Tower corrosion safety assessment system and method thereof

Abstract

The invention discloses a tower corrosion safety assessment system and method. The system comprises a data analysis module, a safety assessment module and an early warning module. The data analysis module is used for carrying out preprocessing, feature extraction, classification evaluation and integrated analysis on the obtained tower related data; the data preprocessing unit comprises a denoising module, a normalization module and a missing value module; and comprehensive data analysis: carrying out deep mining and comprehensive analysis on tower corrosion related data through a plurality of links such as data preprocessing, feature extraction, classification evaluation and integrated analysis, and improving the accuracy and comprehensiveness of evaluation. And multi-dimensional assessment: atmospheric corrosion, soil corrosion and chemical corrosion are assessed respectively, the influence of various corrosion factors is comprehensively considered, and the actual corrosion condition of the tower can be reflected more accurately. According to the scientific assessment method, corrosion rate calculation and risk assessment are carried out by adopting various mathematical models and formulas, and a scientific basis is provided for safe operation of electric power facilities.

Description

Technical Field

[0001] The present invention relates to the technical field of power facility safety assessment, and in particular to a tower corrosion safety assessment system and method thereof. Background Art

[0002] In the power system, pole towers are important supporting structures for transmission lines, and their safety performance is of vital importance. However, due to long-term exposure to the natural environment, pole towers are susceptible to various corrosion factors, such as atmospheric corrosion, soil corrosion, chemical corrosion, etc. Corrosion can lead to reduced strength and structural deformation of pole towers, which seriously threatens the stable operation of the power system.

[0003] At present, the assessment of tower corrosion mainly has the following deficiencies and omissions:

[0004] Single evaluation method: Existing evaluation schemes often use a single indicator or method for evaluation, which lacks comprehensiveness and accuracy. For example, judging the corrosion of the tower only by visual inspection or simple instrument measurement cannot accurately reflect the overall corrosion condition of the tower.

[0005] Lack of real-time performance: Traditional assessment methods usually involve regular inspections, which cannot monitor the corrosion changes of poles and towers in real time, making it difficult to detect potential safety hazards in a timely manner.

[0006] Insufficient data integration and analysis: The existing assessment schemes do not integrate and analyze the acquired tower-related data deeply enough, and cannot fully tap the potential information in the data, making it difficult to make a scientific and accurate assessment.

[0007] Imperfect early warning mechanism: After the tower corrosion problem is discovered, there is a lack of an effective early warning mechanism, and relevant personnel cannot be notified in time to take measures, which may cause the problem to deteriorate further. Summary of the invention

[0008] The purpose of the present invention is to overcome the deficiencies of the prior art. To achieve the above purpose, the present invention adopts the following technical solutions:

[0009] A tower corrosion safety assessment system, comprising a data analysis module, a safety assessment module and an early warning module;

[0010] The data analysis module is used to perform preprocessing, feature extraction, classification evaluation and integrated analysis on the acquired tower-related data;

[0011] The data preprocessing unit includes a denoising module, a normalization module and a missing value module processing;

[0012] The denoising module processes the original data of the tower corrosion to reduce the interference to the subsequent analysis, which specifically includes mean filtering, median filtering and Gaussian filtering:

[0013] The feature extraction unit can effectively predict or diagnose the corrosion condition of the tower by extracting features related to corrosion;

[0014] Mean: describes the overall level of corrosion data and reflects the average state of long-term corrosion. The expression is:

[0015] [\mu=\frac{1}{n}\sum_{i=1}^{n}x(i)]

[0016] (\mu) is the mean;

[0017] (x(i)) is the eroded data point.

[0018] Specifically, the mean value of the tower surface potential reflects the long-term change in corrosion potential. The larger the mean value, the higher the corrosion potential.

[0019] Variance is used to measure the degree of discreteness of data. The larger the variance, the greater the fluctuation of corrosion data, and there may be sudden corrosion. The expression is:

[0020] [\s igma^2=\frac{1}{n}\sum_{i=1}^{n}(x(i)-\mu)^2]

[0021] (\s igma^2) is the variance;

[0022] It includes the fluctuation of potential or current at different time periods. The variance is used to measure the severity of the change. A larger variance may indicate that corrosion is accelerating.

[0023] The standard deviation is the square root of the variance, reflecting the fluctuation range of the data. The expression is:

[0024] [\s igma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x(i)-\mu)^2}]

[0025] The standard deviation is used to determine the stability of the corrosion state. A larger standard deviation indicates unstable data.

[0026] Maximum and minimum values:

[0027] The maximum value indicates the peak value in the corrosion process, while the minimum value indicates the valley value. The expression is:

[0028] [x_{\text{max}}=\max(x(i)),\quad x_{\text{min}}=\min(x(i))]

[0029] (x_{\text{max}}) and (x_{\text{min}}) are the maximum and minimum values ​​of the data respectively;

[0030] The maximum value of corrosion current may correspond to the peak period of corrosion rate, and the minimum value may correspond to the slow corrosion period;

[0031] Time domain characteristics are used to reflect the temporal variation trend of corrosion data;

[0032] Peak value: reflects the maximum amplitude of corrosion current or voltage; the specific expression is:

[0033] [A_p=\max(x(i))]

[0034] (A_p) is the peak value;

[0035] (x(i)) is the corrosion data point;

[0036] The peak value of the voltage on the tower surface is related to local corrosion and is used to identify the corrosion point;

[0037] Mean absolute value: used to reflect the overall amplitude of the signal; the expression is:

[0038] [A_{\text{mean}}=\frac{1}{n}\sum_{i=1}^{n}|x(i)|]

[0039] (A_{\text{mean}}) is the absolute value of the mean;

[0040] (x(i)) is the corrosion data point;

[0041] The mean absolute value of the tower surface current is used to identify the overall corrosion trend, and a larger trend indicates an increase in the corrosion rate;

[0042] (3) Classification and evaluation unit The classification and evaluation unit is designed to classify and evaluate the corrosion environment of the tower according to different characteristic parameters. The classification and evaluation unit mainly uses the following methods for classification and evaluation:

[0043] ① Atmospheric corrosion assessment objectives:

[0044] Evaluate the data related to atmospheric corrosion, analyze the effects of humidity, temperature and pollutants in the atmosphere on tower corrosion, and calculate the atmospheric corrosion rate and extent in combination with the atmospheric corrosion model;

[0045] Collect environmental data related to atmospheric corrosion; you can use data recorded by weather stations, data provided by environmental monitoring stations, or install sensors yourself to obtain real-time data.

[0046] Atmospheric Corrosion Model Calculation

[0047] Input the preprocessed environmental data into the formula of the atmospheric corrosion model:

[0048] [C=A\times t^n]

[0049] The coefficient A is related to factors such as humidity, temperature, and pollutant concentration in the atmosphere, and can be determined by looking up a table or empirical formula. The index n is usually between 0.6 and 1.

[0050] Corrosion rate calculation and evaluation

[0051] The atmospheric corrosion model is used to calculate the annual corrosion rate (μm / year) of the tower surface, and the corrosion rates in different areas are compared and analyzed.

[0052] ②Soil corrosion assessment objectives:

[0053] Evaluate data related to soil corrosion, analyze the effects of soil resistivity, pH, and water content on the corrosion of the tower foundation; use soil corrosion models to calculate corrosion rates and assess corrosion risks;

[0054] Soil data collection

[0055] Collect soil samples around the tower foundation and measure key parameters;

[0056] Soil Corrosion Rate Calculation

[0057] Input the collected soil data into the formula of soil erosion model:

[0058] [R=\frac{2\pi a V}{I}]

[0059] Where R represents soil resistivity. Soil with lower resistivity is more corrosive. Soil resistivity is inversely proportional to corrosion rate. The corrosion rate is calculated using existing empirical formulas or standards in combination with actual soil data.

[0060] Corrosion Assessment and Grading

[0061] Based on parameters such as resistivity, pH value, and water content, soil corrosion standards are used to assess the level of soil corrosion environment, including mild, moderate, and severe corrosion;

[0062] ③Chemical corrosion assessment objectives:

[0063] Evaluate data related to chemical corrosion and analyze the impact of chemical substances around the tower on corrosion. Use chemical corrosion models to calculate chemical corrosion rates and assess corrosion risks;

[0064] Chemical Environmental Data Collection

[0065] The focus is on collecting chemical environmental data, including:

[0066] Acid rain data: Obtain the pH value of acid rain through environmental monitoring, especially focusing on acid rain with pH < 5.5;

[0067] Industrial waste gas data: Evaluate sulfur dioxide (SO 2 ), nitrogen oxides (NO x ) and other gases, and analyze the distribution of these pollutants in combination with wind speed and direction data;

[0068] Chemical sediments: Collect sediments from the soil or water near the tower to detect whether they contain corrosive chemicals.

[0069] Data preprocessing and classification

[0070] Preprocess and classify chemical data such as acid rain and industrial waste gas to assess the concentration of various pollutants and their potential impact on corrosion;

[0071] Chemical corrosion rate calculation

[0072] According to the formula of chemical corrosion model:

[0073] [C_{\text{chem}}=k\t imes\left(\frac{1}{\text{pH}}\r ight)

[0074] \t imes t]

[0075] Where k is a constant related to the chemical environment and materials, determined by experiments or experience; the lower the pH value, the higher the corrosion rate; the chemical corrosion rate is calculated based on actual data;

[0076] Corrosion Assessment and Protection Measures

[0077] Assess the corrosion risk of the tower based on the chemical corrosion rate and provide corresponding protective measures;

[0078] (4) Integrated analysis unit

[0079] The integrated analysis is to integrate the results of the classified assessment, comprehensively consider the influence of various corrosion factors, and obtain the overall corrosion status assessment of the tower;

[0080] The safety assessment module is used to assess the corrosion safety status of the tower and classify the risk level according to the overall corrosion status assessment results obtained by the data analysis module;

[0081] The early warning module is used to issue corresponding early warning information according to the corrosion risk level of the tower, so that relevant personnel can take timely measures to reduce the operating risks of power facilities.

[0082] Furthermore, the mean filter is used for data with slow fluctuations caused by external factors such as temperature and humidity. The data is smoothed by taking the average value of the data in the data window. The expression is:

[0083] y(i)=\frac{1}{n}\sum_{k=-(n-1) / 2}^{(n-1) / 2}x(i+k)

[0084] y(i) is the filtered data point;

[0085] x(i+k) is the original data point;

[0086] n is the size of the filter window (i.e., the number of data points contained in the window);

[0087] (\frac{1}{n}) is a normalization coefficient used to ensure the average value of all data points in the window. It represents the contribution of each data point in calculating the mean, and the sum of the contributions of all data points is equal to 1;

[0088] Median filtering removes isolated sharp noise points by selecting the median of the data in the window while maintaining the edge characteristics of the signal;

[0089] The expression is:

[0090] [y(i)=\text{med ian}(x(i-(n-1) / 2),\ldots,x(i+(n-1) / 2))]

[0091] n is the window size;

[0092] \text{med ian} means taking the median of all data in the window;

[0093] (x(i-(n-1) / 2)) to (x(i+(n-1) / 2)) represent a series of values ​​of the original data point within a window range;

[0094] (n) is the size of the filter window (usually an odd number, such as 3, 5, 7, etc.);

[0095] “\ldots” represents a continuous range of all data points in the window;

[0096] Therefore, "(\ldots)" represents all data points from the starting point (x(i-(n-1) / 2)) to the ending point (x(i+(n-1) / 2)) within a given window range; the median of these data points (i.e. the value in the middle after sorting) is used to calculate the new filtered data point (y(i));

[0097] Gaussian filtering performs weighted averaging on the data, with weights allocated according to Gaussian distribution, to smooth out high-frequency noise; the specific expression is:

[0098] [y(i)=\frac{1}{\sqrt{2\pi\s igma^2}}\sum_{k=-(n-1) / 2}^{(n-1) / 2}x(i+k)e^{-\frac{k^2}{2\s igma^2}}]

[0099] \s igma is the standard deviation of the Gaussian distribution, which controls the smoothness of the filter;

[0100] x(i+k) is the original data point.

[0101] Furthermore, the normalization includes:

[0102] Min-max normalization:

[0103] Scale the data to the range of [0,1]. The specific expression is:

[0104] [x'=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}]

[0105] (x) is the original data;

[0106] (x_{\text{min}}) and (x_{\text{max}}) are the minimum and maximum values ​​of the data, respectively.

[0107] Z-score normalization:

[0108] Standardize the data to ensure that the data follows a normal distribution, with a mean of 0 and a standard deviation of 1. The specific expression is:

[0109] [x'=\frac{x-\mu}{\s igma}]

[0110] (\mu) is the mean of the data;

[0111] (\s igma) is the standard deviation of the data.

[0112] Furthermore, the specific expression of the interpolation method is:

[0113] [x(i)=x(i-1)+\frac{x(i+1)-x(i-1)}{2}]

[0114] (x(i)) is the missing point;

[0115] (x(i-1)) and (x(i+1)) are adjacent data points.

[0116] Use the global mean of the data to fill missing values;

[0117] formula:

[0118] [x(i)=\frac{1}{n}\sum_{j=1}^{n}x(j)]

[0119] (x(i)) is a missing value;

[0120] (x(j)) are other data points.

[0121] In the case of multiple experiments or data aggregation of multiple sensors, the mean filling method can be used to fill in the short-term missing corrosion data; the specific expression is:

[0122] [x(i)=x(i-1)+\frac{x(i+1)-x(i-1)}{2}]

[0123] (x(i)) is the missing point;

[0124] (x(i-1)) and (x(i+1)) are adjacent data points;

[0125] Use the global mean of the data to fill in missing values; the specific expression is:

[0126] [x(i)=\frac{1}{n}\sum_{j=1}^{n}x(j)]

[0127] (x(i)) is a missing value;

[0128] (x(j)) are other data points;

[0129] In the case of multiple experiments or data aggregation from multiple sensors, the mean filling method can be used to fill in the short-term missing corrosion data.

[0130] A tower corrosion safety assessment method, the specific steps are as follows:

[0131] (1) Data collection and preprocessing

[0132] Obtain data related to tower corrosion through existing databases, monitoring systems and other channels.

[0133] 1) The denoising module in the data preprocessing unit processes the original data and can use mean filtering, median filtering, and Gaussian filtering methods to reduce noise interference in the data;

[0134] 2) Feature extraction

[0135] Statistical characteristics include mean, variance, standard deviation, maximum value, and minimum value;

[0136] 3) Classification evaluation

[0137] Atmospheric corrosion assessment: Collect environmental data related to atmospheric corrosion;

[0138] The pre-processed environmental data is input into the atmospheric corrosion model formula, where the coefficient A is related to factors such as humidity, temperature, and pollutant concentration in the atmosphere, and the index n is usually between 0.6 and 1, to calculate the atmospheric corrosion rate and degree;

[0139] The atmospheric corrosion model is used to calculate the annual corrosion rate (μm / year) of the tower surface, and the corrosion rates in different areas are compared and analyzed;

[0140] Soil corrosion assessment: Collect soil samples around the tower foundation and measure key parameters;

[0141] The collected soil data is input into the soil corrosion model formula to calculate the soil resistivity R. The soil with lower resistivity is more corrosive.

[0142] Based on parameters such as resistivity, pH value, and water content, soil corrosion standards are used to assess the level of soil corrosion environment, including mild, moderate, and severe corrosion;

[0143] Chemical Corrosion Assessment:

[0144] The focus is on collecting chemical environmental data, including acid rain data (obtaining the pH value of acid rain through environmental monitoring, especially focusing on acid rain with pH < 5.5), industrial waste gas data (assessing sulfur dioxide (SO 2 ), nitrogen oxides (NO x ) and other gas concentrations, and analyze the distribution of these pollutants in combination with wind speed and direction data), chemical sediments (collect sediments in the soil or water near the tower to detect whether they contain corrosive chemicals);

[0145] Preprocess and classify chemical data such as acid rain and industrial waste gas to assess the concentration of various pollutants and their potential impact on corrosion;

[0146] According to the chemical corrosion model formula, where k is a constant related to the chemical environment and material, determined by experiment or experience; calculate the chemical corrosion rate.

[0147] Assess the corrosion risk of the tower based on the chemical corrosion rate and provide corresponding protective measures;

[0148] 4) Integrated analysis

[0149] The integrated analysis unit integrates and analyzes the results of the classified assessment, comprehensively considers the influence of various corrosion factors, and obtains an overall corrosion status assessment of the tower;

[0150] (2) Safety assessment module, which assesses the corrosion safety status of the tower based on the overall corrosion status assessment results obtained by the data analysis module;

[0151] Risk level classification

[0152] The safety assessment module classifies the corrosion risks of poles and towers according to the assessment results, into three levels: high risk, medium risk and low risk.

[0153] (3) Early warning module, which issues corresponding early warning information according to the corrosion risk level of the tower. The early warning level can be divided into three levels: red warning, orange warning and yellow warning, which correspond to the three corrosion risk levels of high risk, medium risk and low risk respectively. The early warning information can be sent to relevant personnel via SMS, email and mobile phone APP.

[0154] The advantage of the present invention is that it provides a pole tower corrosion safety assessment system and method thereof, and comprehensive data analysis: through multiple links such as data preprocessing, feature extraction, classification assessment and integrated analysis, in-depth mining and comprehensive analysis of pole tower corrosion related data are carried out, thereby improving the accuracy and comprehensiveness of the assessment.

[0155] Multi-dimensional assessment: Atmospheric corrosion, soil corrosion and chemical corrosion are assessed separately, and the influence of various corrosion factors is comprehensively considered to more accurately reflect the actual corrosion condition of the tower.

[0156] Scientific assessment methods: A variety of mathematical models and formulas are used to calculate corrosion rates and conduct risk assessments, providing a scientific basis for the safe operation of power facilities.

[0157] Perfect early warning mechanism: timely issue early warning information according to the corrosion risk level of the tower, improving the safety and reliability of power facilities. DETAILED DESCRIPTION

[0158] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with 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.

[0159] Those skilled in the art should understand that these implementations are only used to explain the technical principles of the present invention, and are not intended to limit the protection scope of the present invention.

[0160] A tower corrosion safety assessment system, comprising a data analysis module, a safety assessment module and an early warning module;

[0161] Data Analysis Module

[0162] The data analysis module is the core part of the entire system. It mainly performs preprocessing, feature extraction, classification evaluation and integrated analysis on the acquired tower-related data to provide accurate data support for subsequent safety assessment and early warning.

[0163] (1) Data preprocessing unit

[0164] Data Acquisition

[0165] Data related to tower corrosion are obtained through existing databases, monitoring systems and other channels. These data include material properties, structural forms, geographical locations, meteorological conditions, soil parameters, and corrosion monitoring data of towers.

[0166] Data analysis

[0167] (1) Data preprocessing

[0168] 1. Data preprocessing unit Data preprocessing is a key step to ensure that tower corrosion data can be accurately and cleanly input into the model. Common preprocessing methods include denoising, normalization and missing value processing.

[0169] Denoising The raw data of tower corrosion (such as image data, sensor data, etc.) are usually accompanied by certain noise, which may come from environmental interference, sensor instability, etc. Denoising can reduce the interference to subsequent analysis and improve the accuracy of feature extraction.

[0170] Mean filter:

[0171] Smoothing the data by averaging the data within the data window is suitable for dealing with noise with slowly changing trends.

[0172] formula:

[0173] y(i)=\frac{1}{n}\sum_{k=-(n-1) / 2}^{(n-1) / 2}x(i+k)

[0174] y(i) is the filtered data point;

[0175] x(i+k) is the original data point;

[0176] n is the size of the filter window (i.e. the number of data points contained in the window).

[0177] (\frac{1}{n}) is a normalization coefficient used to ensure the average value of all data points in the window. It represents the contribution of each data point in calculating the mean, and the sum of the contributions of all data points is equal to 1. Through this coefficient, mean filtering can effectively smooth out noise and make data fluctuations smoother.

[0178] Application to corrosion data: Mean filtering is suitable for data with slow fluctuations caused by external factors such as temperature and humidity. For example, the resistance data collected from towers under different climatic conditions will fluctuate slowly due to environmental influences, and these fluctuations can be reduced through mean filtering.

[0179] Median filter:

[0180] Median filtering is mainly used to remove sharp, isolated noise, such as individual outliers, which are often found in sudden disturbances in sensor data.

[0181] formula:

[0182] [y(i)=\text{med ian}(x(i-(n-1) / 2),\ldots,x(i+(n-1) / 2))]

[0183] n is the window size;

[0184] \text{med ian} means taking the median of all data in the window.

[0185] (x(i-(n-1) / 2)) to (x(i+(n-1) / 2)) represent a series of values ​​of the original data point within a window range;

[0186] (n) is the size of the filter window (usually an odd number, such as 3, 5, 7, etc.);

[0187] "\ldots" represents a continuous range of all data points in the window.

[0188] Therefore, "(\ldots)" represents all data points from the starting point (x(i-(n-1) / 2)) to the ending point (x(i+(n-1) / 2)) within a given window. The median of these data points (i.e., the value in the middle after sorting) is used to calculate the new filtered data point (y(i)). The median filter removes isolated sharp noise points by selecting the median of the data in the window while maintaining the edge characteristics of the signal.

[0189] Applied to corrosion data: For example, when monitoring the voltage and current of a tower, occasional high or low values ​​may be caused by sensor failure and other reasons. Median filtering can be used to eliminate these abnormal values ​​and maintain the stability of the data.

[0190] Gaussian filtering:

[0191] Gaussian filtering performs weighted averaging on data, with weights distributed according to Gaussian distribution, which is suitable for smoothing high-frequency noise. The specific expression is:

[0192] [y(i)=\frac{1}{\sqrt{2\pi\s igma^2}}\sum_{k=-(n-1) / 2}^{(n-1) / 2}x(i+k)e^{-\frac{k^2}{2\s igma^2}}]

[0193] \s igma is the standard deviation of the Gaussian distribution, which controls the smoothness of the filter;

[0194] x(i+k) is the original data point.

[0195] Applied to corrosion data: For example, the slight noise in the tower corrosion image can be smoothed by Gaussian filtering, making the edge of the corrosion area clearer.

[0196] 1.2 Normalization Corrosion data usually have different dimensions, such as corrosion current, voltage, ambient temperature, etc. Their value ranges vary greatly. Directly using these data will cause the model training to be biased towards certain features, so the data needs to be normalized.

[0197] Min-max normalization:

[0198] The data is scaled to the range of [0,1] to eliminate the influence of different dimensions. The specific expression is:

[0199] [x'=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}]

[0200] (x) is the original data;

[0201] (x_{\text{min}}) and (x_{\text{max}}) are the minimum and maximum values ​​of the data, respectively.

[0202] Applied to corrosion data: such as normalization of corrosion depth and surface potential index of towers, so that data of different dimensions can be compared on the same scale.

[0203] Z-score normalization:

[0204] Standardize the data to ensure that the data follows a normal distribution, with a mean of 0 and a standard deviation of 1. The specific expression is:

[0205] [x'=\frac{x-\mu}{\s igma}]

[0206] (\mu) is the mean of the data;

[0207] (\s igma) is the standard deviation of the data.

[0208] For example, fluctuations in environmental monitoring data (temperature, humidity) can be normalized using the Z-score to facilitate model analysis of corrosion severity.

[0209] Missing Value Processing The corrosion monitoring data of the tower may be missing due to equipment failure or other reasons. These missing values ​​will affect the subsequent model training. Common processing methods are:

[0210] Interpolation method:

[0211] Linear interpolation using adjacent data points is the simplest interpolation method.

[0212] formula:

[0213] [x(i)=x(i-1)+\frac{x(i+1)-x(i-1)}{2}]

[0214] (x(i)) is the missing point;

[0215] (x(i-1)) and (x(i+1)) are adjacent data points.

[0216] When the sensor fails occasionally, the monitored resistance and potential values ​​can be filled in through interpolation to ensure data continuity.

[0217] Mean filling method:

[0218] Use the global mean of the data to fill missing values.

[0219] formula:

[0220] [x(i)=\frac{1}{n}\sum_{j=1}^{n}x(j)]

[0221] (x(i)) is a missing value;

[0222] (x(j)) are other data points.

[0223] In the case of multiple experiments or data aggregation from multiple sensors, the mean filling method can be used to fill in the short-term missing corrosion data.

[0224] 2. Feature Extraction Unit After completing data preprocessing, feature extraction is a key step in analyzing the corrosion of towers. By extracting features related to corrosion, the corrosion condition of towers can be effectively predicted or diagnosed.

[0225] 2.1 Statistical feature extraction Statistical features can help describe the distribution and fluctuation of corrosion data.

[0226] Mean:

[0227] Describes the overall level of corrosion data and reflects the average state of long-term corrosion.

[0228] formula:

[0229] [\mu=\frac{1}{n}\sum_{i=1}^{n}x(i)]

[0230] (\mu) is the mean;

[0231] (x(i)) is the eroded data point.

[0232] Applied to corrosion data: For example, the mean value of the surface potential of the tower reflects the long-term change of corrosion potential. The larger the mean value, the higher the corrosion potential.

[0233] variance:

[0234] It is used to measure the degree of discreteness of data. The larger the variance, the greater the fluctuation of corrosion data, and there may be sudden corrosion.

[0235] formula:

[0236] [\s igma^2=\frac{1}{n}\sum_{i=1}^{n}(x(i)-\mu)^2]

[0237] (\s igma^2) is the variance.

[0238] Applied to corrosion data: For example, potential or current fluctuations in different time periods, the variance is used to measure the severity of the change. A larger variance may indicate that corrosion is accelerating.

[0239] Standard Deviation:

[0240] The standard deviation is the square root of the variance and reflects the range of fluctuation of the data.

[0241] formula:

[0242] [\s igma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x(i)-\mu)^2}]

[0243] Applied to corrosion data: Standard deviation can be used to determine the stability of the corrosion state. A larger standard deviation indicates unstable data.

[0244] Maximum and minimum values:

[0245] The maximum values ​​can indicate peaks in the corrosion process, while the minimum values ​​indicate valleys.

[0246] formula:

[0247] [x_{\text{max}}=\max(x(i)),\quad x_{\text{min}}=\min(x(i))]

[0248] (x_{\text{max}}) and (x_{\text{min}}) are the maximum and minimum values ​​of the data, respectively.

[0249] Applied to corrosion data: For example, the maximum value of corrosion current may correspond to the peak period of corrosion rate, and the minimum value may correspond to the period of slow corrosion.

[0250] 2.2 Time domain feature extraction,Time domain features can reflect the temporal variation trend of corrosion data and are an important means to analyze the corrosion evolution process.

[0251] Peak:

[0252] Reflects the maximum amplitude of corrosion current or voltage.

[0253] formula:

[0254] [A_p=\max(x(i))]

[0255] (A_p) is the peak value;

[0256] (x(i)) is the corrosion data point.

[0257] For example, monitoring the peak voltage on the tower surface may be associated with local corrosion and can be used to identify possible corrosion points.

[0258] Mean absolute value:

[0259] Used to reflect the overall amplitude of the signal.

[0260] formula:

[0261] [A_{\text{mean}}=\frac{1}{n}\sum_{i=1}^{n}|x(i)|]

[0262] (A_{\text{mean}}) is the absolute value of the mean;

[0263] (x(i)) is the corrosion data point.

[0264] For example, the mean absolute value of the tower surface current may be used to identify the overall corrosion trend, and a larger trend may indicate an increase in the corrosion rate.

[0265] Through the above detailed steps, combined with specific corrosion data processing and feature extraction methods, the corrosion condition of the tower can be analyzed more accurately, providing effective support for corrosion monitoring and prevention.

[0266] \quad x_{\text{min}}=\min(x(i))]

[0267] Monitoring the maximum potential or current value of the tower can reflect the possible existence of severe corrosion areas.

[0268] Through the above detailed steps, the tower corrosion monitoring data can be effectively processed and analyzed, thereby improving the accuracy and reliability of corrosion prediction.

[0269] (3) Classification evaluation unit

[0270] The goal of the classification assessment unit is to conduct a detailed analysis of the tower according to different corrosion characteristic parameters to better understand its corrosion situation and predict potential risks. By evaluating various corrosion environments such as atmosphere, soil, and chemistry, it can provide an accurate basis for corrosion prevention and guide the corresponding protection design and maintenance strategies. The following is a further expansion and refinement of the atmospheric corrosion, soil corrosion, and chemical corrosion assessment steps.

[0271] ① Atmospheric corrosion assessment objectives:

[0272] Evaluate the data related to atmospheric corrosion, analyze the impact of atmospheric humidity, temperature, pollutants and other factors on tower corrosion, and calculate the atmospheric corrosion rate and degree in combination with the atmospheric corrosion model.

[0273] step:

[0274] Data collection

[0275] Collect environmental data related to atmospheric corrosion. You can use data recorded by weather stations, data provided by environmental monitoring stations, or install sensors to obtain real-time data. Key data include:

[0276] Humidity: Records the relative humidity (RH) in the air.

[0277] Temperature: Get the average daily temperature, extreme temperature, etc. of the area.

[0278] Pollutant concentration: Monitor sulfur dioxide (SO 2 ), chloride ion (Cl - ) and other pollutants, especially in industrial or coastal areas.

[0279] Wind speed and direction: Records the strength and direction of wind, which have an important impact on the dispersion and deposition of pollutants.

[0280] Data preprocessing

[0281] Clean and process the collected data, remove abnormal data and missing values, and ensure the accuracy and consistency of the data. Interpolation or mean method can be used to fill in missing data, and scatter plots or box plots can be used to detect abnormal values.

[0282] Atmospheric Corrosion Model Calculation

[0283] Input the preprocessed environmental data into the formula of the atmospheric corrosion model:

[0284] [C=A\times t^n]

[0285] The coefficient A is related to factors such as humidity, temperature, and pollutant concentration in the atmosphere, and can be determined by looking up a table or empirical formula. The index n is usually between 0.6 and 1.

[0286] Corrosion rate calculation and evaluation

[0287] The atmospheric corrosion model is used to calculate the annual corrosion rate (μm / year) of the tower surface, and the corrosion rates in different areas are compared and analyzed. According to the ISO 9223 standard, the corrosivity of the area is divided into different levels (such as C1-C5) to evaluate the degree of corrosion. Based on the evaluation results, it is determined whether additional protective measures are needed.

[0288] Note:

[0289] A high humidity environment (relative humidity > 60%) can easily cause condensation on the surface of the tower, accelerating the occurrence of corrosion, so special attention needs to be paid.

[0290] When the tower is located in a marine environment or industrial area, where the concentration of pollutants such as chloride ions and sulfur dioxide is high, the corrosion rate may be higher and needs to be given special consideration.

[0291] ②Soil corrosion assessment objectives:

[0292] Evaluate data related to soil corrosion and analyze the effects of soil resistivity, pH, water content and other factors on the corrosion of the tower foundation. Use soil corrosion models to calculate corrosion rates and assess corrosion risks.

[0293] step:

[0294] Soil data collection

[0295] Soil samples were collected around the tower foundations and key parameters were measured, including:

[0296] Soil resistivity: The Wenner four-electrode method is used to measure soil resistivity in Ω·cm.

[0297] Acidity (pH): Use a pH meter to determine the acidity or alkalinity of the soil, paying special attention to acidic soils with a pH < 7.

[0298] Moisture content: Use drying methods or sensors to monitor the moisture content of the soil. Higher moisture content will lead to lower resistivity and increase the risk of corrosion.

[0299] Data preprocessing and analysis

[0300] The collected soil data were preprocessed and analyzed, statistical indicators such as mean and variance were calculated, the uniformity of soil conditions was evaluated, and the soil resistivity distribution map was drawn using geological modeling tools.

[0301] Soil Corrosion Rate Calculation

[0302] Input the collected soil data into the formula of soil erosion model:

[0303] [R=\frac{2\pi a V}{I}]

[0304] Where R represents soil resistivity. Soil with lower resistivity is more corrosive. Soil resistivity is inversely proportional to corrosion rate. The corrosion rate is calculated using existing empirical formulas or standards in combination with actual soil data.

[0305] Corrosion Assessment and Grading

[0306] Based on parameters such as resistivity, pH value, and water content, soil corrosion standards (such as NACE standards) are used to evaluate the level of soil corrosion environment, such as mild, moderate, and severe corrosion. Based on the evaluation results, corresponding corrosion protection recommendations are given, such as whether cathodic protection or coating protection measures are needed.

[0307] Note:

[0308] Soil resistivity varies greatly with water content, so in long-term monitoring, it is necessary to regularly measure soil moisture and adjust the corrosion rate assessment results.

[0309] Under special geological conditions (such as saline-alkali land or swampland), the corrosiveness of the soil may increase significantly, requiring the introduction of special protective designs.

[0310] ③Chemical corrosion assessment objectives:

[0311] Evaluate data related to chemical corrosion and analyze the impact of chemical substances (such as acid rain and industrial waste gas) around the tower on corrosion. Use chemical corrosion models to calculate chemical corrosion rates and assess corrosion risks.

[0312] step:

[0313] Chemical Environmental Data Collection

[0314] The focus is on collecting chemical environmental data, including:

[0315] Acid rain data: Obtain the pH value of acid rain through environmental monitoring, especially focusing on acid rain with pH < 5.5.

[0316] Industrial waste gas data: Evaluate sulfur dioxide (SO 2), nitrogen oxides (NO x ) and other gases, and analyze the distribution of these pollutants in combination with wind speed and direction data.

[0317] Chemical sediments: Collect sediments from the soil or water near the tower to detect whether they contain corrosive chemicals (such as chlorides and sulfates).

[0318] Data preprocessing and classification

[0319] Preprocess and classify chemical data such as acid rain and industrial waste gas to evaluate the concentration of various pollutants and their potential impact on corrosion. If necessary, use chemical models to simulate the diffusion of acid rain and waste gas at different wind speeds and temperatures.

[0320] Chemical corrosion rate calculation

[0321] According to the formula of chemical corrosion model:

[0322] [C_{\text{chem}}=k\t imes\left(\frac{1}{\text{pH}}\r ight)

[0323] \t imes t]

[0324] Where k is a constant related to the chemical environment and material, determined experimentally or empirically. The lower the pH value, the higher the corrosion rate. Calculate the chemical corrosion rate based on actual data.

[0325] Corrosion Assessment and Protection Measures

[0326] The corrosion risk of the tower is assessed based on the chemical corrosion rate, and corresponding protective measures are recommended. For example, in areas with severe acid rain, it may be necessary to use corrosion-resistant materials or take anti-corrosion coatings.

[0327] Note:

[0328] The impact of acid rain is wide, and the monitoring area should cover all potential impact areas of the tower.

[0329] The corrosive components in industrial waste gas have a greater impact on corrosion in a high humidity environment, and the atmospheric corrosion assessment results need to be comprehensively considered for analysis.

[0330] Summarize:

[0331] Through the refinement and implementation of the atmospheric, soil and chemical corrosion assessment units, the corrosion environment of the tower can be fully analyzed and the corrosion rate can be calculated. The corrosion level can be determined based on the corrosion rate and corresponding protection suggestions can be provided. These refinement steps make the classification assessment units more specific and provide a reasonable basis for the design and maintenance of corrosion protection measures.

[0332] (4) Integrated analysis unit

[0333] Integrated analysis is to conduct integrated analysis on the results after classification and evaluation, comprehensively consider the influence of various corrosion factors, and obtain the overall corrosion condition evaluation of the pole tower. The integrated analysis unit mainly uses the following two methods for integrated analysis:

[0334] (1) Specific application and implementation steps of the weighted average method

[0335] 1). Application content: The weighted average method is an analysis method that weights and aggregates the evaluation results of multiple factors according to their importance. It is applicable to situations that need to comprehensively consider multiple different influencing factors, and can assign different weights to each factor to obtain the overall evaluation result. In the pole tower corrosion assessment, different corrosion factors such as environmental conditions, material properties, service life, etc. have different influences on corrosion. The weighted average method can comprehensively consider the influence of each factor to evaluate the overall corrosion condition.

[0336] 2). Implementation steps Step 1: Determine the evaluation indicators - According to the actual situation of pole tower corrosion, select the main evaluation indicators affecting the corrosion condition, such as: humidity, pollution level, corrosion rate, material properties, etc.

[0337] Step 2: Determine the weights - Use the expert scoring method or the analytic hierarchy process (AHP) to determine the importance weights of each evaluation indicator. The expert scoring method requires multiple experts to score the importance of each indicator, and then take the average value to determine the weight. The analytic hierarchy process constructs a judgment matrix by pairwise comparing the importance of each indicator and calculates the weight.

[0338] Step 3: Standardization processing - Nondimensionalize (standardize) the values of each evaluation indicator to ensure that the dimensions of all indicators are consistent. Commonly used standardization methods include the range method, the standard deviation method, etc.

[0339] Step 4: Weighted calculation - Perform weighted calculation on each standardized evaluation indicator according to its weight. The calculation formula is:

[0340] [R_{\text{comprehensive}}=\sum_{i = 1}^{n}W_i\times R_i]

[0341] Among them, (R_{\text{comprehensive}}) is the comprehensive evaluation result, (W_i) is the weight of the i-th evaluation indicator, and (R_i) is the standardized result of the i-th evaluation indicator.

[0342] Step 5: Result analysis - According to the comprehensive evaluation result, obtain the corrosion condition grade of the pole tower. For example, set different score intervals to divide the corrosion condition into three grades: excellent, good, and poor.

[0343] (2) Specific application and implementation steps of fuzzy comprehensive evaluation method

[0344] 1. Application content The fuzzy comprehensive evaluation method is a mathematical method for dealing with complex and fuzzy problems, and is suitable for multi-factor, multi-level system evaluation. It can convert qualitative evaluation results into quantitative comprehensive evaluation values, and is particularly suitable for dealing with uncertain factors or fuzzy boundaries in tower corrosion evaluation.

[0345] 2. Implementation steps Step 1: Determine the evaluation factors and evaluation levels - According to the corrosion condition of the tower, determine the evaluation factor set (U = {U_1, U_2, \dots, U_n}), such as corrosion depth, humidity, environmental pollution, etc.

[0346] Determine the evaluation level set (V = {V_1, V_2, \dots, V_m}), which is usually divided into five levels: excellent, good, medium, poor, and inferior.

[0347] Step 2: Determine the weight set - The expert scoring method or hierarchical analysis method can also be used to determine the weight set of each evaluation factor (W = {w_1, w_2,\dots, w_n}).

[0348] Step 3: Establish fuzzy relationship matrix - According to the specific situation of each evaluation factor, use experts or historical data to establish the membership matrix (R) of the evaluation factor to each level. Each element (r_{ij}) of the matrix represents the membership of the i-th evaluation factor to the j-th level, ranging from [0,1].

[0349] For example: If the membership of humidity to corrosion assessment is (R_1={0.2,0.5,0.3,0.1,0.0}), it means that the membership of humidity to the evaluation levels "excellent", "good", "medium", "poor", and "inferior" are 0.2, 0.5, 0.3, 0.1, and 0.0 respectively.

[0350] Step 4: Fuzzy comprehensive calculation - use weighted average method or other calculation methods to perform fuzzy comprehensive calculation on each evaluation factor. The commonly used calculation method is fuzzy matrix operation, that is:

[0351] [B=W\times R]

[0352] Among them, (W) is the weight vector, (R) is the membership matrix, and the obtained (B) is the fuzzy vector of the comprehensive evaluation result, which represents the comprehensive membership of each level.

[0353] Step 5: Determine the final evaluation result - Determine the final grade of the evaluation object according to the maximum membership principle of the fuzzy vector (B). For example, if (B = {0.1, 0.3, 0.5, 0.1, 0.0}), the corrosion condition of the tower belongs to the "medium" grade (because the maximum membership is medium).

[0354] Step 6: Analyze the results and make decisions - Based on the final evaluation results, determine the corrosion status of the tower and make corresponding repair or maintenance decisions based on the results.

[0355] Application comparison-weighted average method is more suitable for evaluation scenarios with clear data and relatively certain weights, and the evaluation process is simple and intuitive.

[0356] The fuzzy comprehensive evaluation rule performs better when the evaluation factors are ambiguous, uncertain or the data boundaries are unclear, and can handle more complex multi-factor evaluation problems.

[0357] Security Assessment Module

[0358] The safety assessment module evaluates the corrosion safety status of the tower and divides the risk level according to the overall corrosion status assessment results obtained by the data analysis module.

[0359] (1) Safety Assessment Unit

[0360] The safety assessment unit mainly uses the following methods to assess the corrosion safety status of the tower:

[0361] Strength Assessment:

[0362] Strength assessment mainly analyzes the residual bearing capacity of tower materials under the influence of corrosion. The assessment methods usually include:

[0363] Stability Assessment:

[0364] Stability assessment mainly analyzes the tower's ability to resist overturning under corrosion, especially under external forces (such as wind load and snow load). Commonly used assessment methods include:

[0365] Stability analysis model: A mathematical model is established for the stability of the tower in a corrosive environment to calculate its stability loss under different external forces.

[0366] Dynamic load analysis: Consider the impact of load changes on the tower under different climatic conditions, such as changes in wind speed and increased snow thickness.

[0367] Life expectancy prediction:

[0368] By analyzing the corrosion rate and residual strength of the tower, its remaining service life can be predicted. The core of life prediction is to determine the time variation characteristics of the corrosion rate and evaluate it in combination with the following methods:

[0369] Big data analysis: Combine historical data and actual monitoring data to further predict the life under different corrosion conditions.

[0370] (2) Risk classification unit Based on the results of the safety assessment unit, the risk classification unit classifies the corrosion risks of the towers to help determine the priority of maintenance and renewal.

[0371] High risk:

[0372] When the degree of corrosion of the tower seriously affects its strength and stability, the assessment results show that its remaining bearing capacity and anti-overturning capacity are obviously insufficient, and there are significant safety hazards. At this time, measures should be taken immediately to repair or replace it.

[0373] Specific features:

[0374] The residual strength drops by more than 50% of the design allowable value.

[0375] Obvious corrosion pits or cracks appear in key load-bearing parts.

[0376] Stability analysis showed a significant risk of capsizing in extreme weather.

[0377] Medium risk:

[0378] The corrosion of the tower is relatively serious, but the remaining strength and stability have not yet reached a critical state. Although it does not need to be replaced immediately in the short term, it is necessary to strengthen monitoring and maintenance and formulate a medium- and long-term repair plan.

[0379] Specific features:

[0380] The residual strength decreases between 20% and 50%.

[0381] The surface corrosion is severe, but has not penetrated into the core structure of the tower.

[0382] Stability analysis shows that there is still sufficient safety margin under normal weather conditions.

[0383] Low risk:

[0384] The degree of corrosion is relatively light, and the remaining strength and stability of the tower basically meet the design requirements. Such towers only need regular inspection and maintenance to prevent further corrosion.

[0385] Specific features:

[0386] The residual strength decreases by no more than 20%.

[0387] Surface corrosion can be treated through routine maintenance.

[0388] Stability analysis shows that it still has good anti-overturning ability under various external forces.

[0389] Through comprehensive analysis and safety assessment of corrosion conditions, the integrated analysis unit and safety assessment module jointly ensure the structural safety and service life of the tower.

[0390] The early warning module issues corresponding early warning information based on the corrosion risk level of the tower so that relevant personnel can take timely measures to reduce the operating risks of power facilities.

[0391] (1) Early Warning Unit

[0392] The early warning unit mainly uses the following methods to determine the warning level and issue corresponding warning information:

[0393] ① Determination of warning level: Determine the warning level according to the corrosion risk level of the tower. The warning level can be divided into three levels: red warning, orange warning and yellow warning, corresponding to the three corrosion risk levels of high risk, medium risk and low risk respectively.

[0394] ② Early warning information release: According to the determined early warning level, the corresponding early warning information is released. The early warning information may include the location of the tower, corrosion status, risk level, recommended measures, etc. The early warning information can be sent to relevant personnel via SMS, email, mobile phone APP, etc.

[0395] (2) Communication unit

[0396] The communication unit is the key link in sending the warning information to relevant personnel. The communication unit can use a variety of communication methods, such as mobile communication, satellite communication, Internet communication, etc., to ensure that the warning information can be sent to relevant personnel in a timely and accurate manner.

[0397] Early Warning

[0398] (1) Determination of warning level

[0399] The early warning unit determines the early warning level according to the corrosion risk level and issues corresponding early warning information. The early warning level can be divided into three levels: red warning, orange warning and yellow warning, corresponding to the three corrosion risk levels of high risk, medium risk and low risk respectively.

[0400] (2) Early warning information release

[0401] The communication unit sends the warning information to relevant personnel so that timely measures can be taken to deal with it.

[0402] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A tower corrosion safety assessment system, characterized in that: Including data analysis module, safety assessment module and early warning module; The data analysis module is used to perform preprocessing, feature extraction, classification evaluation and integrated analysis on the acquired tower-related data; The data preprocessing unit includes a denoising module, a normalization module and a missing value module processing; The denoising module processes the original data of the tower corrosion to reduce the interference to the subsequent analysis, which specifically includes mean filtering, median filtering and Gaussian filtering: The feature extraction unit can effectively predict or diagnose the corrosion condition of the tower by extracting features related to corrosion; Mean: describes the overall level of corrosion data and reflects the average state of long-term corrosion. The expression is: [\mu=\frac{1}{n}\sum_{i=1}^{n}x(i)] (\mu) is the mean; (x(i)) is the eroded data point. Specifically, the mean value of the tower surface potential reflects the long-term change in corrosion potential. The larger the mean value, the higher the corrosion potential. Variance is used to measure the degree of discreteness of data. The larger the variance, the greater the fluctuation of corrosion data, and there may be sudden corrosion. The expression is: [\sigma^2=\frac{1}{n}\sum_{i=1}^{n}(x(i)-\mu)^2] (\sigma^2) is the variance; It includes the fluctuation of potential or current at different time periods. The variance is used to measure the severity of the change. A larger variance may indicate that corrosion is accelerating. The standard deviation is the square root of the variance, reflecting the fluctuation range of the data. The expression is: [\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x(i)-\mu)^2}] The standard deviation is used to determine the stability of the corrosion state. A larger standard deviation indicates unstable data. Maximum and minimum values: The maximum value indicates the peak value in the corrosion process, while the minimum value indicates the valley value. The expression is: [x_{\text{max}}=\max(x(i)),\quad x_{\text{min}}=\min(x(i))] (x_{\text{max}}) and (x_{\text{min}}) are the maximum and minimum values ​​of the data respectively; The maximum value of corrosion current may correspond to the peak period of corrosion rate, and the minimum value may correspond to the slow corrosion period; Time domain characteristics are used to reflect the temporal variation trend of corrosion data; Peak value: reflects the maximum amplitude of corrosion current or voltage; the specific expression is: [A_p=\max(x(i))] (A_p) is the peak value; (x(i)) is the corrosion data point; The peak value of the voltage on the tower surface is related to local corrosion and is used to identify the corrosion point; Mean Absolute value: used to reflect the overall amplitude of the signal; the expression is: [A_{\text{mean}}=\frac{1}{n}\sum_{i=1}^{n}|x(i)|] (A_{\text{mean}}) is the absolute value of the mean; (x(i)) is the corrosion data point; The mean absolute value of the tower surface current is used to identify the overall corrosion trend, and a larger trend indicates an increase in the corrosion rate; (3) Classification and evaluation unit The classification and evaluation unit is designed to classify and evaluate the corrosion environment of the tower according to different characteristic parameters. The classification and evaluation unit mainly uses the following methods for classification and evaluation: ① Atmospheric corrosion assessment objectives: Evaluate the data related to atmospheric corrosion, analyze the effects of humidity, temperature and pollutants in the atmosphere on tower corrosion, and calculate the atmospheric corrosion rate and extent in combination with the atmospheric corrosion model; Collect environmental data related to atmospheric corrosion; you can use data recorded by weather stations, data provided by environmental monitoring stations, or install sensors yourself to obtain real-time data. Atmospheric Corrosion Model Calculation Input the preprocessed environmental data into the formula of the atmospheric corrosion model: [C=A\times t^n] The coefficient A is related to factors such as humidity, temperature, and pollutant concentration in the atmosphere, and can be determined by looking up a table or empirical formula. The index n is usually between 0.6 and 1. Corrosion rate calculation and evaluation The atmospheric corrosion model is used to calculate the annual corrosion rate (μm / year) of the tower surface, and the corrosion rates in different areas are compared and analyzed. ②Soil corrosion assessment objectives: Evaluate data related to soil corrosion, analyze the effects of soil resistivity, pH, and water content on the corrosion of the tower foundation; use soil corrosion models to calculate corrosion rates and assess corrosion risks; Soil data collection Collect soil samples around the tower foundation and measure key parameters; Soil Corrosion Rate Calculation Input the collected soil data into the formula of soil erosion model: [R=\frac{2\pi a V}{I}] Where R represents soil resistivity. Soil with lower resistivity is more corrosive. Soil resistivity is inversely proportional to corrosion rate. The corrosion rate is calculated using existing empirical formulas or standards in combination with actual soil data. Corrosion Assessment and Grading Based on parameters such as resistivity, pH value, and water content, soil corrosion standards are used to assess the level of soil corrosion environment, including mild, moderate, and severe corrosion; ③Chemical corrosion assessment objectives: Evaluate data related to chemical corrosion and analyze the impact of chemical substances around the tower on corrosion. Use chemical corrosion models to calculate chemical corrosion rates and assess corrosion risks; Chemical Environmental Data Collection The focus is on collecting chemical environmental data, including: Acid rain data: Obtain the pH value of acid rain through environmental monitoring, especially focusing on acid rain with pH < 5.5; Industrial waste gas data: assess sulfur dioxide (SO2), nitrogen oxides (NO x ) and other gases, and analyze the distribution of these pollutants in combination with wind speed and direction data; Chemical sediments: Collect sediments from the soil or water near the tower to detect whether they contain corrosive chemicals. Data preprocessing and classification Preprocess and classify chemical data such as acid rain and industrial waste gas to assess the concentration of various pollutants and their potential impact on corrosion; Chemical corrosion rate calculation According to the formula of chemical corrosion model: [C_{\text{chem}}=k\times\left(\frac{1}{\text{pH}}\right)\times t] Where k is a constant related to the chemical environment and materials, determined by experiments or experience; the lower the pH value, the higher the corrosion rate; the chemical corrosion rate is calculated based on actual data; Corrosion Assessment and Protection Measures Assess the corrosion risk of the tower based on the chemical corrosion rate and provide corresponding protective measures; (4) Integrated analysis unit The integrated analysis is to integrate the results of the classified assessment, comprehensively consider the influence of various corrosion factors, and obtain the overall corrosion status assessment of the tower; The safety assessment module is used to assess the corrosion safety status of the tower and classify the risk level according to the overall corrosion status assessment results obtained by the data analysis module; The early warning module is used to issue corresponding early warning information according to the corrosion risk level of the tower, so that relevant personnel can take timely measures to reduce the operating risks of power facilities.

2. A tower corrosion safety assessment system according to claim 1, characterized in that: The mean filter is used for data with slow fluctuations caused by external factors such as temperature and humidity. The data is smoothed by taking the average value of the data in the data window. The expression is: y(i)=\frac{1}{n}\sum_{k=-(n-1) / 2}^{(n-1) / 2}x(i+k) y(i) is the filtered data point; x(i+k) is the original data point; n is the size of the filter window (i.e., the number of data points contained in the window); (\frac{1}{n}) is a normalization coefficient used to ensure the average value of all data points in the window. It represents the contribution of each data point in calculating the mean, and the sum of the contributions of all data points is equal to 1; Median filtering removes isolated sharp noise points by selecting the median of the data in the window while maintaining the edge characteristics of the signal; The expression is: [y(i)=\text{median}(x(i-(n-1) / 2),\ldots,x(i+(n-1) / 2))] n is the window size; \text{median} means taking the median of all data in the window; (x(i-(n-1) / 2)) to (x(i+(n-1) / 2)) represent a series of values ​​of the original data point within a window range; (n) is the size of the filter window (usually an odd number, such as 3, 5, 7, etc.); "\ldots" represents a continuous range of all data points in the window; Therefore, "(\ldots)" represents all data points within a given window range, from the starting point (x(i-(n-1) / 2)) to the ending point (x(i+(n-1) / 2)); the median of these data points (i.e. the value in the middle after sorting) is used to calculate the new filtered data point (y(i)); Gaussian filtering performs weighted averaging on the data, with weights allocated according to Gaussian distribution, to smooth out high-frequency noise; the specific expression is: [y(i)=\frac{1}{\sqrt{2\pi\sigma^2}}\sum_{k=-(n-1) / 2}^{(n-1) / 2}x(i+k)e^{-\frac{k^2}{2\sigma^2}}] \sigma is the standard deviation of the Gaussian distribution, which controls the smoothness of the filter; x(i+k) is the original data point.

3. A tower corrosion safety assessment system according to claim 1, characterized in that: The normalization comprises: Min-max normalization: Scale the data to the range of [0,1]. The specific expression is: [x'=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}](x) is the original data; (x_{\text{min}}) and (x_{\text{max}}) are the minimum and maximum values ​​of the data, respectively. Z-score normalization: Standardize the data to ensure that the data follows a normal distribution, with a mean of 0 and a standard deviation of 1. The specific expression is: [x'=\frac{x-\mu}{\sigma}] (\mu) is the mean of the data; (\sigma) is the standard deviation of the data.

4. A tower corrosion safety assessment system according to claim 1, characterized in that: The specific expression of the interpolation method is: [x(i)=x(i-1)+\frac{x(i+1)-x(i-1)}{2}] (x(i)) is the missing point; (x(i-1)) and (x(i+1)) are adjacent data points. Use the global mean of the data to fill missing values; formula: [x(i)=\frac{1}{n}\sum_{j=1}^{n}x(j)] (x(i)) is a missing value; (x(j)) are other data points. In the case of multiple experiments or data aggregation of multiple sensors, the mean filling method can be used to fill in the short-term missing corrosion data; the specific expression is: [x(i)=x(i-1)+\frac{x(i+1)-x(i-1)}{2}] (x(i)) is the missing point; (x(i-1)) and (x(i+1)) are adjacent data points; Use the global mean of the data to fill in missing values; the specific expression is: [x(i)=\frac{1}{n}\sum_{j=1}^{n}x(j)] (x(i)) is a missing value; (x(j)) are other data points; In the case of multiple experiments or data aggregation from multiple sensors, the mean filling method can be used to fill in the short-term missing corrosion data.

5. A tower corrosion safety assessment method, characterized in that: The specific steps are as follows: (1) Data collection and preprocessing Obtain data related to tower corrosion through existing databases, monitoring systems and other channels. 1) The denoising module in the data preprocessing unit processes the original data and can use mean filtering, median filtering, and Gaussian filtering methods to reduce noise interference in the data; 2) Feature extraction Statistical characteristics include mean, variance, standard deviation, maximum value, and minimum value; 3) Classification evaluation Atmospheric corrosion assessment: Collect environmental data related to atmospheric corrosion; The pre-processed environmental data is input into the atmospheric corrosion model formula, where the coefficient A is related to factors such as humidity, temperature, and pollutant concentration in the atmosphere, and the index n is usually between 0.6 and 1, to calculate the atmospheric corrosion rate and degree; The atmospheric corrosion model is used to calculate the annual corrosion rate (μm / year) of the tower surface, and the corrosion rates in different areas are compared and analyzed; Soil corrosion assessment: Collect soil samples around the tower foundation and measure key parameters; The collected soil data is input into the soil corrosion model formula to calculate the soil resistivity R. The soil with lower resistivity is more corrosive. Based on parameters such as resistivity, pH value, and water content, soil corrosion standards are used to assess the level of soil corrosion environment, including mild, moderate, and severe corrosion; Chemical Corrosion Assessment: The focus is on collecting chemical environmental data, including acid rain data (obtaining the pH value of acid rain through environmental monitoring, especially focusing on acid rain with pH < 5.5), industrial waste gas data (assessing sulfur dioxide (SO2) and nitrogen oxides (NO x ) and other gas concentrations, and analyze the distribution of these pollutants in combination with wind speed and direction data), chemical sediments (collect sediments in the soil or water near the tower to detect whether they contain corrosive chemicals); Preprocess and classify chemical data such as acid rain and industrial waste gas to assess the concentration of various pollutants and their potential impact on corrosion; According to the chemical corrosion model formula, where k is a constant related to the chemical environment and material, determined by experiment or experience; calculate the chemical corrosion rate. Assess the corrosion risk of the tower based on the chemical corrosion rate and provide corresponding protective measures; 4) Integrated analysis The integrated analysis unit integrates and analyzes the results of the classified assessment, comprehensively considers the influence of various corrosion factors, and obtains an overall corrosion status assessment of the tower; (2) Safety assessment module, which assesses the corrosion safety status of the tower based on the overall corrosion status assessment results obtained by the data analysis module; Risk level classification The safety assessment module classifies the corrosion risks of poles and towers according to the assessment results, into three levels: high risk, medium risk and low risk. (3) Early warning module, which issues corresponding early warning information according to the corrosion risk level of the tower. The early warning level can be divided into three levels: red warning, orange warning and yellow warning, corresponding to the three corrosion risk levels of high risk, medium risk and low risk respectively; Warning information can be sent to relevant personnel via SMS, email, or mobile APP.