplitude and the true reflected amplitude.

8. The system for non-destructive corrosion assessment of typical grounding electrodes according to claim 7, characterized in that, The system also includes: The visualization output module is used to generate a corrosion distribution map based on the corrosion state determination results, thereby enabling the visualization output of the corrosion state determination results.

9. A computer device comprising a memory and a processor, the memory storing code, characterized in that, The processor is configured to acquire the code and execute the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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