Gas transmission pipeline four-dimensional coupling risk assessment and dynamic safety management system and method

Abstract

The four-dimensional coupling risk assessment and dynamic safety management system of the gas transmission pipeline comprises a data acquisition module, a data coupling module, a four-dimensional coupling module and a cross-scale risk assessment module. The data acquisition module is used for acquiring monitoring data at each position of the pipeline in real time. The monitoring data comprises sensor monitoring data, InSAR satellite data, pipeline size parameters and GIS geographic information. The sensor data comprises pressure, temperature and corrosion rate. The data coupling module is used for constructing an evaluation model of a plurality of risk factors according to the monitoring data and historical data thereof. The evaluation model comprises an erosion rate model, a corrosion rate model, a geological subsidence model and a stress field model. An interactive coupling mechanism between the evaluation models is established, and a multi-dimensional coupling model representing a pipeline damage evolution process is generated. The cross-scale risk assessment module is used for performing multi-scale risk assessment on the pipeline according to the multi-dimensional coupling model.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology for gas pipelines, specifically to a four-dimensional coupled risk assessment and dynamic safety management system and method for gas pipelines. Background Technology

[0002] In the field of safe operation of gas pipelines, risk assessment and management technologies have evolved from manual inspections to semi-quantitative evaluations and then to quantitative analysis. Currently, the industry commonly uses risk assessment methods including remaining life prediction based on corrosion rate models, crack propagation analysis based on fracture mechanics, and settlement monitoring based on geological surveys. These methods typically rely on periodic monitoring data. For example, intelligent pipeline pigs acquire data on corrosion defects on the pipeline's inner wall every few years, combining this data with empirical formulas to calculate remaining strength; or settlement observation points are installed in key areas to periodically measure ground displacement and determine whether the pipeline is affected by geological activity. Although operating parameters such as pressure and temperature are collected in real time, they are often only used for over-limit alarms and have not been quantitatively correlated with damage mechanisms such as corrosion, erosion, and settlement. In failure mode identification, most existing technologies perform isolated analysis on a single defect type. For example, crack defects are evaluated separately using fracture mechanics, and corrosion pits are judged separately using the remaining wall thickness criterion. However, actual pipelines often have multiple defects at the same time, and different damage mechanisms promote each other. For example, erosion accelerates corrosion, and settlement changes the stress distribution, thus affecting the crack propagation rate. This coupling effect is difficult to reflect in existing methods.

[0003] While traditional risk assessments have incorporated GIS (Geographic Information System), they are primarily limited to pipeline centerline location and surrounding environment visualization, failing to deeply integrate GIS coordinates with physical quantities such as defect evolution and stress distribution. For special pipe sections like elbows, tees, and compressor station outlets, existing methods typically employ uniform evaluation standards, lacking targeted weight allocation, potentially leading to underestimation of high-risk areas. Regarding geological hazard monitoring, satellite remote sensing technologies such as InSAR have been gradually applied to ground settlement monitoring along pipelines, but data is often provided in monthly or quarterly reports, disconnected from real-time operational data, and unable to achieve dynamic updates and early warnings of settlement strain. Furthermore, existing risk assessment systems mostly remain at the static scoring level of pipe sections, such as using scorecards to weighted summations of various risk factors to obtain a fixed risk value. This value cannot reflect the dynamic process of risk changes over time, nor can it reflect the impact of micro-defect evolution on macro-network safety.

[0004] Meanwhile, existing technologies primarily rely on pre-set emergency plans. When a leak occurs, on-site personnel judge valve shut-off and evacuation areas based on experience, lacking precise simulations based on real-time weather, terrain, and population distribution. The chain reaction of disasters, such as the failure of one pipe segment triggering subsequent failures of adjacent segments due to pressure fluctuations, currently lacks effective prediction methods. Furthermore, assessments of the cross-impact of pipelines with other lifeline projects such as power grids, water conservancy, and transportation are rarely addressed, resulting in a lack of systematic assurance of the resilience of critical infrastructure. Summary of the Invention

[0005] The technical problem solved by this invention is to provide a four-dimensional coupled risk assessment and dynamic safety management system for gas transmission pipelines, which can provide accurate and timely early warning and decision support when the long-term safe operation of high-pressure gas transmission pipelines is required.

[0006] The basic solution provided by this invention is a four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines, including a data acquisition module, a data coupling module, a four-dimensional coupling module, and a cross-scale risk assessment module. The data acquisition module is used to acquire monitoring data from various parts of the pipeline in real time. The monitoring data includes sensor monitoring data, InSAR satellite data, pipeline size parameters, and GIS geographic information. The sensor data includes pressure, temperature, and corrosion rate. The data coupling module is used to construct assessment models for multiple risk factors based on monitoring data and historical data. The assessment models include erosion rate model, corrosion rate model, geological subsidence model and stress field model, and establish an interactive coupling mechanism between the assessment models to generate a multi-dimensional coupling model characterizing the pipeline damage evolution process. The cross-scale risk assessment module is used to perform multi-scale risk assessment on pipelines based on a multi-dimensional coupling model. The cross-scale risk assessment module includes a micro-analysis module, a meso-analysis module, and a macro-analysis module. The microscopic analysis module is used to calculate the evolution of a single defect and determine the failure type of the pipeline based on the single defect. The mesoscopic analysis module is used to calculate the dynamic safety factor of the pipeline based on the diffusion rate of a single defect, combined with the pipeline type and corresponding GIS geographic information. The macro-analysis module is used to simulate the disaster chain reaction field and the failure propagation path of the pipeline based on the dynamic safety factor of each pipeline and combined with GIS geographic information.

[0007] The principle and advantages of this invention are as follows: First, a data acquisition module acquires sensor data, InSAR satellite data, pipeline dimensional parameters, and GIS geographic information from various locations along the pipeline. This data is then fed into a data coupling module, which constructs four assessment models—erosion rate, corrosion rate, geological subsidence, and stress field—based on monitoring data and historical data, and establishes an interactive coupling mechanism between these models to ultimately generate a multi-dimensional coupled model capable of characterizing the pipeline damage evolution process. The model is then input into a cross-scale risk assessment module, which includes micro, meso, and macro levels of analysis. The micro-analysis module calculates the evolution of individual defects on the pipeline's inner wall and determines the specific failure type that a defect might cause based on its characteristics. The meso-analysis module calculates the dynamic safety factor of the pipeline segment based on the defect's propagation rate, combined with the specific type of pipeline and GIS geographic information. The macro-analysis module summarizes the dynamic safety factors of each pipeline segment and, combined with GIS geographic information, simulates the disaster chain reaction field and the propagation path of pipeline failure.

[0008] Traditional risk assessment methods typically employ static evaluation or single-physical-field analysis, making it difficult to capture the changes in risk over time and the interactions between different risk factors. This solution, through multi-dimensional data collection, multi-model coupling, and cross-scale analysis, achieves dynamic risk assessment across the entire chain, from microscopic defects to macroscopic pipeline networks.

[0009] Furthermore, the data coupling module includes an erosion module, a corrosion module, a settlement module, and a stress module; The erosion module is used to calculate the erosion rate using a preset erosion rate model. :

[0010] Where E is the erosion rate. The erosion coefficient is... For the density of the multiphase flow medium, For fluid velocity, The velocity index is the flow rate index. Where m is the diameter of the eroded particles; The corrosion module is used to calculate the synergistic effect of corrosion and erosion using a synergistic coefficient model. :

[0011] in, The total corrosion rate of the corrosion-erosion coupling is given. Let be the pure corrosion rate without erosion. The coefficient for promoting corrosion by erosion. Erosion rate; The settlement module is used to monitor ground displacement using InSAR satellite data, perform finite element analysis of pipeline stress distribution, and calculate settlement strain. :

[0012] in For settlement strain, The vertical ground displacement obtained from InSAR monitoring is represented by L, which is the original length of the settlement pipe. The stress module is used to calculate the stress intensity factor of the pipe. :

[0013] in Y is a type I stress intensity factor, and Y is a geometric factor. The total stress borne by the pipeline. This is a dimensional defect.

[0014] Furthermore, the microscopic analysis module is used to output the current risk factor based on the evaluation model of each risk factor by inputting real-time monitoring data, identify the location and type of a single defect on the inner wall of the pipeline, calculate the evolution rate of the single defect, and determine the failure type of the pipeline. The meso-level analysis module is used to determine the weight coefficients of each risk factor based on the preset type of the pipeline segment, and to perform weighted fusion of the various risk factors to obtain the dynamic safety factor of the pipeline segment. ; The macroscopic analysis module is used to simulate a disaster chain reaction field based on the dynamic safety factor of each pipe section and combined with GIS geographic information, and to identify the pipeline failure propagation path and high-risk areas at the macro level.

[0015] The specific functions of the three analysis modules—microscopic, mesoscopic, and macroscopic—were further clarified. The microscopic analysis module receives the erosion rate E and corrosion rate from the data coupling module. Settlement strain ε and stress intensity factor This data is used to identify the location and type of individual defects on the pipeline's inner wall, calculate the evolution rate of these defects over time, and determine the specific failure type that the defect may cause based on the evolution. The mesoscopic analysis module then determines the E, based on the type of pipeline section, such as elbows, goaf areas, or high-sulfur sections. ε Different weighting coefficients are assigned to risk factors, and then these factors are weighted and fused to calculate the dynamic safety factor SFt for the pipeline segment. The macro-analysis module summarizes the data for each pipeline segment. By combining GIS geographic information, this method simulates a disaster chain reaction field, identifying the failure propagation path and high-risk areas of the entire pipeline network from a macroscopic perspective. Traditional risk assessment methods struggle to link the evolution of micro-defects with the failure risk of the macro-level pipeline network. This solution, through clear hierarchical division, uses the evolution rate of micro-defects as the basis for calculating the meso-level safety factor, and then uses the meso-level safety factor as input for macro-level disaster simulation. This achieves a comprehensive risk assessment from local to overall, and from micro to macro levels, providing systematic technical support for the full lifecycle management of pipeline networks.

[0016] Furthermore, the microscopic analysis module includes a failure type discrimination module, used to determine the possible pipeline failure type of the defect based on the defect type, evolution rate, and size parameters, and by referring to preset failure criterion rules; the defect types include cracks, corrosion pits, erosion pits, depressions, and deposits; the failure types include fractures, perforations, leaks, deformation, and blockages; wherein the failure criterion rules include: For crack-type defects, when the stress intensity factor Greater than the toughness of the material At that time, it was determined to be a fracture failure; For corrosion pit defects, when the remaining wall thickness Less than minimum operating wall thickness When this occurs, it is determined to be a perforation failure; For erosion pit defects, when the erosion rate E is greater than the critical erosion rate At that time, it was determined to be a puncture failure; For dent-type defects, if the dent depth is greater than 6% of the pipe diameter, it is judged as deformation failure; For sediment defects, when the blockage rate of the pipe cross-sectional area is greater than 80%, it is judged as a blockage failure.

[0017] First, based on real-time monitoring data and GIS coordinate positioning, the types of defects on the pipeline inner wall are identified, including cracks, corrosion pits, erosion pits, depressions, and deposits. Then, the failure type discrimination module determines the specific failure mode that may result from different defect types, according to preset criteria rules. Traditional methods often only calculate the evolution rate of defects but fail to directly correlate it with specific failure consequences. This solution establishes a direct correspondence between defect types and failure types and sets clear quantitative criteria, enabling the system to directly output the most likely failure mode at the micro level, providing more accurate input for subsequent meso-level risk assessment and macro-level emergency decision-making.

[0018] Furthermore, the meso-level analysis module includes a weight allocation module, a safety factor technology module, and a risk classification module; The weight allocation module is used to dynamically determine the weight coefficients of each risk factor based on the type of pipeline section and GIS geographic information. The pipeline section types include elbows, tees, compressor station outlets, goaf areas, H2S high-concentration sections, and third-party construction areas. The weight coefficients are preset according to the historical failure expert rule base. The safety factor technology module is used to calculate the dynamic safety factor based on each risk factor and its weighting coefficient. :

[0019] in, As a baseline safety factor, The remaining wall thickness is obtained by subtracting the cumulative corrosion depth from the initial wall thickness. Where D is the pressure inside the pipe, and D is the pipe diameter. , , as well as These are the reference thresholds for each risk factor; The risk grading module is used to assign dynamic safety factors. The risk level is output by comparing it with a preset threshold, and the dynamic safety factor is adjusted accordingly. When the value is less than the first preset threshold, a yellow warning is triggered. When the value is less than the second preset threshold, a red alert is triggered and the emergency shutdown logic is activated.

[0020] The weight allocation module first determines the erosion rate E and corrosion rate based on the type of pipeline section, such as elbows, compressor station outlets, goaf areas, or high-sulfur sections, and combines this with GIS geographic information. Settlement strain ε and stress intensity factor Assign corresponding weighting coefficients to These weighting coefficients, based on historical failure data and expert rule base presets, reflect the dominant role of different risk factors in different types of pipe sections. The safety factor technology module then uses these weighting coefficients, combined with the pipeline's real-time operating parameters, to calculate the dynamic safety factor. This formula uses the baseline safety factor. Multiply by the remaining wall thickness The ratio of pressure P to pipe diameter D yields a basic safety margin. This margin is then subtracted from the weighted normalized damage term for each risk factor, quantifying the reduction in safety factor by risk factors. The risk grading module compares the calculated SFt with preset thresholds. When SFt is less than the first preset threshold, a yellow alert is triggered, indicating the need for enhanced monitoring; when SFt is less than the second, even lower, preset threshold, a red alert is triggered, and the emergency shutdown logic is activated, automatically executing a shutdown operation. Traditional safety factors are typically fixed values, failing to reflect dynamic changes in risk. This solution, through dynamic weight allocation and real-time parameter calculation, achieves real-time updates to the safety factor and automatically triggers different levels of response measures based on the risk level, significantly improving the accuracy and automation of risk management.

[0021] Furthermore, the macro-analysis module includes a disaster chain simulation module, a diffusion simulation module, and a cross-impact assessment module; The disaster chain simulation module is used to construct a pipeline network topology map with pipe segments as nodes and connection relationships as edges based on the real-time risk values ​​and GIS geographic information of each pipeline. It uses the Monte Carlo simulation method to randomly generate initial failure events, simulate the failure process, and calculate the failure propagation path under different failure scenarios. The diffusion simulation module is used to calculate the concentration distribution of leaked substances in space when a pipeline leak occurs, based on the leak location, medium properties, real-time meteorological data, and terrain data, using a Gaussian smoke diffusion model or computational fluid dynamics model. Combined with GIS buffer analysis, it delineates the personnel evacuation range and environmentally sensitive areas, and dynamically updates the affected high-consequence areas. The cross-impact assessment module is used to spatially overlay the impact range of pipeline failure with the geographic layers of power grids, water conservancy, and transportation projects to assess the indirect impact of pipeline failure on critical infrastructure and generate a comprehensive risk heat map through a multi-criteria decision-making method.

[0022] The disaster chain simulation module utilizes real-time risk values ​​and GIS geographic information for each pipe segment to construct a topological graph of the entire pipeline network, with pipe segments as nodes and connections as edges. Initial failure events are randomly generated using Monte Carlo simulation methods, and based on preset failure probability propagation rules, the module simulates the chain reaction process of failure within the pipeline network, calculating failure propagation paths and cascading failure probabilities under different failure scenarios. The diffusion simulation module, targeting pipeline leak scenarios, calculates the spatial concentration distribution of leaked substances using Gaussian puff diffusion models or computational fluid dynamics models based on leak location, medium properties, real-time meteorological data, and topographic data. Combined with GIS buffer analysis, it dynamically delineates the areas requiring evacuation and affected environmentally sensitive zones, updating high-consequence area information in real time. The cross-impact assessment module spatially overlays the impact range of pipeline failure with geographic layers of critical infrastructure such as power grids, water conservancy, and transportation, assessing the indirect impact of pipeline failure on other lifeline projects and generating a comprehensive risk heat map through a multi-criteria decision-making method. Traditional emergency responses are often based on static plans, making it difficult to cope with complex and ever-changing real-world situations. This solution provides dynamic and visualized decision support for emergency command by simulating disaster chains, diffusion paths, and cross-impacts. It effectively guides the deployment of repair forces, the determination of personnel evacuation areas, and cross-departmental emergency coordination. Furthermore, the data acquisition module includes a third-party construction monitoring submodule, used to access real-time data from construction machinery vibration sensors, construction area video surveillance data, and the construction plan's GIS layer. When the detected mechanical vibration amplitude exceeds a preset threshold or construction machinery enters the pipeline's protection zone, a third-party construction early warning is triggered, and stress mutation monitoring is initiated.

[0023] Furthermore, it also includes a detection cycle optimization module, used to optimize the detection cycle based on the dynamic safety factor of each pipe segment. Based on the confidence level of historical testing data, the non-destructive testing cycle is dynamically adjusted; among them, the testing cycle for high-risk pipe sections is shortened to 30% of the normal cycle, and the testing cycle for low-risk pipe sections is extended to 150% of the normal cycle.

[0024] Furthermore, it also includes an emergency repair prioritization module, which generates a repair priority ranking list based on the severity of the failure consequences of each failed pipe section and the repair difficulty coefficient, combined with GIS traffic network data, when a leakage accident occurs; wherein, the severity of the failure consequences is calculated based on the population density, environmental sensitivity level and important user type within the affected area.

[0025] This invention also discloses a four-dimensional coupled risk assessment and dynamic safety management method for gas pipelines, which is applied to the aforementioned four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines. Attached Figure Description

[0026] Figure 1This is a schematic diagram of an embodiment of the four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines of the present invention. Detailed Implementation

[0027] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown: The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines includes a data acquisition module, a data coupling module, a four-dimensional coupling module, and a cross-scale risk assessment module. The data acquisition module is used to acquire monitoring data from various parts of the pipeline in real time. The monitoring data includes sensor monitoring data, InSAR satellite data, pipeline size parameters, and GIS geographic information. The sensor data includes pressure, temperature, and corrosion rate.

[0028] Specifically, taking a certain operating natural gas pipeline as an example, the pipeline is approximately 120 kilometers long, with a diameter of 1016 millimeters and a design pressure of 10 MPa. 120 pressure sensors are installed along the pipeline, one every kilometer, to monitor pressure changes in real time, with a sampling frequency of once per minute. Temperature sensors are also installed every kilometer to monitor the gas temperature inside the pipeline. Corrosion rate is monitored using resistance probe sensors, densely installed at high-risk locations such as pipeline bends and welds, totaling 80 sensors, which automatically upload corrosion rate data every 24 hours. InSAR satellite data is obtained through commercial satellite remote sensing services, providing monthly ground subsidence monitoring data along the pipeline with millimeter-level accuracy. Pipeline dimensional parameters, including design data such as pipe diameter, wall thickness, and pipe material grade, are imported into the system from pipeline construction archives. GIS geographic information includes the pipeline centerline coordinates, administrative divisions along the route, locations of densely populated areas, environmentally sensitive areas, road networks, and river distribution; this data is obtained from surveying and mapping departments and entered into the system. The third-party construction monitoring submodule accesses vibration sensor data and video streams from 30 construction monitoring points along the pipeline, enabling the system to detect machinery operating near the pipeline in real time.

[0029] The data coupling module is used to construct assessment models for multiple risk factors based on monitoring data and historical data. The assessment models include erosion rate model, corrosion rate model, geological subsidence model and stress field model, and establish an interactive coupling mechanism between the assessment models to generate a multi-dimensional coupling model characterizing the pipeline damage evolution process.

[0030] Specifically, the data coupling module includes an erosion module, a corrosion module, a settlement module, and a stress module; The erosion module is used to calculate the erosion rate using a preset erosion rate model. :

[0031] Where E is the erosion rate. The erosion coefficient is... For the density of the multiphase flow medium, For fluid velocity, The velocity index is the flow rate index. denoted as 'm', where 'm' is the particle diameter index.

[0032] For example, in a certain pipe section, the measured gas velocity was 15 meters per second, the sand content in the gas was 0.5 grams per cubic meter, and the average diameter of the sand particles was 0.1 millimeters. The erosion module called a preset erosion rate model to calculate the erosion rate. The erosion coefficient K was taken as an empirical value of 0.0023, the velocity index n was taken as 2.3, and the particle size index m was taken as 0.2. Substituting these values ​​into the formula, the current erosion rate of this pipe section was calculated to be 0.12 millimeters per year. This calculation result was compared and verified with ultrasonic thickness measurement data from the same period, and the error was within 10%.

[0033] The corrosion module is used to calculate the synergistic effect of corrosion and erosion using a synergistic coefficient model. :

[0034] in, The total corrosion rate of the corrosion-erosion coupling is given. Let be the pure corrosion rate without erosion. The coefficient for promoting corrosion by erosion. The erosion rate is denoted as .

[0035] For example, in the same pipe section, the pure corrosion rate, measured by the stripping test, was 0.2 mm per year. The erosion-promoting coefficient α was taken as 0.8 based on the pipe material and media conditions. Substituting the erosion rate of 0.12 mm per year into the synergistic coefficient model, the total corrosion rate of the corrosion-erosion coupling was calculated to be 0.296 mm per year. This result indicates that erosion increased the corrosion rate by approximately 48%.

[0036] The settlement module is used to monitor ground displacement using InSAR satellite data, perform finite element analysis of pipeline stress distribution, and calculate settlement strain. :

[0037] in For settlement strain, The vertical displacement of the ground obtained by InSAR monitoring is represented by L, which is the original length of the settlement pipe.

[0038] For example, InSAR satellite data shows that a pipe section traversing a mined-out area has subsided by 8 millimeters in the past three months. The original length of the pipe section is 500 meters. The settlement module calculates the settlement strain to be 1.6 × 10⁻⁵. This strain is input as a boundary condition into the finite element model to perform stress analysis on the pipeline, and the axial stress added to the pipe section due to settlement is found to be 12 MPa.

[0039] The stress module is used to calculate the stress intensity factor of the pipe. :

[0040] in Y is a type I stress intensity factor, and Y is a geometric factor. The total stress borne by the pipeline. This is a dimensional defect.

[0041] For example, in the aforementioned goaf section, when the internal pressure is 7 MPa, the total stress σ borne by the pipeline, including the circumferential stress caused by the internal pressure and the additional stress from settlement, is calculated to be 180 MPa. Internal inspection revealed a crack with a length of 5 mm and a depth of 1.5 mm at this location. The geometric factor Y is taken as 1.2 based on the crack shape. Substituting into the stress intensity factor formula, the calculation yields... The value is 16.8 MPa². The fracture toughness of the material in this pipe section is... The value is 30 MPa square root, which is currently within a safe range.

[0042] The cross-scale risk assessment module is used to perform multi-scale risk assessment of pipelines based on a multi-dimensional coupling model. The cross-scale risk assessment module includes a micro-analysis module, a meso-analysis module, and a macro-analysis module.

[0043] The microscopic analysis module is used to calculate the evolution of a single defect and determine the failure type of the pipeline based on the single defect.

[0044] The microscopic analysis module is used to output the current risk factor based on the evaluation model of each risk factor by inputting real-time monitoring data, identify the location and type of a single defect on the inner wall of the pipeline, calculate the evolution rate of the single defect, and determine the failure type of the pipeline.

[0045] The microscopic analysis module includes a failure type discrimination module, which is used to determine the type of pipeline failure that the defect may cause based on the defect type, evolution rate and size parameters, and by referring to preset failure criterion rules. The defect types include cracks, corrosion pits, erosion pits, depressions and deposits, and the failure types include fractures, perforations, leaks, deformation and blockages.

[0046] Specifically, the failure criteria rules include: For crack-type defects, when the stress intensity factor Greater than the material's fracture toughness At that time, it was judged as a fracture failure. For example, the crack defect in the above-mentioned goaf pipe section, The calculated value is 16.8 MPa square root. The value is 30 MPa·m², which does not meet the fracture criterion.

[0047] For corrosion pit defects, when the remaining wall thickness Less than minimum operating wall thickness In such cases, the pipe is deemed to have failed due to perforation. For example, in the corrosion pit at the aforementioned elbow, the original pipe wall thickness was 12 mm, the corrosion pit depth was 2.3 mm, and the remaining wall thickness was 9.7 mm. The minimum operating wall thickness specified in the design for this pipe section is 8 mm. The remaining wall thickness is greater than the minimum operating wall thickness, and therefore the perforation criterion is not met.

[0048] For erosion pit defects, when the erosion rate E is greater than the critical erosion rate When the erosion rate of the outlet pipe section of a certain compressor station is 0.3 mm per year, and the critical erosion rate of this pipe section is set at 0.25 mm per year, the system judges it to be at high risk of puncture leakage failure.

[0049] For dent-type defects, a dent depth greater than 6% of the pipe diameter is considered a deformation failure. A dent was found in a pipe section with a depth of 65 mm. The pipe diameter is 1016 mm, and the dent depth accounts for 6.4% of the diameter, exceeding the 6% threshold. The system therefore classifies it as a deformation failure.

[0050] For sediment defects, a blockage rate exceeding 80% of the pipe's cross-sectional area is considered a blockage failure. In a low-lying pipe section, differential pressure monitoring revealed a significant sediment buildup, with a calculated blockage rate of 85%, leading the system to classify it as a blockage failure.

[0051] The meso-level analysis module is used to calculate the dynamic safety factor of the pipeline based on the propagation rate of a single defect, combined with the pipeline type and corresponding GIS geographic information. This module is also used to determine the weight coefficients of each risk factor based on the preset type of the pipeline segment, and to perform weighted fusion of the various risk factors to obtain the dynamic safety factor of that pipeline segment. .

[0052] Specifically, in this embodiment, the weight allocation table is as follows:

[0053] The meso-level analysis module includes a weight allocation module, a safety factor technology module, and a risk classification module; The weight allocation module is used to dynamically determine the weight coefficients of each risk factor based on the type of pipeline section and GIS geographic information. The pipeline section types include elbows, tees, compressor station outlets, goaf areas, H2S high-concentration sections, and third-party construction areas. The weight coefficients are preset according to the historical failure expert rule base. The safety factor technology module is used to calculate the dynamic safety factor based on each risk factor and its weighting coefficient. :

[0054] in, As a baseline safety factor, The remaining wall thickness is obtained by subtracting the cumulative corrosion depth from the initial wall thickness. Where D is the pressure inside the pipe, and D is the pipe diameter. , , as well as These are the reference thresholds for each risk factor; The risk grading module is used to assign dynamic safety factors. The risk level is output by comparing it with a preset threshold, and the dynamic safety factor is adjusted accordingly. When the value is less than the first preset threshold, a yellow warning is triggered. When the value is less than the second preset threshold, a red alert is triggered and the emergency shutdown logic is activated.

[0055] Specifically, in this embodiment, the preset first threshold for a yellow warning is 1.5, and the preset second threshold for a red warning is 1.2. In the aforementioned high-H2S concentration pipe section, the SFt value is negative, much less than 1.2, triggering a red warning and initiating emergency shutdown logic, automatically sending a shutdown command to the station control system. In another conventional pipe section, the calculated SFt value is 1.8, greater than 1.5, and the system determines it to be in a normal state. In a certain elbow pipe section, the calculated SFt value is 1.4, less than 1.5 but greater than 1.2, triggering a yellow warning and prompting the system to arrange ultrasonic testing.

[0056] The macro-analysis module is used to simulate the disaster chain reaction field and the failure propagation path of the pipeline based on the dynamic safety factor of each pipeline and combined with GIS geographic information.

[0057] The macroscopic analysis module is used to simulate a disaster chain reaction field based on the dynamic safety factor of each pipe section and combined with GIS geographic information, and to identify the pipeline failure propagation path and high-risk areas at the macro level.

[0058] The macro-analysis module includes a disaster chain simulation module, a diffusion simulation module, and a cross-impact assessment module; The disaster chain simulation module is used to construct a pipeline network topology map with pipe segments as nodes and connection relationships as edges based on the real-time risk values ​​and GIS geographic information of each pipeline. It uses the Monte Carlo simulation method to randomly generate initial failure events, simulate the failure process, and calculate the failure propagation path under different failure scenarios.

[0059] Specifically, the aforementioned 120-kilometer pipeline was divided into 120 segments, each 1 kilometer long. The dynamic safety factor of each segment was used to calculate the probability of failure; a lower safety factor indicates a higher probability of failure. A Monte Carlo simulation was conducted 10,000 times. In each simulation, an initial failed segment was randomly selected, and it was determined whether adjacent segments would experience cascading failures due to pressure fluctuations or flow redistribution. Simulation results showed that the four consecutive segments numbered K45-K48 were located in a goaf area with a low safety factor. If any one of these segments failed, there was a 75% probability of triggering a cascading failure of the adjacent three segments. This area was marked as a high-risk area for the disaster chain.

[0060] The diffusion simulation module is used to calculate the concentration distribution of leaked substances in space when a pipeline leak occurs, based on the leak location, medium properties, real-time meteorological data, and terrain data, using a Gaussian smoke diffusion model or computational fluid dynamics model. Combined with GIS buffer analysis, it delineates the personnel evacuation range and environmentally sensitive areas, and dynamically updates the affected high-consequence areas.

[0061] Specifically, assuming a leak occurs in the aforementioned goaf section of the pipeline, with a leak diameter of 20 mm and a leak pressure of 7 MPa, and the natural gas's main component is methane. Real-time meteorological data shows a northeasterly wind direction of 3 m / s and an atmospheric stability of Class D. The terrain data indicates a hilly area. Gaussian smoke cloud diffusion model calculations show that the natural gas concentration within 500 meters downwind reaches 50% of the lower explosive limit, requiring immediate evacuation. GIS buffer zone analysis shows two residential areas within this range, with a total permanent population of approximately 80 people, and a provincial highway passes through this area. The system automatically generates an evacuation area map and pushes it to relevant departments through the emergency command platform.

[0062] The cross-impact assessment module is used to spatially overlay the impact range of pipeline failure with the geographic layers of power grids, water conservancy, and transportation projects to assess the indirect impact of pipeline failure on critical infrastructure and generate a comprehensive risk heat map through a multi-criteria decision-making method.

[0063] Specifically, overlaying the evacuation area map with the power grid line layer revealed a 10kV high-voltage line located within the evacuation area. A potential fire or explosion caused by a pipeline leak could damage this line, affecting the electricity supply to approximately 2,000 downstream households. Overlaying with the water conservancy facility layer showed no direct impact. Overlaying with the transportation layer revealed that provincial highways might need temporary closure due to evacuation and emergency response needs. The system generated a comprehensive risk heat map, marking the aforementioned cross-impact effects with different colors on the GIS map to support emergency command and decision-making.

[0064] The data acquisition module also includes a third-party construction monitoring submodule, which is used to access construction machinery vibration sensor data, construction area video monitoring data and construction plan GIS layer in real time. When the mechanical vibration amplitude exceeds the preset threshold or the construction machinery enters the pipeline protection range, the third-party construction early warning is triggered and stress change monitoring is started.

[0065] For example, at 2:23 PM on a certain day, a vibration sensor located at pipeline kilometer marker K78+300 detected a continuous vibration signal, the frequency of which matched the characteristics of excavating machinery. The system automatically retrieved video surveillance footage of the construction area, showing an excavator leveling the land within the pipeline protection zone. The GIS layer showed that the pipeline was buried at a depth of 1.5 meters, and the protection zone extended 5 meters on each side of the pipeline's centerline. The system determined that the construction machinery had entered the protection zone and immediately triggered a third-party construction warning, pushing the warning information to the patrolman's mobile terminal and the dispatch center. Simultaneously, stress mutation monitoring was activated, increasing the sampling frequency of the stress sensor for that section from once per hour to once per minute.

[0066] It also includes a detection cycle optimization module, used to optimize the detection cycle based on the dynamic safety factor of each pipe section. Based on the confidence level of historical testing data, the non-destructive testing cycle is dynamically adjusted; among them, the testing cycle for high-risk pipe sections is shortened to 30% of the normal cycle, and the testing cycle for low-risk pipe sections is extended to 150% of the normal cycle.

[0067] Specifically, for example, the system dynamically adjusts the non-destructive testing cycle based on the dynamic safety factor and the confidence level of historical inspection data for each pipe section. For instance, if the SFt of a certain elbow pipe section has remained below 1.5 for two months, the system shortens its non-destructive testing cycle from the usual 12 months to 4 months, or 33% of the usual cycle. For another straight pipe section located in the Gobi Desert, where the SFt has remained consistently above 2.0 and no abnormalities have been found in any inspections over the past five years, the system extends its testing cycle from 12 months to 18 months, or 150% of the usual cycle.

[0068] It also includes an emergency repair prioritization module, which generates a priority list for repairs based on the severity of the consequences of each failed pipe section and the difficulty coefficient of repair, combined with GIS traffic network data, when a leak occurs. The severity of the consequences is calculated by weighting the population density, environmental sensitivity level, and important user types within the affected area.

[0069] Specifically, assuming two leaks occur simultaneously, one in a densely populated area affecting 500 residents, with a 30-minute repair time; the other in an uninhabited area, affecting no residents but near a highway, with a 60-minute repair time. The severity of the failure consequences is calculated based on a weighted average of population density, environmental sensitivity level, and key user type. The densely populated area section scores 85 points, and the uninhabited area section scores 45 points. The repair difficulty coefficient is calculated based on the road network and terrain; the densely populated area has good road accessibility, resulting in a difficulty coefficient of 0.8; the uninhabited area has complex terrain, resulting in a difficulty coefficient of 1.5. The final priority score is calculated by dividing the consequence score by the difficulty coefficient: the densely populated area section scores 106, and the uninhabited area section scores 30. The system prioritizes the densely populated area section and marks the optimal repair route on the GIS map.

[0070] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines, characterized in that: It includes a data acquisition module, a data coupling module, a four-dimensional coupling module, and a cross-scale risk assessment module; The data acquisition module is used to acquire monitoring data from various parts of the pipeline in real time. The monitoring data includes sensor monitoring data, InSAR satellite data, pipeline size parameters, and GIS geographic information. The sensor data includes pressure, temperature, and corrosion rate. The data coupling module is used to construct assessment models for multiple risk factors based on monitoring data and historical data. The assessment models include erosion rate model, corrosion rate model, geological subsidence model and stress field model, and establish an interactive coupling mechanism between the assessment models to generate a multi-dimensional coupling model characterizing the pipeline damage evolution process. The cross-scale risk assessment module is used to perform multi-scale risk assessment on pipelines based on a multi-dimensional coupling model. The cross-scale risk assessment module includes a micro-analysis module, a meso-analysis module, and a macro-analysis module. The microscopic analysis module is used to calculate the evolution of a single defect and determine the failure type of the pipeline based on the single defect. The mesoscopic analysis module is used to calculate the dynamic safety factor of the pipeline based on the diffusion rate of a single defect, combined with the pipeline type and corresponding GIS geographic information. The macro-analysis module is used to simulate the disaster chain reaction field and the failure propagation path of the pipeline based on the dynamic safety factor of each pipeline and combined with GIS geographic information.

2. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 1, characterized in that: The data coupling module includes an erosion module, a corrosion module, a settlement module, and a stress module; The erosion module is used to calculate the erosion rate using a preset erosion rate model. : Where E is the erosion rate. The erosion coefficient is... For the density of the multiphase flow medium, For fluid velocity, The velocity index is the flow rate index. Where m is the diameter of the eroded particles; The corrosion module is used to calculate the synergistic effect of corrosion and erosion using a synergistic coefficient model. : in, The total corrosion rate of the corrosion-erosion coupling is given. Let be the pure corrosion rate without erosion. The coefficient for promoting corrosion by erosion. Erosion rate; The settlement module is used to monitor ground displacement using InSAR satellite data, perform finite element analysis of pipeline stress distribution, and calculate settlement strain. : in For settlement strain, The vertical ground displacement obtained from InSAR monitoring is represented by L, which is the original length of the settlement pipe. The stress module is used to calculate the stress intensity factor of the pipe. : in Y is a type I stress intensity factor, and Y is a geometric factor. The total stress borne by the pipeline. This is a dimensional defect.

3. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 2, characterized in that: The microscopic analysis module is used to output the current risk factor based on the evaluation model of each risk factor by inputting real-time monitoring data, identify the location and type of a single defect on the inner wall of the pipeline, calculate the evolution rate of the single defect, and determine the failure type of the pipeline. The meso-level analysis module is used to determine the weight coefficients of each risk factor based on the preset type of the pipeline segment, and to perform weighted fusion of the various risk factors to obtain the dynamic safety factor of the pipeline segment. ; The macroscopic analysis module is used to simulate a disaster chain reaction field based on the dynamic safety factor of each pipe section and combined with GIS geographic information, and to identify the pipeline failure propagation path and high-risk areas at the macro level.

4. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 3, characterized in that: The microscopic analysis module includes a failure type discrimination module, used to determine the possible pipeline failure type of a defect based on its type, evolution rate, and size parameters, against preset failure criterion rules. The defect types include cracks, corrosion pits, erosion pits, depressions, and deposits; the failure types include fractures, perforations, leaks, deformation, and blockages. The failure criterion rules include: For crack-type defects, when the stress intensity factor Greater than the toughness of the material At that time, it was determined to be a fracture failure; For corrosion pit defects, when the remaining wall thickness Less than minimum operating wall thickness When this occurs, it is determined to be a perforation failure; For erosion pit defects, when the erosion rate E is greater than the critical erosion rate At that time, it was determined to be a puncture failure; For dent-type defects, if the dent depth is greater than 6% of the pipe diameter, it is judged as deformation failure; For sediment defects, when the blockage rate of the pipe cross-sectional area is greater than 80%, it is judged as a blockage failure.

5. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 4, characterized in that: The meso-level analysis module includes a weight allocation module, a safety factor technology module, and a risk classification module; The weight allocation module is used to dynamically determine the weight coefficients of each risk factor based on the type of pipeline section and GIS geographic information. The pipeline section types include elbows, tees, compressor station outlets, goaf areas, H2S high-concentration sections, and third-party construction areas. The weight coefficients are preset according to the historical failure expert rule base. The safety factor technology module is used to calculate the dynamic safety factor based on each risk factor and its weighting coefficient. : in, As a baseline safety factor, The remaining wall thickness is obtained by subtracting the cumulative corrosion depth from the initial wall thickness. Where D is the pressure inside the pipe, and D is the pipe diameter. , , as well as These are the reference thresholds for each risk factor; The risk grading module is used to assign dynamic safety factors. The risk level is output by comparing it with a preset threshold, and the dynamic safety factor is adjusted accordingly. When the value is less than the first preset threshold, a yellow warning is triggered. When the value is less than the second preset threshold, a red alert is triggered and the emergency shutdown logic is activated.

6. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 5, characterized in that: The macro-analysis module includes a disaster chain simulation module, a diffusion simulation module, and a cross-impact assessment module; The disaster chain simulation module is used to construct a pipeline network topology map with pipe segments as nodes and connection relationships as edges based on the real-time risk values ​​and GIS geographic information of each pipeline. It uses the Monte Carlo simulation method to randomly generate initial failure events, simulate the failure process, and calculate the failure propagation path under different failure scenarios. The diffusion simulation module is used to calculate the concentration distribution of leaked substances in space when a pipeline leak occurs, based on the leak location, medium properties, real-time meteorological data, and terrain data, using a Gaussian smoke diffusion model or computational fluid dynamics model. Combined with GIS buffer analysis, it delineates the personnel evacuation range and environmentally sensitive areas, and dynamically updates the affected high-consequence areas. The cross-impact assessment module is used to spatially overlay the impact range of pipeline failure with the geographic layers of power grids, water conservancy, and transportation projects to assess the indirect impact of pipeline failure on critical infrastructure and generate a comprehensive risk heat map through a multi-criteria decision-making method.

7. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 6, characterized in that: The data acquisition module also includes a third-party construction monitoring submodule, which is used to access construction machinery vibration sensor data, construction area video monitoring data and construction plan GIS layer in real time. When the mechanical vibration amplitude exceeds the preset threshold or the construction machinery enters the pipeline protection range, the third-party construction early warning is triggered and stress change monitoring is started.

8. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 7, characterized in that: It also includes a detection cycle optimization module, used to optimize the detection cycle based on the dynamic safety factor of each pipe section. Based on the confidence level of historical testing data, the non-destructive testing cycle is dynamically adjusted; among them, the testing cycle for high-risk pipe sections is shortened to 30% of the normal cycle, and the testing cycle for low-risk pipe sections is extended to 150% of the normal cycle.

9. The four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines according to claim 8, characterized in that: It also includes an emergency repair prioritization module, which generates a priority list for repairs based on the severity of the consequences of each failed pipe section and the difficulty coefficient of repair, combined with GIS traffic network data, when a leak occurs. The severity of the consequences is calculated by weighting the population density, environmental sensitivity level, and important user types within the affected area.

10. A four-dimensional coupled risk assessment and dynamic safety management method for gas transmission pipelines, characterized by: It is applied to the four-dimensional coupled risk assessment and dynamic safety management system for gas pipelines as described in any one of claims 1-9.

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