An intelligent monitoring system for grounding grid corrosion

Abstract

The present invention relates to the field of corrosion detection technology, and specifically to an intelligent monitoring system for grounding grid corrosion, the system comprising an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability assessment module, and a monitoring strategy optimization module. In the present invention, by filtering and correcting data errors and outliers, the overall quality of the data is improved, and the accuracy of the analysis results is ensured. In the corrosion behavior analysis, the future trend of corrosion is effectively predicted by detecting the dynamic changes of electrochemical properties and calculating the corrosion rate. The time series analysis of electrochemical impedance data not only enhances the accuracy of the prediction, but also improves the depth of understanding of the corrosion behavior. The corrosion probability assessment module, through the application of Markov chains, adds consideration of random changes to the entire prediction model, making the model closer to the uncertainty of the real world. By optimizing the processing flow, the real-time performance and early warning capabilities of the monitoring system are significantly improved, and the reliability of the system and the accuracy of operation are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of corrosion detection, and in particular to an intelligent monitoring system for grounding grid corrosion. Background Art

[0002] The field of corrosion detection technology involves the use of various methods and equipment to monitor and assess corrosion conditions on or within objects. These techniques are not limited to traditional chemical methods but also include advanced technologies such as electrochemistry, electronic detection, and acoustic detection. The key goal of corrosion detection technology is to accurately monitor and diagnose corrosion processes in real time to ensure the safety, reliability, and long-term operational capability of equipment, structures, or systems. Through continuous monitoring and analysis, preventive or remedial measures can be implemented promptly, extending the service life of facilities and reducing maintenance costs.

[0003] The intelligent grounding grid corrosion monitoring system, a specific application in the field of corrosion detection technology, aims to develop an intelligent monitoring system for detecting and assessing corrosion in grounding grid structures for power systems or facilities requiring grounding. Its primary uses include real-time monitoring of grounding grid corrosion and early warning system operators to perform necessary maintenance and repairs, ensuring efficient operation and long-term stability of the grounding system. Leveraging advanced sensing technology and data analysis, this system provides accurate corrosion monitoring data, enabling comprehensive equipment management and safety control.

[0004] While existing intelligent monitoring systems for grounding grid corrosion include electrochemical and electronic detection methods, they still have limitations in terms of real-time performance and accuracy. Traditional corrosion detection technologies rely on periodic physical inspections or chemical analysis, which is not only time-consuming but can also miss early stages of corrosion. They also lack efficient real-time data processing and analysis capabilities, preventing real-time feedback on corrosion status. This often leads to corrosion being discovered only after it has already reached a more severe stage. This delayed detection not only increases maintenance costs but also threatens the safe operation of the equipment. Existing technologies are also limited in the accuracy and reliability of monitoring data, lacking effective data correction mechanisms, which can lead to biased data analysis results. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent monitoring system for grounding grid corrosion.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an intelligent monitoring system for grounding grid corrosion includes:

[0007] The electrochemical data capture module collects electrochemical impedance data from grounding grid nodes based on platinum electrode sensors, transmits the data to a central processing unit via a dedicated line, and obtains an impedance raw data set. Based on the impedance raw data set, data errors and outliers are filtered and corrected to obtain a corrected impedance data set.

[0008] The corrosion behavior analysis module detects dynamic changes in electrochemical properties based on the calibrated impedance data set, calculates the corrosion rate and the influence of environmental factors on the corrosion behavior, generates an electrochemical change index, and performs time series analysis on the electrochemical impedance data based on the electrochemical change index to obtain a corrosion trend prediction;

[0009] The corrosion probability assessment module establishes a random change model through a Markov chain based on the corrosion trend prediction, simulates the corrosion process in the future time period, obtains a corrosion probability model, and updates and optimizes the probability distribution based on the corrosion probability model to obtain a refined risk assessment result;

[0010] The monitoring strategy optimization module adjusts the monitoring strategy and platinum electrode sensors based on the refined risk assessment results, optimizes the data capture frequency and parameters, and obtains an optimized monitoring configuration. Based on the optimized monitoring configuration, the platinum electrode sensors are redeployed and the data transmission settings are adjusted to obtain an enhanced corrosion early warning solution.

[0011] As a further solution of the present invention, the step of obtaining the correction impedance data set is specifically as follows:

[0012] Based on platinum electrode sensors, electrochemical impedance data of grounding grid nodes are collected and transmitted to the central processing unit through a dedicated line to obtain the original impedance data set. The difference between consecutive data points in the data set is calculated using the formula:

[0013]

[0014] Get the difference data set, where Indicates the data points, Indicates the data points, is the difference between these two data points, and is the adjustment factor, is the total number of data points, is the base of natural logarithm;

[0015] Based on the difference data set, calculate the mean and standard deviation, identify and remove outliers, and use the formula:

[0016]

[0017] The calculation generates the standard deviation of the difference, where is the difference, is the mean value of the difference data set, is the number of data points in the difference dataset, is the weight coefficient, Indicates the standard deviation of the difference results;

[0018] Based on the difference standard deviation results, a threshold is set, and the data points in the impedance original data set are screened and corrected according to the difference data set and the standard deviation, and the correction is performed using the formula:

[0019]

[0020] Generate a corrected impedance data set where represents the original data points, is the difference, and are the mean and standard deviation of the difference data set, is the set threshold, is the corrected data point.

[0021] As a further solution of the present invention, the steps of obtaining the electrochemical change index are specifically as follows:

[0022] The electrochemical impedance value at each time point was extracted from the corrected impedance data set, and the change between adjacent time points was calculated, and with reference to the measured time interval and current density, the equation was used:

[0023]

[0024] Generate time series change data, where Indicates time The electrochemical change energy, and are the impedance values at two consecutive time points, is the total number of data points, is the measurement interval, is the current density;

[0025] Using the time series variation data and the temperature adjustment coefficient, the corrosion rate is calculated with reference to the influence of the ambient temperature using the formula:

[0026]

[0027] Generates the Corrosion Rate result where, is the corrosion rate, and are the electrochemical change energies at two consecutive time points, is the temperature adjustment coefficient, is the ambient temperature;

[0028] Combining the corrosion rate results, temperature and humidity, multivariate analysis was performed to determine the influence of multiple factors, using the formula:

[0029]

[0030] Generate electrochemical change indicators, where is an indicator of electrochemical changes. is the corrosion rate, is the temperature, It's humidity, 、 Represent the influence weights of corrosion rate, temperature and humidity respectively.

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

[0032] Based on the electrochemical change indicators, the corrosion rate is extracted and the time series data is calculated using the formula:

[0033]

[0034] With reference to the influence of periodic factors and noise, seasonal adjustment and random disturbance are introduced to generate time series extended data, where: and Represent the corrosion rates at two consecutive time points, is the seasonal adjustment factor, is a random disturbance term, is the length of the cycle, Indicates a point in time, Represents time series data;

[0035] Based on the time series extended data, the autoregressive moving average model is used to process the time series extended data and enhance the prediction accuracy of the model. The formula is:

[0036]

[0037] The trend analysis results are calculated, where Indicates the trend analysis results. and is the current and previous time series extended data, 、 and are model parameters, is the error term of the previous step;

[0038] Based on the trend analysis results, environmental factors are summarized and a new prediction model is used, using the formula:

[0039]

[0040] Generates corrosion trend forecasts where It is the corrosion trend prediction, is the result of trend analysis. and are the current readings of temperature and humidity respectively, 、 and Represent the influencing parameters of control trend, temperature and humidity respectively.

[0041] As a further solution of the present invention, the steps for obtaining the corrosion probability model are specifically as follows:

[0042] The data obtained from the corrosion trend prediction is combined with external environmental factors, including temperature and humidity, through the formula:

[0043]

[0044] Introducing influencing factors and adjusting the weights of environmental factors to generate integrated data, where is the weight of the corrosion trend data, and are the adjustment coefficients for temperature and humidity, and Represents the real-time measured temperature and humidity, Indicates integrated data;

[0045] The integrated data is used to establish a Markov chain model to predict the probability of state transition in the future time period, using the formula:

[0046]

[0047] Smooth the probability transition and generate the state transition matrix, where Indicates status To status The transition probability, and Status and The integrated data value of is a parameter that controls the transfer sensitivity;

[0048] Based on the state transition matrix, the state probability in the future time period is calculated using the formula:

[0049]

[0050] Generate a corrosion probability model where is the corrosion probability distribution in the future time period, It is through The state transition matrix after the step, is the initial state probability distribution.

[0051] As a further solution of the present invention, the steps for obtaining the detailed risk assessment results are specifically as follows:

[0052] Based on the corrosion probability model, the risk metric is updated using real-time corrosion rate and environmental factors, and the nonlinear weighted method is used to optimize the response sensitivity of the model. The formula is:

[0053]

[0054] Generate an updated risk metric where and is the weight factor, Indicates the current environment parameters. represents the updated risk measure.

[0055] Based on the updated risk metric, the risk factor is calculated by combining the historical data with the results of the prediction model using regression analysis method, through the formula:

[0056]

[0057] Generate refined risk factors, where is the refined risk factor, and is the adjustment parameter, represents the updated risk measure;

[0058] Combining the refined risk factors and key thresholds, the formula is:

[0059]

[0060] Generate detailed risk assessment results, where: is to refine the risk assessment results, Adjust the steepness of the curve, is the safety threshold, It is the refined risk factor.

[0061] As a further solution of the present invention, the steps for obtaining the optimized monitoring configuration are specifically as follows:

[0062] According to the refined risk assessment results, the data capture frequency of the platinum electrode sensor is adjusted, and the sensitivity of the risk assessment and the environmental impact factor are referred to, through the formula:

[0063]

[0064] Get the adjusted capture frequency, where Indicates the adjusted capture frequency, is the frequency-adjusted baseline coefficient, To regulate environmental factors, is the environmental benchmark threshold, Indicates the results of the detailed risk assessment;

[0065] According to the adjusted capture frequency, the voltage and current parameters of the platinum electrode sensor are optimized by the formula:

[0066]

[0067] The optimized sensor parameters are obtained, where are the optimized sensor parameters, and are the adjustment coefficients of voltage and current respectively, represents the adjusted capture frequency;

[0068] Combining the adjusted capture frequency and optimized sensor parameters, a monitoring configuration is formed and optimized using the formula:

[0069]

[0070] The optimized monitoring configuration is obtained, where is the optimized monitoring configuration, and is the coefficient that integrates the effects of frequency and parameter settings, Indicates the adjusted capture frequency, are the optimized sensor parameters.

[0071] As a further solution of the present invention, the steps for obtaining the enhanced corrosion early warning solution are specifically as follows:

[0072] Under the guidance of the optimized monitoring configuration, the platinum electrode sensors are redeployed to cover the key corrosion areas, using the formula:

[0073]

[0074] Get the sensor deployment efficiency, where represents the efficiency factor of sensor deployment, is the environmental sensitivity adjustment coefficient, represents the area of the monitoring area, and is the distance adjustment parameter, Indicates adjustment distance;

[0075] Based on the sensor deployment efficiency, the frequency and parameters of data transmission are adjusted according to the redeployed platinum electrode sensor positions, using the formula:

[0076]

[0077] Get the optimized data transmission frequency, where is the data transmission frequency, is the transmission efficiency coefficient, and is the regulating factor, represents the efficiency factor of sensor deployment;

[0078] Combining the optimized data transmission frequency and sensor configuration, an enhanced corrosion early warning scheme is generated using the formula:

[0079]

[0080] in, Is to enhance the corrosion early warning program, and is the integration parameter, Indicates monitoring configuration, is the data transmission frequency.

[0081] Compared with the prior art, the advantages and positive effects of the present invention are:

[0082] In the present invention, by filtering and correcting data errors and outliers, the overall quality of the data is improved and the accuracy of the analysis results is ensured. In the corrosion behavior analysis, the dynamic change detection of electrochemical properties and the calculation of corrosion rate are used to effectively predict the future trend of corrosion. This time series analysis of electrochemical impedance data not only enhances the accuracy of the prediction, but also improves the depth of understanding of corrosion behavior. The corrosion probability assessment module adds considerations of random changes to the entire prediction model through the application of Markov chains, making the model closer to the uncertainty of the real world. By optimizing the data acquisition and processing process, the real-time and early warning capabilities of the monitoring system are significantly improved, and the reliability of the system and the accuracy of operation are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 is a system flow chart of the present invention;

[0084] Figure 2 A flowchart of the present invention for correcting an impedance data set;

[0085] Figure 3 A flow chart of the electrochemical change indicators in the present invention;

[0086] Figure 4 A flowchart of corrosion trend prediction in the present invention;

[0087] Figure 5 This is a flow chart of the corrosion probability model in the present invention;

[0088] Figure 6 A flow chart for refining the risk assessment results in the present invention;

[0089] Figure 7 This is a flow chart of the optimized monitoring configuration in the present invention;

[0090] Figure 8 This is a flow chart of the enhanced corrosion early warning solution in the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0092] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined. Example 1

[0093] See also Figure 1 , an intelligent monitoring system for grounding grid corrosion includes:

[0094] The electrochemical data capture module uses platinum electrode sensors to collect electrochemical impedance data from ground grid nodes and transmits the data to the central processing unit via a dedicated line to obtain an impedance raw data set. Based on the impedance raw data set, data errors and outliers are filtered and corrected to obtain a corrected impedance data set.

[0095] The corrosion behavior analysis module detects the dynamic changes of electrochemical properties based on the calibrated impedance data set, calculates the corrosion rate and the impact of environmental factors on corrosion behavior, generates electrochemical change indicators, and performs time series analysis on the electrochemical impedance data based on the electrochemical change indicators to obtain corrosion trend predictions.

[0096] The corrosion probability assessment module is based on corrosion trend prediction. It establishes a random change model through Markov chain, simulates the corrosion process in the future time period, and obtains a corrosion probability model. Based on the corrosion probability model, it updates and optimizes the probability distribution to obtain detailed risk assessment results.

[0097] The monitoring strategy optimization module adjusts the monitoring strategy and platinum electrode sensors based on the refined risk assessment results, optimizes the data capture frequency and parameters, and obtains the optimized monitoring configuration. Based on the optimized monitoring configuration, the platinum electrode sensors are redeployed and the data transmission settings are adjusted to obtain an enhanced corrosion early warning solution.

[0098] The original impedance data set includes resistance value, capacitance value, and inductance value. The corrected impedance data set includes error rate, correction timestamp, and correction technology. The electrochemical change indicators include chemical kinetic parameters, current density, and potential difference. The corrosion trend prediction includes corrosion depth prediction value, prediction reliability score, and impact time range. The corrosion probability model includes probability distribution type, model validity, and sensitivity analysis. The refined risk assessment results include risk level classification, risk occurrence probability, and key influencing factors. The optimized monitoring configuration includes sensor position adjustment, data sampling frequency, and transmission efficiency. The enhanced corrosion early warning plan includes warning level setting, warning response time, and warning measure details.

[0099] See also Figure 2 , the steps for obtaining the corrected impedance data set are as follows:

[0100] Based on platinum electrode sensors, electrochemical impedance data of grounding grid nodes are collected and transmitted to the central processing unit through a dedicated line to obtain the original impedance data set. The difference between consecutive data points in the data set is calculated using the formula:

[0101]

[0102] Get the difference data set, where Indicates the data points, Indicates the data points, is the difference between these two data points, and is the adjustment factor, is the total number of data points, is the base of natural logarithm;

[0103] Based on the difference data set, calculate the mean and standard deviation, identify and remove outliers, and use the formula:

[0104]

[0105] The calculation generates the standard deviation of the difference, where is the difference, is the mean value of the difference data set, is the number of data points in the difference dataset, is the weight coefficient, Indicates the standard deviation of the difference results;

[0106] Based on the difference standard deviation results, a threshold is set. According to the difference data set and standard deviation, the data points in the impedance original data set are screened and corrected. The correction is performed using the formula:

[0107]

[0108] Generate a corrected impedance data set where represents the original data points, is the difference, and are the mean and standard deviation of the difference data set, is the set threshold, is the corrected data point.

[0109] The formula for establishing the difference data set is:

[0110]

[0111] This formula is used to calculate the difference of each data point in the difference data set, and the adjustment factor refers to the difference between the data points in

[0112] The position in the sequence makes the data points in the middle have less influence on the results, while the data points at the ends have a greater influence;

[0113] and : This is a continuous electrochemical impedance data point;

[0114] and : Adjustment factor, assuming and ,The parameters are determined by experimental tuning in electrochemical measurements to suit the sensitivity of the specific sensor and test conditions;

[0115] : Total number of data points, assuming , that is, the entire data set contains 100 data points;

[0116] : The index of the current data point.

[0117] Suppose that in a certain situation, , , (midpoint), the calculation formula is:

[0118]

[0119]

[0120] This result indicates that the adjusted difference between the data points at position 50 is 3.33.

[0121] Formula for the standard deviation of the difference result

[0122]

[0123] This formula is used to calculate the weighted standard deviation of a difference data set to identify and remove outliers.

[0124] : Elements in the difference dataset;

[0125] : The average value of the difference data set, assuming ;

[0126] : weight coefficient, , assuming ;

[0127] : The number of data points, assumed to be 100;

[0128] For the 50th data point, , the calculation formula is:

[0129]

[0130]

[0131] This standard deviation result is used for subsequent data correction and outlier screening.

[0132] Formula for the corrected data point:

[0133]

[0134] This formula is used to calculate the standard deviation and the set threshold to decide whether to keep the original data point.

[0135] : Difference standard deviation result;

[0136] : The average value of the difference data set, assuming ;

[0137] : is the difference between these two data points;

[0138] : threshold, assuming , represents two times the standard deviation;

[0139] For the 50th data point, if:

[0140]

[0141] Because 9.88 is greater than 2 (T value), will be marked as outliers and removed from the dataset.

[0142] See also Figure 3 , the specific steps for obtaining electrochemical change indicators are as follows:

[0143] Extract the electrochemical impedance value at each time point from the corrected impedance data set, calculate the change between adjacent time points, and refer to the measured time interval and current density through the formula:

[0144]

[0145] Generate time series change data, where Indicates time The electrochemical change energy, and are the impedance values at two consecutive time points, is the total number of data points, is the measurement interval, is the current density;

[0146] Using time series variation data and temperature adjustment coefficients, the corrosion rate is calculated with reference to the influence of ambient temperature using the formula:

[0147]

[0148] Generates the Corrosion Rate result where, is the corrosion rate, and are the electrochemical change energies at two consecutive time points, is the temperature adjustment coefficient, is the ambient temperature;

[0149] Combining the corrosion rate results, temperature and humidity, multivariate analysis was used to determine the influence of multiple factors, using the formula:

[0150]

[0151] Generate electrochemical change indicators, where is an indicator of electrochemical changes. is the corrosion rate, is the temperature, It's humidity, 、 Represent the influence weights of corrosion rate, temperature and humidity respectively.

[0152] time The formula for electrochemical change energy is:

[0153]

[0154] and Represents the time and Electrochemical impedance value;

[0155] represents the time interval of measurement;

[0156] represents the current density;

[0157] represents the total number of data points;

[0158] Assume that at time The impedance value is and time The impedance value is , measurement interval hours, current density A / , the total number of data points ;

[0159] Calculate the change in impedance:

[0160]

[0161] Adjust the amount of change based on the time interval:

[0162]

[0163] Calculate the square of the adjusted change:

[0164]

[0165] Reference current density:

[0166]

[0167] Take the average over all data points:

[0168]

[0169] A / It represents the square of the average resistance change caused by the passage of current per unit time, reflecting the dynamic changes in electrochemical properties.

[0170] The calculation formula of corrosion rate is:

[0171]

[0172] Assumed value: Use the previously obtained and the previous time point , temperature adjustment coefficient , ambient temperature ;

[0173] Calculate the energy ratio:

[0174]

[0175] Take the logarithm:

[0176]

[0177] Effect of reference temperature:

[0178]

[0179] Calculate the corrosion rate:

[0180]

[0181] Represents the adjusted corrosion rate, taking into account the effect of temperature. A larger value indicates a higher corrosion rate.

[0182] The formula of electrochemical change index:

[0183]

[0184] Hypothesized values: regression coefficients ,humidity ;

[0185] Calculate the effect of corrosion rate:

[0186]

[0187] Calculate the effect of temperature:

[0188]

[0189] Calculate the effect of humidity:

[0190]

[0191] The summation gives the electrochemical change index:

[0192]

[0193] It represents an electrochemical change indicator that combines corrosion rate, temperature, and humidity. A larger value indicates a more significant electrochemical change, indicating a higher corrosion risk.

[0194] See also Figure 4 ,The steps for obtaining the corrosion trend prediction are as follows:

[0195] Based on the electrochemical change indicators, the corrosion rate is extracted and the time series data is calculated using the formula:

[0196]

[0197] With reference to the influence of periodic factors and noise, seasonal adjustment and random disturbance are introduced to generate time series extended data, where: and Represent the corrosion rates at two consecutive time points, is the seasonal adjustment factor, is a random disturbance term, is the length of the cycle, Indicates a point in time, Represents time series data;

[0198] Based on the time series extended data, the autoregressive sliding average model is used to process the time series extended data and enhance the prediction accuracy of the model. The formula is:

[0199]

[0200] The trend analysis results are calculated, where Indicates the trend analysis results. and is the current and previous time series extended data, 、 and are model parameters, is the error term of the previous step;

[0201] Based on the trend analysis results, environmental factors are summarized and a new prediction model is used, through the formula:

[0202]

[0203] Generates corrosion trend forecasts where It is the corrosion trend prediction, is the result of trend analysis. and are the current readings of temperature and humidity respectively, 、 and Represent the influencing parameters of control trend, temperature and humidity respectively.

[0204] Time series data formula:

[0205]

[0206] :time The corrosion rate;

[0207] :time The corrosion rate;

[0208] : seasonal adjustment factor;

[0209] : random disturbance term;

[0210] : cycle length;

[0211] Derivation of the example:

[0212] Assume that within a certain period of time, the observed corrosion rate as follows:

[0213] mm / year, mm / year;

[0214] Set seasonal adjustment factor ,cycle Months, random disturbance , for time

[0215] ,calculate as follows:

[0216]

[0217]

[0218]

[0219]

[0220] This represents a seasonally adjusted corrosion rate increase of 0.055 mm / year at the second time point.

[0221] Trend analysis result formula:

[0222]

[0223] : Current extended time series data;

[0224] : previous time series data;

[0225] : autoregressive parameter;

[0226] : sliding average parameter;

[0227] : error term of the previous time step;

[0228] Assumption parameters , the error of the previous step , known and (Continue from the previous step), for :

[0229]

[0230]

[0231]

[0232] This means that according to the model, the predicted corrosion trend at the second time point is an increase of 0.0515 mm / year.

[0233] Corrosion trend prediction formula:

[0234]

[0235] : Trend analysis results;

[0236] :temperature;

[0237] :humidity;

[0238] : Adjust parameters;

[0239] Assumption parameters , , ,temperature C, humidity :

[0240]

[0241]

[0242]

[0243] This means that under the given environmental conditions, the predicted corrosion trend is an increase of 4.1992 mm / year, showing the sensitivity of the model to environmental changes.

[0244] See also Figure 5 , the steps to obtain the corrosion probability model are as follows:

[0245] The data obtained from the corrosion trend prediction is combined with external environmental factors, including temperature and humidity, through the formula:

[0246]

[0247] Introducing influencing factors and adjusting the weights of environmental factors to generate integrated data, where is the weight of the corrosion trend data, and are the adjustment coefficients for temperature and humidity, and Represents the real-time measured temperature and humidity, Indicates integrated data;

[0248] Apply the integrated data to establish a Markov chain model to predict the probability of state transition in the future time period, through the formula:

[0249]

[0250] Smooth the probability transition and generate the state transition matrix, where Indicates status To status The transition probability, and Status and The integrated data value of is a parameter that controls the transfer sensitivity;

[0251] Based on the state transition matrix, the state probability in the future time period is calculated using the formula:

[0252]

[0253] Generate a corrosion probability model where is the corrosion probability distribution in the future time period, It is through The state transition matrix after the step, is the initial state probability distribution.

[0254] Integrate the data set to build the formula:

[0255]

[0256] : Weight of corrosion trend data, assuming ;

[0257] : Temperature adjustment coefficient, assuming ;

[0258] : Adjustment factor for humidity, assuming ;

[0259] : In time The corrosion trend prediction value is assumed to be ;

[0260] : Temperature value, assuming ;

[0261] : Humidity value, assuming ;

[0262] calculate :

[0263]

[0264]

[0265]

[0266] The value Indicates time The integrated data value is used as input to the Markov chain model.

[0267] Transition probability formula:

[0268]

[0269] : Parameters controlling transfer sensitivity, assuming ;

[0270] :state In time The integrated data value of ;

[0271] :state In time The integrated data value of ;

[0272] calculate :

[0273]

[0274]

[0275]

[0276]

[0277] The transition probability Indicates time , from the state To status The transition probability.

[0278] The corrosion probability distribution formula in the future time period is:

[0279]

[0280] :go through The state transfer matrix after the step, assuming ,but ;

[0281] : Initial state probability distribution, assuming ;

[0282] calculate :

[0283]

[0284]

[0285]

[0286]

[0287]

[0288] Should represents the corrosion probability distribution at the next time step.

[0289] See also Figure 6 , the steps for obtaining risk assessment results are as follows:

[0290] Based on the corrosion probability model, the risk metric is updated using real-time corrosion rate and environmental factors, and the nonlinear weighted method is used to optimize the response sensitivity of the model. The formula is:

[0291]

[0292] Generate an updated risk metric where and is the weight factor, Indicates the current environment parameters. represents the updated risk measure.

[0293] Based on the updated risk metrics, the risk factor is calculated by combining the historical data with the results of the prediction model and using regression analysis method, through the formula:

[0294]

[0295] Generate refined risk factors, where is the refined risk factor, and is the adjustment parameter, represents the updated risk measure;

[0296] Combining the refined risk factors and key thresholds, the formula is:

[0297]

[0298] Generate detailed risk assessment results, where: is to refine the risk assessment results, Adjust the steepness of the curve, is the safety threshold, It is the refined risk factor.

[0299] Updated risk measurement formula:

[0300]

[0301] : Weight parameters, determined based on historical data analysis or expert opinions;

[0302] : The risk measurement result at the previous moment, assumed to be 0.5 (50% risk probability);

[0303] : Current environmental parameters, such as corrosion rate or environmental variables, assumed to be 10;

[0304] Assumptions ;

[0305] calculate :

[0306]

[0307]

[0308]

[0309]

[0310] This value should be normalized to the range [0, 1] and indicates that weights or parameters need to be rescaled to make the model more realistic.

[0311] Refined risk factor formula:

[0312]

[0313] : The risk measure from the previous formula, using the results of the above example;

[0314] : Adjustment parameters, Control the steepness of the curve, is the risk threshold, assuming and ;

[0315] calculate :

[0316]

[0317]

[0318]

[0319] This value should be normalized to indicate a high risk and the threshold needs to be adjusted to match the actual situation.

[0320] Refine the risk assessment result formula:

[0321]

[0322] : Risk factor from the previous formula;

[0323] : Adjustment parameters, Control the steepness of the curve, Assume that and ;

[0324] calculate :

[0325]

[0326]

[0327]

[0328]

[0329] This value means that under the given conditions, the risk assessment result is 26.8%, indicating a medium risk.

[0330] See also Figure 7 , the steps to obtain the optimized monitoring configuration are as follows:

[0331] In order to refine the risk assessment results, adjust the data capture frequency of the platinum electrode sensor, refer to the sensitivity of the risk assessment and the environmental impact factor, and use the formula:

[0332]

[0333] Get the adjusted capture frequency, where Indicates the adjusted capture frequency, is the frequency-adjusted baseline coefficient, To regulate environmental factors, is the environmental benchmark threshold, Indicates the results of the detailed risk assessment;

[0334] According to the adjusted capture frequency, the voltage and current parameters of the platinum electrode sensor are optimized by the formula:

[0335]

[0336] The optimized sensor parameters are obtained, where are the optimized sensor parameters, and are the adjustment coefficients of voltage and current respectively, represents the adjusted capture frequency;

[0337] Combine the adjusted capture frequency and optimized sensor parameters to form a monitoring configuration and optimize the configuration using the formula:

[0338]

[0339] The optimized monitoring configuration is obtained, where is the optimized monitoring configuration, and is the coefficient that integrates the effects of frequency and parameter settings, Indicates the adjusted capture frequency, are the optimized sensor parameters.

[0340] Adjusted capture frequency formula:

[0341]

[0342] : Adjusted capture frequency

[0343] : Baseline coefficient for frequency adjustment

[0344] :Influence coefficient of environmental factors

[0345] : Refine risk assessment results

[0346] : Environmental benchmark threshold

[0347] Assume the following specific values: (Risk assessment result, assuming this is a score obtained from a risk model, indicating medium risk), Hz (baseline frequency, typical settings based on the device), (Environmental impact coefficient, more sensitive), (environmental benchmark threshold, assuming average risk level);

[0348] calculate :

[0349]

[0350] Final Hz represents the adjusted data capture frequency, adapted to the current risk environment.

[0351] Optimized sensor parameter formula:

[0352]

[0353] : Optimized sensor parameters;

[0354] : voltage regulation coefficient;

[0355] : Current adjustment coefficient;

[0356] : The adjusted capture frequency (obtained from step 1);

[0357] Assumptions: ;

[0358] calculate :

[0359]

[0360]

[0361] here Indicates the new parameter settings of the sensor to better adapt to the current frequency and environmental changes.

[0362] Optimized monitoring configuration formula:

[0363]

[0364] : Optimized monitoring configuration;

[0365] : parameter integration coefficient;

[0366] : frequency integration coefficient;

[0367] : Optimized sensor parameters;

[0368] : Adjusted capture frequency;

[0369] Assumptions: , ;

[0370] calculate :

[0371]

[0372] here It is the final monitoring configuration parameter, which integrates the adjustment of sensor parameters and capture frequency.

[0373] See also Figure 8 , the steps to obtain the enhanced corrosion early warning solution are as follows:

[0374] Under the guidance of the optimized monitoring configuration, the platinum electrode sensors are redeployed to cover the key corrosion areas, using the formula:

[0375]

[0376] Get the sensor deployment efficiency, where represents the efficiency factor of sensor deployment, is the environmental sensitivity adjustment coefficient, represents the area of the monitoring area, and is the distance adjustment parameter, Indicates adjustment distance;

[0377] Based on the sensor deployment efficiency, the frequency and parameters of data transmission are adjusted according to the redeployed platinum electrode sensor positions, using the formula:

[0378]

[0379] Get the optimized data transmission frequency, where is the data transmission frequency, is the transmission efficiency coefficient, and is the regulating factor, represents the efficiency factor of sensor deployment;

[0380] Combined with the optimized data transmission frequency and sensor configuration, an enhanced corrosion early warning solution is generated using the formula:

[0381]

[0382] in, Is to enhance the corrosion early warning program, and is the integration parameter, Indicates monitoring configuration, is the data transmission frequency.

[0383] The formula for the efficiency factor of sensor deployment is:

[0384]

[0385] : Environmental sensitivity adjustment coefficient, which depends on the sensitivity of the sensor to environmental changes;

[0386] : Area of the monitoring area (square meters);

[0387] : distance adjustment parameter, which affects the effect of distance on efficiency;

[0388] : Baseline adjustment coefficient, which ensures that the denominator is not zero and also adjusts the basic efficiency level;

[0389] : distance from the monitoring center to the sensor (meters);

[0390] Assumptions: , square meters, , , rice;

[0391] calculate :

[0392]

[0393] this A higher value indicates that the sensor deployment is more efficient, taking into account distance and environmental factors.

[0394] Data transmission frequency formula:

[0395]

[0396] : transmission efficiency coefficient;

[0397] : Adjustment coefficient to ensure the denominator is non-zero;

[0398] :adjust right The coefficient of influence;

[0399] Assumptions: , , , using the previous step ;

[0400] calculate :

[0401]

[0402] this A higher value indicates a higher data transmission frequency, which is suitable for real-time data monitoring of sensors.

[0403] Formula for enhanced corrosion early warning scheme:

[0404]

[0405] :comprehensive The coefficient of influence;

[0406] :adjust Impact on early warning programmes;

[0407] :Monitoring configuration index, not detailed above, here assume ;

[0408] Assumptions: , , using the previous step ;

[0409] calculate :

[0410]

[0411]

[0412] this The values represent the enhanced corrosion early warning capability, combining the comprehensive effect of transmission frequency and monitoring configuration.

[0413] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent monitoring system for grounding grid corrosion, characterized in that: The system comprises: The electrochemical data capture module collects electrochemical impedance data from grounding grid nodes based on platinum electrode sensors, transmits the data to a central processing unit via a dedicated line, and obtains an impedance raw data set. Based on the impedance raw data set, data errors and outliers are filtered and corrected to obtain a corrected impedance data set. The corrosion behavior analysis module detects dynamic changes in electrochemical properties based on the calibrated impedance data set, calculates the corrosion rate and the influence of environmental factors on the corrosion behavior, generates an electrochemical change index, and performs time series analysis on the electrochemical impedance data based on the electrochemical change index to obtain a corrosion trend prediction; The steps for obtaining the corrosion trend prediction are specifically as follows: Based on the electrochemical change indicators, the corrosion rate is extracted and the time series data is calculated using the formula: ； With reference to the influence of periodic factors and noise, seasonal adjustment and random disturbance are introduced to generate time series extended data, where: and Represent the corrosion rates at two consecutive time points, is the seasonal adjustment factor, is a random disturbance term, is the length of the cycle, Indicates a point in time, Represents time series data; Based on the time series extended data, the autoregressive moving average model is used to process the time series extended data and enhance the prediction accuracy of the model. The formula is: ； The trend analysis results are calculated, where Indicates the trend analysis results. and is the current and previous time series extended data, 、 and are model parameters, is the error term of the previous step; Based on the trend analysis results, environmental factors are summarized and a new prediction model is used, using the formula: ； Generates corrosion trend forecasts where It is the corrosion trend prediction, and are the current readings of temperature and humidity respectively, 、 and represent the influencing parameters of control trend, temperature and humidity respectively; The corrosion probability assessment module establishes a random change model through a Markov chain based on the corrosion trend prediction, simulates the corrosion process in the future time period, obtains a corrosion probability model, and updates and optimizes the probability distribution based on the corrosion probability model to obtain a refined risk assessment result; The steps for obtaining the corrosion probability model are specifically as follows: The data obtained from the corrosion trend prediction is combined with external environmental factors, including temperature and humidity, through the formula: ； Introducing influencing factors and adjusting the weights of environmental factors to generate integrated data, where is the weight of the corrosion trend data, and are the adjustment coefficients for temperature and humidity, and Represents the real-time measured temperature and humidity, Indicates integrated data; The integrated data is used to establish a Markov chain model to predict the probability of state transition in the future time period, using the formula: ； Smooth the probability transition and generate the state transition matrix, where Indicates status To status The transition probability, and Status and The integrated data value of is a parameter that controls the transfer sensitivity; Based on the state transition matrix, the state probability in the future time period is calculated using the formula: ； Generate a corrosion probability model where is the corrosion probability distribution in the future time period, It is through The state transition matrix after the step, is the initial state probability distribution; The steps for obtaining the detailed risk assessment results are as follows: Based on the corrosion probability model, the risk metric is updated using real-time corrosion rate and environmental factors, and the nonlinear weighted method is used to optimize the response sensitivity of the model. The formula is: ； Generate an updated risk metric where and is the weight factor, Indicates the current environment parameters. represents the updated risk measure; Based on the updated risk metric, the risk factor is calculated by combining the historical data with the results of the prediction model using regression analysis method, through the formula: ； Generate refined risk factors, where is the refined risk factor, and is the adjustment parameter; Combining the refined risk factors and key thresholds, the formula is: ； Generate detailed risk assessment results, where: is to refine the risk assessment results, Adjust the steepness of the curve, is the safety threshold; The monitoring strategy optimization module adjusts the monitoring strategy and platinum electrode sensors based on the refined risk assessment results, optimizes the data capture frequency and parameters, and obtains an optimized monitoring configuration. Based on the optimized monitoring configuration, the platinum electrode sensors are redeployed and the data transmission settings are adjusted to obtain an enhanced corrosion early warning solution.

2. The intelligent monitoring system for grounding grid corrosion according to claim 1 is characterized in that: The steps for obtaining the correction impedance data set are specifically as follows: Based on platinum electrode sensors, electrochemical impedance data of grounding grid nodes are collected and transmitted to the central processing unit through a dedicated line to obtain the original impedance data set. The difference between consecutive data points in the data set is calculated using the formula: ； Get the difference data set, where Indicates the data points, Indicates the data points, is the difference between these two data points, and is the adjustment factor, is the total number of data points, is the base of natural logarithm; Based on the difference data set, calculate the mean and standard deviation, identify and remove outliers, and use the formula: ； The calculation generates the standard deviation of the difference, where is the mean value of the difference data set, is the number of data points in the difference dataset, is the weight coefficient, Indicates the standard deviation of the difference results; Based on the difference standard deviation results, a threshold is set, and the data points in the impedance original data set are screened and corrected according to the difference data set and the standard deviation, and the correction is performed using the formula: ； Generate a corrected impedance data set where represents the original data points, is the difference, is the set threshold, is the corrected data point.

3. The intelligent monitoring system for grounding grid corrosion according to claim 2 is characterized in that: The steps for obtaining the electrochemical change index are specifically as follows: The electrochemical impedance value at each time point was extracted from the corrected impedance data set, and the change between adjacent time points was calculated, and with reference to the measured time interval and current density, the equation was used: ； Generate time series change data, where Indicates time The electrochemical change energy, and are the impedance values at two consecutive time points, is the total number of data points, is the measurement interval, is the current density; Using the time series variation data and the temperature adjustment coefficient, the corrosion rate is calculated with reference to the influence of the ambient temperature using the formula: ； Generates the Corrosion Rate result where, is the corrosion rate, It's time The electrochemical change energy, is the temperature adjustment coefficient, is the ambient temperature; Combining the corrosion rate results, temperature and humidity, multivariate analysis was performed to determine the influence of multiple factors, using the formula: ； Generate electrochemical change indicators, where is an indicator of electrochemical changes. is the temperature, It's humidity, 、 Represent the influence weights of corrosion rate, temperature and humidity respectively.

4. The intelligent monitoring system for grounding grid corrosion according to claim 1, characterized in that: The steps for obtaining the optimized monitoring configuration are specifically as follows: According to the refined risk assessment results, the data capture frequency of the platinum electrode sensor is adjusted, and the sensitivity of the risk assessment and the environmental impact factor are referred to, through the formula: ； Get the adjusted capture frequency, where Indicates the adjusted capture frequency, is the frequency-adjusted baseline coefficient, To regulate environmental factors, is the environmental benchmark threshold, Indicates the results of the detailed risk assessment; According to the adjusted capture frequency, the voltage and current parameters of the platinum electrode sensor are optimized by the formula: ； The optimized sensor parameters are obtained, where are the optimized sensor parameters, and are the adjustment coefficients of voltage and current respectively; Combining the adjusted capture frequency and optimized sensor parameters, a monitoring configuration is formed and optimized using the formula: ； The optimized monitoring configuration is obtained, where is the optimized monitoring configuration, and is the coefficient that integrates the effects of frequency and parameter settings, Indicates the adjusted capture frequency.

5. The intelligent monitoring system for grounding grid corrosion according to claim 4 is characterized in that: The steps for obtaining the enhanced corrosion early warning scheme are specifically as follows: Under the guidance of the optimized monitoring configuration, the platinum electrode sensors are redeployed to cover the key corrosion areas, using the formula: ； Get the sensor deployment efficiency, where represents the efficiency factor of sensor deployment, is the environmental sensitivity adjustment coefficient, represents the area of the monitoring area, and is the distance adjustment parameter, Indicates adjustment distance; Based on the sensor deployment efficiency, the frequency and parameters of data transmission are adjusted according to the redeployed platinum electrode sensor positions, using the formula: ； Get the optimized data transmission frequency, where is the data transmission frequency, is the transmission efficiency coefficient, and is a regulating factor; Combining the optimized data transmission frequency and sensor configuration, an enhanced corrosion early warning scheme is generated using the formula: ； in, Is to enhance the corrosion early warning program, and is the integration parameter, Indicates monitoring configuration.

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