A grounding grid corrosion analysis and prediction system

Abstract

The present invention relates to the technical field of soil corrosion, and specifically to a grounding grid corrosion analysis and prediction system. The grounding grid corrosion analysis and prediction system includes an initial data collection module, a corrosion location analysis module, a fusion analysis and prediction module, and a dynamic monitoring and warning module. In the present invention, by comprehensively utilizing frequency domain reflectance analysis and acoustic echo testing methods to analyze electromagnetic wave and acoustic wave data sets, the positioning accuracy of the corrosion location and the visualization of structural changes are significantly improved, the corrosion degree and structural stability of the grounding grid can be more accurately evaluated, more accurate durability prediction results can be provided, and the maintenance strategy of the grounding grid is optimized through the fusion of data matching and comparison technology and pattern recognition, realizing real-time data monitoring and dynamic adjustment of the grounding grid state, effectively ensuring the safety and functional stability of the grounding grid, significantly improving the accuracy and timeliness of prediction, enhancing the ability of preventive maintenance, and effectively improving the maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of soil corrosion, and particularly to a grounding grid corrosion analysis and prediction system. Background Art

[0002] The technical field of soil corrosion involves the study and evaluation of the corrosion behavior of various materials in the soil environment under natural or artificial conditions. This field uses a variety of methods, including electrochemical testing, chemical analysis, and physical monitoring, to predict and quantify the corrosion rate, mechanism, and potential damage. The importance of this technical field lies in its direct impact on the long-term stability and safety of infrastructure, especially in the maintenance of bridges, pipelines, buildings, and underground or underwater structures. In addition, soil corrosion technology also focuses on how to slow down or prevent corrosion by changing environmental conditions or adopting protective measures.

[0003] Among them, the grounding grid corrosion analysis and prediction system refers to a system specifically designed to evaluate and predict the corrosion status of the grounding grid (usually the safety grounding part of power systems and communication infrastructure) in the soil. The purpose of the system is to identify in advance the corrosion problems that may lead to a reduction in grounding efficiency, thereby allowing timely repair or improvement measures to be taken to ensure the electrical safety and functional stability of the facility. The system usually integrates sensors, data processing software, and simulation tools, and can provide real-time data analysis and long-term corrosion trend prediction, helping maintenance personnel formulate effective maintenance plans and anti-corrosion strategies.

[0004] The existing technology mainly relies on electrochemical testing, chemical analysis, and physical monitoring in operation. These methods have deficiencies in the accuracy of corrosion location and real-time dynamic monitoring, resulting in delays in the identification and evaluation of the corrosion process. This limitation affects the timely maintenance and repair of the grounding grid. For example, if the corrosion area cannot be quickly and accurately located and evaluated, it will lead to misjudgment of the health status of the grounding grid, thereby affecting the safe operation of the power system. The existing technology fails to make full use of advanced data analysis and pattern recognition technologies, and is not accurate enough in predicting the long-term durability of the grounding grid, limiting the foresight and pertinence of maintenance strategies, resulting in premature aging or accidental failures of the grounding grid, increasing maintenance costs and having an adverse impact on system stability. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a grounding grid corrosion analysis and prediction system.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A grounding grid corrosion analysis and prediction system includes:

[0007] The initial data collection module collects electromagnetic signals and acoustic signals through sensors, monitors the collected electromagnetic wave signals in real time, and performs frequency analysis on the acoustic signals to generate an electromagnetic wave data set and an acoustic wave data set;

[0008] The corrosion location analysis module analyzes the corrosion signals in the electromagnetic wave data set, locates the corrosion position, generates corrosion position data, and draws a three-dimensional structure change diagram of the grounding grid by analyzing the acoustic wave data set to generate structure change data;

[0009] The fusion analysis and prediction module combines the corrosion position data and the structure change data, uses data matching and comparison techniques to analyze the structural stability of the grounding grid, and predicts the durability of the grounding grid based on the analysis results combined with pattern recognition technology to generate a durability prediction result;

[0010] The dynamic monitoring and warning module performs real-time data monitoring of the grounding grid based on the durability prediction result, tracks the health status of the grounding grid, dynamically adjusts the grounding grid maintenance strategy, and generates a dynamic record of the grounding grid health status.

[0011] As a further solution of the present invention, the steps for obtaining the electromagnetic wave data set are as follows:

[0012] Deploy sensors to collect electromagnetic signals, and use a peak detection algorithm to calculate the real-time intensity of each electromagnetic signal , using the formula,

[0013]

[0014] Generate a real-time signal intensity data set, where, represents the maximum amplitude of the electromagnetic signal, represents the electromagnetic signal frequency, represents time, represents the phase difference, represents the signal attenuation constant;

[0015] Extract signals exceeding a preset threshold from the real-time signal intensity data set, and use the formula,

[0016]

[0017] Generate a normalized signal data set, where, represents the normalized electromagnetic signal intensity, represents the maximum intensity value among all electromagnetic signals, represents the standard deviation of the data distribution, represents the natural exponential function;

[0018] Analyze the normalized signal dataset, and use the weighted average method. The formula is

[0019]

[0020] to obtain the electromagnetic wave dataset , where represents the weight of the i-th electromagnetic signal, represents the total number of electromagnetic signals, represents the weight adjustment coefficient, which controls the influence of the deviation on the weighted result, represents the average value of the electromagnetic signals.

[0021] As a further solution of the present invention, the steps for obtaining the acoustic wave dataset are as follows:

[0022] Deploy sensors to collect acoustic wave signals, and analyze the frequency distribution of each acoustic wave signal through Fourier transform , and use the formula

[0023]

[0024] to generate the frequency distribution dataset, where represents the frequency of the i-th acoustic wave signal, represents the period of the target acoustic wave signal, represents the amplitude of the acoustic wave signal, represents the nonlinear response coefficient.

[0025] Perform normalization processing on the frequency distribution dataset, and apply the formula

[0026]

[0027] to generate the normalized frequency dataset, where represents the normalized frequency, and represent the minimum and maximum frequencies respectively, represents the median of the frequencies.

[0028] Use the clustering algorithm to perform clustering analysis on the frequencies in the normalized frequency dataset to identify the key acoustic wave frequency groups, and use the formula

[0029]

[0030] to generate the acoustic wave dataset , where represents the weight of the j-th group of frequencies, represents the number of clusters, represents the exponential coefficient that emphasizes the frequency difference.

[0031] As a further solution of the present invention, the steps for obtaining the corrosion position data are as follows:

[0032] According to the electromagnetic wave data set, by applying Fourier transform, the electromagnetic wave signal in the time domain is converted to the frequency domain, and using the formula,

[0033]

[0034] Generate a frequency domain signal data set, where, represents the electromagnetic signal in the frequency domain, is the original electromagnetic signal, is the angular frequency, is the time;

[0035] Utilize the frequency domain signal data set to identify abnormal reflectivity peaks within the target frequency range, representing the corrosion position, and adopt an enhanced peak detection formula,

[0036]

[0037] Generate a preliminary corrosion position identification result, where, is the adjusted maximum peak in the frequency domain signal, emphasizes the influence of high-frequency components, is the standard deviation for normalization processing;

[0038] Convert the preliminary corrosion position identification result data into three-dimensional space coordinates ( ), and use the space mapping formula,

[0039]

[0040] Generate corrosion position data, where, represents the accurate measurement distance of the corrosion position, is the corrosion position coordinate, and The parameters for adjusting the peak influence match the actual physical conditions.

[0041] As a further solution of the present invention, the steps for obtaining the structural change data are as follows:

[0042] Collect and convert the acoustic wave signal into frequency domain data, apply Fourier transform, and through the formula,

[0043]

[0044] Generate an acoustic wave frequency domain data set, where, <( represents the acoustic wave signal in the frequency domain, is the received acoustic wave signal, is the angular frequency, is the time;

[0045] Analyze the acoustic frequency domain data set, identify the frequency shift and amplitude change caused by the change of the grounding grid structure, and use the amplitude difference analysis formula,

[0046]

[0047] Generate the preliminary analysis result of the structural change, where, Indicates the quantization index of the change of the grounding grid structure, Is the baseline frequency domain data, Emphasize the influence of low-frequency components;

[0048] Utilize the preliminary analysis result of the structural change, combine with 3D modeling technology, create a 3D view of the change of the grounding grid, and adopt the formula,

[0049]

[0050] Generate the structural change data, where, Indicates the change intensity of each point in the 3D model, Adjust the parameter of the depth influence, Is the depth coordinate.

[0051] As a further solution of the present invention, the analysis steps of the grounding grid structure stability are:

[0052] Merge the corrosion location data and the structural change data, and adopt the data merging formula,

[0053]

[0054] Obtain the comprehensive data set, where, Indicates the merged data set, And Respectively represent the corrosion location data point and the structural change data point, Is the weight coefficient, which is used to emphasize the importance of the data point;

[0055] Analyze the comprehensive data set, and apply the data analysis formula,

[0056]

[0057] Generate the structural stability analysis result Where, Is the data point in the comprehensive data set, Is the weight factor, which is used to evaluate the influence of each data point;

[0058] Evaluate the structural stability analysis result, and classify the risk level, and adopt the evaluation formula,

[0059]

[0060] Generate risk assessment results , where is the risk sensitivity coefficient is the risk baseline value, used to determine the risk threshold

[0061] As a further solution of the present invention, the steps for obtaining the durability prediction result are as follows

[0062] According to the risk assessment results, feature extraction is performed, and a feature extraction formula is used

[0063]

[0064] to obtain a feature data set, where represents the feature data extracted from the risk assessment results and are adjustment coefficients, used to adjust the data sensitivity

[0065] Based on the feature data set, durability prediction is performed, and a prediction formula is used

[0066]

[0067] to generate a preliminary prediction result , where and are model parameters, used to adjust the sensitivity and speed of the prediction reaction

[0068] Based on the preliminary prediction result, correction is performed according to industry standards and historical data, and a correction formula is used

[0069]

[0070] to generate a durability prediction result , where is the historical data weight, used to balance the relationship between the prediction result and the historical trend is the historical average value, used to provide a baseline reference

[0071] As a further solution of the present invention, the steps for obtaining the dynamic record of the grounding grid health status are as follows

[0072] Based on the durability prediction result, real-time monitoring is performed, and a formula is used

[0073]

[0074] to generate real-time monitoring data , where is the durability prediction result is the adjustment coefficient, is the exponential coefficient that enhances the influence of the prediction result;

[0075] Using the real-time monitoring data, conduct a health status assessment of the grounding grid, and adopt the formula,

[0076]

[0077] Generate a health status score , where is the data point 's weight, is the data sensitivity adjustment coefficient;

[0078] According to the health status score, adjust the maintenance strategy of the grounding grid, and adopt the formula,

[0079]

[0080] Generate a new maintenance strategy , where is the influence factor of the new score, used to limit the output range to ensure smooth adjustment;

[0081] Apply the new maintenance strategy to update the dynamic record of the grounding grid health status, and adopt the formula,

[0082]

[0083] Generate the dynamic record of the grounding grid health status , where is the adjustment factor of the update frequency, is the sine function, used to provide a periodic adjustment function to cope with seasonal or periodic changes.

[0084] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0085] In the present invention, by comprehensively using the frequency domain reflectivity analysis and the acoustic echo test method to analyze the electromagnetic wave and acoustic wave data sets, the positioning accuracy of the corrosion location and the visualization of the structural changes are significantly improved, the corrosion degree and structural stability of the grounding grid can be evaluated more accurately, and a more accurate durability prediction result can be provided. Through the fusion of data matching and comparison technology and pattern recognition, the maintenance strategy of the grounding grid is optimized, real-time data monitoring and dynamic adjustment of the grounding grid state are realized, the safety and functional stability of the grounding grid are effectively guaranteed, the accuracy and timeliness of the prediction are significantly improved, the preventive maintenance ability is enhanced, and the maintenance efficiency is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1It is the system flow chart of the present invention;

[0087] Figure 2 It is the acquisition flow chart of the electromagnetic wave data set of the present invention;

[0088] Figure 3 It is the acquisition flow chart of the acoustic wave data set of the present invention;

[0089] Figure 4 It is the acquisition flow chart of the corrosion position data of the present invention;

[0090] Figure 5 It is the acquisition flow chart of the structure change data of the present invention;

[0091] Figure 6 It is the analysis flow chart of the grounding grid structure stability of the present invention;

[0092] Figure 7 It is the acquisition flow chart of the durability prediction result of the present invention;

[0093] Figure 8 It is the acquisition flow chart of the dynamic record of the grounding grid health status of the present invention. Detailed implementation manners

[0094] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0095] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0096] Embodiment 1

[0097] Please refer to Figure 1 , a grounding grid corrosion analysis and prediction system includes:

[0098] The initial data collection module collects electromagnetic signals and acoustic signals through sensors, monitors the collected electromagnetic wave signals in real time, and performs frequency analysis on the acoustic signals to generate an electromagnetic wave data set and an acoustic wave data set;

[0099] The corrosion location analysis module analyzes the corrosion signals in the electromagnetic wave dataset, locates the corrosion positions, generates corrosion position data, analyzes the acoustic wave dataset, draws the three-dimensional structure change diagram of the grounding grid, and generates structure change data;

[0100] The fusion analysis and prediction module combines the corrosion position data and the structure change data, uses data matching and comparison technologies to analyze the structural stability of the grounding grid, and predicts the durability of the grounding grid based on the analysis results combined with pattern recognition technology, generating a durability prediction result;

[0101] The dynamic monitoring and warning module, based on the durability prediction result, monitors the real-time data of the grounding grid, tracks the health status of the grounding grid, dynamically adjusts the grounding grid maintenance strategy, and generates a dynamic record of the grounding grid health status.

[0102] The electromagnetic wave dataset includes signal frequency, signal amplitude, and signal phase. The acoustic wave dataset includes frequency response records, acoustic wave intensity, and echo delay. The corrosion position data includes corrosion depth, corrosion area, and corrosion type. The structure change data includes structure defect positions, defect sizes, and defect morphologies. The durability prediction result includes predicted life, risk level, and degradation rate. The dynamic record of the grounding grid health status includes maintenance frequency, fault records, and alarm status.

[0103] Please refer to Figure 2 , the steps for obtaining the electromagnetic wave dataset are as follows:

[0104] Deploy sensors to collect electromagnetic signals, use the peak detection algorithm to calculate the real-time intensity of each electromagnetic signal , use the formula,

[0105]

[0106] Generate a real-time signal intensity dataset, where, represents the maximum amplitude of the electromagnetic signal, represents the electromagnetic signal frequency, represents time, represents the phase difference, represents the signal attenuation constant;

[0107] Extract the signals exceeding the preset threshold from the real-time signal intensity dataset, and use the formula,

[0108]

[0109] Generate a normalized signal dataset, where, represents the normalized electromagnetic signal intensity, represents the maximum intensity value among all electromagnetic signals, Represents the standard deviation of the data distribution, represents the natural exponential function;

[0110] Analyze the normalized signal dataset, using the weighted average method, with the formula being,

[0111]

[0112] Obtain the electromagnetic wave dataset , where, represents the weight of the i-th electromagnetic signal, represents the total number of electromagnetic signals, represents the weight adjustment coefficient, controlling the influence of the deviation on the weighted result, represents the average value of the electromagnetic signals.

[0113] : The maximum amplitude of the signal, usually depending on the strength of the signal source, assumed to be 5V.

[0114] : The signal frequency, which can be set according to the signal source characteristics, assumed to be 60Hz.

[0115] : Time, the time starting from signal acquisition, assumed to be calculated at 0.1 seconds.

[0116] : Phase difference, according to the displacement of the signal waveform synchronized with time, assumed to be radians.

[0117] : The attenuation constant of the signal, describing the attenuation rate of the signal over time, assumed to be 0.05 seconds.

[0118] Calculate at :

[0119]

[0120]

[0121]

[0122] The calculated V represents the electromagnetic signal intensity at the 0.1 second moment, which is the real-time measurement value considering the attenuation of the electromagnetic signal.

[0123] : The standard deviation of the data distribution, affecting the shape of the exponential function, assumed to be 1V.

[0124] Assume the maximum obtained It is 1V. Substitute it into the formula and calculate :

[0125]

[0126]

[0127]

[0128] The calculation result represents the signal intensity after normalization, considering the actual amplitude of the signal and non-linear adjustment.

[0129] : The weight of the i-th signal. Assume that the weights of all signals are 1.

[0130] : The weight adjustment coefficient, which controls the influence of the deviation on the weighted result. Assume it is 0.1.

[0131] : The average signal intensity. Assume it was 0.05 in the previous step.

[0132] Assume there is only one type of signal, that is .

[0133] Calculate :

[0134]

[0135]

[0136] represents the weighted average intensity of the entire electromagnetic wave data set, considering a small adjustment of the signal deviation. This is the final data expression for signal monitoring and analysis.

[0137] Please refer to Figure 3 , the steps for obtaining the acoustic wave data set are as follows:

[0138] Deploy sensors to collect acoustic wave signals, and analyze the frequency distribution of each acoustic wave signal through Fourier transform , using the formula,

[0139]

[0140] Generate a frequency distribution data set, where represents the frequency of the i-th acoustic wave signal, represents the period of the target acoustic wave signal, represents the amplitude of the acoustic wave signal, represents the non-linear response coefficient.

[0141] Normalize the frequency distribution dataset and apply the formula,

[0142]

[0143] Generate a normalized frequency dataset, where, represents the normalized frequency, and represent the minimum and maximum frequencies respectively, represents the median of the frequencies.

[0144] Use the clustering algorithm to perform clustering analysis on the frequencies in the normalized frequency dataset, identify the key acoustic frequency groups, and adopt the formula,

[0145]

[0146] Generate an acoustic wave dataset , where, represents the weight of the j-th group of frequencies, represents the number of clusters, represents the exponential coefficient that emphasizes the frequency difference.

[0147] : The period of the target signal, usually determined by the repetition interval of the signal, is assumed to be 0.02 seconds.

[0148] : The amplitude of the signal, depending on the intensity of the acoustic wave signal, is assumed to be 3V.

[0149] : The non-linear response coefficient, a parameter that measures the influence of the amplitude on the frequency calculation, is assumed to be 0.5.

[0150] Substitute into the formula for calculation to get:

[0151]

[0152]

[0153] The calculated Hz represents the frequency of the acoustic wave signal with a period of 0.02 seconds under the influence of the given amplitude and non-linear response coefficient, which is the characteristic frequency of the acoustic wave signal.

[0154] and : Are the minimum and maximum frequency values in the dataset respectively, assuming Hz, Hz.

[0155] : The median of the frequencies, is assumed to be 25Hz.

[0156] Combined with the aforementioned calculation results (for the convenience of calculation, rounded to 20 Hz), substitute into the formula for calculation :

[0157]

[0158]

[0159] represents the standardized frequency value, which quantifies the degree of signal standardization in combination with the frequency deviation. This value is used for further data analysis.

[0160] : The weight of the j-th group of frequencies, assuming each group is equal, all being 1.

[0161] : The standardized frequency obtained from the aforementioned calculation.

[0162] : The number of clusters, assuming there is one cluster.

[0163] : The exponential coefficient emphasizing the frequency difference, assuming it is 2.

[0164] Calculate :

[0165]

[0166]

[0167] represents the weighted sum of the dataset based on the acoustic wave frequency. This value reflects the result of the weighted clustering analysis and is used to describe the main frequency characteristics in the acoustic wave dataset.

[0168] Please refer to Figure 4 , the steps for obtaining the corrosion location data are as follows:

[0169] According to the electromagnetic wave dataset, by applying the Fourier transform, the electromagnetic wave signal in the time domain is converted to the frequency domain, using the formula,

[0170]

[0171] generate the frequency domain signal dataset, where, represents the electromagnetic signal in the frequency domain, is the original electromagnetic signal, is the angular frequency, is the time;

[0172] Using a frequency-domain signal dataset, identify abnormal reflectivity peaks within a target frequency range, representing corrosion locations, and employ an enhanced peak detection formula,

[0173]

[0174] to generate a preliminary identification result of the corrosion location, where, is the adjusted maximum peak in the frequency-domain signal, emphasizes the influence of high-frequency components, is the standard deviation for normalization;

[0175] Convert the preliminary identification result of the corrosion location into three-dimensional space coordinates ( ), using a spatial mapping formula,

[0176]

[0177] to generate corrosion location data, where, represents the accurate measurement distance of the corrosion location, is the corrosion location coordinate, and are parameters for adjusting the peak influence to match the actual physical conditions.

[0178] Assume the signal: , where volts, hertz.

[0179] Given (corresponding to 60 hertz), the first thing to solve is the expression for the Fourier transform. For the Fourier transform, use the form of complex exponential to simplify the calculation:

[0180]

[0181] Substitute this expression into the definition of the Fourier transform, we get:

[0182]

[0183] The integral can be divided into two parts:

[0184]

[0185] Analyzing the formula (considering the Dirac delta function property of the Fourier transform) shows that the two integrals generate delta peaks at and respectively. Therefore, for hertz, we calculate the situation when :

[0186]

[0187]

[0188] In mathematics and physics, the delta function is theoretically infinite and represents that there is a strong frequency component at

[0189] At it is theoretically infinite, indicating a significant energy concentration at a frequency of 60 Hz, which is a key characteristic in signal analysis, meaning that the energy of the electromagnetic wave data is particularly strong at this frequency and can be used for further signal processing and feature extraction. Especially in the context of corrosion detection, the significance of this frequency may be related to the physical phenomena associated with corrosion.

[0190] Assume that the value at is 2, and , calculate : :

[0191]

[0192]

[0193] The high value of

[0194] indicates a possible corrosion location, emphasizing the abnormal reflection signal with a larger amplitude at higher frequencies. : The three-dimensional coordinates of the corrosion location, assumed to be

[0195] : The adjustment factor to make the model more adaptable to the actual physical conditions, let :

[0196] : The peak value obtained from the aforementioned calculation is 60318.8.

[0197] Calculate the straight-line distance in three-dimensional space :

[0198]

[0199]

[0200]

[0201]

[0202] meters. This result represents the adjusted distance from the origin to the corrosion location, taking into account the potential signal attenuation and spatial distribution effects due to corrosion peaks. This value can be used to precisely specify the exact point of the corrosion location in three-dimensional space for further grounding grid structural health monitoring and maintenance strategy formulation.

[0203] Please refer to Figure 5 , the steps for obtaining structural change data are as follows:

[0204] Collect and convert the acoustic wave signal into frequency domain data, apply the Fourier transform, through the formula,

[0205]

[0206] Generate an acoustic wave frequency domain data set, where, represents the acoustic wave signal in the frequency domain, is the received acoustic wave signal, is the angular frequency, is the time;

[0207] Analyze the acoustic wave frequency domain data set, identify the frequency shift and amplitude change caused by the grounding grid structure change, use the amplitude difference analysis formula,

[0208]

[0209] Generate a preliminary analysis result of the structural change, where, represents the quantification index of the grounding grid structure change, is the baseline frequency domain data, emphasizes the influence of low-frequency components;

[0210] Utilize the preliminary analysis result of the structural change, combined with three-dimensional modeling technology, to create a three-dimensional view of the grounding grid change, using the formula,

[0211]

[0212] Generate the structural change data, where, represents the change intensity of each point in the three-dimensional model, parameters for adjusting the depth influence, is the depth coordinate.

[0213] : The received acoustic wave signal, assumed to be a simple sine wave , where is the amplitude, is the frequency of the acoustic wave.

[0214] : Angular frequency, related to the frequency domain characteristics of the acoustic wave signal.

[0215] : Time variable.

[0216] Assume Volts, Hertz, considering the calculation (corresponding to 50 Hz) :

[0217]

[0218] The integral is through Euler's formula Solution:

[0219]

[0220]

[0221] The first integral is an infinite constant term, and the second integral yields , which is infinite at but is 0 at .

[0222] This indicates that for the signal sin(100πt), there is no significant frequency component at ω = 100π, i.e., there is no energy at this frequency. For acoustic signal analysis, it shows the absence or weakening of this frequency component in the signal.

[0223] : Baseline frequency domain data, assumed to be (no energy at this frequency in the baseline).

[0224] Assume (the maximum frequency domain amplitude), calculate at:

[0225]

[0226] It is pointed out that no significant change is observed in the comparison of the current and baseline frequency domain data. This means that the acoustic signal does not show structural changes at this specific frequency, which is crucial for formulating maintenance strategies.

[0227] : Parameter affecting the adjustment depth, assumed to be .

[0228] : Depth coordinate, assumed to be 3 meters.

[0229]

[0230] This indicates that no significant changes in the grounding grid structure were observed within the current inspection range, which is important for determining the stability of the structure and future inspection strategies.

[0231] See also Figure 6 , the analysis steps of the grounding grid structure stability are:

[0232] Combine the corrosion location data and the structural change data, using the data merging formula,

[0233]

[0234] Get a comprehensive data set, where represents the merged dataset, and Represent the corrosion location data points and structure change data points respectively, is the weight coefficient, which is used to emphasize the importance of the data point;

[0235] Analyze comprehensive data sets and apply data analysis formulas,

[0236]

[0237] Generate structural stability analysis results ,in, is a data point in the synthetic dataset, is a weight factor used to evaluate the influence of each data point;

[0238] Evaluate the structural stability analysis results and classify the risk level using the evaluation formula.

[0239]

[0240] Generate risk assessment results ,in, is the risk sensitivity coefficient, It is the risk benchmark value, which is used to determine the risk threshold.

[0241] Suppose there are two data points, the corresponding corrosion location data are 3 and 5, the structural change data are 2 and 4, and the weights are 1.5 and 2.0 respectively.

[0242] Compute the synthetic dataset:

[0243]

[0244]

[0245] The result value of 5.05 represents the standardized merged value of the comprehensive data set, which can be used for the next step of data matching analysis and reflects the combined influence of corrosion location and structural change data.

[0246] Continue to use the data points and weights from the previous step. Here the values are respectively taken from the square of:

[0247]

[0248]

[0249] The result value 57 is the value of the structural stability analysis after weight adjustment. A high value may indicate poor structural stability, and further analysis is required to determine the specific risk level.

[0250] Assume and , use :

[0251]

[0252] The obtained result 0.668 can be interpreted as the risk level of the structure. The closer the value is to 1, the higher the risk. In this example, 0.668 indicates medium to high risk, and it is recommended to further reinforce or monitor the grounding grid structure.

[0253] Please refer to Figure 7 , and the steps to obtain the durability prediction result are as follows:

[0254] According to the risk assessment result, perform feature extraction, and use the feature extraction formula,

[0255]

[0256] to obtain the feature data set, where represents the feature data extracted from the risk assessment result, and are adjustment coefficients used to adjust the data sensitivity;

[0257] Based on the feature data set, perform durability prediction, and use the prediction formula,

[0258]

[0259] to generate the preliminary prediction result , where and are model parameters used to adjust the sensitivity and speed of the prediction response;

[0260] Based on the preliminary prediction result, correct it according to industry standards and historical data, and use the correction formula,

[0261]

[0262] Generate durability prediction results , where is the historical data weight, used to balance the relationship between the prediction result and the historical trend, is the historical average, used to provide a baseline reference.

[0263] Use the and hypothesis ,

[0264]

[0265]

[0266] The result represents the feature data extracted from the risk assessment, and the numerical size reflects the correlation strength between the structural stability and durability. A higher feature value indicates a greater potential risk and requires more intensive monitoring or maintenance.

[0267] Hypothesis and , use :

[0268]

[0269] The obtained result represents the preliminary durability prediction result. The closer the numerical value is to 5 (the maximum possible value is determined by ), the better the durability. This indicates that under the current risk assessment, the expected functional persistence of the grounding grid is stronger.

[0270] Hypothesis and = 3, use

[0271]

[0272] The result is the final durability prediction value, which is 3 higher than the historical average, indicating that the prediction result is better than the past performance, possibly due to improved maintenance strategies or technological upgrades. This value is used to guide future maintenance plans and budget allocations to maintain or improve the performance of the grounding grid.

[0273] Please refer to Figure 8 , the steps to obtain the dynamic record of the grounding grid health status are as follows:

[0274] Based on the durability prediction results, conduct real-time monitoring, using the formula,

[0275]

[0276] Generate real-time monitoring data , where is the durability prediction result, is the adjustment coefficient, is the exponential coefficient that enhances the influence of the prediction result;

[0277] Using the real-time monitoring data, conduct a health status assessment of the grounding grid, and adopt the formula

[0278]

[0279] Generate a health status score , where is the data point weight of is the data sensitivity adjustment coefficient;

[0280] According to the health status score, adjust the maintenance strategy of the grounding grid, and adopt the formula

[0281]

[0282] Generate a new maintenance strategy , where is the influence factor of the new score, used to limit the output range to ensure smooth adjustment;

[0283] Apply the new maintenance strategy to update the dynamic record of the grounding grid health status, and adopt the formula

[0284]

[0285] Generate the dynamic record of the grounding grid health status , where is the adjustment factor of the update frequency, is the sine function, used to provide a periodic adjustment function to cope with seasonal or periodic changes.

[0286] Assume:

[0287]

[0288]

[0289]

[0290] Calculation process:

[0291]

[0292] It means that when the given power is 16 units, the output of the system is 8 units.

[0293] Assume:

[0294]

[0295]

[0296] Calculation process:

[0297]

[0298] means that the overall state index of the system at time is 8.33, showing the weighted system state.

[0299] Hypothesis:

[0300]

[0301]

[0302] Calculation process:

[0303]

[0304]

[0305] means that the maintenance index at the current time is 3.8, which reflects the combination of the immediate response of the system state to maintenance requirements and historical data.

[0306] Hypothesis:

[0307]

[0308]

[0309] Calculation process:

[0310]

[0311]

[0312] indicates that at time the risk assessment index of the grounding grid is , reflecting the current risk level of the grounding grid and its change trend, which is conducive to the judgment and maintenance of the grounding grid.

[0313] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A grounding grid corrosion analysis and prediction system, characterized in that The system includes: The initial data collection module collects electromagnetic signals and acoustic signals through sensors, monitors the collected electromagnetic wave signals in real time, and analyzes the frequency of the acoustic signals to generate an electromagnetic wave data set and an acoustic wave data set; The corrosion location analysis module analyzes the corrosion signals in the electromagnetic wave data set, locates the corrosion position, generates corrosion position data, and draws a three-dimensional structure change diagram of the grounding grid by analyzing the acoustic wave data set to generate structure change data; The fusion analysis and prediction module combines the corrosion position data and the structure change data, uses data matching and comparison technologies to analyze the structural stability of the grounding grid, and predicts the durability of the grounding grid based on the analysis results combined with pattern recognition technology to generate a durability prediction result; The dynamic monitoring and warning module, based on the durability prediction result, monitors the real-time data of the grounding grid, tracks the health status of the grounding grid, dynamically adjusts the grounding grid maintenance strategy, and generates a dynamic record of the grounding grid health status; The acquisition steps of the acoustic wave data set are as follows: Deploy sensors to collect acoustic wave signals, analyze the frequency distribution of each acoustic wave signal through Fourier transform, and use the formula ； Generate a frequency distribution data set, where, represents the frequency of the i-th acoustic wave signal, represents the period of the target acoustic wave signal, represents the amplitude of the acoustic wave signal, represents the nonlinear response coefficient; Perform standardization processing on the frequency distribution data set, and apply the formula ； Generate a standardized frequency dataset, where, represents the standardized frequency, and represent the minimum and maximum frequencies respectively, represents the median of the frequencies; Use the clustering algorithm to perform clustering analysis on the frequencies in the standardized frequency data set to identify the key acoustic wave frequency groups, and use the formula ； Generate an acoustic wave data set , where represents the weight of the j-th group of frequencies, represents the number of clusters, represents the exponential coefficient that emphasizes the frequency difference; The acquisition steps of the structure change data are as follows: Collect and convert the acoustic wave signals into frequency domain data, apply Fourier transform, and use the formula ； Generate an acoustic wave frequency domain data set, where, represents the acoustic wave signal in the frequency domain, is the received acoustic wave signal, is the angular frequency, is the time; Analyze the acoustic wave frequency domain data set, identify the frequency offset and amplitude change caused by the structure change of the grounding grid, and use the amplitude difference analysis formula ； Generate the preliminary analysis results of the structural change, where represents the quantification index of the structural change of the grounding grid, is the baseline frequency-domain data, emphasizes the influence of the low-frequency components; Use the preliminary analysis result of the structure change, combined with 3D modeling technology, to create a 3D view of the change of the grounding grid, and use the formula ； Generate structure change data, where represents the change intensity of each point in the three-dimensional model, represents a parameter for adjusting the influence of depth, is the depth coordinate; The analysis steps of the structural stability of the grounding grid are as follows: Combine the corrosion position data and the structure change data, and use the data combination formula ； Obtain a comprehensive data set, where represents the merged data set, and represent the corrosion position data points and the structure change data points respectively, is the weight coefficient, which is used to emphasize the importance of the data points; Analyze the comprehensive data set and apply the data analysis formula ； Generate the results of structural stability analysis , where is a data point in the comprehensive dataset is a weight factor used to evaluate the influence of each data point; Evaluate the structural stability analysis result and classify the risk level, and use the evaluation formula ； Generate a risk assessment result , where is the risk sensitivity coefficient is the risk benchmark value, which is used to determine the risk threshold; The acquisition steps of the durability prediction result are as follows: According to the risk assessment result, perform feature extraction, and use the feature extraction formula ； Obtain a feature dataset, where represents the feature data extracted from the risk assessment result, and is a regulation coefficient for regulating data sensitivity; Based on the feature data set, perform durability prediction, and use the prediction formula ； Generate preliminary prediction results , where and are model parameters used to adjust the sensitivity and speed of the prediction reaction; Based on the preliminary prediction result, correct according to industry standards and historical data, and use the correction formula ； Generate durability prediction results , where is the historical data weight, which is used to balance the relationship between the prediction result and the historical trend, is the historical average, which is used to provide a baseline reference.

2. The grounding grid corrosion analysis and prediction system according to claim 1, wherein: The acquisition steps of the electromagnetic wave data set are as follows: Deploy sensors to collect electromagnetic signals, adopt a peak detection algorithm, and calculate the real-time intensity of each electromagnetic signal , and use the formula ； Generate a real-time signal strength data set, where represents the maximum amplitude of the electromagnetic signal, represents the electromagnetic signal frequency, represents time, represents the phase difference, represents the attenuation constant of the signal; Extract signals from the real-time signal strength dataset that exceed a preset threshold and use the formula ； Generate a normalized signal dataset, where represents the normalized electromagnetic signal intensity, represents the maximum intensity value among all electromagnetic signals, represents the standard deviation of the data distribution, represents the natural exponential function; Analyze the normalized signal data set, use the weighted average method, and use the formula ； Obtain an electromagnetic wave data set , where represents the weight of the i-th electromagnetic signal, represents the total number of electromagnetic signals, represents a weight adjustment coefficient that controls the influence of the deviation on the weighted result, represents the average value of the electromagnetic signals.

3. The grounding grid corrosion analysis and prediction system according to claim 2, wherein: The acquisition steps of the corrosion position data are as follows: According to the electromagnetic wave data set, by applying Fourier transform, convert the electromagnetic wave signal in the time domain to the frequency domain, and use the formula ； Generate a frequency-domain signal dataset, where, represents an electromagnetic signal in the frequency domain, is the original electromagnetic signal, is the angular frequency, is the time; Use the frequency domain signal data set to identify the abnormal reflectivity peak value within the target frequency range, which represents the corrosion position, and use the enhanced peak detection formula ； Generate a preliminary recognition result of the corrosion location, where is the adjusted maximum peak value in the frequency-domain signal, emphasize the influence of high-frequency components, is the standard deviation for normalization processing; Convert the preliminary identification result data of the corrosion position into three-dimensional space coordinates, and use the space mapping formula ； Generate corrosion location data, where represents the accurate measurement distance of the corrosion location, is the corrosion location coordinate, and parameters for adjusting the peak influence to match the actual physical conditions.

4. The grounding grid corrosion analysis and prediction system according to claim 1, wherein: The acquisition steps of the dynamic record of the grounding grid health status are as follows: Based on the durability prediction result, perform real-time monitoring, and use the formula ； Generate real-time monitoring data , where is the durability prediction result, is the adjustment coefficient, is the exponential coefficient that enhances the impact of the prediction result; Using the real-time monitoring data, conduct a health status assessment of the grounding grid, using a formula ； Generate a health status score , where is the weight of the data point , and is the data sensitivity adjustment coefficient; According to the health status score, adjust the maintenance strategy of the grounding grid, using a formula ； Generate a new maintenance strategy , where is the influencing factor of the new score used to limit the output range and ensure smooth adjustment; Apply the new maintenance strategy to update the dynamic record of the grounding grid health status, using a formula ； Generate a dynamic record of the health status of the grounding grid , where is an adjustment factor for the update frequency is a sine function used to provide a periodic adjustment function to cope with seasonal or periodic changes

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