Oil pipeline area risk monitoring method

Abstract

The invention discloses an oil pipeline area risk monitoring method, and relates to the technical field of risk monitoring, and the method comprises the steps: calculating an electrostatic charge accumulation risk value Qst (t), and calculating an electrostatic discharge threshold value Qth through combining the equivalent capacitance Cp of an oil pipeline and the material breakdown electric field intensity Eb; and judging whether the pipeline has the electrostatic discharge risk or not through preliminary comparison and evaluation of the electrostatic charge accumulation risk value Qst (t) and the electrostatic discharge threshold value Qth. And when the current exceeds the safety range, triggering a lightning stroke analysis mechanism, and further calculating the transient corrosion current density Icorr (t) through a transient current corrosion algorithm. And meanwhile, the surface metal corrosion rate CR of the pipeline is calculated in combination with the static charge accumulation risk value Qst (t) and external environment disturbance parameters such as a sine periodic term. And for a lightning stroke high-incidence area, calculating a cumulative effect index Lacc through a lightning stroke cumulative effect algorithm, and comprehensively evaluating the influence of lightning stroke on long-term corrosion of the pipeline.

Description

Technical Field

[0001] The invention relates to the technical field of risk monitoring, and in particular to a method for monitoring regional risks in an oil pipeline. Background Art

[0002] As an important infrastructure for energy transportation, oil pipelines occupy a core position in the energy and chemical industry. With the development of long-distance oil pipeline technology, the demand for its deployment in complex geographical environments is increasing, including crossing special environments such as mountainous areas, deserts, oceans, and areas with high incidence of lightning. Among them, lightning strikes or electromagnetic interference pose a significant threat to the safety of oil pipelines, especially in areas with frequent lightning. Static electricity accumulation, local discharge and corrosion may occur on the surface of oil pipelines, seriously affecting the long-term stable operation of oil pipelines. Therefore, it is of great engineering and economic significance to study a regional risk monitoring method for oil pipelines based on real-time data monitoring, focusing on the corrosion problems caused by lightning strikes and electromagnetic interference.

[0003] At present, oil pipelines usually rely on periodic manual inspections or single monitoring methods such as corrosion current probes to assess safety status. However, these methods have obvious shortcomings: first, the manual inspection cycle is long and delayed, making it difficult to detect sudden risks caused by lightning strikes or electromagnetic interference in a timely manner; second, the coverage of a single sensor monitoring is limited, and it is impossible to capture the dynamic changes of static electricity accumulation, lightning current and corrosion rate in the entire pipeline area; third, traditional risk assessment is mostly based on static parameters and lacks multi-parameter dynamic coupling analysis, resulting in low accuracy in predicting the cumulative effect of lightning strikes and their corrosion risks. These shortcomings may lead to missed detections or misjudgments, increasing the safety risks of oil pipelines in areas with high lightning incidence. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for monitoring regional risks in an oil pipeline, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for monitoring regional risks of oil pipelines, comprising the following steps: 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. Based on the acquired standard data set, static electricity accumulation analysis is performed on the oil pipeline in the lightning-prone area, and the static charge accumulation risk value Qst(t) at the current moment is calculated and output, and then the static discharge threshold Qth is preset for preliminary comparative evaluation to determine the static electricity accumulation discharge risk in the oil pipeline, and the lightning analysis mechanism is executed based on the evaluation results; S3. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, the lightning analysis mechanism is executed. The lightning analysis mechanism calculates the output 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); S4. At the same time, according to the lightning accumulation algorithm formula, the lightning cumulative effect index Lacc is calculated and output, and the lightning high-incidence areas and the cumulative corrosion effect of lightning on the oil pipeline are analyzed. Then, based on the static charge cumulative risk value Qst (t), 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, a comprehensive calculation is performed to obtain the corrosion risk index CRR; 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.

[0006] Preferably, 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, transient corrosion current density Ic and lightning current intensity Ir; S12. Receive risk data in real time in the central monitoring system, and pre-process 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.

[0007] Preferably, S2 includes S21 and S22; S21, extracting the pipeline surface gradient Eg and soil conductivity Gs in the standard data set, and calculating the static charge accumulation risk value Qst(t) on the surface of the oil pipeline; The static charge accumulation risk value Qst(t) is calculated and outputted by the following algorithm 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.

[0008] Preferably, 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, the electrostatic discharge threshold Qth of the oil pipeline is calculated and outputted, and the specific calculation formula of the electrostatic discharge threshold Qth is: Where d represents the distance between the oil pipeline and the ground; Then, a preliminary comparative evaluation is performed with the electrostatic charge accumulation risk value Qst(t), the electrostatic charge on the current oil pipeline surface is analyzed, and based on the preliminary comparative evaluation results, the electrostatic accumulation discharge risk in the oil pipeline is judged, and a lightning strike analysis mechanism is executed. The specific evaluation contents are as follows; When the static charge accumulation risk value Qst (t) is greater than the static discharge threshold Qth, it means that the static charge on the pipeline surface exceeds the safety threshold. At this time, static discharge may occur and the lightning strike analysis mechanism is executed; 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.

[0009] Preferably, said S3 includes S31 and S32; S31. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, the 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; The transient corrosion current density Icorr(t) is calculated and outputted by the following transient current corrosion algorithm formula; ; 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 conductivity effective depth, pipeline depth D and conductivity effective depth The specific value is initially set by the user.

[0010] Preferably, 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 on the corrosion rate CR of the output oil pipeline; The corrosion rate CR is calculated and outputted by the following algorithm formula: ; 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 and T represents the total time period over which the corrosion rate is evaluated.

[0011] Preferably, said S4 includes S41 and S42; S41, construct a lightning accumulation algorithm formula, extract the soil conductivity Gs and the strike current intensity Ir in the standard data set and input them into the lightning accumulation algorithm formula, calculate and output the lightning cumulative 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, It represents the attenuation coefficient of lightning strike effect due to lightning strike frequency.

[0012] Preferably, S42, the static charge cumulative risk value Qst(t), the transient corrosion current density Icorr(t), the metal corrosion rate CR and the lightning cumulative effect index Lacc are comprehensively calculated, and the corrosion risk index CRR is output, and the lightning strike is analyzed to conduct a comprehensive analysis of the lightning strike risk of the current oil pipeline; The corrosion risk index CRR is calculated and output by the following algorithm formula: ; Preferably, said S5 includes S51 and S52; S51, based on the initial setting of the corrosion risk threshold Cth by the user, a secondary comparative evaluation and analysis is performed with the corrosion risk index CRR to analyze the corrosion of the oil pipeline caused by lightning strikes. The specific evaluation content is 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.

[0013] Preferably, S52, through front-end and back-end programming technology, a visualization interface is constructed in the central detection system, and 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.

[0014] The present invention provides a method for monitoring regional risks of oil pipelines. It has the following beneficial effects: (1) This method installs a sensor group, including electric field sensors, soil conductivity meters, electrochemical corrosion probes and lightning monitors, in areas with high lightning incidence on oil pipelines. This method can collect multiple key risk data such as electric field strength E, soil conductivity Gs, transient corrosion current density Ic and lightning current strength Ir in real time. The collected data is transmitted to the central monitoring system via the 5G network to achieve efficient and low-latency data reception. In the central monitoring system, the data is filtered and denoised using fast Fourier transform (FFT), normalized to unify the dimensions, and calibrated 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, and provide high-quality basic data for risk analysis of static electricity accumulation and lightning corrosion.

[0015] (2) This method uses the electrostatic accumulation model to calculate the electrostatic charge accumulation risk value Qst(t), and combines the equivalent capacitance Cp of the oil pipeline and the material breakdown electric field strength Eb to calculate the electrostatic discharge threshold Qth. Through the preliminary comparative evaluation of Qst(t) and Qth, it is judged whether the pipeline has an electrostatic discharge risk. When the electrostatic accumulation exceeds the safe range, the lightning analysis mechanism is triggered, and the transient corrosion current density Icorr(t) is further calculated by the transient current corrosion algorithm. At the same time, the metal corrosion rate CR on the pipeline surface is calculated by combining the electrostatic charge accumulation risk value Qst(t) and the external environmental disturbance parameters such as the sine period term. In addition, for areas with high incidence of lightning strikes, this method calculates the cumulative effect index Lacc through the lightning cumulative effect algorithm to comprehensively evaluate the impact of lightning strikes on long-term pipeline corrosion. This multi-parameter, multi-level risk modeling method dynamically couples the electrostatic effect, lightning transient impact and cumulative effect, significantly improving the scientificity and comprehensiveness of pipeline corrosion risk assessment.

[0016] (3) This method takes the corrosion risk indicator CRR as the core, and comprehensively calculates and analyzes the electrostatic charge accumulation risk value Qst(t), transient corrosion current density Icorr(t), corrosion rate CR and lightning cumulative effect indicator Lacc. After a secondary comparative evaluation of CRR and the preset corrosion risk threshold Cth, if it is found that the corrosion risk indicator CRR ≥ Cth, the system immediately generates a risk warning and automatically starts the grounding device to eliminate static electricity accumulation. At the same time, relevant information is displayed in the visual interface of the central monitoring system, including the regional lightning risk status, static charge accumulation and comprehensive corrosion risk indicators. This visual intelligent management method not only improves the real-time and intuitiveness of pipeline risk monitoring, but also can effectively reduce the risk of electrostatic discharge and pipeline corrosion caused by lightning strikes, avoid energy transmission interruptions and environmental pollution accidents, and greatly improve the safety and reliability of pipeline operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a schematic diagram of the steps of a method for monitoring regional risks in an oil pipeline according to the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1: The present invention provides a method for monitoring regional risks in an oil pipeline. Figure 1 , including the following steps: 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. Based on the acquired standard data set, static electricity accumulation analysis is performed on the oil pipeline in the lightning-prone area, and the static charge accumulation risk value Qst(t) at the current moment is calculated and output, and then the static discharge threshold Qth is preset for preliminary comparative evaluation to determine the static electricity accumulation discharge risk in the oil pipeline, and the lightning analysis mechanism is executed based on the evaluation results; S3. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, the lightning analysis mechanism is executed. The lightning analysis mechanism calculates the output 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); S4. At the same time, according to the lightning accumulation algorithm formula, the lightning cumulative effect index Lacc is calculated and output, and the lightning high-incidence areas and the cumulative corrosion effect of lightning on the oil pipeline are analyzed. Then, based on the static charge cumulative risk value Qst (t), 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, a comprehensive calculation is performed to obtain the corrosion risk index CRR; S5. Perform secondary comparative evaluation on the preset corrosion risk threshold Cth and the corrosion risk index CRR to analyze the comprehensive corrosion risk of the current oil pipeline. At the same time, build a visual interface in the central monitoring system to generate early warning information based on the secondary comparative evaluation results, and generate relevant control mechanisms through the visual interface.

[0020] In this embodiment, the method provides a full-process solution of real-time collection, dynamic analysis and intelligent evaluation by targeting the problems of pipeline static electricity accumulation, transient current corrosion and lightning cumulative effect that may be caused in areas with high lightning incidence. In the specific implementation, the 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 is filtered, de-noised, normalized and time synchronized to form a standard data set. Based on the standard data set, the electrostatic charge risk value Qst(t) is calculated by the electrostatic accumulation model, and the preliminary assessment of the electrostatic discharge risk is achieved in combination with the set electrostatic discharge threshold Qth; when the discharge anomaly is detected, the lightning analysis mechanism is triggered, and the transient corrosion current density Icorr(t) is calculated by the transient current corrosion algorithm, and the corrosion impact is further quantified by combining the environmental disturbance simulation and the metal corrosion rate CR; at the same time, the lightning cumulative effect algorithm is used to calculate the lightning cumulative index Lacc, and the corrosion risk index CRR is comprehensively output and a secondary assessment is performed with the corrosion risk threshold Cth. The beneficial effects of this implementation plan are: 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 transient effects and long-term cumulative effects of lightning strikes is more refined; third, intelligent early warning and visual display closely combine risk monitoring and control mechanisms, realizing the transition from passive monitoring to active protection. Through the above optimization measures, this method effectively reduces the risk of pipeline corrosion caused by lightning strikes and electromagnetic interference, significantly improves the safety, stability and management efficiency of oil pipeline operation, and provides accurate data support and scientific basis for pipeline maintenance and resource allocation.

[0021] Example 2: This example is an explanation of Example 1. Figure 1 , specifically: 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 set includes electric field sensors, geological conductivity meters, electrochemical corrosion probes, and lightning strike monitors; Risk data include electric field strength E, soil conductivity Gs, transient corrosion current density Ic and lightning current intensity Ir; The electric field strength E is acquired through the electric field sensor; The soil conductivity Gs is collected and obtained by the address conductivity meter; The transient corrosion current density Ic is acquired by electrochemical corrosion probe; The lightning current intensity Ir is acquired through the lightning monitor; S12. Receive risk data in real time in the central monitoring system, and pre-process the risk data to obtain a standard data set; Preprocessing methods include filtering denoising, time synchronization and normalization; Filter denoising filters the risk data using fast Fourier transform FFT; Normalization is used to normalize the filtered risk data, unify the risk data of different dimensions, and obtain a standard data set; Time synchronization is used to time align standard data sets; Then extract the electric field intensity E and perform gradient calculation to obtain the pipeline surface gradient Eg. The specific algorithm formula is: , where △E represents the change in electric field strength between adjacent measuring points, and △d represents the distance between adjacent measuring points.

[0022] In this embodiment, the method collects key risk parameters such as electric field strength E, soil conductivity Gs, transient corrosion current density Ic and lightning current intensity Ir in real time by installing a sensor group in areas prone to lightning, including electric field sensors, address conductivity meters, electrochemical corrosion probes and lightning monitors, and transmits the data to the central monitoring system through the communication module and the 5G network. The central monitoring system filters and denoises the received data using fast Fourier transform FFT, normalization processing and time synchronization operations to generate a standardized data set to ensure data consistency and accuracy. In addition, by performing gradient calculation on the electric field strength E, the electric field gradient Eg on the pipeline surface is further obtained to provide key parameters for subsequent analysis. The core purpose of this implementation plan is to comprehensively improve the risk monitoring capability of oil pipelines in complex lightning environments through multi-parameter acquisition and efficient data processing. Its beneficial effects include: first, strong real-time performance, which can quickly capture key data changes caused by lightning strikes; second, accurate data processing, 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 static 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, which effectively reduces the underreporting and misjudgment of sudden risks and enhances the safety and reliability of pipeline operation.

[0023] Example 3: This example is an explanation of Example 2. Figure 1 , specifically: S2 includes S21 and S22; S21, extracting the pipeline surface gradient Eg and soil conductivity Gs in the standard data set, and calculating the static charge accumulation risk value Qst(t) on the surface of the oil pipeline; The static charge accumulation risk value Qst(t) is calculated and output by the following algorithm 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.

[0024] 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, calculate and output the electrostatic discharge threshold Qth of the oil pipeline. The specific calculation formula of the electrostatic discharge threshold Qth is: Where d represents the distance between the oil pipeline and the ground; Then, a preliminary comparative evaluation is conducted with the electrostatic charge accumulation risk value Qst (t), and the electrostatic charge on the current surface of the oil pipeline is analyzed. Based on the preliminary comparative evaluation results, the electrostatic accumulation discharge risk in the oil pipeline is judged, and the lightning strike analysis mechanism is implemented. The specific evaluation contents are as follows; When the static charge accumulation risk value Qst (t) is greater than the static discharge threshold Qth, it means that the static charge on the pipeline surface exceeds the safety threshold. At this time, static discharge may occur and the lightning strike analysis mechanism is executed; 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.

[0025] 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 the specific implementation, by extracting the pipeline surface gradient Eg and soil conductivity Gs in the standard data set, the electrostatic accumulation model is used to calculate the static charge accumulation risk value Qst(t). 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. By comparing and evaluating Qst(t) and Qth, it is judged whether there is an electrostatic discharge risk in the pipeline: when Qst(t)>Qth, electrostatic discharge may occur in the pipeline, triggering the lightning strike analysis mechanism; when Qst(t)≤Qth, the static charge of the pipeline is within the safe range, maintaining normal operation and real-time monitoring. The core purpose of this method is to dynamically evaluate the electrostatic accumulation risk on the pipeline surface, so as to identify possible electrostatic discharge hazards in advance and take corresponding measures. Its beneficial effects include: first, through the accurate calculation of the static electricity accumulation model and the discharge threshold, the scientific quantification of the electrostatic discharge risk is achieved; second, the dynamic monitoring and real-time evaluation capabilities effectively improve the timeliness of pipeline static electricity 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 identification and response speed of pipeline electrostatic discharge risks are significantly improved, effectively avoiding the aggravation of corrosion and safety accidents caused by electrostatic discharge, and further improving the operational reliability and safety of oil pipelines.

[0026] Example 4: This example is an explanation of Example 1. Figure 1 , specifically: S3 includes S31 and S32; S31. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, the 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; The transient corrosion current density Icorr(t) is calculated and output by the following transient current corrosion algorithm formula; ; 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.

[0027] S32. Based on the transient corrosion current density Icorr(t) and the static charge accumulation risk value Qst(t), the influence of environmental changes on the corrosion rate is simulated by the sinusoidal periodic term, and the corrosion rate CR of the output oil pipeline is calculated. The corrosion rate CR is calculated and output by the following algorithm formula; ; 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, and the above parameters are obtained by the user's initial settings.

[0028] In this embodiment, through the above method steps, this method realizes the accurate analysis of the transient corrosion impact of oil pipelines caused by lightning strikes and the dynamic evaluation of corrosion rate. In the specific implementation, when the abnormal static electricity accumulation discharge in the pipeline is identified, the lightning analysis mechanism is immediately executed. Based on the transient current corrosion algorithm, the soil conductivity Gs and lightning current intensity Ir(t) in the standard data set are extracted, and the corrosion intensity and influence of the lightning current transmitted through the soil to the pipeline surface are analyzed by calculating the transient corrosion current density Icorr(t). At the same time, Icorr(t) is combined with the static charge accumulation risk value Qst(t), and a sinusoidal periodic term is introduced to simulate the dynamic influence of environmental disturbance on the corrosion rate, and 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 transient and dynamic corrosion of oil pipelines and provide a scientific basis for the safe operation of pipelines. Its beneficial effects include: first, the transient current corrosion algorithm accurately reflects the short-term high-intensity corrosion behavior caused by lightning strikes; second, combined with the dynamic simulation of environmental disturbances, it fully captures the impact of long-term environmental factors on the corrosion rate; third, the real-time calculated corrosion rate CR provides quantitative data support for predicting pipeline life and planning protective measures. Through this method, the monitoring accuracy of the transient and dynamic corrosion effects of pipelines is significantly improved, the risk assessment capability is more comprehensive, and the possibility of local corrosion failure of pipelines caused by lightning strikes is further reduced, which comprehensively improves the operational safety and reliability of oil pipelines.

[0029] Example 5: This example is an explanation of Example 4. Figure 1 , specifically: S4 includes S41 and S42; S41, construct a lightning accumulation algorithm formula, extract the soil conductivity Gs and the strike current intensity Ir in the standard data set and input them into the lightning accumulation algorithm formula, calculate and output the lightning cumulative 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, It represents the attenuation coefficient of lightning strike effect due to lightning strike frequency.

[0030] S42, perform comprehensive calculation on the static charge cumulative risk value Qst(t), transient corrosion current density Icorr(t), metal corrosion rate CR and lightning cumulative effect index Lacc, output the corrosion risk index CRR, and perform comprehensive lightning risk analysis on the current oil pipeline; The corrosion risk index CRR is calculated and output by the following algorithm formula; ; In this embodiment, through the above method steps, this method realizes the scientific quantitative analysis of the cumulative effect of lightning strikes and the comprehensive risk of corrosion, and systematically evaluates the long-term cumulative effect of lightning strikes on oil pipelines and the superposition effect of transient corrosion effects. In the specific implementation, by extracting the soil conductivity Gs and lightning current intensity IrI in the standard data set, the lightning cumulative algorithm formula is constructed, and the lightning cumulative effect index Lacc is dynamically calculated to quantify the long-term impact of lightning strike frequency, soil conductivity change and lightning effect attenuation on pipeline corrosion. At the same time, the static charge cumulative risk value Qst(t), transient corrosion current density Icorr(t), metal corrosion rate CR and lightning cumulative effect index Lacc are integrated to calculate the corrosion risk index CRR through a comprehensive algorithm. This index quantifies the overall corrosion risk of the pipeline in a high-incidence lightning strike environment, providing a reliable basis for subsequent protection decisions. The core purpose of this method is to comprehensively reveal the cumulative effect of lightning strikes and their long-term threat to pipeline corrosion through dynamic modeling and multi-parameter fusion analysis, thereby realizing the quantitative assessment of corrosion risk. Its beneficial effects include: first, the lightning accumulation algorithm accurately captures the cumulative corrosion characteristics of the high-frequency area of ​​lightning strikes, significantly improving the scientific nature of long-term corrosion assessment; second, the corrosion risk index CRR combined with multi-parameter analysis provides strong support for the accurate quantification of the comprehensive risk of pipelines; third, the dynamic evaluation and quantitative analysis results lay the foundation for the intelligent monitoring and early warning mechanism of pipelines. Through this method, the pipeline risk management capability has been greatly improved, the risk of safety accidents caused by cumulative corrosion failure has been effectively reduced, and the operating efficiency and reliability of the oil pipeline system have been comprehensively enhanced.

[0031] Example 6: This example is an explanation of Example 5. Please refer to Figure 1 , specifically: S5 includes S51 and S52; S51, based on the initial setting of the corrosion risk threshold Cth by the user, a secondary comparative evaluation and analysis is performed with the corrosion risk index CRR to analyze the corrosion of the oil pipeline caused by lightning strikes. 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.

[0032] S52. Through front-end and back-end programming technology, a visual interface is constructed in the central detection system. 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 visual interface to display the risk monitoring situation of the lightning strike area.

[0033] In this embodiment, through the above method steps, the method realizes the secondary evaluation analysis and intelligent risk early warning management based on the corrosion risk index CRR, providing an effective guarantee for the safe operation of the oil pipeline in the high lightning strike area. In the specific implementation, through the corrosion risk threshold Cth set by the user, the calculated corrosion risk index CRR is evaluated by secondary comparison. When it is detected that CRR≥Cth, the system automatically generates a risk early warning and triggers the grounding device to eliminate static electricity, so as to reduce the corrosion risk of the pipeline; when CRR<Cth, the pipeline is in a safe range and no early warning needs to be generated. At the same time, through the front-end and back-end programming technologies, a visual interface is constructed in the central detection system to dynamically display the regional 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 makes the risk status and regional distribution presented intuitively, providing convenient operation and decision-making support for pipeline management. The core purpose of this method is to realize the intelligent early warning and dynamic display of the lightning strike cumulative risk through the comparative analysis of the corrosion risk threshold and the comprehensive risk index, so as to ensure 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 early 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, the pipeline management has changed from static monitoring to dynamic early warning, greatly enhancing the control ability of the lightning strike corrosion risk, reducing the accident rate, and improving the operation efficiency and safety of the oil pipeline system.

[0034] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it is understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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. Based on the acquired standard data set, the static electricity accumulation analysis is performed on the oil pipeline in the lightning-prone area, and the static charge accumulation risk value Qst(t) at the current moment is calculated and output, and then the static electricity discharge threshold Qth is preset for preliminary comparative evaluation to determine the static electricity accumulation discharge risk in the oil pipeline, and the lightning analysis mechanism is executed based on the evaluation results; S3. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, the lightning analysis mechanism is executed. The lightning analysis mechanism calculates the output 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); S4. At the same time, according to the lightning accumulation algorithm formula, the lightning cumulative effect index Lacc is calculated and output, and the lightning high-incidence areas and the cumulative corrosion effect of lightning on the oil pipeline are analyzed. Then, based on the static charge cumulative risk value Qst (t), 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, a comprehensive calculation is performed to obtain the corrosion risk index CRR; 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, transient corrosion current density Ic and lightning current intensity Ir; S12. Receive risk data in real time in the central monitoring system, and pre-process 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 2, characterized in that: The S2 includes S21 and S22; S21, extracting the pipeline surface gradient Eg and soil conductivity Gs in the standard data set, and calculating the static charge accumulation risk value Qst(t) on the surface of the oil pipeline; The static charge accumulation risk value Qst(t) is calculated and outputted by the following algorithm 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.

4. The method for monitoring regional risks of oil pipelines according to claim 3 is characterized by: 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, calculate and output the electrostatic discharge threshold Qth of the oil pipeline. The specific calculation formula of the electrostatic discharge threshold Qth is: Where d represents the distance between the oil pipeline and the ground; Then, a preliminary comparative evaluation is performed with the electrostatic charge accumulation risk value Qst(t), the electrostatic charge on the current oil pipeline surface is analyzed, and based on the preliminary comparative evaluation results, the electrostatic accumulation discharge risk in the oil pipeline is judged, and a lightning strike analysis mechanism is executed. The specific evaluation contents 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.

5. The method for monitoring regional risks of oil pipelines according to claim 4, characterized in that: The S3 includes S31 and S32; S31. When the static electricity accumulation discharge abnormality in the oil pipeline is identified, the 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; The transient corrosion current density Icorr(t) is calculated and outputted by the following transient current corrosion algorithm formula; ； 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.

6. The method for monitoring regional risks of oil pipelines according to claim 5, characterized in that: 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 oil pipeline; The corrosion rate CR is calculated and outputted by the following algorithm formula: ； 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 and T represents the total time period over which the corrosion rate is evaluated.

7. The method for monitoring regional risks of oil pipelines according to claim 6, characterized in that: The S4 includes S41 and S42; S41, construct a lightning accumulation algorithm formula, extract the soil conductivity Gs and the strike current intensity Ir in the standard data set and input them into the lightning accumulation algorithm formula, calculate and output the lightning cumulative 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, It represents the attenuation coefficient of lightning strike effect due to lightning strike frequency.

8. The method for monitoring regional risks of oil pipelines according to claim 7, characterized in that: S42, perform comprehensive calculation on the static charge cumulative risk value Qst(t), transient corrosion current density Icorr(t), metal corrosion rate CR and lightning cumulative effect index Lacc, output the corrosion risk index CRR, and perform comprehensive lightning risk analysis on the current oil pipeline; The corrosion risk index CRR is calculated and output by the following algorithm formula: 。 9. The method for monitoring regional risks of oil pipelines according to claim 8, characterized in that: The S5 includes S51 and S52; S51, based on the initial setting of the corrosion risk threshold Cth by the user, a secondary comparative evaluation and analysis is performed with the corrosion risk index CRR to analyze the corrosion of the oil pipeline caused by lightning strikes. The specific evaluation content is 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.

10. The method for monitoring regional risks of oil pipelines according to claim 9, characterized in that: S52. Through front-end and back-end programming technology, a visual interface is constructed in the central detection system. 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 visual interface to display the risk monitoring situation of the lightning strike area.

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