Method and system for carrying out nondestructive corrosion evaluation on typical grounding body

Abstract

The invention discloses a method and system for carrying out nondestructive corrosion evaluation on a typical grounding body, and the method comprises the steps: carrying out the field calibration scanning of a reference grounding body in a known health state, and obtaining a dielectric constant and a health reference amplitude; a ground penetrating radar system is used for carrying out data acquisition on a to-be-measured grounding body to obtain original GPR data; performing data processing and analysis on the original GPR data based on the dielectric constant, and positioning the space coordinates of the grounding body to be measured at each scanning position from the original GPR data; based on the space coordinates of the to-be-measured grounding body, extracting the real reflection amplitude of the to-be-measured grounding body; and based on the healthy reference amplitude and the real reflection amplitude, carrying out corrosion state determination on the to-be-detected grounding body. The method has the remarkable effects that the problem of detection uncertainty caused by complex and non-uniform soil media is effectively solved; and quantitative evaluation and accurate positioning of the corrosion state of the grounding body are realized.

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 reflection signals in underground media. 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 GPR signals. However, when applying GPR technology to the quantitative assessment of the corrosion state of buried grounding bodies, it still faces serious challenges at the current technical level: Severe interference of complex soil environment: Soil is an extremely complex and non-uniform natural medium. Its composition, compaction, and especially the dramatic changes in water content, will significantly affect its dielectric constant. For example, a small area of clay with high water content may have an attenuation effect on GPR signals comparable to or even stronger than that caused by grounding body corrosion. This makes it difficult to simply attribute signal attenuation to target corrosion in practical applications, and it is easy to make false judgments.

[0006] Complexity of data processing and interpretation: In the GPR raw data (profile), the response of the grounding body usually presents a hyperbolic shape, which needs to be processed by complex migration algorithms to focus its energy back to the true spatial location. The processing effect is heavily dependent on the accurate estimation of the dielectric constant of the underground medium. Incorrect parameters can cause 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.

[0007] In summary, the existing grounding grid detection technology system has obvious shortcomings, and although GPR technology has great potential, it still has technical bottlenecks in solving soil environment interference, establishing reliable evaluation criteria, and realizing process-based and high-precision interpretation. Therefore, the industry urgently needs 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

[0008] 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 evaluation and accurate positioning of the corrosion state of grounding bodies under non-excavation conditions.

[0009] To achieve the above purpose, the technical solution adopted by the present application is as follows: In a first aspect, the present application provides a method for non-destructive corrosion evaluation of typical grounding bodies, which is characterized by comprising 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; Step 4, which involves extracting the true reflection amplitude of the grounding body 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.

[0010] Furthermore, 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.

[0011] Furthermore, the data processing and analysis of the raw GPR data described in step 3 includes the following procedures: 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.

[0012] Furthermore, 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.

[0013] Furthermore, 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.

[0014] Furthermore, the method also includes the step of generating a corrosion distribution map based on the corrosion state determination result, thereby realizing the visual output of the corrosion state determination result.

[0015] Secondly, a system for non-destructive corrosion assessment of typical grounding electrodes using ground penetrating radar, for implementing the steps of the method described in the first aspect, includes: 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.

[0016] Furthermore, 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.

[0017] Thirdly, the present invention provides a computer device comprising a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the method described in the first aspect.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0019] The significant effects of this invention are: This invention first performs on-site calibration scanning on a reference grounding electrode with known health status to obtain the dielectric constant for precise offset processing and the health reference amplitude for quantitative assessment. Then, it systematically acquires ground-penetrating radar (GPR) data from the grounding electrode under test. Based on the calibrated dielectric constant, it performs FK offset processing on the acquired data to focus the reflected signal energy of the grounding electrode. A machine learning clustering algorithm is then used to automatically identify the precise spatial coordinates of the grounding electrode at each scan location. Next, based on the extracted spatial coordinates, it queries the high-fidelity true reflection amplitude in the pre-processed raw data before offsetting. Using the calibrated maximum reference amplitude, i.e., the health reference amplitude, it calculates the relative attenuation of the reflection amplitude at each measurement point. Finally, it compares the relative attenuation of each measurement point with a preset corrosion judgment threshold to determine whether severe corrosion exists at that location and generates a corrosion distribution map.

[0020] Therefore, this application, by introducing mandatory on-site calibration, obtains a reliable evaluation benchmark, effectively overcoming the uncertainty in detection caused by the complex and heterogeneous nature of the soil medium. Furthermore, through a systematic data processing workflow, combining signal focusing, target positioning, and high-fidelity amplitude extraction, it achieves quantitative assessment and precise location of the corrosion state of the grounding electrode. This method avoids the indirectness and misjudgment risks of traditional electrical measurement methods, and also avoids the destructive, blind, and inefficient nature of excavation inspections. It can detect serious corrosion hazards before the grounding electrode undergoes physical fracture, thereby greatly improving the accuracy, reliability, and efficiency of detection, and providing precise technical support for the preventive maintenance and safe operation of the grounding grid. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method described in this invention; Figure 2 This is a flowchart of the corrosion state determination method described in this invention; Figure 3 This is a schematic diagram of the system described in this invention; Figure 4 This is a schematic diagram of the structure of the computer device described in this invention. Detailed Implementation

[0022] The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] First, it should be noted that the grounding grid mentioned in this invention specifically refers to a simple lightning protection grounding grid (such as a lightning protection grounding grid specially laid in places like gas stations, oil depots, and hazardous chemical warehouses). Grounding grids of ordinary buildings that use foundation steel bars for discharge or grounding grids of power stations with complex structures and environments are not within the scope of application. Typical grounding electrodes refer to horizontal grounding electrodes made of 40mm*4mm flat steel or φ12mm round steel. Other types of materials and vertical grounding electrodes are not within the scope of application.

[0024] Secondly, the basic principles and applicability of this invention are analyzed as follows: 1. Electrochemical basis of corrosion The corrosion of steel is an electrochemical process, essentially transforming metallic iron (Fe) into a more thermodynamically stable oxide form, namely rust (Fe₂O₃·xH₂O). This process requires three basic elements: an anode (the region where the oxidation reaction occurs), a cathode (the region where the reduction reaction occurs), and an electrolyte. In buried grounding applications, different areas of the grounding conductor itself can act as the anode and cathode, while the surrounding moist soil provides the electrolyte environment for ion flow.

[0025] This electrochemical process mainly involves two core chemical reactions: Oxidation of steel (anodic reaction): Metallic iron loses electrons and is converted into ferrous ions.

[0026]

[0027] Water reduction (cathode reaction): In the presence of oxygen, water and oxygen gain electrons to form hydroxide ions.

[0028]

[0029] Subsequently, at the interface between the steel and the soil, ferrous ions combine with hydroxide ions to eventually form hydrated iron oxide, commonly known as rust. The formation of corrosion products not only consumes the original metal material, weakening the structural integrity and conductivity of the grounding electrode, but its volume is also much larger than the original steel. This volume expansion will exert compressive stress on the surrounding soil or wrapping material, further altering the physical environment around the grounding electrode.

[0030] 2. Interaction between GPR signal and corrosive materials The core principle of ground-penetrating radar (GPR) technology is to detect underground targets by emitting 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 its dielectric constant and conductivity. When GPR electromagnetic waves encounter an interface with different electromagnetic properties, some of the energy is reflected back to the ground receiving antenna.

[0031] The corrosion process of a grounding electrode significantly alters the electromagnetic properties of its surrounding microenvironment, forming the physical basis for GPR detection. Specifically, the formation of corrosion products (rust) and the accumulation of moisture during corrosion lead to two key changes: Change in dielectric constant: The dielectric constant of the mixture of corrosion products and moisture differs significantly from that of the surrounding dry soil or intact metal, forming a clear electromagnetic wave reflection interface.

[0032] Signal attenuation and scattering: Corroded regions are typically rich in ions and moisture, creating a localized environment with high conductivity and high loss. When a GPR signal passes through this region, its energy is drastically attenuated due to conduction losses. Simultaneously, the irregular morphology of corrosion products and the micro-cracks in the surrounding medium caused by corrosion cause scattering of the GPR signal, further weakening the signal energy that can be effectively reflected back to the receiving antenna.

[0033] Therefore, compared with healthy, uncorroded grounding electrodes, the amplitude of the GPR reflected signal generated by grounding electrodes in severely corroded areas will be significantly reduced. The degree of attenuation of this signal amplitude has become the core indicator for quantitatively assessing the corrosion status.

[0034] 3. ASTM D6087 standard as the analytical framework To standardize and quantitatively assess the attenuation of GPR signal amplitude, this invention draws on the core analytical logic of the ASTM D6087 standard test method. This standard provides a non-destructive assessment method that can predict whether there is deterioration in concrete or steel reinforcement at or above the top layer of steel reinforcement. This method is mainly used to assess the corrosion status of steel reinforcement in concrete bridge decks, but its basic principle can be applied to the detection of buried grounding electrodes.

[0035] The core of this method is a relative comparison method, which compares the reflected signal amplitude of the grounding electrode in the area under test with the reflected signal amplitude of a known "healthy" (uncorroded or with very low corrosion) reference grounding electrode. To more effectively characterize the vast dynamic range of signal strength, the evaluation is performed on a logarithmic decibel (dB) scale. The conversion formula for signal amplitude is as follows:

[0036] 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.

[0037] The analysis process is as follows: Within the entire testing area, the amplitude of reflected signals from all detectable grounding bodies was first identified and measured.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 4. Applicability Analysis 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.

[0042] 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.

[0043] Secondly, the validity of the entire ASTM D6087 method depends entirely on an accurate "health" reference signal A. max The selection of reference points. In bridge decks, it is generally reasonable to assume that the reinforcing steel in certain areas is intact. However, for a continuous buried grounding system that may be hundreds of meters long, it is impossible to guarantee a priori that any section is completely "healthy." If the grounding electrode selected as the reference point already has slight corrosion, its A... maxThe value would be lower than that of a conductor in its true original state. This would cause the entire decibel scale to be compressed, making it possible for signal attenuation in some severely corroded areas to fail to reach the -8 dB threshold, thus producing false negatives. The most critical step in this invention is not the GPR scan itself, but rather establishing a validated and reliable "health" reference standard in the field. This requirement must be explicitly stated as a mandatory prerequisite in this invention.

[0044] Based on the above description, in order to solve the problems existing in the detection of corrosion status of typical grounding electrodes, the technical solutions adopted are as described in the following embodiments: Example

[0045] like Figure 1 As shown, a method for non-destructive corrosion assessment of a typical grounding electrode is described, with the following specific steps: Step 1: Perform field calibration scanning on the reference grounding electrode with known health status to obtain the dielectric constant for accurate offset processing and the health reference amplitude for quantitative assessment; 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; Step 6: Generate a corrosion distribution map based on the corrosion state determination results to achieve a visual output of the corrosion state determination results.

[0046] In this embodiment of the invention, on-site calibration is a mandatory and indispensable initial step, providing a reliable anchor point for the entire data processing and analysis process. Before evaluation, a dedicated GPR on-site calibration scan must be performed on a target with known conditions. This target with known conditions can be: a. Establish a new reference conductor: Near the site, bury a brand-new grounding conductor (40mm*4mm flat steel or 12mm round steel) of the exact same specifications as the conductor under test at the same depth of 80cm. The scanning results of this target will serve as a benchmark for a "perfectly healthy" state to determine the optimal dielectric constant. The maximum amplitude of the reference is the healthy reference amplitude Amax.

[0047] b. Excavation verification point: Select a convenient location on the grounding electrode to be tested, excavate a small area, and visually inspect its corrosion status; then backfill and scan this location to correlate its GPR response with its actual condition.

[0048] This calibration phase can greatly reduce the risks associated with soil uncertainties and instrument setup.

[0049] In some alternative implementations, step 2, which involves using a ground-penetrating radar system to collect data from the grounding electrode 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; Specifically, for targets buried at a depth of 80 cm, and considering the detection requirements for targets of similar sizes such as flat steel and round steel, the choice of antenna frequency needs to strike a balance between detection depth and resolution. Low-frequency antennas have strong penetration capabilities but low resolution, while high-frequency antennas have high resolution but limited penetration depth. Therefore, this embodiment selects a GPR antenna with a center frequency in the range of 250-400 MHz. This frequency band can effectively penetrate to a depth of 80 cm under typical soil conditions, while providing sufficient resolution for targets such as 40mm*4mm flat steel and 12mm round steel.

[0050] In addition, shielded antennas must be used to minimize electromagnetic interference from objects above ground (such as operators, vegetation, and air-to-ground interfaces). Unshielded antennas receive a large amount of airborne clutter, which severely contaminates the effective signals from underground targets and poses significant challenges to subsequent data processing.

[0051] Step 2.2: Systematically set up the measurement network and perform geographical registration for the grounding electrode to be measured; 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.

[0052] 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.

[0053] Step 2.3: After setting the acquisition parameters of the ground penetrating radar system, data acquisition is performed to obtain the raw GPR data.

[0054] 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.

[0055] In this example, the recommended GPR acquisition parameter benchmarks are shown in Table 1: Table 1 Recommended GPR Acquisition Parameters

[0056] In some alternative implementations, the data processing and analysis of the raw GPR data in step 3 is performed in the following manner: Step 3.1: Perform data preprocessing and signal conditioning on the raw GPR data; 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: First, the basic signal model of GPR data can be represented as:

[0057] 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; The source wavelet is the basic waveform of the GPR transmitted pulse; For the first i road The true time zero point is the precise moment when the radar pulse leaves the antenna and enters the underground medium; The total number of effective reflective targets (such as reinforcing bars, cavities, rock interfaces, etc.) existing in the underground medium; For the first road In the signal, from the first The amplitude of the reflected wave from the target; For the first road In the signal, from the first Two-way travel time of the reflected wave from a target; This refers to random noise introduced during the measurement process.

[0058] 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: Zero-point time calibration: The zero-point time is the reference point for depth calculation. Due to factors such as instrument delay and differences in antenna coupling, the start time of each signal may have systematic deviations. This step aims to accurately align the start time of all A-scan signals to ensure the accuracy of depth calculation.

[0059] Specifically, the "scan-by-scan" time zero-point correction method is used to detect the strongest direct wave or ground reflection wave in each signal and define its position as the time zero point of that signal.

[0060] Estimate the first road zero time of the signal :

[0061] in, The length of the time window set for searching the peak value (usually the first 5%-10% of the total recording time).

[0062] Perform the correction:

[0063] in, For the first time before correction The original A-scan signal; This is for the signal after zero-point time correction.

[0064] Background Removal: This step aims to eliminate horizontal stripe-like coherent noise caused by internal antenna reflections, antenna coupling, or direct waves from the air-to-ground interface. This noise appears as horizontal stripes in B-scan images and severely masks the true reflected signals from underground targets.

[0065] Specifically: A horizontal moving average filter is used to subtract the average value of spatially adjacent multiple channels from the current channel signal, thereby preserving local abnormal signals.

[0066] Subtract the average of its spatially adjacent channels from the current signal:

[0067] in, The input signal (from the zero-point time correction step); The signal after background removal; Input signal The average value of multiple adjacent signals in space, i.e., the local average background signal.

[0068] In this example, the local average background signal The calculation method is as follows:

[0069] in, The window size for the moving average filter represents the number of neighboring gathers used to calculate the average.

[0070] Gain compensation: When electromagnetic waves propagate underground, their energy decays exponentially due to geometric diffusion and medium absorption, resulting in very weak reflected signals from deep targets. Gain compensation aims to compensate for this energy loss, amplify deep signals, and enable targets at different depths to have comparable amplitude levels.

[0071] Specifically: Apply a gain function that increases with time (corresponding to depth) to the signal. In this example, we use the "Spreading and Exponential Compensation (SEC)" gain.

[0072] Application gain:

[0073] In the formula, The input signal (from the background removal step); This is the signal after applying the gain. For the SEC gain function:

[0074] in, This is the exponential gain parameter (typical value: 0.01-0.1), mainly used to compensate for the exponential decay caused by medium absorption; This is a power gain parameter (typical value: 1-2), mainly used to compensate for attenuation caused by wavefront geometric diffusion; For time (proportional to depth).

[0075] Low-frequency drift removal: The application of the gain function can cause a low-frequency drift in the signal baseline, which can cause the baseline to deviate from zero. This step aims to remove this low-frequency component, restoring the signal baseline to near its zero mean, which is crucial for subsequent amplitude analysis.

[0076] Specifically: This is achieved through high-pass filtering, which subtracts the low-frequency components obtained by the moving average filter from the signal.

[0077] Subtract its low-frequency components from the signal:

[0078] in, The input signal (from the gain compensation step); This is the final preprocessed signal after removing low-frequency drift.

[0079] Low frequency components The calculation method is as follows:

[0080] in, This represents the number of sampling points corresponding to half the width of the time-domain moving average window. The time sampling interval is ns.

[0081] In summary, the complete GPR data preprocessing workflow in this example is as follows: Zero-point time correction: Aligns the start time of each signal channel; Background removal: Eliminates horizontal coherent noise and direct waves; Exponential gain: compensates for depth decay; Low-frequency drift removal: Removes low-frequency drift and DC offset; The output of each step serves as the input for the next step, forming a cascaded processing chain:

[0082] The final result is the preprocessed GPR data.

[0083] Step 3.2: Perform FK offset processing on the preprocessed GPR data to focus the reflected signal energy of the grounding electrode; 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.

[0084] The variables used in this step are described in Table 2.

[0085] Table 2. Explanation of variables in FK offset processing steps

[0086] It should be noted here that the preprocessed data output in step 3.1 With this step The correspondence is as follows:

[0087] 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.

[0088] The implementation steps of the FK offset algorithm are as follows: Step A1: Calculate the electromagnetic wave velocity based on the known dielectric constant. :

[0089] 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. ;

[0090] The specific implementation method is to perform an FFT transformation in the x-direction to obtain... Performing an FFT along the t direction yields... ; Step A3, Depth Offset: In the FK domain, use the wave velocity from step A1. According to the dispersion relation Perform coordinate mapping for each imaging depth Perform phase shift:

[0091] Step A4, Inverse Transform Imaging: Perform an inverse Fourier transform on the mapped FK domain data to transform the offset data back into the spatial domain:

[0092] The algorithm flow for the above process is as follows: enter: - : Preprocessed GPR data matrix [N_x×N_t] - Given dielectric constant - Spatial sampling interval (m) - Time sampling interval (ns) - Maximum imaging depth (m) Output: - I(x,z): Offset image [Nx × Nz] algorithm: 1. # Calculate wave speed 2. # Transform to fk domain 3. Construct the frequency axis: 4. Construct the wavenumber axis: 5. For each depth from 0 to : For each : # Vertical wavenumber if For real numbers: # Phase Shift else: # Evanescent wave attenuation # Depth imaging 6. Output

[0093] Step 3.3: Automatically identify the spatial coordinates of the grounding body under test at each scanning position using machine learning clustering algorithms.

[0094] 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: 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. 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; 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; 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; Step B5, Iteration: Repeat steps B3 and B4 until the position of the centroid no longer changes significantly or the preset number of iterations is reached, to obtain the coordinates (x, z) of K centroids, which are the spatial coordinates of the grounding body to be tested.

[0095] Step 3 in this embodiment utilizes signal processing and machine learning algorithms to automatically identify the location of the grounding body under test from the preprocessed data.

[0096] In some optional implementations, the extraction of the true reflection amplitude of the grounding body based on its spatial coordinates in step 4 is specifically implemented as follows: Step 4.1, Coordinate Transformation: Transform the spatial coordinates of the grounding electrode under test into time domain coordinates; Due to the spatial coordinates of the grounding electrode to be tested ( x , z The coordinates are obtained from the image after FK offset and need to be converted back to time domain coordinates first. x , t ):

[0097] in, z The depth coordinates are in meters (m). t For time (ns); It is the dielectric constant; c The speed of light in a vacuum (0.3 m / ns); x The horizontal coordinate remains unchanged.

[0098] Step 4.2, Two-dimensional interpolation: Based on the time domain coordinates of the grounding body under test, its initial reflection amplitude is obtained through bilinear interpolation; Due to the time domain coordinates ( x , t () is a floating-point number, and is a preprocessed but not FK-offset GPR data matrix. It is a discrete grid (where Corresponding spatial index, (corresponding to the time index), bilinear interpolation is required to obtain the accurate amplitude.

[0099] For the target point Find the four grid points that surround it: , , , ,in:

[0100] in, For spatial sampling interval, The time sampling interval is denoted as .

[0101] Bilinear interpolation formula:

[0102] in, For target point The interpolated amplitude at that point is also the initial reflection amplitude (mV); Let be the amplitude value (mV) at grid point (m, n).

[0103] The weighting coefficients in the above formula Determined by the distance from the target point to the grid point:

[0104] in,( , ) represents the time domain coordinates of grid point (m, n).

[0105] Understandably, in the bilinear interpolation formula... m , n It refers to the index within the double summation operator, not a variable. Specifically... m and n This indicates the indices of the four grid points surrounding the target point: m = i arrive i +1: In space ( x Traverse two adjacent grid points in the direction of ) n = j arrive j +1: In time ( t Traverse two adjacent grid points in the direction of ) Specifically, this double summation will traverse 4 grid points: ( i , j - Bottom left grid point, ( i +1, j - Bottom right corner grid point; ( i , j +1) - Top left corner grid point, ( i +1, j +1) - Top right grid point.

[0106] Therefore, for each grid point ( m , n Each of them has an amplitude value s. (4) [m,n] and a corresponding weight .

[0107] 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.

[0108] In this way, the amplitude value at any floating-point coordinate position can be estimated using data from four known grid points.

[0109] 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; The geometric diffusion and medium absorption effects during electromagnetic wave propagation are compensated using the following formula:

[0110] 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.

[0111] Therefore, the purpose of this step is to obtain the target amplitude for quantitative analysis without distortion.

[0112] 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.

[0113] 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… Rerun the offset test and visually inspect the quality of the results. Only after confirming that the offset effect is optimal should subsequent clustering and amplitude extraction be performed.

[0114] In some alternative implementations, the process of determining the corrosion status of the grounding electrode under test based on the health reference amplitude in step 5 is as follows: Figure 2 As shown, the specific implementation process is as follows: Step 5.1: Construct the final dataset: Construct a final dataset containing the locations of all identified grounding bodies. The final dataset is a final data table, which should contain at least three columns: the coordinates (x, y) and depth z of the identified grounding bodies to be tested, and the high-fidelity original amplitude A extracted at that location. Step 5.2: Determine the healthy reference amplitude (A) max ): From the final dataset, or based on the reference standard of the known grounding electrode as the health status determined during the field calibration phase, find its corresponding maximum reflection amplitude value as the health reference amplitude Amax, thereby determining the benchmark for the entire relative comparison analysis; Step 5.3: Convert all amplitudes to decibels (dB): Using the standard formula, all the true reflection amplitude data in the final data table are converted into decibel values ​​A. dB :

[0115] Step 5.4: Calculate the relative attenuation: For each measurement point, calculate its value relative to the health reference point. The relative attenuation. The calculation formula is: Relative attenuation (dB)

[0116] It should be noted that, due to Always less than or equal to The calculated relative attenuation value will be negative or zero.

[0117] Step 5.5, Determining the Corrosion State: The calculated relative attenuation value at each measurement point is compared with a preset threshold, and the corrosion status is determined based on the comparison result. In this example, the preset threshold is -8 dB. That is, if the relative attenuation (dB) value at a point is < -8 dB, then that location is marked as having a high probability of severe corrosion.

[0118] This step converts the extracted true reflection amplitude into a relative attenuation (dB) and compares it with a threshold, thereby enabling the automatic identification of potentially severe corrosion locations and determining the corrosion status of the grounding body under test.

[0119] In some specific optional embodiments, step 6, which generates a corrosion distribution map based on the corrosion state determination result to achieve a visual output of the corrosion state determination result, is implemented as follows: Spatial interpolation: Using a suitable spatial interpolation algorithm (such as linear interpolation, Kriging interpolation, or splines), a continuous attenuation data grid20 covering the entire exploration area is generated based on discrete data points to obtain a two-dimensional contour map; Color Coding and Visualization: The generated grid data is visualized, using colors to represent different attenuation levels, thus generating a corrosion distribution map. This embodiment employs a clear and intuitive color mapping scheme. For example, all areas with attenuation exceeding the -8 dB threshold are marked with high-contrast, eye-catching colors such as bright red or dark red, allowing decision-makers to easily identify potential corrosion hotspots. The final map should resemble the corrosion assessment results shown in the reference document.

[0120] This step transforms discrete corrosion condition assessment results into continuous two-dimensional contour maps, enabling decision-makers to visually display the spatial distribution of corrosion throughout the survey area.

[0121] In summary, the embodiments of this invention propose a systematic process based on ground penetrating radar (GPR) technology for detecting severe corrosion of grounding electrodes buried at a depth of 80 cm, which mainly consists of the following stages: Phase 1: On-site Calibration: This is a mandatory initial step in the project. A reliable ground truth is established by scanning targets with known health conditions. This ground truth is used to fine-tune key parameters (such as dielectric constant) in subsequent data processing and to set the baseline for corrosion assessment. max ); Phase 2: Data Acquisition: Based on the high-resolution, precise positioning measurement network design, systematically collect GPR data covering the entire area to be measured; Phase 3: Data Processing: Execute a multi-step, iterative computational workflow. Specifically corresponding to steps 3 and 4 in this embodiment, this process includes data preprocessing, automatic target localization via FK offset and K-means clustering, and high-fidelity extraction of true reflection amplitude; Phase 4: Assessment and Reporting: The extracted amplitude is converted into relative attenuation (dB) and compared with a threshold to identify potentially severe corrosion locations, ultimately generating an intuitive corrosion distribution map and / or a detailed technical report.

[0122] Example 2: See appendix Figure 3 This embodiment describes a non-destructive corrosion assessment system for typical grounding electrodes using ground penetrating radar, used to implement the steps of the method described in Embodiment 1, including: 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.

[0123] In this example, the system further 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.

[0124] Example 3: In some embodiments, reference Figure 4 The figure is a schematic diagram of a computer device for implementing a method for non-destructive corrosion assessment of a typical grounding electrode according to some embodiments of this application. The method for non-destructive corrosion assessment of a typical grounding electrode in Embodiment 1 above can be implemented through... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor, a communication bus, a memory, and at least one communication interface.

[0125] A processor can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0126] A communication bus can be used to transmit information between the aforementioned components.

[0127] The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. The memory can exist independently and be connected to the processor via a communication bus. The memory can also be integrated with the processor.

[0128] The memory stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor executes the program code stored in the memory. The program code may 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.

[0129] A communication interface is a device that uses any transceiver or similar device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0130] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0131] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0132] Example 4: Embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1 above.

[0133] In summary, this invention first performs on-site calibration scanning on a reference grounding electrode with known health status to obtain the dielectric constant for precise offset processing and the health reference amplitude for quantitative assessment. Then, it systematically acquires ground-penetrating radar (GPR) data from the grounding electrode under test. Based on the calibrated dielectric constant, it performs FK offset processing on the acquired data to focus the reflected signal energy of the grounding electrode. A machine learning clustering algorithm is then used to automatically identify the precise spatial coordinates of the grounding electrode at each scanning location. Next, based on the extracted spatial coordinates, it queries the unoffset high-fidelity reflection amplitude in the preprocessed raw data and calculates the relative attenuation of the reflection amplitude at each measurement point based on the calibrated maximum reference amplitude. Finally, it compares the relative attenuation of each measurement point with a preset corrosion threshold to determine whether severe corrosion exists at that location and generates a corrosion distribution map.

[0134] Therefore, this invention not only effectively overcomes the uncertainty in detection caused by the complex and heterogeneous nature of soil media, but also achieves quantitative assessment and precise location of the corrosion state of grounding electrodes. It also avoids the indirectness and misjudgment risks of traditional electrical measurement methods, as well as the destructive, blind, and inefficient nature of excavation inspection. It can detect serious corrosion hazards before the grounding electrode undergoes physical fracture, greatly improving the accuracy, reliability, and efficiency of detection, and providing precise technical support for the preventive maintenance and safe operation of grounding grids.

[0135] The technical solution provided by this invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make several improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

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 body 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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