Cathode protection optimization method for complex buried pipe network under current interference

Abstract

The invention relates to the technical field of cathode protection, in particular to a method for optimizing cathode protection of a complex buried pipe network under current interference, which comprises the following steps of: collecting data of soil resistivity, pipeline material and environment humidity from differentiated industrial areas by adopting an on-site sampling mode, and analyzing the variation and relevance of the data to obtain the cathode protection of the complex buried pipe network. And obtaining environmental parameter characteristics. According to the invention, through the scene test adjustment model of the simulation environment, various complex interference conditions can be accurately corresponding, the distribution of current and potential can be optimized, the stability of the cathode protection system in the variable interference environment is improved, and through the simulation of the electromagnetic field data and the multi-round simulation test, the fine adjustment of the current and potential parameters is realized; the anti-corrosion effect of the buried pipe network and the overall efficiency of the system are remarkably improved, long-term safety and stability of the buried pipe network in a complex current interference environment are ensured through real-time monitoring and adjustment, and the service life of facilities is remarkably prolonged.

Description

Technical Field

[0001] The invention relates to the technical field of cathodic protection, and in particular to a method for optimizing cathodic protection of a complex buried pipe network under current interference. Background Art

[0002] Cathodic protection technology is an electrochemical protection method widely used to prevent metal structures from corrosion in specific environments. It is mainly used to protect metal structures such as 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 keep the potential on the surface of the structure at a sufficiently low level through a DC power supply or the use of an anode to prevent electrochemical corrosion reactions in the environment. This technology effectively slows down or prevents the dissolution of metal ions from the metal surface, protecting the metal from corrosion.

[0003] Among them, the optimization method of cathodic protection of complex buried pipeline networks under current interference refers to a method of technically optimizing the cathodic protection system of buried pipelines under the condition of current interference. This interference originates from nearby power lines, rail transit or electrical facilities. The stray current affects the normal operation of the cathodic protection system and reduces the 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 that even in complex interference environments, the pipeline network can be effectively protected from corrosion, extend its service life and ensure safe operation.

[0004] Although cathodic protection methods in the prior art are widely used for corrosion protection of various metal structures, in complex interference environments, such as under the influence of power lines, rail transit or electrical facilities, conventional cathodic protection systems are difficult to cope with the ever-changing current and potential distribution in the environment, resulting in poor corrosion protection and inefficient system operation. This technology relies on an external DC power supply or anode to maintain the low potential of the metal structure, but fails to fully consider the specific effects of environmental factors such as soil resistivity and humidity, and lacks the ability to dynamically respond to electromagnetic interference. In the operation of the prior art, deficiencies lead to uneven corrosion protection, increased maintenance costs and safety risks. The lack of an effective real-time monitoring and adjustment mechanism makes the response not quick or effective enough when problems arise, further exacerbating the corrosion and damage of the facilities. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method for optimizing cathodic protection of a complex buried pipeline network under current interference.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for optimizing cathodic protection of a complex buried pipeline network under current interference, comprising the following steps: S1: Using field sampling, we collected data on soil resistivity, pipeline material, and ambient humidity from differentiated industrial areas, and obtained environmental parameter characteristics by analyzing the variability and correlation of the data; S2: Select information gain to select decision points based on the environmental parameter characteristics, input variables including current density, potential difference and soil type, and construct an initialization decision tree model; S3: applying the initialized decision tree model to a simulation environment for scenario testing, comparing the model output with the buried pipeline network cathodic protection effect data, adjusting the branch conditions and thresholds in the decision tree, and obtaining an adjusted decision tree model; S4: using the adjusted decision tree model, collecting electromagnetic field data of the interference source, simulating the electric field and magnetic field, recording the intensity and direction changes of the simulated data points, and generating electromagnetic field simulation data; S5: Based on the electromagnetic field simulation data, adjusting the parameters of the output current and potential distribution, adjusting the parameters through multiple rounds of simulation tests, monitoring the changes of the electromagnetic interference after the adjustment, and forming optimized current and potential parameters; S6: Based on the optimized current and potential parameters, the cathodic protection effect of the buried pipeline network is monitored in real time, the stable operation efficiency under the complex current interference environment is verified, and the strategy implementation result is obtained.

[0007] As a further solution of the present invention, the environmental parameter characteristics include soil resistivity distribution map, reaction data of differentiated pipeline materials and environmental humidity measurement values ​​of multiple industrial areas, the initialized decision tree model includes inputs of current density, potential difference and soil type, the organization form of variables in the model and the initial threshold, the adjusted decision tree model includes optimized branch conditions, updated node thresholds and model prediction response speed and efficiency, the electromagnetic field simulation data includes change data of the intensity and direction of the electric field and magnetic field, the optimized current and potential parameters include adjusted current density values, potential distribution maps and corresponding simulation test results, and the strategy implementation results include real-time monitoring data of the cathodic protection effect of buried pipelines under complex current interference environments, the stability of the protection effect and indicators of operating efficiency.

[0008] As a further solution of the present invention, the data of soil resistivity, pipeline material and environmental humidity from differentiated industrial areas are collected by field sampling. The steps of obtaining environmental parameter characteristics by analyzing the variability and correlation of the data are as follows: S101: Select representative locations within the industrial area for soil sampling, check to cover differentiated types of pipelines and varying humidity environments, record the location and environmental conditions of each sampling point, and generate soil sample and environmental data tables; S102: Based on the soil sample and the environmental data table, the resistivity and environmental humidity of the soil are measured on site, the material of the pipeline is recorded, the collected data is sorted, the integrity of the data is checked, and an original measurement data record is generated; S103: Perform data quality control based on the original measurement data records, remove deviant data points, classify data based on soil types, and perform statistical analysis to analyze the correlation between resistivity, pipeline material and ambient humidity to generate environmental parameter characteristics.

[0009] As a further solution of the present invention, the information gain is selected to select the decision point through the environmental parameter characteristics, and the variables are input, including current density, potential difference and soil type. The steps of constructing the initialization decision tree model are specifically as follows: S201: selecting current density, potential difference and soil type as variables based on the environmental parameter characteristics, evaluating the influence of the differentiated variables on the classification effect of the decision tree, determining the optimal segmentation node, and generating a decision variable evaluation table; S202: Based on the decision variable evaluation table, select the variable with information gain as the root node, select the variables with the next highest information gain in turn for branch construction, set the initialization structure parameters of the decision tree, and generate a decision tree structure framework; S203: According to the decision tree structure framework, current density, potential difference and soil type data are input to train and verify the model, and the model performance is optimized by adjusting parameters in real time to generate an initialized decision tree model.

[0010] As a further solution of the present invention, the initialized decision tree model is applied to a simulation environment for scenario testing, the model output is compared with the buried pipe network cathodic protection effect data, the branch conditions and thresholds in the decision tree are adjusted, and the steps of obtaining the adjusted decision tree model are specifically as follows: S301: according to the initialized decision tree model, setting a simulation test environment, using simulated current density, potential difference and soil type data to perform scenario testing, and recording model output to generate simulation test results; S302: Based on the simulation test results, a random forest algorithm is used to compare the model output with the real-time buried pipeline network cathodic protection effect data, identify the advantages and disadvantages of the model, analyze the error sources, and generate model effect comparison analysis results; S303: According to the comparative analysis results of the model effects, the branch conditions and thresholds in the decision tree are adjusted to optimize the model structure and generalization capability, the adjustment effects are verified through multiple tests, and an adjusted decision tree model is generated.

[0011] As a further solution of the present invention, the formula of the random forest algorithm is as follows:

[0012] in, is the error value between the model and the 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 time when the pipeline network is buried.

[0013] As a further solution of the present invention, the adjusted decision tree model is used to collect electromagnetic field data of the interference source, simulate the electric field and magnetic field, and record the intensity and direction changes of the simulated data points. The steps of generating electromagnetic field simulation data are specifically as follows: S401: According to the adjusted decision tree model, configure electromagnetic field test equipment, collect electromagnetic field data of interference sources, record electric field and magnetic field measurement data, and generate electromagnetic field initialization measurement records; S402: Based on the electromagnetic field initialization measurement record, simulate the electric field and magnetic field, adjust the position and strength of the electromagnetic source, record the changes in the strength and direction of the data points during the simulation, and generate a direction change diagram; S403: Integrate simulation data through the direction change diagram, perform data analysis and visualization processing, identify key changes in electromagnetic field intensity and direction, and generate electromagnetic field simulation data.

[0014] As a further solution of the present invention, based on the electromagnetic field simulation data, the parameters of the output current and potential distribution are adjusted, the parameters are adjusted through multiple rounds of simulation tests, and the changes in the electromagnetic interference after the adjustment are monitored. The steps of optimizing the current and potential parameters are specifically as follows: S501: using the electromagnetic field simulation data, setting the initial output current and potential distribution parameters, performing an electromagnetic field simulation test, recording the current and potential response data, and generating a simulation test record; S502: According to the simulation test record, using a genetic algorithm, adjusting the parameters of the output current and potential distribution, performing multiple rounds of simulation tests, recording changes in key parameters and electromagnetic interference after each adjustment, and generating multiple rounds of simulation adjustment records; S503: Based on the multiple rounds of simulation adjustment records, the simulation data is integrated to avoid electromagnetic interference, and the cathodic protection effect of the complex buried pipeline network is adjusted through simulation verification to generate optimized current and potential parameters.

[0015] As a further solution of the present invention, the formula of the genetic algorithm is as follows:

[0016] in, 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 resistance.

[0017] As a further solution of the present invention, according to the optimized current and potential parameters, the cathodic protection effect of the buried pipe network is monitored in real time, and the stable operation efficiency under the complex current interference environment is verified. The steps of obtaining the strategy implementation results are specifically as follows: S601: According to the optimized current and potential parameters, the effect of cathodic protection of the complex buried pipe network is monitored in real time, and the initial protection effect and key performance indicators are recorded by comparing the data before and after the adjustment, so as to generate an initial monitoring data record; S602: using the initial monitoring data record, analyzing the cathodic protection effect in a complex current interference environment, adjusting the monitoring strategy and protection parameters to cope with changing environmental factors, continuously recording the adjusted effect data, and generating a cathodic protection strategy; S603: Using the cathodic protection strategy, by analyzing long-term operation data, verifying the stable operation efficiency of the current and potential parameter adjustment strategy in a complex current interference environment, and generating strategy implementation results.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by collecting soil resistivity, pipeline material and environmental humidity data of specific industrial areas, analyzing the variability and correlation of the data to identify environmental parameter characteristics, the adaptability of the cathodic protection strategy for a specific environment is effectively improved. Information gain is selected to select decision points and construct an initial decision tree model. The model is further adjusted through scenario testing in a simulated environment, which can accurately correspond to various complex interference conditions and optimize the distribution of current and potential. Not only is the stability of the cathodic protection system improved in a variable interference environment, but also through the simulation of electromagnetic field data and multiple rounds of simulation testing, the fine adjustment of current and potential parameters is achieved, which significantly improves the anti-corrosion effect of the buried pipeline network and the overall efficiency of the system. Through real-time monitoring and adjustment, the long-term safety and stability of the buried pipeline network in a complex current interference environment is ensured, and the service life of the facility is significantly extended. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 It is a schematic diagram of the refinement of S1 of the present invention; Figure 3 It is a schematic diagram of the refinement of S2 of the present invention; Figure 4It is a schematic diagram of the refinement of S3 of the present invention; Figure 5 It is a schematic diagram of the refinement of S4 of the present invention; Figure 6 It is a detailed schematic diagram of S5 of the present invention; Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0022] See also Figure 1 The present invention provides a technical solution, a method for optimizing cathodic protection of a complex buried pipeline network under current interference, comprising the following steps: S1: Using field sampling, we collected data on soil resistivity, pipeline material, and ambient humidity from differentiated industrial areas, and obtained environmental parameter characteristics by analyzing the variability and correlation of the data; S2: Select information gain to select decision points through environmental parameter characteristics, conduct critical evaluation of variables, including current density, potential difference and soil type, input variables, and construct an initial decision tree model; S3: Apply the initialized decision tree model to the simulation platform for multi-scenario testing, compare the model output with the buried pipeline network cathodic protection effect data, adjust the branch conditions and thresholds in the decision tree, monitor the adjustment effect in real time, and obtain the adjusted decision tree model; 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 conditions, and generate electromagnetic field simulation data; 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, evaluate the effect of cathodic protection, and form optimized current and potential parameters; S6: Based on the optimized current and potential parameters, the cathodic protection effect of the buried pipeline network is monitored in real time, and the settings are adjusted according to the feedback data to verify the stable operation efficiency under complex current interference environment and obtain the results of strategy implementation.

[0023] Environmental parameter characteristics include soil resistivity distribution map, response data of differentiated pipeline materials and ambient humidity measurements of multiple industrial areas. Initialization of the decision tree model includes inputs of current density, potential difference and soil type, organization of variables in the model and initial thresholds. The adjusted decision tree model includes optimized branch conditions, updated node thresholds and model prediction response speed and efficiency. Electromagnetic field simulation data includes data on changes in the intensity and direction of electric and magnetic fields. Optimization of current and potential parameters includes 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, stability of protection effects and indicators of operating efficiency.

[0024] See also Figure 2 , using field sampling to collect data on soil resistivity, pipeline material and ambient humidity from differentiated industrial areas. By analyzing the variability and correlation of the data, the steps to obtain the environmental parameter characteristics are as follows: S101: Select representative locations in the industrial area for soil sampling, check the coverage of differentiated types of pipelines and changing humidity environments, record the location and environmental conditions of each sampling point, and generate the soil sample and environmental data table. The execution process is as follows; Soil sampling is carried out in industrial areas, and representative points are selected for sampling based on different locations and humidity conditions. The selection of each sampling point must be based on the geographical location and the diversity of soil types to ensure the representativeness of the samples and the breadth of coverage. Soil samples must be collected in accordance with scientific methods, and the geographical coordinates and environmental factors of each sample point must be accurately recorded to facilitate subsequent data comparison and analysis. Sampling tools and containers must also maintain consistency and cleanliness to avoid sample contamination. Environmental conditions should also be strictly controlled during the storage and transportation of samples to ensure the stability of the samples and generate soil sample and environmental data tables.

[0025] S102: Based on the soil sample and the environmental data table, the resistivity and environmental humidity of the soil are measured on site, the material of the pipeline is recorded, the collected data is sorted, the integrity of the data is checked, and the execution process of generating the original measurement data record is as follows; The resistivity of the soil and the ambient humidity were measured on site, and the formula , calculate soil resistivity ( ). In the formula, represents the measured soil resistance, represents the distance between electrodes, Represents the cross-sectional area of ​​the soil column. Detailed explanation of the formula and the process of formula calculation: Setting the distance between electrodes The soil resistance is 2.0 m. is 300.0 ohms, the cross-sectional area of ​​the soil column is 0.01 square meters. According to the formula, The results show that soils with higher resistivity are caused by low water content or high salt content in the soil. Comparing the soil resistivity in different regions with numerical results helps to understand the environmental differences between regions.

[0026] S103: Perform data quality control based on the original measurement data records, remove the data points with deviations, classify the data based on the soil type, and perform statistical analysis to analyze the correlation between resistivity, pipeline material and environmental humidity. The execution process of generating environmental parameter characteristics is as follows; Quality control and data elimination are performed based on the original measurement data. It is necessary to accurately analyze whether the data points are within the normal range. Data that deviates from the normal range needs to be eliminated. Data classification is based on soil type to make the data more targeted and effective. Comprehensive statistical analysis is performed on parameters such as resistivity, pipeline material and ambient humidity to explore the potential correlation between parameters. The correlation and influence between various types of data are analyzed through statistical software and data analysis methods. The purpose is to identify the key environmental factors that affect changes in soil resistivity. The analysis results can help 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.

[0027] See also Figure 3 ,Through the environmental parameter characteristics, the information gain is selected to select the decision point, the variables are input, including the current density, potential difference and soil type, and the steps of constructing the initialization decision tree model are as follows: S201: According to the environmental parameter characteristics, current density, potential difference and soil type are selected as variables, the influence of the differentiation variables on the classification effect of the decision tree is evaluated, the optimal split node is determined, and the execution process of generating the decision variable evaluation table is as follows; Evaluate the influence of differentiation variables on the classification effect of decision tree according to the formula , calculate the information gain. In the formula, represents information gain, represents the training data set, represents characteristic variables (such as current density, potential difference, soil type), represents entropy, Represented in Features The value of A subset of the data, Representative features Detailed explanation of the formula and the process of formula calculation: Calculate the entropy of the entire data set , if the dataset There are Class results, the proportion of each class is , then entropy for: For each feature , such as the current density, the value is , the data subset corresponding to each value Entropy The same calculation is performed according to the above formula. If there are three different classifications of soil types, and the sample size ratio and entropy of each classification are , the entropy of the entire data set is , then the information gain Calculated as: . This shows that soil type is an important factor affecting the classification results.

[0028] S202: Based on the decision variable evaluation table, select the variable with information gain as the root node, select the variable with the next highest information gain in turn for branch construction, set the initialization structure parameters of the decision tree, and generate the execution process of the decision tree structure framework as follows; According to 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 resolution is maximized. The variable with the second highest information gain is used for further branching. The key in this process is to accurately calculate the information gain of each variable. It is necessary to integrate all values ​​of current density, potential difference and soil type and the corresponding classification results, and use statistical software to calculate and analyze data to ensure that the information gain is calculated accurately. Selecting variables and building trees based on information gain values ​​can not only improve the classification accuracy of the model, but also effectively simplify the model structure, avoid overfitting, and generate the structural framework of the decision tree.

[0029] S203: According to the decision tree structure framework, current density, potential difference and soil type data are input to train and verify the model, and the model performance is optimized by adjusting parameters in real time. The execution process of generating the initialization decision tree model is as follows; Through the decision tree structure framework, the data of current density, potential difference and soil type are input. This step is the core link of model training and verification. The performance optimization of the model is achieved by adjusting the parameters of the decision tree in real time, including the depth of the tree, the minimum number of samples of the branch, etc. Through iterative training, the model gradually adapts to the characteristics of the data. In the verification stage, the generalization ability and accuracy of the model are evaluated through cross-validation or independent test sets. The results of each training need to be recorded and analyzed, and the parameters of the model need to be adjusted in time to achieve the best model performance and generate an initialized decision tree model.

[0030] See also Figure 4 , the initialization decision tree model is applied to the simulation environment for scenario testing, the model output is compared with the buried pipeline network cathodic protection effect data, the branch conditions and thresholds in the decision tree are adjusted, and the steps to obtain the adjusted decision tree model are as follows: S301: According to the initialization decision tree model, a simulation test environment is set, a scenario test is performed using simulated current density, potential difference and soil type data, and the model output is recorded. The execution process of generating simulation test results is as follows; A simulation test environment is set up, and scenario tests are performed using simulated current density, potential difference, and soil type data. In this process, it is necessary to accurately simulate various scenarios that occur in the real world to ensure the breadth and representativeness of the test results. Through simulation data, the decision tree model can be tested under a variety of different conditions. Recording the model output of each test is a key step, which can provide a direct basis for subsequent model optimization. Through simulation test results, the classification effect of the model under specific conditions can be intuitively understood. The test not only verifies the current performance of the model, but also provides a test basis for future application scenarios. After the simulation test is completed, the simulation test results are generated.

[0031] S302: Based on the simulation test results, the random forest algorithm is used to compare the model output with the real-time buried pipeline network cathodic protection effect data, identify the advantages and disadvantages of the model, analyze the source of the error, and generate the model effect comparison analysis results. The execution process is as follows; The formula for the random forest algorithm is as follows:

[0032] in, is the error value between the model and the 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 time when the pipeline network is buried.

[0033] Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the error between the buried pipeline network cathodic protection effect data and the model prediction. The parameters in the formula are as follows: :Actual cathodic protection data is obtained through real-time monitoring of sensors in the buried pipe network. For example, the protection potential of a certain pipe network section is -0.85 volts.

[0034] : Cathodic protection data predicted by the model, predicted by the random forest algorithm. The prediction result for the same pipe network section is set to -0.80 volts.

[0035] : Weight coefficient, calculated based on data quality and network length. Data quality is evaluated by sensor accuracy and signal strength, and network length is directly measured. If the data quality of a certain network segment is high, the weight is set to 1.5.

[0036] : The depth of the pipe network, obtained through geological measurement tools, for example, 2 meters.

[0037] : The time the pipeline network is buried can be determined based on the installation records, for example, 10 years.

[0038] Example of formula calculation process: 1. Calculation and The absolute value of the difference between volt.

[0039] 2. Multiply the difference by the weight coefficient ,Right now .

[0040] 3. Calculation The value of .

[0041] 4. Multiply the result of step 2 by the result of step 3, that is .

[0042] 5. Add the calculated results of all data points and divide by the total number of data points , and get the error .

[0043] The results show that by carefully analyzing the difference between the actual protection potential of each pipeline network segment and the model prediction, a more accurate model error assessment can be obtained by considering data quality, pipeline length, depth and service life. The assessment results can be used to further optimize the model to ensure that the prediction results are closer to the actual situation and improve the prediction accuracy of cathodic protection effect.

[0044] S303: According to the results of the comparative analysis of the model effect, the branch conditions and thresholds in the decision tree are adjusted to optimize the model structure and generalization ability. The adjustment effect is verified through multiple tests, and the execution process of generating the adjusted decision tree model is as follows; Adjust the branch conditions and thresholds in the decision tree according to the formula , calculate the model error rate. In the formula, represents the total error rate of the model, Represents the number of test data, is the indicator function, when (Actual Category) is not equal to (prediction category), the value is 1, otherwise it is 0. Detailed explanation of the formula and the process of formula calculation: 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 has 2 prediction errors in 10 tests, the error rate is calculated as: .

[0045] That is, the total error rate of the model is 20%. By adjusting the branch conditions and thresholds of the decision tree, such as modifying a certain threshold to improve the accuracy of a specific classification, multiple tests are performed to verify the effect of the adjustment, in order to reduce the error rate, enhance the generalization ability of the model, and ensure that the model can achieve higher accuracy in future practical applications.

[0046] See also Figure 5 ,Using the adjusted decision tree model, collect the electromagnetic field data of the interference source, simulate the electric field and magnetic field, record the intensity and direction changes of the simulated data points, and the steps to generate electromagnetic field simulation data are as follows: S401: According to the adjusted decision tree model, the electromagnetic field test equipment is configured, the electromagnetic field data of the interference source is collected, the measurement data of the electric field and the magnetic field are recorded, and the execution process of generating the electromagnetic field initialization measurement record is as follows; Configure electromagnetic field test equipment and collect electromagnetic field data of interference sources, including measurement data of electric and magnetic fields. The setting of each test should ensure that the strength and direction of electric and magnetic fields can be accurately recorded. Recording of measurement data is a key step to provide basic data for subsequent data analysis. The collection of electromagnetic field data should include multiple measurements under different conditions to ensure the comprehensiveness and accuracy of the data. The data will be used to evaluate the stability of the electromagnetic field environment and the impact of interference sources. A preliminary quantitative description of the on-site electromagnetic environment can be obtained, which is the basis for further simulation and analysis, and to generate electromagnetic field initialization measurement records.

[0047] S402: Based on the electromagnetic field initialization measurement record, the electric field and magnetic field are simulated, the position and strength of the electromagnetic source are adjusted, and the changes in the strength and direction of the data points during the simulation are recorded to generate a direction change diagram. The execution process is as follows; To simulate the electric and magnetic fields, according to the formula , calculate the magnetic field strength. Where, represents the magnetic field vector, represents the magnetic constant, represents the amount of charge, represents the charge velocity vector, represents the unit vector from the charge to the observation point, Represents the distance from the charge to the observation point. Detailed explanation of the formula and the process of formula calculation: When the charge At speed When in motion, the magnetic field strength generated can be calculated using the above formula. , , the observation point is 1m away from the charge in the due east direction, that is , magnetic field strength Calculated as: This means that in this configuration, the magnetic field strength is in a plane perpendicular to the velocity and the direction of the observation point. By adjusting the position and strength of the electromagnetic source and recording the changes in strength and direction of the data points during the simulation, a map of the direction changes of the electric and magnetic fields is generated.

[0048] S403: Through the direction change diagram, the simulation data is integrated, data analysis and visualization are performed, key changes in electromagnetic field intensity and direction are identified, and the execution process of generating electromagnetic field simulation data is as follows; The simulation data is integrated through the direction change diagram, and data analysis and visualization are performed, including identifying the key changes in the intensity and direction of the electromagnetic field. Advanced data analysis tools, such as statistical software and visualization platforms, are used to convert the change data of the electromagnetic field into graphics and charts, making the dynamic changes of the electromagnetic field more intuitive and easy to understand. Analytical data can help identify the specific characteristics of the interference source, such as location, intensity and range of influence, and how to reduce adverse effects by adjusting parameters. The integrated data not only provides an in-depth understanding of the electromagnetic field environment, but also provides a scientific basis for further research and application, and generates electromagnetic field simulation data.

[0049] See also Figure 6 Based on the electromagnetic field simulation data, the parameters of the output current and potential distribution are adjusted. The parameters are adjusted through multiple rounds of simulation tests, and the changes in electromagnetic interference after adjustment are monitored. The specific steps for optimizing the current and potential parameters are as follows: S501: Using electromagnetic field simulation data, set the initial output current and potential distribution parameters, perform electromagnetic field simulation test, record the current and potential response data, and generate the simulation test record. The execution process is as follows; Set the initial output current and potential distribution parameters, perform electromagnetic field simulation test, and follow the formula , calculate the potential difference. In the formula, represents the potential difference, represents the current, Represents resistance. Detailed explanation of the formula and the process of formula calculation: In the electromagnetic field simulation test, the output current is set For 10A and resistance is 5Ω, the potential difference It can be calculated according to the formula: The results show that when a 10A current is passed through a circuit with a resistance of 5Ω, a potential difference of 50V will be generated. By recording the response data of current and potential, the accuracy of the simulation model and its ability to respond to electromagnetic field interference can be verified, providing a basis for the next step of optimization.

[0050] S502: According to the simulation test record, the parameters of the output current and potential distribution are adjusted by using a genetic algorithm, and multiple rounds of simulation tests are performed. After each adjustment, the key parameters and electromagnetic interference changes are recorded. The execution process of generating multiple rounds of simulation adjustment records is as follows; The formula of genetic algorithm is as follows:

[0051] in, 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 resistance.

[0052] Detailed explanation of the formula and the process of formula calculation and derivation: In the simulation model for minimizing electromagnetic interference, the output current , potential difference , Real-time electromagnetic interference value , Target electromagnetic interference value and resistor is a key parameter. The following will explain in detail the meaning of each parameter, the calculation method, and how to apply it to the formula, and give specific numerical examples.

[0053] Output Current (Ampere, A): This parameter is monitored by circuit simulation software and is the result of adjustments to improve electromagnetic interference. The setting is achieved through actual measurement and the output current is 2A under a specific configuration.

[0054] Potential difference (Volt, V): Potential difference is measured directly between two points in a circuit using a voltmeter, set to 5V.

[0055] Current electromagnetic interference value (Volt / meter, V / m): It is measured by an electromagnetic field strength meter at different positions and configurations. The measured value is used to evaluate the initial strength of electromagnetic interference in the simulation. The current electromagnetic interference value is set to 3V / m.

[0056] Target electromagnetic interference value (Volt / meter, V / m): It indicates the interference level that is expected to be achieved under ideal conditions. It is a value set based on environmental safety standards, and the target electromagnetic interference value is set to 0.5V / m.

[0057] resistance (Ohm, Ω): Resistance is measured in a circuit using an ohmmeter. Suppose the resistance is 50Ω.

[0058] Formula calculation derivation process: It is necessary to calculate the change in electromagnetic interference, that is, In the given values, V / m and ,so:

[0059] The square root of the value:

[0060] Substitute into the formula to calculate :

[0061] The results show that by adjusting the current and voltage and optimizing the circuit design, the electromagnetic interference can be significantly reduced to below the target interference value, promoting the stable operation of the system performance.

[0062] S503: Based on multiple rounds of simulation adjustment records, comprehensive simulation data, avoiding electromagnetic interference, adjusting the cathodic protection effect of the complex buried pipeline network through simulation verification, and generating the execution process of optimizing current and potential parameters are as follows; Through multiple rounds of simulation adjustment records and comprehensive simulation data, the purpose is to avoid electromagnetic interference, especially in complex buried pipe network systems. By accurately adjusting the distribution parameters of current and potential, continuously testing and recording the results, using advanced simulation tools for multiple rounds of testing, and gradually optimizing the parameters, it is ensured that each adjustment is based on the results of the previous simulation, thereby improving the accuracy and reliability of the simulation. At the same time, through this repeated simulation process, it is possible to effectively verify the improvement of the cathodic protection effect by the adjustment measures and generate optimized current and potential parameters.

[0063] See also Figure 7According to the optimized current and potential parameters, the cathodic protection effect of the buried pipeline network is monitored in real time, and the stable operation efficiency under complex current interference environment is verified. The specific steps to obtain the strategy implementation results are as follows: S601: According to the optimized current and potential parameters, the effect of cathodic protection of the complex buried pipe network 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 of generating the initial monitoring data record is as follows; According to the optimization of current and potential parameters, the effect of cathodic protection of complex buried pipeline networks 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 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 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 generating initial monitoring data records.

[0064] S602: Using the initial monitoring data record, analyzing the cathodic protection effect in a complex current interference environment, adjusting the monitoring strategy and protection parameters to cope with the changing environmental factors, continuously recording the adjusted effect data, and generating the execution process of the cathodic protection strategy as follows; Analyze the cathodic protection effect in a complex current interference environment, according to the formula , calculate the cathodic protection efficiency. In the formula, represents the efficiency of cathodic protection, represents the leakage current, Represents the total current. Detailed explanation of the formula and the derivation process of the formula calculation: Set the total current during a specific monitoring period. The leakage current is 100A. is 5A, then the cathodic protection efficiency Calculated as: The results show that under the current and potential parameters, the efficiency of cathodic protection is 95%. By continuously recording the effect data after adjustment, the influence of various adjustment measures on the protection efficiency can be verified, with the aim of optimizing the cathodic protection strategy under complex current interference environments.

[0065] S603: Using the cathodic protection strategy, by analyzing the long-term operation data, verifying the stable operation efficiency of the current and potential parameter adjustment strategy in a complex current interference environment, the execution process of generating the strategy implementation result is as follows; By using the cathodic protection strategy and analyzing the long-term operation data, the stable operation efficiency of the current and potential parameter adjustment strategy in a complex current interference environment is verified. The long-term data provides in-depth insights into the effectiveness of the strategy. The data can be used to understand the impact of different environmental factors on the cathodic protection effect. In particular, after the current and potential parameters have been adjusted many times, the stability and adaptability of the strategy can be significantly improved. The analysis of the data not only helps optimize the cathodic protection parameters, but also predicts future adjustment needs, providing a scientific basis for ensuring the long-term stable operation of the buried pipeline network in a complex current interference environment and generating strategy implementation results.

[0066] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing cathodic protection of complex buried pipe networks under current interference, characterized in that: The following steps are involved: By using field sampling, we collected data on soil resistivity, pipeline material, and ambient humidity from differentiated industrial areas, and obtained environmental parameter characteristics by analyzing the variability and correlation of the data. According to the environmental parameter characteristics, information gain is selected to select the decision point, and variables including current density, potential difference and soil type are input to construct an initialization decision tree model; Applying the initialized decision tree model to a simulation environment for scenario testing, comparing the model output with the buried pipe network cathodic protection effect data, adjusting the branch conditions and thresholds in the decision tree, and obtaining an adjusted decision tree model; Using the adjusted decision tree model, collecting electromagnetic field data of the interference source, simulating the electric field and magnetic field, recording the intensity and direction changes of the simulated data points, and generating electromagnetic field simulation data; Based on the electromagnetic field simulation data, adjusting the parameters of the output current and potential distribution, adjusting the parameters through multiple rounds of simulation tests, monitoring the changes in electromagnetic interference after adjustment, and forming optimized current and potential parameters; According to the optimized current and potential parameters, the cathodic protection effect of the buried pipeline network is monitored in real time, the stable operation efficiency under the complex current interference environment is verified, and the strategy implementation result is obtained.

2. The method for optimizing cathodic protection of complex buried pipeline networks under current interference according to claim 1 is characterized in that: The environmental parameter characteristics include soil resistivity distribution map, response data of differentiated pipeline materials and environmental humidity measurement values ​​of multiple industrial areas. The initialized decision tree model includes inputs of current density, potential difference and soil type, the organization form of variables in the model and the initial threshold. The adjusted decision tree model includes optimized branch conditions, updated node thresholds and model prediction response speed and efficiency. The electromagnetic field simulation data includes data on 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 operating efficiency.

3. The method for optimizing cathodic protection of complex buried pipeline networks under current interference according to claim 1 is characterized in that: By using field sampling, we collected data on soil resistivity, pipeline material, and ambient humidity from differentiated industrial areas. By analyzing the variability and correlation of the data, we obtained the characteristics of environmental parameters in the following steps: Select representative locations within the industrial area for soil sampling, check and cover different types of pipelines and varying humidity environments, record the location and environmental conditions of each sampling point, and generate soil sample and environmental data tables; Based on the soil sample and the environmental data table, the resistivity and environmental humidity of the soil are measured on site, the material of the pipeline is recorded, the collected data is sorted, the integrity of the data is checked, and the original measurement data record is generated; According to the original measurement data records, data quality control is performed to eliminate deviant data points, data classification is performed in combination with soil types, and statistical analysis is performed to analyze the correlation between resistivity, pipeline material and ambient humidity to generate environmental parameter characteristics.

4. The method for optimizing cathodic protection of complex buried pipeline networks under current interference according to claim 1 is characterized in that: According to the environmental parameter characteristics, information gain is selected to select the decision point, and the variables are input, including current density, potential difference and soil type. The steps of constructing the initialization decision tree model are as follows: According to the environmental parameter characteristics, current density, potential difference and soil type are selected as variables, the influence of differentiation variables on the classification effect of the decision tree is evaluated, the optimal segmentation node is determined, and a decision variable evaluation table is generated; Based on the decision variable evaluation table, select the variable with information gain as the root node, select the second highest variable in turn for branch construction, set the initialization structure parameters of the decision tree, and generate the decision tree structure framework; According to the decision tree structure framework, current density, potential difference and soil type data are input to train and verify the model, and the model performance is optimized by adjusting parameters in real time to generate an initialized decision tree model.

5. The method for optimizing cathodic protection of complex buried pipeline networks under current interference according to claim 1, characterized in that: The steps of applying the initialized decision tree model to a simulation environment for scenario testing, comparing the model output with the buried pipe network cathodic protection effect data, adjusting the branch conditions and thresholds in the decision tree, and obtaining the adjusted decision tree model are as follows: According to the initialized decision tree model, a simulated test environment is set, a scenario test is performed using simulated current density, potential difference and soil type data, and the model output is recorded to generate a simulated test result; Based on the simulation test results, the random forest algorithm is used to compare the model output with the real-time buried pipeline network cathodic protection effect data, identify the advantages and disadvantages of the model, analyze the source of error, and generate model effect comparison analysis results; According to the comparative analysis results of the model effects, the branch conditions and thresholds in the decision tree are adjusted, the model structure and generalization ability are optimized, the adjustment effect is verified through multiple tests, and the adjusted decision tree model is generated.

6. The method for optimizing cathodic protection of complex buried pipe networks under current interference according to claim 5 is characterized in that: The formula of the random forest algorithm is as follows: in, is the error value between the model and the 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 time when the pipeline network is buried.

7. The method for optimizing cathodic protection of complex buried pipeline networks under current interference according to claim 1, characterized in that: The adjusted decision tree model is used to collect electromagnetic field data of interference sources, simulate electric and magnetic fields, and record the intensity and direction changes of simulated data points. The specific steps of generating electromagnetic field simulation data are as follows: According to the adjusted decision tree model, configuring electromagnetic field test equipment, collecting electromagnetic field data of interference sources, recording measurement data of electric fields and magnetic fields, and generating electromagnetic field initialization measurement records; Based on the electromagnetic field initialization measurement record, simulating the electric field and the magnetic field, adjusting the position and strength of the electromagnetic source, recording the changes in the strength and direction of the data points during the simulation, and generating a direction change graph; Through the direction change diagram, the simulation data is integrated, data analysis and visualization processing are performed, key changes in electromagnetic field intensity and direction are identified, and electromagnetic field simulation data is generated.

8. The method for optimizing cathodic protection of complex buried pipeline networks under current interference according to claim 1, characterized in that: Based on the electromagnetic field simulation data, the parameters of the output current and potential distribution are adjusted, the parameters are adjusted through multiple rounds of simulation tests, and the changes in the electromagnetic interference after the adjustment are monitored. The steps of optimizing the current and potential parameters are specifically as follows: By using the electromagnetic field simulation data, the output current and potential distribution parameters are initialized, an electromagnetic field simulation test is performed, the response data of the current and potential are recorded, and a simulation test record is generated; According to 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 performed. After each adjustment, changes in key parameters and electromagnetic interference are recorded to generate multiple rounds of simulation adjustment records; Based on the multiple rounds of simulation adjustment records and integrated simulation data, electromagnetic interference is avoided, the cathodic protection effect of the complex buried pipeline network is adjusted through simulation verification, and optimized current and potential parameters are generated.

9. The method for optimizing cathodic protection of complex buried pipe networks under current interference according to claim 8, characterized in that: The formula of the genetic algorithm is as follows: in, 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 resistance.

10. The method for optimizing cathodic protection of complex buried pipe networks 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 pipeline network is monitored in real time, and the stable operation efficiency under the complex current interference environment is verified. The specific steps to obtain the strategy implementation results are as follows: According to the optimized current and potential parameters, the effect of cathodic protection of complex buried pipe networks is monitored in real time, and the initial protection effect and key performance indicators are recorded by comparing the data before and after adjustment, so as to generate initial monitoring data records; Using the initial monitoring data record, analyzing the cathodic protection effect in a complex current interference environment, adjusting the monitoring strategy and protection parameters, responding to changing environmental factors, continuously recording the adjusted effect data, and generating a cathodic protection strategy; By using the cathodic protection strategy, the long-term operation data is analyzed to verify the stable operation efficiency of the current and potential parameter adjustment strategy in a complex current interference environment, and the strategy implementation results are generated.

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