A method for optimizing cathodic protection of complex buried pipe network under current interference

Abstract

The present application relates to the technical field of cathodic protection, in particular to a complex buried pipe network cathodic protection optimization method under current interference, comprising the following steps: collecting the data of soil resistivity, pipeline material and environmental humidity from differentiated industrial areas by adopting field sampling method, obtaining environmental parameter characteristics by analyzing the variability and correlation of the data.In the present application, the model can be accurately adjusted to various complex interference conditions by simulating the scene test of the environment, optimizing the distribution of current and potential, not only improving the stability of the cathodic protection system in the variable interference environment, but also realizing the fine adjustment of current and potential parameters through the simulation and multiple simulation tests of electromagnetic field data, significantly improving the corrosion prevention effect of buried pipe network and the overall efficiency of the system, ensuring the long-term safety and stability of buried pipe network in complex current interference environment through real-time monitoring and adjustment, significantly prolonging the service life of the facility.

Description

Technical Field

[0001] This invention relates to the field of cathodic protection technology, and in particular to an optimized method for cathodic protection of complex buried pipeline networks under current interference. Background Technology

[0002] Cathodic protection is a widely used electrochemical protection method to prevent corrosion of metal structures in specific environments. It is primarily used to protect buried or underwater metal pipelines, storage tanks, reinforced concrete structures, and ships. The principle of cathodic protection is to use the metal structure as the cathode in the electrolyte, and through a DC power supply or by using the anode, maintain the potential of the structural surface at a sufficiently low level to prevent electrochemical corrosion reactions in the environment. This technology effectively slows down or prevents metal ions from dissolving from the metal surface, protecting the metal from corrosion.

[0003] The optimization method for cathodic protection of complex buried pipelines under current interference refers to a method of technically optimizing the cathodic protection system of buried pipelines under conditions of current interference. This interference originates from nearby power lines, rail transit, or electrical facilities; stray currents affect the normal operation of the cathodic protection system and reduce its anti-corrosion effect. Optimization methods include improving current distribution, using more efficient cathodic protection materials, or adopting advanced control and monitoring systems. This optimization ensures effective protection of the pipeline network from corrosion even in complex interference environments, extending its service life and ensuring safe operation.

[0004] While existing cathodic protection methods are widely used for corrosion protection of various metal structures, conventional cathodic protection systems struggle to cope with constantly changing current and potential distributions in complex interference environments, such as those caused by power lines, rail transit, or electrical facilities. This results in poor corrosion protection and low system efficiency. This technology relies on an external DC power supply or anode to maintain a low potential in the metal structure, but it fails to adequately consider the specific effects of environmental factors such as soil resistivity and humidity, and lacks dynamic response capabilities to electromagnetic interference. In operation, these shortcomings lead to uneven corrosion protection, increasing maintenance costs and safety risks. The lack of effective real-time monitoring and adjustment mechanisms results in slow or ineffective responses when problems occur, further exacerbating corrosion and damage to the facilities. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and propose an optimized method for cathodic protection of complex buried pipelines under current interference.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an optimization method for cathodic protection of complex buried pipeline networks under current interference, comprising the following steps:

[0007] S1: Using on-site sampling, data on soil resistivity, pipe material, and ambient humidity from differentiated industrial areas are collected. By analyzing the variability and correlation of the data, environmental parameter characteristics are obtained.

[0008] S2: Based on the environmental parameter characteristics, select the information gain to choose the decision point, input variables including current density, potential difference and soil type, and construct an initial decision tree model;

[0009] S3: Apply the initial decision tree model to a simulated environment for scenario testing, compare the model output with the cathodic protection effect data of buried pipelines, adjust the branch conditions and thresholds in the decision tree, and obtain the adjusted decision tree model.

[0010] S4: Using the adjusted decision tree model, collect electromagnetic field data of the interference source, simulate electric and magnetic fields, record the intensity and direction changes of the simulated data points, and generate electromagnetic field simulation data.

[0011] S5: Based on the electromagnetic field simulation data, adjust the parameters of the output current and potential distribution, adjust the parameters through multiple rounds of simulation tests, monitor the changes in electromagnetic interference after adjustment, and form optimized current and potential parameters;

[0012] S6: Based on the optimized current and potential parameters, monitor the cathodic protection effect of the buried pipeline network in real time, verify the stable operation efficiency under complex current interference environment, and obtain the strategy implementation results.

[0013] As a further aspect of the present invention, the environmental parameter features include soil resistivity distribution maps, response data of differentiated pipeline materials, and environmental humidity measurements from multiple industrial areas. The initial decision tree model includes inputs of current density, potential difference, and soil type, the organization of variables in the model, and initial thresholds. The adjusted decision tree model includes optimized branching conditions, updated node thresholds, and model prediction response speed and efficiency. The electromagnetic field simulation data includes data on the changes in the intensity and direction of electric and magnetic fields. The optimized current and potential parameters include adjusted current density values, potential distribution maps, and corresponding simulation test results. The strategy implementation results include real-time monitoring data of the cathodic protection effect of buried pipelines under complex current interference environments, and indicators of the stability of the protection effect and operational efficiency.

[0014] As a further aspect of the present invention, the steps of collecting data on soil resistivity, pipe material, and ambient humidity from differentiated industrial areas through on-site sampling, and obtaining environmental parameter characteristics by analyzing the variability and correlation of the data, are as follows:

[0015] S101: Select representative locations within the industrial area to collect soil samples, examine the pipes covering different types and the changing humidity environment, record the location and environmental conditions of each sampling point, and generate soil sample and environmental data tables.

[0016] S102: Based on the soil sample and environmental data table, the resistivity of the soil and the ambient humidity are measured on site, the pipe material is recorded, the collected data is organized, the integrity of the data is checked, and the original measurement data record is generated.

[0017] S103: Based on the original measurement data records, perform data quality control, remove data points with deviations, classify the data according to soil type, and perform statistical analysis to analyze the correlation between resistivity, pipe material and ambient humidity, and generate environmental parameter characteristics.

[0018] As a further aspect of the present invention, the steps for selecting decision points by selecting information gain based on the environmental parameter characteristics, and by inputting variables including current density, potential difference, and soil type to construct an initial decision tree model are as follows:

[0019] S201: Based on the environmental parameter characteristics, select current density, potential difference and soil type as variables, evaluate the influence of differential variables on the classification effect of decision tree, determine the optimal split node, and generate a decision variable evaluation table.

[0020] S202: Based on the decision variable evaluation table, select the variable with the highest information gain as the root node, select the second highest variable in sequence to construct branches, set the initial structure parameters of the decision tree, and generate the decision tree structure framework.

[0021] S203: Based on the decision tree structure framework, input current density, potential difference and soil type data, train and validate the model, optimize model performance by adjusting parameters in real time, and generate an initial decision tree model.

[0022] As a further aspect of the present invention, the steps of applying the initial decision tree model to a simulated environment for scenario testing, comparing the model output with the cathodic protection effect data of buried pipelines, and adjusting the branch conditions and thresholds in the decision tree to obtain the adjusted decision tree model are as follows:

[0023] S301: Based on the initial decision tree model, set up a simulation test environment, use simulated current density, potential difference and soil type data to conduct scenario tests, record model output, and generate simulation test results;

[0024] S302: Based on the simulation test results, the random forest algorithm is used to compare the model output with the real-time cathodic protection effect data of the buried pipeline network, identify the advantages and disadvantages of the model, analyze the sources of error, and generate model effect comparison analysis results;

[0025] S303: Based on the comparative analysis results of the model performance, adjust the branch conditions and thresholds in the decision tree, optimize the model structure and generalization ability, verify the adjustment effect through multiple tests, and generate the adjusted decision tree model.

[0026] As a further aspect of the present invention, the formula for the random forest algorithm is as follows:

[0027]

[0028] in, This represents the error value between the model and the real-time result. Data representing the effectiveness of cathodic protection This represents the cathodic protection effect predicted by the model. Represents the weighting coefficient. Represents the depth of the pipeline network. This indicates the time when the pipeline was laid.

[0029] As a further aspect of the present invention, the steps of collecting electromagnetic field data of interference sources using the adjusted decision tree model, simulating electric and magnetic fields, recording the intensity and direction changes of the simulated data points, and generating electromagnetic field simulation data are as follows:

[0030] S401: Based on the adjusted decision tree model, configure the electromagnetic field testing equipment, collect the electromagnetic field data of the interference source, record the measurement data of the electric field and magnetic field, and generate the electromagnetic field initialization measurement record.

[0031] S402: Based on the electromagnetic field initialization measurement record, simulate the electric field and magnetic field, adjust the position and intensity of the electromagnetic source, record the changes in intensity and direction of the data points during the simulation, and generate a direction change diagram;

[0032] S403: By integrating the simulation data through the aforementioned direction change diagram, data analysis and visualization processing are performed to identify key changes in electromagnetic field strength and direction, and electromagnetic field simulation data is generated.

[0033] As a further aspect of the present invention, based on the electromagnetic field simulation data, the parameters of the output current and potential distribution are adjusted, and the parameters are adjusted through multiple rounds of simulation tests. The changes in electromagnetic interference after adjustment are monitored to form optimized current and potential parameters. The specific steps are as follows:

[0034] S501: Using the electromagnetic field simulation data, set the initial output current and potential distribution parameters, perform electromagnetic field simulation tests, record the current and potential response data, and generate simulation test records.

[0035] S502: Based on the simulation test records, a genetic algorithm is used to adjust the parameters of the output current and potential distribution, and multiple rounds of simulation tests are conducted. After each adjustment, the key parameters and electromagnetic interference changes are recorded to generate multiple rounds of simulation adjustment records.

[0036] S503: Based on the multi-round simulation adjustment records, combined simulation data, electromagnetic interference is avoided, and the cathodic protection effect of the complex buried pipeline network is verified through simulation, generating optimized current and potential parameters.

[0037] As a further aspect of the present invention, the formula of the genetic algorithm is as follows:

[0038]

[0039] in, These are the optimal parameter values ​​after simulation. This represents the adjusted output current. Represents potential difference. Represents real-time electromagnetic interference values. Represents the target electromagnetic interference value. Represents resistance.

[0040] As a further aspect of the present invention, the steps for real-time monitoring of the cathodic protection effect of the buried pipeline network based on the optimized current and potential parameters, verifying the stable operation efficiency under complex current interference environments, and obtaining the strategy implementation results are as follows:

[0041] S601: Based on the optimized current and potential parameters, monitor the effect of cathodic protection of complex buried pipelines in real time, and record the initial protection effect and key performance indicators by comparing the data before and after adjustment, and generate initial monitoring data records.

[0042] S602: Using the initial monitoring data recorded, analyze the cathodic protection effect under complex current interference environment, adjust the monitoring strategy and protection parameters to cope with changing environmental factors, continuously record the effect data after adjustment, and generate a cathodic protection strategy.

[0043] S603: Using the aforementioned cathodic protection strategy, by analyzing long-term operating data, verify the stable operating efficiency of the current and potential parameter adjustment strategy under complex current interference environment, and generate strategy implementation results.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] This invention collects soil resistivity, pipeline material, and environmental humidity data from specific industrial areas, analyzes the variability and correlation of the data to identify environmental parameter characteristics, and effectively improves the adaptability of cathodic protection strategies for specific environments. Information gain is selected for decision point selection and an initial decision tree model is constructed. Further model adjustments are made through scenario testing in simulated environments, enabling precise responses to various complex interference conditions and optimizing current and potential distribution. This not only improves the stability of the cathodic protection system in variable interference environments but also allows for fine-tuning of current and potential parameters through electromagnetic field data simulation and multiple rounds of simulation testing, significantly enhancing the corrosion resistance of buried pipelines and the overall efficiency of the system. Real-time monitoring and adjustment ensure the long-term safety and stability of buried pipelines under complex current interference environments, significantly extending the service life of the facilities. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0048] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0049] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0050] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0051] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 This is a detailed schematic diagram of S6 of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0054] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0055] Please see Figure 1 This invention provides a technical solution: an optimized method for cathodic protection of complex buried pipeline networks under current interference, comprising the following steps:

[0056] S1: Using on-site sampling, data on soil resistivity, pipe material, and ambient humidity from differentiated industrial areas are collected. By analyzing the variability and correlation of the data, environmental parameter characteristics are obtained.

[0057] S2: Based on environmental parameter characteristics, select the information gain to choose the decision point, conduct a criticality assessment of variables, including current density, potential difference and soil type, input the variables, and construct an initial decision tree model;

[0058] S3: Apply the initial decision tree model to the simulation platform for multi-scenario testing, compare the model output with the cathodic protection effect data of buried pipelines, adjust the branch conditions and thresholds in the decision tree, monitor the adjustment effect in real time, and obtain the adjusted decision tree model.

[0059] S4: Using the adjusted decision tree model, collect electromagnetic field data of interference sources, simulate electric and magnetic fields, record the intensity and direction changes of simulated data points, match complex current interference situations, and generate electromagnetic field simulation data.

[0060] S5: Based on electromagnetic field simulation data, adjust the parameters of output current and potential distribution, adjust the parameters through multiple rounds of simulation tests, monitor the changes in electromagnetic interference after adjustment, evaluate the effect of cathodic protection, and form optimized current and potential parameters;

[0061] S6: Based on the optimized current and potential parameters, monitor the cathodic protection effect of the buried pipeline network in real time, continue to adjust the settings based on the feedback data, verify the stable operation efficiency under complex current interference environment, and obtain the strategy implementation results.

[0062] Environmental parameter characteristics include soil resistivity distribution maps, response data of differentiated pipeline materials, and environmental humidity measurements from multiple industrial areas. The initial decision tree model includes inputs of current density, potential difference, and soil type, the organization of variables in the model, and initial thresholds. The adjusted decision tree model includes optimized branching conditions, updated node thresholds, and model prediction response speed and efficiency. Electromagnetic field simulation data includes data on the changes in the intensity and direction of electric and magnetic fields. Optimized current and potential parameters include adjusted current density values, potential distribution maps, and corresponding simulation test results. Strategy implementation results include real-time monitoring data of the cathodic protection effect of buried pipelines under complex current interference environments, and indicators of the stability of the protection effect and operational efficiency.

[0063] Please see Figure 2 The specific steps for collecting soil resistivity, pipe material, and ambient humidity data from differentiated industrial areas using on-site sampling, and obtaining environmental parameter characteristics by analyzing the variability and correlation of the data, are as follows:

[0064] S101: The execution process for selecting representative locations within the industrial area to collect soil samples, inspecting pipes of varying types and changing humidity environments, recording the location and environmental conditions of each sampling point, and generating soil sample and environmental data tables is as follows;

[0065] Soil sampling is conducted in industrial areas, selecting representative points based on different locations and humidity conditions. The selection of each sampling point must be based on the diversity of geographical location and soil type to ensure the representativeness and wide coverage of the samples. Soil samples must be collected using scientific methods, and the geographical coordinates and environmental factors of each sampling point must be recorded accurately to facilitate subsequent data comparison and analysis. Sampling tools and containers must also maintain consistency and cleanliness to avoid sample contamination. Environmental conditions should be strictly controlled during sample storage and transportation to ensure sample stability. Soil sample and environmental data tables are then generated.

[0066] S102: Based on soil samples and environmental data sheets, the resistivity of the soil and the ambient humidity are measured on-site, the pipe material is recorded, the collected data is organized, the integrity of the data is checked, and the original measurement data record is generated. The execution process is as follows:

[0067] On-site measurements of soil resistivity and ambient humidity were performed according to the formula. Calculate soil resistivity ( In the formula, This represents the measured soil resistivity. Represents the distance between electrodes. This represents the cross-sectional area of ​​the soil column. Detailed formula explanation and calculation derivation: Setting the distance between electrodes. The soil resistivity was measured at a distance of 2.0 meters. The cross-sectional area of ​​the soil column is 300.0 ohms. It is 0.01 square meters, calculated according to the formula. The results indicate that the soil has a high resistivity, which is caused by low water content or high salt content. Comparing the soil resistivity in different regions helps to understand the environmental differences between regions.

[0068] S103: Based on the original measurement data records, perform data quality control, remove biased data points, classify the data according to soil type, and perform statistical analysis to analyze the correlation between resistivity, pipe material and ambient humidity. The execution process for generating environmental parameter characteristics is as follows.

[0069] Quality control and data removal based on raw measurement data require precise analysis to determine whether data points fall within the normal range. Data deviating from the normal range must be removed. Data classification is based on soil type to make the data more targeted and effective. Comprehensive statistical analysis is conducted on parameters such as resistivity, pipe material, and environmental humidity to explore the potential correlations between parameters. Statistical software and data analysis methods are used to analyze the correlations and influences among various data types. The aim is to identify key environmental factors affecting changes in soil resistivity. The analysis results can help to better understand the impact of soil conditions on infrastructure, provide a scientific basis for subsequent soil improvement and infrastructure design, and generate environmental parameter characteristics.

[0070] Please see Figure 3 The steps for constructing an initial decision tree model are as follows: Based on environmental parameter characteristics, information gain is selected to choose the decision point. Variables, including current density, potential difference, and soil type, are input.

[0071] S201: The execution flow of selecting current density, potential difference and soil type as variables based on environmental parameter characteristics, evaluating the influence of differential variables on the classification effect of decision tree, determining the optimal split node, and generating the decision variable evaluation table is as follows;

[0072] To assess the impact of differential variables on the classification performance of decision trees, follow the formula... Calculate the information gain. Where, Represents information gain. Represents the training dataset. Representative characteristic variables (such as current density, potential difference, soil type). Represents entropy. Representative in characteristics The value is A subset of data Representative characteristics All values. Detailed formula explanation and calculation derivation: Calculating the entropy of the entire dataset. If the dataset There is The results are categorized, with each category having a proportion of [missing information]. Then entropy for: For each feature For example, current density, the value is The data subset corresponding to each value entropy The calculation is performed using the same formula as above. If there are three different soil types, and the sample size percentage and entropy of each type are respectively... The entropy of the entire dataset is Information gain The calculation is as follows: This indicates that soil type is an important factor affecting classification results.

[0073] S202: Based on the decision variable evaluation table, select the variable with the highest information gain as the root node, and then select the second highest variable in sequence to build branches. Set the initial structure parameters of the decision tree and generate the decision tree structure framework. The execution flow is as follows:

[0074] Based on the decision variable evaluation table, the variable with the highest information gain is selected as the root node of the decision tree. This selection is to ensure that the model's discriminative power is maximized. The variable with the second highest information gain is used for further branching. The key to this process is to accurately calculate the information gain of each variable. This requires integrating all values ​​of current density, potential difference, and soil type, as well as the corresponding classification results. Statistical software is used for data calculation and analysis to ensure that the information gain calculation is accurate. Selecting variables and constructing the tree based on the information gain value can not only improve the model's classification accuracy but also effectively simplify the model structure, avoid overfitting, and generate the structural framework of the decision tree.

[0075] S203: Based on the decision tree structure framework, input current density, potential difference and soil type data, train and validate the model, optimize model performance by adjusting parameters in real time, and generate the initial decision tree model. The execution flow is as follows:

[0076] Using a decision tree framework, input data on current density, potential difference, and soil type are input. This step is the core of model training and validation. Model performance optimization is achieved by adjusting the parameters of the decision tree in real time, including tree depth, minimum number of samples per branch, etc. Through iterative training, the model gradually adapts to the characteristics of the data. In the validation phase, cross-validation or independent test sets are used to evaluate the model's generalization ability and accuracy. Each training result needs to be recorded and analyzed, and the model parameters need to be adjusted in a timely manner to achieve the best model performance and generate an initial decision tree model.

[0077] Please see Figure 4 The steps to apply the initial decision tree model to a simulated environment for scenario testing, compare the model output with the data on the cathodic protection effect of buried pipelines, and adjust the branch conditions and thresholds in the decision tree to obtain the adjusted decision tree model are as follows:

[0078] S301: Based on the initial decision tree model, set up the simulation test environment, use simulated current density, potential difference and soil type data to conduct scenario tests, record model output, and generate simulation test results. The execution flow is as follows:

[0079] A simulated testing environment is set up, and scenario tests are conducted using simulated current density, potential difference, and soil type data. This process requires accurately simulating various scenarios that occur in the real world to ensure the breadth and representativeness of the test results. Through simulated data, the decision tree model can be tested to assess its performance under various conditions. Recording the model output for each test is a crucial step, providing direct evidence for subsequent model optimization. Through the simulated test results, the classification effect of the model under specific conditions can be intuitively understood. The test not only verifies the current effectiveness of the model but also provides a testing foundation for future application scenarios. After the simulation test is completed, the simulation test results are generated.

[0080] S302: Based on the simulation test results, the random forest algorithm is used to compare the model output with the real-time cathodic protection effect data of the buried pipeline network, identify the advantages and disadvantages of the model, analyze the source of error, and generate the model effect comparison analysis results. The execution flow is as follows:

[0081] The formula for the Random Forest algorithm is as follows:

[0082]

[0083] in, This represents the error value between the model and the real-time result. Data representing the effectiveness of cathodic protection This represents the cathodic protection effect predicted by the model. Represents the weighting coefficient. Represents the depth of the pipeline network. This indicates the time when the pipeline was laid.

[0084] Detailed explanation of the formula and its calculation derivation:

[0085] The formula is used to calculate the error between the cathodic protection effect data of buried pipelines and the model predictions. The specific parameters in the formula are as follows:

[0086] Actual cathodic protection data is obtained through real-time monitoring by sensors in the buried pipeline network. For example, the protection potential of a certain pipeline section is -0.85 volts.

[0087] The cathodic protection data predicted by the model is obtained through the random forest algorithm. The prediction result for the same pipeline segment is set to -0.80 volts.

[0088] Weighting coefficient, calculated based on data quality and pipeline length. Data quality is assessed through sensor accuracy and signal strength, while pipeline length is directly measured. A pipeline segment with high data quality is assigned a weight of 1.5.

[0089] The depth of the pipeline network is obtained through geological surveying tools, for example, 2 meters.

[0090] The pipeline installation time is determined from the installation records, for example, 10 years.

[0091] Example of formula calculation process:

[0092] 1. Calculation and The absolute value of the difference, i.e. volt.

[0093] 2. Multiply the difference by the weighting factor. ,Right now .

[0094] 3. Calculation The value of, i.e. .

[0095] 4. Multiply the result of step 2 by the result of step 3, that is... .

[0096] 5. Sum the calculation results for all data points and divide by the total number of data points. The error was obtained. .

[0097] The results show that by carefully analyzing the difference between the actual protection potential and the model prediction for each pipeline segment, and considering data quality, pipeline length, depth, and service life, a more accurate model error assessment can be obtained. The assessment results can be used to further optimize the model, ensuring that the prediction results are closer to the actual situation and improving the accuracy of cathodic protection predictions.

[0098] S303: Based on the comparative analysis of model performance, adjust the branch conditions and thresholds in the decision tree, optimize the model structure and generalization ability, verify the adjustment effect through multiple tests, and generate the following execution flow of the adjusted decision tree model;

[0099] Adjust the branch conditions and thresholds in the decision tree according to the formula. Calculate the model error rate. Where, Represents the total error rate of the model. Represents the number of test data. It is an indicator function, when (Actual category) is not equal to The value is 1 for (predicted category) and 0 otherwise. Detailed formula explanation and derivation: When optimizing the model structure, the total error rate of the model can be calculated by performing error analysis on the results of each simulation test. For example, if the model makes 2 prediction errors in 10 tests, the error rate is calculated as follows: .

[0100] That is, the model's total error rate is 20%. By adjusting the branching conditions and thresholds of the decision tree, such as modifying a certain threshold to improve the accuracy of a specific classification, and conducting multiple tests to verify the effect of the adjustment, the aim is to reduce the error rate, enhance the model's generalization ability, and ensure that the model can achieve higher accuracy in future practical applications.

[0101] Please see Figure 5 The steps for generating electromagnetic field simulation data are as follows: Using the adjusted decision tree model, electromagnetic field data of interference sources are collected, electric and magnetic fields are simulated, and the intensity and direction changes of the simulated data points are recorded.

[0102] S401: The execution flow of configuring electromagnetic field testing equipment, collecting electromagnetic field data of interference sources, recording electric and magnetic field measurement data, and generating electromagnetic field initialization measurement records based on the adjusted decision tree model is as follows;

[0103] Configure electromagnetic field testing equipment to collect electromagnetic field data from interference sources, including measurements of electric and magnetic fields. Each test setup should ensure accurate recording of the intensity and direction of the electric and magnetic fields. Recording measurement data is a crucial step, providing the foundation for subsequent data analysis. Electromagnetic field data collection should include multiple measurements under different conditions to ensure the comprehensiveness and accuracy of the data. The data will be used to assess the stability of the electromagnetic field environment and the degree of influence of the interference sources, providing a preliminary quantitative description of the on-site electromagnetic environment. This forms the basis for further simulation and analysis, generating initial electromagnetic field measurement records.

[0104] S402: Based on the electromagnetic field initialization measurement record, the electric field and magnetic field are simulated, the position and intensity of the electromagnetic source are adjusted, the changes in intensity and direction of the data points are recorded during the simulation, and the execution flow of generating the direction change diagram is as follows;

[0105] Simulations of electric and magnetic fields are performed according to the formulas. Calculate the magnetic field strength. In the formula, Represents the magnetic field vector. Represents the magnetic constant. Represents the amount of charge. Represents the charge velocity vector. This represents the unit vector from the charge to the observation point. This represents the distance from the charge to the observation point. Detailed explanation of the formula and its derivation: When the charge... With speed The strength of the magnetic field generated during motion can be calculated using the formula above. (Settings) , The observation point is 1m away from the charge in the due east direction, i.e. magnetic field strength The calculation is as follows: This means that, under this configuration, the magnetic field strength lies in a plane perpendicular to the direction of velocity and the observation point. By adjusting the position and intensity of the electromagnetic source, the changes in intensity and direction of data points during the simulation are recorded, generating a graph showing the changes in the direction of the electric and magnetic fields.

[0106] S403: The execution flow for generating electromagnetic field simulation data by integrating simulation data through the direction change diagram, performing data analysis and visualization processing, identifying key changes in electromagnetic field strength and direction, and generating electromagnetic field simulation data is as follows;

[0107] By integrating simulation data through direction change diagrams, data analysis and visualization are performed. This includes identifying key changes in electromagnetic field strength and direction, and using advanced data analysis tools such as statistical software and visualization platforms to convert electromagnetic field variation data into graphs and charts. This makes the dynamic changes of the electromagnetic field more intuitive and easier to understand. Analyzing the data can help identify specific characteristics of interference sources, such as location, intensity, and range of influence, and how to reduce adverse effects by adjusting parameters. The integrated data not only provides a deeper understanding of the electromagnetic field environment but also provides a scientific basis for further research and applications, generating electromagnetic field simulation data.

[0108] Please see Figure 6 Based on electromagnetic field simulation data, the parameters of output current and potential distribution are adjusted. Multiple rounds of simulation tests are used to refine these parameters, and changes in electromagnetic interference are monitored after adjustment. The specific steps for optimizing current and potential parameters are as follows:

[0109] S501: The execution flow of setting the initial output current and potential distribution parameters through electromagnetic field simulation data, performing electromagnetic field simulation tests, recording the current and potential response data, and generating simulation test records is as follows;

[0110] Set the initial output current and potential distribution parameters, perform electromagnetic field simulation tests, and follow the formula. Calculate the potential difference. In the formula, Represents potential difference. Represents current. Represents resistance. Detailed formula explanation and calculation derivation: In electromagnetic field simulation testing, the output current is set... For 10A and resistor 5Ω, potential difference It can be calculated using the formula: The results show that when a 10A current is passed through a circuit with a 5Ω resistor, a potential difference of 50V will be generated. By recording the current and potential response data, the accuracy of the simulation model and its response capability to electromagnetic interference can be verified, providing a basis for further optimization.

[0111] S502: Based on the simulation test records, a genetic algorithm is used to adjust the parameters of the output current and potential distribution, and multiple rounds of simulation tests are conducted. After each adjustment, the key parameters and electromagnetic interference changes are recorded. The execution flow of generating multi-round simulation adjustment records is as follows:

[0112] The formula for the genetic algorithm is as follows:

[0113]

[0114] in, These are the optimal parameter values ​​after simulation. This represents the adjusted output current. Represents potential difference. Represents real-time electromagnetic interference values. Represents the target electromagnetic interference value. Represents resistance.

[0115] Detailed explanation of the formula and its calculation derivation:

[0116] In the simulation model that minimizes electromagnetic interference, the output current Potential difference Real-time electromagnetic interference value Target electromagnetic interference value and resistance These are key parameters. The following will explain in detail the meaning of each parameter, its calculation method, and how it is applied in the formula, and provide specific numerical examples.

[0117] Output current (Ampere, A): The parameter is obtained by monitoring through circuit simulation software. It is the result of adjustments to improve electromagnetic interference. The output current is set to 2A under a specific configuration based on actual measurement.

[0118] Potential difference (Volts): The potential difference is obtained by directly measuring the voltage between two points in the circuit using a voltmeter. The measured value is set to 5V.

[0119] Current electromagnetic interference value (V / m): The values ​​were obtained by measuring the electromagnetic field strength at different locations and configurations using an electromagnetic field strength meter. The measured values ​​were used to evaluate the initial intensity of electromagnetic interference in the simulation. The current electromagnetic interference value was set to 3V / m.

[0120] Target electromagnetic interference value (V / m): This represents the desired level of interference under ideal conditions. It is a value set based on environmental safety standards, with a target electromagnetic interference value of 0.5V / m.

[0121] resistance (Ohm, Ω): The resistance value is obtained by measuring it in the circuit with an ohmmeter. Let the resistance value be 50Ω.

[0122] Formula calculation derivation process:

[0123] It is necessary to calculate the change in electromagnetic interference, i.e. In the given numerical values, V / m and ,so:

[0124]

[0125] The square root of the value:

[0126]

[0127] Substitute into the formula to calculate :

[0128]

[0129] The results show that by adjusting the current and voltage and optimizing the circuit design, electromagnetic interference can be significantly reduced to below the target interference value, thus promoting the stable operation of the system.

[0130] S503: Based on multi-round simulation adjustment records, comprehensive simulation data, and avoiding electromagnetic interference, the execution process of generating optimized current and potential parameters by verifying the cathodic protection effect of complex buried pipelines through simulation is as follows;

[0131] By conducting multiple rounds of simulation adjustments and recordings, and integrating simulation data, the aim is to avoid electromagnetic interference, especially in complex buried pipeline systems. This is achieved by precisely adjusting the distribution parameters of current and potential, continuously testing and recording the results, and using advanced simulation tools for multiple rounds of testing to gradually optimize the parameters. This ensures that each adjustment is based on the results of the previous simulation, improving the accuracy and reliability of the simulation. At the same time, through this iterative simulation process, the improvement of the adjustment measures on the cathodic protection effect can be effectively verified, and optimized current and potential parameters can be generated.

[0132] Please see Figure 7 The specific steps for obtaining the strategy implementation results are as follows: Based on optimized current and potential parameters, the cathodic protection effect of buried pipelines is monitored in real time, the stable operation efficiency under complex current interference environment is verified, and the strategy implementation results are obtained.

[0133] S601: Based on the optimized current and potential parameters, the effect of cathodic protection of complex buried pipelines is monitored in real time. By comparing the data before and after adjustment, the initial protection effect and key performance indicators are recorded, and the execution process for generating initial monitoring data records is as follows;

[0134] Based on optimized current and potential parameters, the effectiveness of cathodic protection in complex buried pipelines is monitored in real time. The key is to compare the data before and after adjustment to ensure the accuracy and real-time nature of the monitoring. By recording the initial protection effect and key performance indicators, the initial state and effect of cathodic protection can be intuitively understood. The data recording not only provides direct evidence of the effectiveness of the protection measures, but also provides basic data for subsequent adjustments. The recorded key performance indicators include current leakage rate, potential deviation, etc., which are important parameters for evaluating the effectiveness of cathodic protection, and generate initial monitoring data records.

[0135] S602: Using initial monitoring data recording, analyze the cathodic protection effect under complex current interference environment, adjust monitoring strategy and protection parameters to cope with changing environmental factors, continuously record the effect data after adjustment, and generate the following cathodic protection strategy execution flow;

[0136] Analyze the cathodic protection effect under complex current interference environment, according to the formula Calculate the cathodic protection efficiency. Where, Represents the efficiency of cathodic protection. Represents leakage current. Represents the total current. Detailed formula explanation and calculation derivation: The total current is set during a specific monitoring period. 100A, leakage current If the value is 5A, then the cathodic protection efficiency is... The calculation is as follows: The results show that the cathodic protection efficiency is 95% under the current and potential parameters. By continuously recording the effect data after adjustment, the impact of various adjustment measures on the protection efficiency can be verified. The aim is to optimize the cathodic protection strategy under complex current interference environment.

[0137] S603: Using the cathodic protection strategy, by analyzing long-term operating data, the stable operating efficiency of the current and potential parameter adjustment strategy under complex current interference environment is verified, and the execution flow of generating strategy implementation results is as follows;

[0138] By utilizing cathodic protection strategies and analyzing long-term operational data, the stable operating efficiency of current and potential parameter adjustment strategies under complex current interference environments is verified. Long-term data provides in-depth insights into the effectiveness of the strategy, revealing the impact of different environmental factors on cathodic protection. In particular, the stability and adaptability of the strategy can be significantly improved after multiple adjustments to current and potential parameters. Data analysis not only helps optimize cathodic protection parameters but also predicts future adjustment needs, providing a scientific basis for ensuring the long-term stable operation of buried pipelines under complex current interference environments and generating strategy implementation results.

[0139] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing cathodic protection of complex buried pipeline networks under current interference, characterized in that, The method comprises the following steps: Collecting soil resistivity, pipeline material and environmental humidity data from different industrial areas through field sampling, obtaining environmental parameter characteristics by analyzing the variability and correlation of the data; Selecting information gain to select decision points based on the environmental parameter characteristics, inputting variables including current density, potential difference and soil type, and constructing an initial decision tree model; Applying the initial decision tree model to a simulated environment for scene testing, comparing the model output with buried pipeline network cathodic protection effect data, adjusting the branch conditions and threshold values in the decision tree, and obtaining an adjusted decision tree model; Collecting electromagnetic field data of interference sources using the adjusted decision tree model, simulating electric and magnetic fields, recording the intensity and direction changes of the simulation data points, and generating electromagnetic field simulation data; Adjusting the parameters of output current and potential distribution based on the electromagnetic field simulation data, adjusting the parameters through multiple rounds of simulation testing, monitoring the changes of the adjusted electromagnetic interference, and forming optimized current and potential parameters; Real-time monitoring of buried pipeline network cathodic protection effect based on the optimized current and potential parameters, verifying the stable operation efficiency in complex current interference environment, and obtaining strategy implementation results.

2. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, The environmental parameter characteristics include soil resistivity distribution map, reaction data of different pipeline materials and environmental humidity measurement values of multiple industrial areas, the initial decision tree model includes input of current density, potential difference and soil type, organization form of variables in the model and initial threshold value, the adjusted decision tree model includes optimized branch conditions, updated node threshold value and model prediction response speed and efficiency, the electromagnetic field simulation data includes intensity and direction change data of electric and magnetic fields, the optimized current and potential parameters include adjusted current density value, potential distribution map and corresponding simulation test results, and the strategy implementation results include real-time monitoring data of buried pipeline network cathodic protection effect in complex current interference environment, stability of protection effect and indicators of operation efficiency.

3. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, The steps of collecting soil resistivity, pipeline material and environmental humidity data from different industrial areas through field sampling, and obtaining environmental parameter characteristics by analyzing the variability and correlation of the data are as follows: Selecting representative positions in the industrial area for soil sampling, checking pipelines covering different types and changing humidity environments, recording the position and environmental conditions of each sampling point, generating soil samples and environmental data table; Based on the soil samples and environmental data table, field determination of soil resistivity and environmental humidity, recording of pipeline material, arrangement of collected data, checking of data integrity, and generation of original measurement data record; According to the original measurement data record, data quality control is performed to eliminate deviated data points, data classification is performed in combination with soil type, statistical analysis is performed, the correlation between resistivity, pipeline material and environmental humidity is analyzed, and environmental parameter characteristics are generated.

4. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, The step of selecting information gain for decision point selection through the environmental parameter characteristics, inputting variables including current density, potential difference and soil type, and constructing an initialization decision tree model is specifically as follows: The step of selecting current density, potential difference and soil type as variables, evaluating the influence of differentiated variables on the classification effect of the decision tree, determining the optimal segmentation node, and generating a decision variable evaluation table through the environmental parameter characteristics is specifically as follows: The step of selecting the variable with the highest information gain as the root node, sequentially selecting the second highest variable for branch construction, setting the initialization structure parameters of the decision tree, and generating a decision tree structure framework based on the decision variable evaluation table is specifically as follows: The step of inputting current density, potential difference and soil type data according to the decision tree structure framework, training and verifying the model, optimizing the model performance through real-time parameter adjustment, and generating an initialization decision tree model is specifically as follows:

5. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, The step of adjusting the branch conditions and threshold values in the decision tree by comparing the model output with the buried pipe network cathodic protection effect data through scene testing of the initialization decision tree model in a simulated environment is specifically as follows: The step of setting a simulated test environment according to the initialization decision tree model, using simulated current density, potential difference and soil type data for scene testing, recording the model output, and generating a simulated test result is specifically as follows: The step of comparing the model output with real-time buried pipe network cathodic protection effect data based on the simulated test result, identifying the advantages and disadvantages of the model, analyzing the error sources, and generating a model effect comparison and analysis result by using a random forest algorithm is specifically as follows: The step of adjusting the branch conditions and threshold values in the decision tree according to the model effect comparison and analysis result, optimizing the model structure to improve the generalization ability, verifying the adjustment effect through multiple tests, and generating an adjusted decision tree model is specifically as follows.

6. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 5, characterized in that, The formula of the random forest algorithm is as follows: wherein, is the error value of the model and real-time effect, represents the cathodic protection effect data, represents the cathodic protection effect predicted by the model, represents the weight coefficient, represents the depth of the pipe network, represents the pipe network burying time.

7. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, The step of collecting electromagnetic field data of interference sources, simulating electric and magnetic fields, recording the intensity and direction changes of data points in the simulation, and generating electromagnetic field simulation data by using the adjusted decision tree model is specifically as follows: The step of configuring electromagnetic field test equipment according to the adjusted decision tree model, collecting electromagnetic field data of interference sources, recording the measurement data of electric and magnetic fields, and generating an electromagnetic field initialization measurement record is specifically as follows: The step of simulating electric and magnetic fields based on the electromagnetic field initialization measurement record, adjusting the position and intensity of electromagnetic sources, recording the intensity and direction changes of data points in the simulation process, and generating a direction change graph is specifically as follows: The step of integrating simulation data, performing data analysis and visualization processing, identifying key changes in electromagnetic field intensity and direction, and generating electromagnetic field simulation data through the direction change graph is specifically as follows:

8. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, The step of adjusting the parameters of output current and potential distribution, adjusting the parameters through multiple rounds of simulation tests, monitoring the changes of electromagnetic interference after adjustment, and forming optimized current and potential parameters based on the electromagnetic field simulation data is specifically as follows: The step of setting initialization output current and potential distribution parameters through the electromagnetic field simulation data, performing electromagnetic field simulation tests, recording the response data of current and potential, and generating a simulation test record is specifically as follows: According to the simulation test record, the parameters of the output current and the potential distribution are adjusted by using a genetic algorithm, a plurality of rounds of simulation tests are performed, key parameters and electromagnetic interference changes are recorded after each adjustment, and a plurality of rounds of simulation adjustment records are generated; Based on the plurality of rounds of simulation adjustment records, the simulation data is synthesized to avoid electromagnetic interference, the cathodic protection effect of the complex buried pipe network is verified by simulation, and optimized current and potential parameters are generated.

9. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 8, characterized in that, The formula of the genetic algorithm is as follows: wherein, is the optimal parameter value after simulation, represents the adjusted output current, represents the potential difference, represents the real-time electromagnetic interference value, represents the target electromagnetic interference value, represents the resistance.

10. The method for complex buried pipeline network cathodic protection optimization under current interference according to claim 1, characterized in that, According to the optimized current and potential parameters, the cathodic protection effect of the buried pipe network is monitored in real time, the stable operation efficiency in a complex current interference environment is verified, and the strategy implementation result is obtained, and the steps are as follows: According to the optimized current and potential parameters, the effect of the cathodic protection of the complex buried pipe network is monitored in real time, the data before and after adjustment are compared, the initial protection effect and key performance indicators are recorded, and initial monitoring data records are generated; Using the initial monitoring data records, the cathodic protection effect in a complex current interference environment is analyzed, the monitoring strategy and protection parameters are adjusted, the changing environmental factors are dealt with, the effect data after adjustment is continuously recorded, and a cathodic protection strategy is generated; Using the cathodic protection strategy, the current and potential parameter adjustment strategy in a complex current interference environment is verified by analyzing long-term operation data, and a strategy implementation result is generated.

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