A method for monitoring regional risks of oil pipelines

Abstract

The present invention discloses a method for monitoring the regional risk of an oil pipeline, which relates to the technical field of risk monitoring. This method calculates the static charge accumulation risk value Qst(t), and calculates the electrostatic discharge threshold Qth in combination with the equivalent capacitance Cp of the oil pipeline and the material breakdown electric field strength Eb. By means of a preliminary comparison and evaluation of the static charge accumulation risk value Qst(t) and the electrostatic discharge threshold Qth, it is judged whether there is an electrostatic discharge risk in the pipeline. When it exceeds the safe range, a lightning strike analysis mechanism is triggered, and the transient corrosion current density Icorr(t) is further calculated through the transient current corrosion algorithm. At the same time, in combination with the static charge accumulation risk value Qst(t) and external environmental disturbance parameters such as sine periodic terms, the metal corrosion rate CR of the pipeline surface is calculated. For areas with a high incidence of lightning strikes, this method calculates the cumulative effect index Lacc through the lightning strike cumulative effect algorithm, and comprehensively evaluates the impact of lightning strikes on the long-term corrosion of the pipeline.

Description

Technical Field

[0001] The present invention relates to the technical field of risk monitoring, and particularly to a risk monitoring method for an oil pipeline area. Background Art

[0002] As an important infrastructure for energy transportation, oil pipelines play a core role in the energy and chemical industry. With the development of long-distance oil pipeline technology, the demand for their deployment in complex geographical environments is increasing, including special environments such as crossing mountains, deserts, oceans, and areas with high lightning incidence. Among them, lightning strikes or electromagnetic interference pose a significant threat to the safety of oil pipelines. Especially in areas with frequent lightning, electrostatic accumulation, partial discharge, and corrosion may occur on the surface of oil pipelines, seriously affecting the long-term stable operation of oil pipelines. Therefore, researching a risk monitoring method for an oil pipeline area based on real-time data monitoring, with a focus on corrosion problems caused by lightning strikes and electromagnetic interference, has important engineering and economic significance.

[0003] At present, oil pipelines usually rely on periodic manual inspections or single monitoring means such as corrosion current probes to evaluate the safety status. However, these methods have obvious deficiencies: First, the manual inspection cycle is long and lagging, making it difficult to detect sudden risks caused by lightning strikes or electromagnetic interference in a timely manner; Second, the coverage of single-sensor monitoring is limited, and it is impossible to capture the dynamic changes of electrostatic accumulation, lightning current, and corrosion rate across the entire pipeline; Third, traditional risk assessments are mostly based on static parameters, lacking multi-parameter dynamic coupling analysis, resulting in low prediction accuracy for the cumulative effect of lightning strikes and their corrosion risks. These deficiencies may lead to missed detections or misjudgments, increasing the safety hazards of oil pipelines in areas with high lightning incidence. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a risk monitoring method for an oil pipeline area, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A risk monitoring method for an oil pipeline area, including the following steps:

[0006] S1. Install a sensor group in the high-lightning-incidence area of the oil pipeline, collect risk data in the high-lightning-incidence area in real time, transmit the collected risk data to a pre-constructed central monitoring system through a network, and perform data processing in the central monitoring system to obtain a standard data set;

[0007] S2. Based on the obtained standard data set, conduct an electrostatic accumulation analysis on the oil pipelines in lightning-prone areas, calculate and output the static charge accumulation risk value Qst(t) at the current moment, and then make a preliminary comparison and evaluation by presetting the electrostatic discharge threshold Qth to determine the risk of electrostatic accumulation discharge in the oil pipeline, and execute the lightning strike analysis mechanism based on the evaluation results;

[0008] S3. When an abnormal electrostatic accumulation discharge in the oil pipeline is identified, execute the lightning strike analysis mechanism. The lightning strike analysis mechanism calculates and outputs the transient corrosion current density Icorr(t) by extracting the standard data set, and then calculates the corrosion rate CR of the metal on the surface of the oil pipeline in combination with the static charge accumulation risk value Qst(t);

[0009] S4. At the same time, according to the lightning strike accumulation algorithm formula, calculate and output the lightning strike accumulation effect index Lacc, analyze the lightning strike-prone area and the cumulative corrosion effect of lightning strikes on the oil pipeline, and then conduct a comprehensive calculation based on the static charge accumulation risk value Qst(t), the transient corrosion current density Icorr(t), the corrosion rate CR of the metal on the surface of the oil pipeline, and the lightning strike accumulation effect index Lacc to obtain the corrosion risk index CRR;

[0010] S5. Conduct a secondary comparison and evaluation by presetting the corrosion risk threshold Cth and the corrosion risk index CRR to analyze the comprehensive corrosion risk situation of the current oil pipeline. At the same time, construct a visualization interface in the central monitoring system, generate a warning message based on the secondary comparison and evaluation results, and generate a relevant control mechanism through the visualization interface.

[0011] Preferably, S1 includes S11 and S12;

[0012] S11. Install a sensor group in the lightning strike-prone area of the oil pipeline to collect risk data in real time, set a communication module CM for the sensor group, and integrate and connect the sensor group with the constructed central monitoring system through a 5G network to transmit the real-time collected risk data to the central monitoring system;

[0013] The sensor group includes an electric field sensor, a geological conductivity meter, an electrochemical corrosion probe, and a lightning strike monitor;

[0014] The risk data includes the electric field strength E, the soil conductivity Gs, the transient corrosion current density Ic, and the lightning strike current intensity Ir;

[0015] S12. Receive the risk data in real time in the central monitoring system and preprocess the risk data to obtain a standard data set;

[0016] The preprocessing methods include filtering and denoising, time synchronization, and normalization processing;

[0017] The filtering and denoising is performed by filtering the risk data using the Fast Fourier Transform (FFT).

[0018] The normalization process is used to normalize the filtered risk data, unify the dimensions of risk data with different dimensions, and obtain a standard data set.

[0019] The time synchronization is used to align the time of the standard data set.

[0020] Then, the electric field strength E is extracted and gradient calculation is performed to obtain the pipeline surface gradient Eg.

[0021] Preferably, S2 includes S21 and S22.

[0022] S21: Extract the pipeline surface gradient Eg and soil conductivity Gs in the standard data set, and calculate the static charge accumulation risk value Qst(t) on the surface of the oil pipeline.

[0023] The static charge accumulation risk value Qst(t) is calculated and output through the following algorithm formula.

[0024] ;

[0025] In the formula, represents the vacuum node parameter, indicating the induction ability of static charges in the electric field, represents the pipeline surface gradient Eg at time t, represents the soil conductivity coefficient, e represents the exponential function, Ac represents the pipeline surface area, represents the non-linear influence coefficient, dt represents the time differential variable, and Eth represents the static electricity accumulation threshold.

[0026] Preferably, S22: Based on the initially set equivalent capacitance Cp of the oil pipeline surface area and the breakdown electric field strength Eb of the material used for the oil pipeline, calculate and output the electrostatic discharge threshold Qth of the oil pipeline. The specific calculation formula for the electrostatic discharge threshold Qth is as follows: In the formula, d represents the distance between the oil pipeline and the ground;

[0027] Then, a preliminary comparison and evaluation is made with the static charge accumulation risk value Qst(t) to analyze the current static charge amount on the surface of the oil pipeline. Based on the results of the preliminary comparison and evaluation, judge the risk of static electricity accumulation and discharge in the oil pipeline, and execute the lightning strike analysis mechanism. The specific evaluation content is as follows;

[0028] When the static charge accumulation risk value Qst(t) > the electrostatic discharge threshold Qth, it indicates that the static charge amount on the pipeline surface exceeds the safety threshold, and electrostatic discharge may occur at this time. Execute the lightning strike analysis mechanism;

[0029] When the static charge accumulation risk value Qst(t) ≤ the electrostatic discharge threshold Qth, it indicates that the static charge amount is within the safe range, no discharge phenomenon will occur, the pipeline is in a safe state, maintaining the current operating state, and continuously monitoring the static charge accumulation situation of the oil pipeline.

[0030] Preferably, the step S3 includes S31 and S32;

[0031] S31. When it is re-identified that there is an abnormal static charge accumulation discharge in the oil pipeline, the lightning strike analysis mechanism is executed. The lightning strike analysis mechanism constructs a transient current corrosion algorithm formula, extracts the soil conductivity Gs and the lightning strike current intensity Ir from the standard dataset, inputs them into the transient current corrosion algorithm formula, calculates and outputs the transient corrosion current density Icorr(t), and analyzes the situation of the lightning strike current conducting to the pipeline through the soil;

[0032] The transient corrosion current density Icorr(t) is calculated and output through the following transient current corrosion algorithm formula;

[0033] ;

[0034] In the formula, Ir(t) represents the lightning strike current intensity Ir at time t, ln represents the natural logarithm, represents the current decay coefficient, represents the point matching coefficient between the soil and the pipeline, exp represents the natural exponential function, D represents the pipeline depth, represents the effective depth of conductivity, the pipeline depth D and the effective depth of conductivity The specific values are initially set by the user.

[0035] Preferably, S32. Based on the transient corrosion current density Icorr(t) and combined with the static charge accumulation risk value Qst(t), the influence of environmental changes on the corrosion rate is simulated through a sine periodic term, and the corrosion rate CR of the oil pipeline is calculated and output through basic calculations;

[0036] The corrosion rate CR is calculated and output through the following algorithm formula;

[0037] ;

[0038] In the formula, k represents the corrosion constant, represents the metal density, h represents the pipeline wall thickness, represents the external disturbance intensity, w represents the periodic frequency of the disturbance, sin represents the sine function, represents the non-linear corrosion coefficient, and T represents the total time period for evaluating the corrosion rate.

[0039] Preferably, the step S4 includes S41 and S42;

[0040] S41. Construct a lightning strike accumulation algorithm formula, extract the soil conductivity Gs and lightning strike current intensity Ir from the standard dataset, input them into the lightning strike accumulation algorithm formula for calculation, and output the lightning strike cumulative effect index Lacc. Analyze the high lightning strike areas and the cumulative corrosion impact of lightning strikes on the oil pipeline.

[0041] The lightning strike cumulative effect index Lacc is calculated and output through the following lightning strike accumulation algorithm formula.

[0042] ;

[0043] In the formula, N represents the lightning strike frequency. represents the conductivity threshold sensitivity coefficient. represents the attenuation coefficient of the lightning strike frequency on the lightning strike effect.

[0044] Preferably, in S42, the static charge accumulation risk value Qst(t), the transient corrosion current density Icorr(t), the corrosion rate CR of the metal, and the lightning strike cumulative effect index Lacc are comprehensively calculated to output the corrosion risk index CRR, and a comprehensive analysis of the lightning strike risk of the current oil pipeline is carried out.

[0045] The corrosion risk index CRR is calculated and output through the following algorithm formula.

[0046] ;

[0047] Preferably, S5 includes S51 and S52.

[0048] S51. Based on the initial setting of the corrosion risk threshold Cth by the user, and then conduct a secondary comparison and evaluation analysis with the corrosion risk index CRR to analyze the corrosion situation of the oil pipeline by lightning strikes. The specific evaluation content is as follows.

[0049] When the corrosion risk index CRR ≥ the corrosion risk threshold Cth, it indicates the electrical accumulation situation of lightning strikes in the current area, and there is a corrosion risk to the oil pipeline. At this time, a risk warning is generated, and the grounding device is automatically activated to eliminate static electricity.

[0050] When the corrosion risk index CRR < the corrosion risk threshold Cth, it indicates the electrical accumulation situation of lightning strikes in the current area, and the oil pipeline is in a safe state. At this time, there is no need to generate a safety warning.

[0051] Preferably, in S52, through front-end and back-end programming technologies, a visual interface is constructed in the central detection system. When a risk warning is generated through the secondary comparison and evaluation, the area information of the current oil pipeline, the static charge accumulation risk value Qst(t), and the corrosion risk index CRR are displayed on the visual interface to display the risk monitoring situation of the lightning strike area.

[0052] The present invention provides a method for monitoring the regional risks of an oil pipeline. It has the following beneficial effects:

[0053] (1) By installing a sensor group in the high-lightning area of the oil pipeline, including an electric field sensor, a soil conductivity meter, an electrochemical corrosion probe, and a lightning strike monitor, this method can collect multiple key risk data such as the electric field strength E, soil conductivity Gs, transient corrosion current density Ic, and lightning strike current intensity Ir in real time. The collected data is transmitted to the central monitoring system through a 5G network to achieve efficient and low-latency data reception. In the central monitoring system, the fast Fourier transform FFT is used to filter and denoise the data, perform normalization processing to unify the dimension, and calibrate the data through time synchronization to generate a standard data set. In addition, for the electric field strength E, the gradient algorithm is used to calculate the pipeline surface gradient Eg. These data processing methods effectively improve the accuracy and consistency of the data, providing high-quality basic data for the risk analysis of electrostatic accumulation and lightning strike corrosion.

[0054] (2) This method uses the electrostatic accumulation model to calculate the static charge accumulation risk value Qst(t), and combines the equivalent capacitance Cp and material breakdown electric field strength Eb of the oil pipeline to calculate the electrostatic discharge threshold Qth. Through the preliminary comparison and evaluation of Qst(t) and Qth, it is judged whether there is an electrostatic discharge risk in the pipeline. When the electrostatic accumulation exceeds the safe range, the lightning strike analysis mechanism is triggered, and the transient corrosion current density Icorr(t) is further calculated through the transient current corrosion algorithm. At the same time, combining the static charge accumulation risk value Qst(t) and external environmental disturbance parameters such as the sine periodic term, the metal corrosion rate CR on the pipeline surface is calculated. In addition, for the high-lightning area, this method calculates the cumulative effect index Lacc through the lightning strike cumulative effect algorithm to comprehensively evaluate the long-term corrosion effect of lightning strikes on the pipeline. This multi-parameter and multi-level risk modeling method dynamically couples the electrostatic effect, lightning strike transient effect, and cumulative effect, significantly improving the scientificity and comprehensiveness of the pipeline corrosion risk assessment.

[0055] (3) This method takes the corrosion risk index CRR as the core, and comprehensively calculates and analyzes the static charge accumulation risk value Qst(t), transient corrosion current density Icorr(t), corrosion rate CR, and lightning strike cumulative effect index Lacc. After the secondary comparison and evaluation of CRR with the preset corrosion risk threshold Cth, if it is found that the corrosion risk index CRR≥Cth, the system immediately generates a risk warning and automatically activates the grounding device to eliminate static charge accumulation, and at the same time displays relevant information in the visualization interface of the central monitoring system, including the regional lightning strike risk status, static charge accumulation situation, and comprehensive corrosion risk index. This visual intelligent management method not only improves the real-time and intuitive nature of pipeline risk monitoring, but also effectively reduces the risks of static discharge and pipeline corrosion caused by lightning strikes, avoids the occurrence of energy transmission interruption and environmental pollution accidents, and greatly improves the safety and reliability of pipeline operation. Description of the Drawings

[0056] Figure 1 Schematic diagram of the steps of a method for monitoring regional risks of an oil pipeline according to the present invention. Detailed Embodiments

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiment 1: The present invention provides a method for monitoring regional risks of an oil pipeline. Please refer to Figure 1 , which includes the following steps:

[0059] S1. Install a sensor group in the high-lightning area of the oil pipeline to collect risk data in the high-lightning area in real time, transmit the collected risk data to a pre-constructed central monitoring system through the network, and perform data processing in the central monitoring system to obtain a standard data set;

[0060] S2. Based on the obtained standard data set, perform static charge accumulation analysis on the oil pipeline in the high-lightning area, calculate and output the static charge accumulation risk value Qst(t) at the current moment, and then perform a preliminary comparison and evaluation with a preset static discharge threshold Qth to judge the static charge accumulation and discharge risk in the oil pipeline, and execute a lightning strike analysis mechanism based on the evaluation result;

[0061] S3. When an abnormal electrostatic cumulative discharge in the oil pipeline is recognized again, the lightning strike analysis mechanism is executed. The lightning strike analysis mechanism calculates and outputs the transient corrosion current density Icorr(t) by extracting the standard data set, and then calculates the corrosion rate CR of the metal on the surface of the oil pipeline in combination with the static charge accumulation risk value Qst(t).

[0062] S4. At the same time, according to the lightning strike cumulative algorithm formula, calculate and output the lightning strike cumulative effect index Lacc, analyze the high lightning strike area and the cumulative corrosion effect of lightning strikes on the oil pipeline, and then perform a comprehensive calculation based on the static charge accumulation risk value Qst(t), the transient corrosion current density Icorr(t), the corrosion rate CR of the metal on the surface of the oil pipeline, and the lightning strike cumulative effect index Lacc to obtain the corrosion risk index CRR.

[0063] S5. Conduct a secondary comparison and evaluation between the preset corrosion risk threshold Cth and the corrosion risk index CRR to analyze the comprehensive corrosion risk situation of the current oil pipeline. At the same time, construct a visualization interface in the central monitoring system, generate warning information based on the results of the secondary comparison and evaluation, and generate relevant control mechanisms through the visualization interface.

[0064] In this embodiment, the method provides a full - process solution for real - time acquisition, dynamic analysis, and intelligent evaluation to address the problems of pipeline static electricity accumulation, transient current corrosion, and lightning strike cumulative effect that may occur in high - lightning - frequency areas. In specific implementation, a sensor group is used to collect risk data such as electric field strength E, soil conductivity Gs, transient corrosion current density Ic, and lightning current intensity Ir in real time. After the data is transmitted to the central monitoring system through the 5G network, it undergoes filtering and denoising, normalization, and time synchronization processing to form a standard data set. Based on the standard data set, the static charge risk value Qst(t) is calculated through the static electricity accumulation model, and the preliminary evaluation of the static electricity discharge risk is realized by combining the set static electricity discharge threshold Qth. When abnormal discharge is detected, the lightning strike analysis mechanism is triggered, and the transient corrosion current density Icorr(t) is calculated through the transient current corrosion algorithm, and the corrosion impact is further quantified by combining environmental disturbance simulation and metal corrosion rate CR. At the same time, the lightning strike cumulative index Lacc is calculated through the lightning strike cumulative effect algorithm, and the corrosion risk index CRR is comprehensively output and re - evaluated with the corrosion risk threshold Cth. The beneficial effects of this implementation plan are as follows: First, the combination of real - time acquisition and dynamic analysis comprehensively improves the accuracy and response speed of risk monitoring; Second, the construction of multi - parameter and multi - level models significantly enhances the scientificity and comprehensiveness of risk assessment, especially the comprehensive analysis of lightning strike transient effects and long - term cumulative effects is more refined; Third, intelligent early warning and visual display closely combine the risk monitoring and control mechanisms, realizing the transformation from passive monitoring to active protection. Through the above optimization measures, this method effectively reduces the pipeline corrosion risk caused by lightning strikes and electromagnetic interference, significantly improves the safety, stability, and management efficiency of oil pipelines, and at the same time provides accurate data support and scientific basis for pipeline maintenance and resource allocation.

[0065] Embodiment 2: This embodiment is an explanatory note based on Embodiment 1. Please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0066] S11. Install a sensor group in the high - lightning - frequency area of the oil pipeline to collect risk data in real time, and set a communication module CM for the sensor group. Integrate and connect the sensor group with the constructed central monitoring system through the 5G network, and transmit the real - time collected risk data to the central monitoring system;

[0067] The sensor group includes an electric field sensor, a geological conductivity meter, an electrochemical corrosion probe, and a lightning strike monitor;

[0068] The risk data includes electric field strength E, soil conductivity Gs, transient corrosion current density Ic, and lightning current intensity Ir;

[0069] The electric field strength E is collected and obtained through an electric field sensor;

[0070] The soil conductivity Gs is collected by a geoelectric conductivity meter;

[0071] The transient corrosion current density Ic is collected by an electrochemical corrosion probe;

[0072] The lightning current intensity Ir is collected by a lightning monitor;

[0073] S12. Receive the risk data in real time in the central monitoring system, and preprocess the risk data to obtain a standard data set;

[0074] The preprocessing methods include filtering and denoising, time synchronization, and normalization processing;

[0075] Filtering and denoising is performed by filtering the risk data using the fast Fourier transform FFT;

[0076] Normalization processing is used to normalize the filtered risk data, unify the dimensions of risk data with different dimensions, and obtain a standard data set;

[0077] Time synchronization is used to align the time of the standard data set;

[0078] Then extract the electric field strength E and perform gradient calculation processing to obtain the pipeline surface gradient Eg. The specific algorithm formula is , where △E represents the change in electric field strength between adjacent measurement points, and △d represents the distance between adjacent measurement points.

[0079] In this embodiment, the method installs a sensor group in areas with high lightning incidence, including an electric field sensor, a ground conductivity meter, an electrochemical corrosion probe, and a lightning strike monitor, to collect key risk parameters such as the electric field strength E, soil conductivity Gs, transient corrosion current density Ic, and lightning strike current intensity Ir in real time, and transmits the data to the central monitoring system through a communication module and the 5G network. The central monitoring system performs filtering and denoising on the received data using the fast Fourier transform FFT, normalization processing, and time synchronization operations to generate a standardized data set, ensuring the consistency and accuracy of the data. In addition, by calculating the gradient of the electric field strength E, the electric field gradient Eg on the pipeline surface is further obtained, providing key parameters for subsequent analysis. The core purpose of this implementation plan is to comprehensively improve the risk monitoring ability of oil pipelines in complex lightning environments through multi-parameter collection and efficient data processing. Its beneficial effects include: First, it has strong real-time performance and can quickly capture key data changes caused by lightning strike events; Second, the data processing is precise, and filtering, denoising, normalization, and time synchronization ensure the integrity and reliability of multi-source data; Third, the calculated electric field gradient Eg provides a scientific basis for electrostatic accumulation analysis and corrosion assessment. Through this method, the accuracy and timeliness of risk monitoring are significantly improved, and the safety management of oil pipelines is upgraded from traditional periodic inspections to real-time dynamic monitoring, effectively reducing the missed reports and misjudgments of sudden risks and enhancing the safety and reliability of pipeline operation.

[0080] Embodiment 3: This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0081] S21. Extract the pipeline surface gradient Eg and soil conductivity Gs from the standard data set, and calculate the static charge accumulation risk value Qst(t) on the surface of the oil pipeline;

[0082] The static charge accumulation risk value Qst(t) is calculated and output through the following algorithm formula;

[0083] ;

[0084] In the formula, represents the vacuum node parameter, indicating the induction ability of static charges in the electric field, represents the pipeline surface gradient Eg at time t, represents the soil conductivity coefficient, e represents the exponential function, Ac represents the pipeline surface area, represents the non-linear influence coefficient, dt represents the time differential variable, and Eth represents the static charge accumulation threshold.

[0085] S22. Based on the initially set equivalent capacitance Cp of the surface area of the oil pipeline and the breakdown electric field strength Eb of the material used for the oil pipeline, calculate and output the electrostatic discharge threshold Qth of the oil pipeline. The specific calculation formula for the electrostatic discharge threshold Qth is as follows: In the formula, d represents the distance between the oil pipeline and the ground;

[0086] Then, make a preliminary comparison and evaluation with the static charge accumulation risk value Qst(t), analyze the situation of the static charge amount on the current oil pipeline surface, and based on the results of the preliminary comparison and evaluation, judge the risk of electrostatic accumulation and discharge in the oil pipeline, and execute the lightning strike analysis mechanism. The specific evaluation content is as follows;

[0087] When the static charge accumulation risk value Qst(t) > the electrostatic discharge threshold Qth, it means that the static charge amount on the pipeline surface exceeds the safety threshold, and electrostatic discharge may occur at this time, and the lightning strike analysis mechanism is executed;

[0088] When the static charge accumulation risk value Qst(t) ≤ the electrostatic discharge threshold Qth, it means that the static charge amount is within the safe range, no discharge phenomenon will occur, the pipeline is in a safe state, maintain the current operating state, and monitor the static charge accumulation situation of the oil pipeline in real time.

[0089] In this embodiment, through the above method steps, this method realizes the accurate calculation and preliminary evaluation of the electrostatic accumulation risk of the oil pipeline, laying a solid foundation for lightning strike analysis and pipeline safety management. In specific implementation, by extracting the pipeline surface gradient Eg and soil conductivity Gs in the standard dataset, the static charge accumulation risk value Qst(t) is calculated using the electrostatic accumulation model. At the same time, combined with the equivalent capacitance Cp of the pipeline surface area and the material breakdown electric field strength Eb, the electrostatic discharge threshold Qth is calculated according to the distance d between the pipeline and the ground. Through the comparison and evaluation of Qst(t) and Qth, it is judged whether there is an electrostatic discharge risk in the pipeline: when Qst(t) > Qth, the pipeline may have electrostatic discharge and trigger the lightning strike analysis mechanism; when Qst(t) ≤ Qth, the static charge amount of the pipeline is within the safe range, maintain normal operation and monitor in real time. The core purpose of this method is to dynamically evaluate the electrostatic accumulation risk on the pipeline surface, so as to identify potential electrostatic discharge hazards in advance and take corresponding measures. Its beneficial effects include: First, through the accurate calculation of the electrostatic accumulation model and discharge threshold, the scientific quantification of the electrostatic discharge risk is realized; Second, the dynamic monitoring and real-time evaluation capabilities effectively improve the timeliness of pipeline electrostatic safety management; Third, the preliminary risk assessment provides a scientific basis for the triggering of the subsequent lightning strike analysis mechanism. Through this method, the recognition and response speed of pipeline electrostatic discharge risk are significantly improved, effectively avoiding the aggravation of corrosion and safety accidents caused by electrostatic discharge, and further enhancing the operation reliability and safety of the oil pipeline.

[0090] Example 4: The explanation in this example is based on Example 1. Please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0091] S31. When electrostatic cumulative discharge anomaly in the oil pipeline is re-identified, the lightning strike analysis mechanism is executed. The lightning strike analysis mechanism constructs a transient current corrosion algorithm formula, extracts the soil conductivity Gs and lightning strike current intensity Ir from the standard dataset, inputs them into the transient current corrosion algorithm formula, calculates and outputs the transient corrosion current density Icorr(t), and analyzes the situation of lightning strike current conducting to the pipeline through the soil;

[0092] The transient corrosion current density Icorr(t) is calculated and output through the following transient current corrosion algorithm formula;

[0093] ;

[0094] In the formula, Ir(t) represents the lightning strike current intensity Ir at time t, ln represents the natural logarithm, represents the current decay coefficient, represents the point matching coefficient between the soil and the pipeline, exp represents the natural exponential function, D represents the pipeline depth, represents the effective depth of conductivity.

[0095] S32. Based on the transient corrosion current density Icorr(t) combined with the static charge accumulation risk value Qst(t), the influence of environmental changes on the corrosion rate is simulated through the sine periodic term, and the corrosion rate CR of the oil pipeline is calculated and output through basic calculations;

[0096] The corrosion rate CR is calculated and output through the following algorithm formula;

[0097] ;

[0098] In the formula, k represents the corrosion constant, represents the metal density, h represents the pipeline wall thickness, represents the external disturbance intensity, w represents the periodic frequency of the disturbance, sin represents the sine function, represents the non-linear corrosion coefficient, T represents the total time period for evaluating the corrosion rate, and the above parameters are all obtained by initial setting of the user.

[0099] In this embodiment, through the above method steps, the method realizes the accurate analysis of the transient corrosion impact of an oil pipeline caused by lightning strikes and the dynamic assessment of the corrosion rate. In a specific implementation, when an abnormal electrostatic accumulation discharge in the pipeline is identified, the lightning strike analysis mechanism is immediately executed. Based on the transient current corrosion algorithm, the soil conductivity Gs and the lightning strike current intensity Ir(t) in the standard dataset are extracted. By calculating the transient corrosion current density Icorr(t), the corrosion intensity and impact of the lightning strike current conducted to the pipeline surface through the soil are analyzed. At the same time, Icorr(t) is combined with the static charge accumulation risk value Qst(t), and a sine periodic term is introduced to simulate the dynamic impact of environmental disturbances on the corrosion rate. Finally, the corrosion rate CR of the pipeline metal is output through a comprehensive calculation formula. The core purpose of this method is to quantify the impact of lightning strikes on the instantaneous and dynamic corrosion of oil pipelines, providing a scientific basis for the safe operation of pipelines. Its beneficial effects include: First, the transient current corrosion algorithm accurately reflects the short-time high-intensity corrosion behavior caused by lightning strike events; Second, by combining the dynamic simulation of environmental disturbances, the long-term environmental factors affecting the corrosion rate are comprehensively captured; Third, the real-time calculated corrosion rate CR provides quantitative data support for predicting the pipeline life and planning protection measures. Through this method, the monitoring accuracy of the instantaneous and dynamic corrosion impacts of pipelines is significantly improved, the risk assessment ability is more comprehensive, the possibility of local corrosion failure of pipelines caused by lightning strikes is further reduced, and the operation safety and reliability of oil pipelines are comprehensively improved.

[0100] Example 5: This example is an explanatory note based on Example 4. Please refer to Figure 1 , specifically: S4 includes S41 and S42;

[0101] S41. Construct a lightning strike accumulation algorithm formula, extract the soil conductivity Gs and the strike current intensity Ir from the standard dataset and input them into the lightning strike accumulation algorithm formula for calculation to output the lightning strike cumulative effect index Lacc, and analyze the high lightning strike areas and the cumulative corrosion impact of lightning strikes on the oil pipeline;

[0102] The lightning strike cumulative effect index Lacc is calculated and output through the following lightning strike accumulation algorithm formula;

[0103] ;

[0104] In the formula, N represents the lightning strike frequency, represents the conductivity threshold sensitivity coefficient, represents the attenuation coefficient of the lightning strike frequency on the lightning strike effect.

[0105] S42. Comprehensively calculate the static charge accumulation risk value Qst(t), transient corrosion current density Icorr(t), corrosion rate CR of the metal, and lightning strike cumulative effect index Lacc, output the corrosion risk index CRR, and conduct a comprehensive analysis of the lightning strike risk on the current oil pipeline;

[0106] The corrosion risk index CRR is calculated and output through the following algorithm formula;

[0107] ;

[0108] In this embodiment, through the above method steps, the method realizes the scientific quantitative analysis of the lightning strike cumulative effect and the comprehensive corrosion risk, and systematically evaluates the long-term cumulative impact of lightning strikes on the oil pipeline and the superposition effect of the transient corrosion effect. In the specific implementation, by extracting the soil conductivity Gs and lightning strike current intensity IrI from the standard dataset, a lightning strike cumulative algorithm formula is constructed to dynamically calculate the lightning strike cumulative effect index Lacc, and quantify the long-term impact of lightning strike frequency, soil conductivity change, and lightning strike effect attenuation on pipeline corrosion. At the same time, integrate the static charge accumulation risk value Qst(t), transient corrosion current density Icorr(t), metal corrosion rate CR, and lightning strike cumulative effect index Lacc, and calculate the corrosion risk index CRR through a comprehensive algorithm. This index quantifies the overall corrosion risk of the pipeline in a high lightning strike environment, providing a reliable basis for subsequent protection decisions. The core purpose of this method is to comprehensively reveal the lightning strike cumulative effect and its long-term threat to pipeline corrosion through dynamic modeling and multi-parameter fusion analysis, so as to realize the quantitative assessment of corrosion risk. Its beneficial effects include: First, the lightning strike cumulative algorithm accurately captures the cumulative corrosion characteristics in the high lightning strike frequency area, significantly improving the scientificity of long-term corrosion assessment; Second, the corrosion risk index CRR combines multi-parameter analysis, providing strong support for the accurate quantification of the comprehensive risk of the pipeline; Third, the dynamic assessment and quantitative analysis results lay the foundation for the intelligent monitoring and early warning mechanism of the pipeline. Through this method, the pipeline risk management ability is greatly improved, the risk of safety accidents caused by cumulative corrosion failure is effectively reduced, and the operation efficiency and reliability of the oil pipeline system are comprehensively enhanced.

[0109] Example 6: This example is an explanatory description based on Example 5. Please refer to Figure 1 , specifically: S5 includes S51 and S52;

[0110] S51. Based on the user's initial setting of the corrosion risk threshold Cth, conduct a secondary comparison and evaluation analysis with the corrosion risk index CRR to analyze the corrosion situation of the oil pipeline by lightning strikes. The specific evaluation content is as follows;

[0111] When the corrosion risk index CRR ≥ the corrosion risk threshold Cth, it indicates the electrical accumulation situation of lightning strikes in the current area, and there is a corrosion risk to the oil pipeline. At this time, a risk warning is generated, and the grounding device is automatically activated to eliminate static electricity;

[0112] When the corrosion risk index CRR < the corrosion risk threshold Cth, it indicates the electrical accumulation situation of lightning strikes in the current area, and the oil pipeline is in a safe state. At this time, there is no need to generate a safety warning.

[0113] S52. Through front-end and back-end programming technologies, a visual interface is constructed in the central detection system. When a risk warning is generated through secondary comparison and evaluation, the area information of the current oil pipeline, the static charge accumulation risk value Qst(t), and the corrosion risk index CRR are displayed on the visual interface, and the risk monitoring situation of the lightning strike area is displayed.

[0114] In this embodiment, through the above method steps, the method realizes the secondary evaluation analysis and intelligent risk warning management based on the corrosion risk index CRR, providing an effective guarantee for the safe operation of the oil pipeline in high lightning strike areas. In specific implementation, through the corrosion risk threshold Cth set by the user, the calculated corrosion risk index CRR is subjected to secondary comparison and evaluation. When it is detected that CRR ≥ Cth, the system automatically generates a risk warning and triggers the grounding device to eliminate static electricity to reduce the corrosion risk of the pipeline; when CRR < Cth, the pipeline is in a safe range and there is no need to generate a warning. At the same time, through front-end and back-end programming technologies, a visual interface is constructed in the central detection system to dynamically display the area information of the oil pipeline, the static charge accumulation risk value Qst(t), the corrosion risk index CRR, and the risk monitoring situation of the lightning strike area. The visual interface intuitively presents the risk status and area distribution, providing convenient operation and decision-making support for pipeline management. The core purpose of this method is to realize the intelligent warning and dynamic display of lightning strike cumulative risks through the comparative analysis of the corrosion risk threshold and the comprehensive risk index, thereby ensuring the safe operation of the oil pipeline. Its beneficial effects include: First, the secondary evaluation based on the risk index CRR improves the accuracy of corrosion risk judgment; Second, the automatic warning and protection trigger mechanism significantly shortens the response time and improves the timeliness of risk management; Third, the real-time dynamic visual display makes the monitoring results more intuitive and helps the scientific decision-making of pipeline operation management. Through this method, pipeline management has changed from static monitoring to dynamic warning, greatly enhancing the control ability of lightning strike corrosion risks, reducing the accident rate, and improving the operation efficiency and safety of the oil pipeline system.

[0115] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring regional risks in an oil pipeline, characterized by: The following steps are involved: S1. Install a sensor group in the lightning-prone area of ​​the oil pipeline to collect risk data of the lightning-prone area in real time, and transmit the collected risk data to the pre-built central monitoring system through the network, and process the data in the central monitoring system to obtain a standard data set; S2, including S21 and S22; S21. Extract the pipeline surface gradient Eg and soil conductivity Gs in the standard data set, perform static electricity accumulation analysis on the oil pipeline in the lightning-prone area, and calculate and output the static charge accumulation risk value Qst(t) on the surface of the oil pipeline at the current moment through the following formula; ； In the formula, represents the vacuum node parameter, which represents the induction ability of static charge in the electric field. represents the pipeline surface gradient Eg at time t, represents the soil conductivity, e represents the exponential function, Ac represents the pipeline surface area, represents the nonlinear influence coefficient, dt represents the time differential variable, and Eth represents the electrostatic accumulation threshold; S22, based on the initially set equivalent capacitance Cp of the oil pipeline surface area and the breakdown electric field strength Eb of the material used in the oil pipeline, the electrostatic discharge threshold Qth of the oil pipeline is calculated and output by the following formula: Where d represents the distance between the oil pipeline and the ground; Perform a preliminary comparative evaluation of the electrostatic charge accumulation risk value Qst(t) and the preset electrostatic discharge threshold value Qth to determine the electrostatic accumulation discharge risk in the oil pipeline, and determine whether to execute the lightning strike analysis mechanism based on the evaluation results; S3, including S31 and S32; S31. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, a lightning analysis mechanism is executed. The lightning analysis mechanism constructs a transient current corrosion algorithm formula, extracts the soil conductivity Gs and the lightning current intensity Ir in the standard data set, inputs them into the transient current corrosion algorithm formula, calculates and outputs the transient corrosion current density Icorr (t), and analyzes the situation of the lightning current being conducted to the pipeline through the soil; ； In the formula, Ir(t) represents the lightning current intensity Ir at time t, ln represents the natural logarithm, represents the current attenuation coefficient, represents the soil-pipe point matching coefficient, exp represents the natural exponential function, D represents the pipeline depth, Indicates the effective depth of conductivity; S32, based on the transient corrosion current density Icorr(t) combined with the static charge accumulation risk value Qst(t), the influence of environmental changes on the corrosion rate is simulated by a sinusoidal periodic term, and a basic calculation is performed to output the corrosion rate CR of the metal on the surface of the oil pipeline; ； In the formula, k represents the corrosion constant, represents metal density, h represents pipe wall thickness, represents the external disturbance intensity, w represents the periodic frequency of the disturbance, sin represents the sine function, represents the nonlinear corrosion coefficient, T represents the total time period for evaluating the corrosion rate; S4, including S41 and S42; S41, construct a lightning accumulation algorithm formula, extract the soil conductivity Gs and lightning current intensity Ir in the standard data set and input them into the lightning accumulation algorithm formula, calculate and output the lightning accumulation effect index Lacc, analyze the lightning high incidence area, and the cumulative corrosion effect of lightning on the oil pipeline; The lightning stroke cumulative effect index Lacc is calculated and output by the following lightning stroke cumulative algorithm formula; ； Where N is the lightning frequency, represents the conductivity threshold sensitivity coefficient, Indicates the attenuation coefficient of lightning strike frequency on lightning strike effect; S42, comprehensively calculating the static charge cumulative risk value Qst(t), the transient corrosion current density Icorr(t), the corrosion rate CR of the metal on the surface of the oil pipeline and the lightning cumulative effect index Lacc, outputting the corrosion risk index CRR, and conducting a comprehensive analysis of the lightning risk of the current oil pipeline; S5. Perform secondary comparative evaluation with the preset corrosion risk threshold Cth and the corrosion risk index CRR to analyze the comprehensive corrosion risk situation of the current oil pipeline. At the same time, build a visualization interface in the central monitoring system, generate early warning information based on the secondary comparative evaluation results, and generate relevant control mechanisms through the visualization interface.

2. The method for monitoring regional risks of oil pipelines according to claim 1, characterized in that: Said S1 includes S11 and S12; S11. Install a sensor group in the lightning-prone area of ​​the oil pipeline to collect risk data in real time, set a communication module CM for the sensor group, integrate and connect the sensor group with the constructed central monitoring system through the 5G network, and transmit the risk data collected in real time to the central monitoring system; The sensor group includes an electric field sensor, a geological conductivity meter, an electrochemical corrosion probe and a lightning strike monitor; The risk data include electric field strength E, soil conductivity Gs and lightning current intensity Ir; S12, receiving risk data in real time from the central monitoring system, and preprocessing the risk data to obtain a standard data set; The preprocessing method includes filtering and denoising, time synchronization and normalization processing; The filtering and denoising is performed by filtering the risk data using Fast Fourier Transform FFT; The normalization process is used to normalize the risk data after filtering, unify the risk data of different dimensions, and obtain a standard data set; The time synchronization is used to perform time alignment on the standard data set; Then extract the electric field intensity E and perform gradient calculation to obtain the pipeline surface gradient Eg.

3. The method for monitoring regional risks of oil pipelines according to claim 1, characterized in that: The specific preliminary comparative evaluation contents in S22 are as follows; When the static charge accumulation risk value Qst(t)>the static discharge threshold Qth, it means that the static charge on the pipeline surface is abnormal, and the lightning strike analysis mechanism is executed at this time; When the static charge accumulation risk value Qst (t) ≤ the electrostatic discharge threshold Qth, it means that the static charge is within the safe range and no discharge will occur. The pipeline is in a safe state and maintains the current operating state. The static charge accumulation of the oil pipeline is monitored in real time.

4. The method for monitoring regional risks of oil pipelines according to claim 1, characterized in that: The corrosion risk index CRR is calculated and output by the following algorithm formula: 。 5. The method for monitoring regional risks of oil pipelines according to claim 4, characterized in that: The S5 includes S51, based on the corrosion risk threshold Cth initially set by the user, a secondary comparative evaluation analysis is performed with the corrosion risk index CRR to analyze the corrosion of the oil pipeline caused by lightning strikes, and the specific evaluation contents are as follows; When the corrosion risk index CRR ≥ the corrosion risk threshold Cth, it indicates the accumulation of electricity from lightning strikes in the current area, and there is a corrosion risk to the oil pipeline. At this time, a risk warning is generated, and the grounding device is automatically started to eliminate static electricity. When the corrosion risk index CRR is less than the corrosion risk threshold Cth, it indicates the electrical accumulation of lightning strikes in the current area and the oil pipeline is safe, so there is no need to generate a safety warning.

6. The method for monitoring regional risks of oil pipelines according to claim 5, characterized in that: The S5 also includes S52, which constructs a visualization interface in the central detection system through front-end and back-end programming technology. When a risk warning is generated through secondary comparative evaluation, the regional information of the current oil pipeline, the static charge accumulation risk value Qst (t) and the corrosion risk index CRR are displayed on the visualization interface to display the risk monitoring situation of the lightning strike area.

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