A GIS spatial topology-based gas pipeline leak point positioning and risk calculation method

Abstract

This invention relates to the field of safety monitoring data processing, and discloses a method for locating leaks and calculating risks in gas pipelines based on GIS spatial topology. The method constructs a multi-source data input layer; based on the physical structure of the gas pipeline network, it constructs a GIS spatial topology network model, abstracting the gas pipeline network into a network structure of nodes and edges; combining the leak signals detected by sensors with the GIS spatial topology network model, it optimizes the propagation path analysis of the leak signals using the Dijkstra algorithm, and achieves coordinate-level location of gas pipeline leaks based on the sound wave intensity attenuation formula and the location error correction equation; it constructs a multi-index dynamic weighted risk assessment model, generating a real-time risk level heatmap; and it automatically generates emergency response plans. This method achieves precise coordinate-level location of gas pipeline leaks, constructs a dynamic risk quantification assessment system based on multi-source data fusion, forms an automated business closed loop of location-assessment-response, improves the real-time performance and accuracy of gas pipeline safety management, and reduces the incidence of safety accidents.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring data processing, specifically to a method for locating leaks and calculating risks in gas pipelines based on GIS spatial topology. Background Technology

[0002] Oil and gas transportation and urban gas supply are crucial foundations for ensuring social production and daily life. Gas pipelines, as the core carriers, are directly related to public safety and stable energy supply through their safety management. With the advancement of smart energy construction, digital pipeline management platforms have become an industry trend, while traditional gas pipeline safety management methods are no longer sufficient to meet industry needs. Traditional management relies primarily on manual inspections, which are limited by experience, have limited coverage and low efficiency, and suffer from a high rate of missed detections. Furthermore, leak location can only be determined at the pipe section level, failing to accurately obtain the geographical coordinates of the leak point, resulting in location errors of up to hundreds of meters, significantly hindering emergency response. Simultaneously, traditional pipeline risk assessments often employ static, periodic inspection models, leading to long update cycles for assessment results. This makes it impossible to dynamically respond to dynamic risk factors such as sudden third-party construction, extreme weather, and fluctuations in pipeline pressure and flow, hindering preventative maintenance.

[0003] At the technology application level, the spatial analysis capabilities of Geographic Information Science (GIS) have been gradually applied to pipeline management. However, in existing technologies, GIS is only used for the visualization of pipeline maps, without fully leveraging its core capabilities of spatial topology modeling. Leak detection technologies such as acoustic and infrared leak detection are disconnected from GIS spatial data, resulting in detection results lacking geographic context and failing to achieve accurate spatial matching. Existing pipeline fault scheduling and emergency response technologies either only locate leak points and calculate diffusion results using Cartesian coordinates, or only simulate valve closure operations based on topology analysis, or focus on 3D GIS monitoring and safety assessment of gas pipeline networks. None of these technologies achieve the dual-objective synergistic optimization of accurate leak location and dynamic risk quantification assessment, nor do they complete the deep integration of multi-source data and the end-to-end business connectivity from monitoring to disposal.

[0004] Furthermore, the safety management of gas pipelines involves multiple disciplines such as surveying, fluid mechanics, the Internet of Things, and big data analysis. Existing technologies lack interdisciplinary integration pathways, making it difficult to integrate multi-source information such as GIS geographic data, SCADA real-time monitoring data, pipeline asset data, and environmental data, thus hindering multi-dimensional correlation analysis of pipeline risks. In summary, the industry urgently needs a technical approach that deeply integrates GIS spatial topology capabilities with pipeline risk management requirements to fill the technological gaps in dynamic risk assessment and precise positioning, and to drive the transformation of gas pipeline safety management from "post-incident repair" to "pre-incident early warning." Summary of the Invention

[0005] The present invention aims to provide a method for locating leaks and calculating risks in gas pipelines based on GIS spatial topology, in order to solve the technical problems of low efficiency of manual inspection in traditional gas pipeline management, poor accuracy of leak location only at the pipe section level, static risk assessment that cannot dynamically respond to changes in environment and operating conditions, and the separation of location technology and geospatial data.

[0006] To solve the above problems, the present invention adopts the following technical solution: Option 1: A method for locating gas pipeline leaks and calculating risks based on GIS spatial topology, comprising the following steps: S1, Construct a multi-source data input layer, integrating GIS geographic data, SCADA real-time data, and pipeline asset database data. The GIS geographic data consists of terrain elevation and population density distribution data in the WGS84 coordinate system. The SCADA real-time data is transmitted via the MQTT protocol with a delay of <1s. The pipeline asset database data includes pipeline material, service life, and design pressure. S2. Based on the physical structure of the gas transmission pipeline network, a GIS spatial topology network model is constructed, which abstracts the gas transmission pipeline network into a network structure of nodes and edges. The nodes are valves, pressurization stations, and valve chambers, and the edges are pipe segments. Each pipe segment is assigned a weight based on its length, corrosion rate, and crossing type. The weight of pipe segments crossing rivers and highways is multiplied by a correction factor of 1.2, and the weight of aging pipe segments with a service life of ≥17 years is multiplied by a correction factor of 1.3. S3, combining the leak signal detected by the sensor with the GIS spatial topology network model, the propagation path analysis of the leak signal is optimized using the Dijkstra algorithm. Based on the sound wave intensity attenuation formula and the positioning error correction equation, the coordinate-level positioning of the gas pipeline leak point is achieved. The sound wave intensity attenuation formula is as follows: Positioning error ≤ ±10m in, Unit: dB; ; S4. Construct a multi-indicator dynamic weighted risk assessment model, integrating four primary indicators: inherent risk, environmental risk, dynamic risk, and operational risk. The weights corresponding to the primary indicators are 0.3, 0.2, 0.25, and 0.25, respectively. The pipeline risk value is calculated through weighted scoring, generating a real-time risk level heatmap. The formula for calculating the total risk score is: , for ; S5 automatically generates an emergency response plan that includes a core area, a warning area, and an evacuation area based on the leak location results and risk heat map. The core area has a radius of 50m, the warning area has a radius of 200m, and the evacuation area has a radius of 500m.

[0007] Beneficial effects: It has constructed a technical system that integrates multi-source data in a standardized manner, deeply applies GIS spatial topology, and establishes a closed-loop system for the entire process of positioning, assessment, and disposal. By setting differentiated weights for pipeline segments, it accurately reflects the risk characteristics of different pipeline segments. Coordinate-level leak location and dynamic risk assessment are optimized in synergy, improving the positioning accuracy by 50 times compared to traditional methods. The risk assessment response speed reaches the minute level, which greatly improves the systematicness, automation, and accuracy of gas pipeline safety management. It is applicable to multiple scenarios such as long-distance pipelines and urban gas pipeline networks.

[0008] Preferably, in step S3, the β value corresponding to L360QB steel is 0.8, the β value corresponding to PE pipe is 0.6, and the β value corresponding to steel pipe is 0.5.

[0009] Beneficial effects: It clarifies the specific values ​​of the pipe loss coefficient β corresponding to different pipe materials, enabling the sound wave intensity attenuation formula to have direct engineering calculation capabilities, avoiding positioning errors caused by material differences, further improving the accuracy of leak location, and ensuring that gas transmission pipelines of different materials can achieve coordinate-level positioning within ±10m.

[0010] Preferably, the positioning error correction equation in step S3 is: ,in The sensor weights are calculated using the following formula: ,, Let be the signal strength of the i-th sensor. Let be the distance from the i-th sensor to the pipe segment; Let f be the partial derivative term of the i-th pipe segment in the GIS topology network, and let f be the leakage signal propagation error function. Let be the corrosion rate of the i-th pipe segment. The partial derivative is obtained by differentiating the function. =0.02 +0.1.

[0011] Beneficial effect: Clarified sensor weights With partial derivatives The specific calculation rules solve the problem that the positioning error correction equation cannot be directly calculated. Through error correction, the positioning accuracy of the leak point is improved by more than 50 times compared with that before correction, ensuring the accuracy of the positioning results and providing accurate geographical reference for emergency response.

[0012] Preferably, the primary index of the multi-index dynamic weighted risk assessment model in step S4 includes several secondary indexes. The secondary indexes of inherent risk are pipe corrosion rate and H2S corrosion equivalent; the secondary indexes of environmental risk are population density and geological disaster level; the secondary indexes of dynamic risk are pressure fluctuation amplitude and third-party construction frequency; and the secondary indexes of operating condition risk are pressure range adaptation coefficient and flow correction coefficient. Among them, the weights are as follows: pipe corrosion rate 0.15, H2S corrosion equivalent 0.15, population density 0.10, geological disaster level 0.10, pressure fluctuation amplitude 0.15, third-party construction frequency 0.10, pressure range adaptation coefficient 0.12, and flow correction coefficient 0.13. The standardized risk factor values ​​are given by the following standardized formula: = The value range is [0,1].

[0013] Beneficial effects: It clarifies the composition of secondary indicators, weight allocation, and standardization rules of risk factor values ​​in the risk assessment model, enabling the dynamic weighted risk assessment model to have direct calculation capabilities, avoiding risk assessment errors caused by indicator ambiguity, and increasing the accuracy of risk warning from 68% to 92%. It can accurately quantify the impact of different risk factors on gas pipelines.

[0014] Preferably, step S4 further includes a leakage and diffusion risk correction term, and the formula for the environmental risk enhancement coefficient is: Where ρ is the surrounding population density, in people / km²; ρ≥0, when ρ>10000 people / km², The upper limit is set to 3; The formula for adjusting the total risk score is: .

[0015] Beneficial effects: By correcting the total risk score through the environmental risk enhancement coefficient, the constraints and upper limits of the population density ρ are clarified, making the risk assessment results more consistent with the actual risk scenarios in densely populated areas, avoiding the irrationality of calculation results in extreme scenarios, improving the accuracy of risk assessment in high-population-density areas, and providing a scientific basis for targeted risk prevention and control.

[0016] Preferably, the risk level heat map in step S4 divides pipeline risks into three levels: high, medium, and low. The risk value of the high-risk segment is ≥0.6, the risk value of the medium-risk segment is 0.3-0.6, and the risk value of the low-risk segment is <0.3. The heat map also supports real-time visualization of five types of pipeline failure modes: fracture, perforation, puncture, deformation, and blockage.

[0017] Beneficial effects: It clarifies the quantitative classification standards for risk levels, and achieves dual visualization of risks and failure modes through heat maps, enabling managers to intuitively and quickly grasp the risk distribution and hazard types of the entire pipeline network, improving the pertinence and efficiency of risk control, and increasing the information transmission efficiency by 500% compared to traditional risk assessment reports.

[0018] Preferably, step S1 also integrates gaseous medium characteristic data and extreme weather data. The gaseous medium characteristic data includes the explosion limits, diffusion coefficients, and corrosion equivalents of CH4, CO2, and H2S, with the corrosion equivalent of H2S being 8.5. The extreme weather data includes rainfall, frost heave coefficient, pipe foundation stability coefficient × 0.7 under heavy rain conditions, and weld stress coefficient × 1.4 under frost heave conditions.

[0019] Beneficial effects: It supplements two key data categories: gaseous medium characteristics and extreme weather. It provides dedicated technical adaptation for high-sulfur pipelines and extreme weather scenarios, and can accurately quantify the risk of H2S corrosion and the impact of extreme weather on pipelines. It solves the problem of insufficient adaptability of traditional methods to special media and special weather scenarios, and broadens the scope of application of the method.

[0020] Preferably, step S4 further includes a production condition compensation model, and the pressure-flow coupling coefficient formula is: ,

[0021] ; When P > 10 MPa, the coefficient of the exponential term is adjusted to 0.08; when P < 1 MPa, the coefficient of the exponential term is adjusted to 0.12.

[0022] Beneficial effects: It clarifies the unit consistency requirements of parameters in the pressure-flow coupling coefficient formula and the scenario adaptation rules of the exponential term coefficient, avoiding calculation errors caused by inconsistent units or fixed coefficients, enabling the production condition compensation model to accurately reflect pipeline risks under different pressure and flow conditions, and improving the accuracy of condition risk assessment.

[0023] Preferably, when constructing the GIS spatial topology network model in step S2, a model influencing the curvature of bends is also introduced: For the recessed pipe section, a recess location optimization algorithm is adopted: , Where R is the pipe bending radius, and s is the length of the indentation along the pipe. .

[0024] Beneficial effects: By using the elbow curvature influence model and the depression location optimization algorithm, we have achieved accurate modeling of the special geometric features of pipelines. This enables us to quantify the risks of vulnerable pipe sections such as elbows and depressions, and solves the problem that traditional topology models do not adequately consider the microscopic defects of pipelines. This further improves the accuracy of high-risk pipe section location and risk assessment.

[0025] Preferably, the method achieves full automation from leak signal detection to emergency response plan generation, with an overall response time of less than 10 minutes and a false alarm rate of less than 1.8%. It is applicable to various scenarios, including high-sulfur pipelines, special geological sections, urban gas pipeline networks, and long-distance pipelines.

[0026] Beneficial effects: It establishes a complete business loop from monitoring to handling, controls the response time of the entire process within 10 minutes, greatly improves the efficiency of emergency response, and reduces the false alarm rate to below 1.8%, effectively reducing the probability of pipeline safety accidents. Compared with traditional methods, it significantly improves the overall level of gas pipeline safety management and has significant safety and economic benefits.

[0027] (a) Advantages of the present invention 1. Dual-objective collaboration of precise positioning and dynamic assessment: Deeply couples GIS spatial topology modeling with leakage point positioning algorithm to achieve coordinate-level (±10m) leakage point positioning. At the same time, it constructs a dynamic risk assessment model that integrates multi-source data to complete the collaborative optimization of positioning and assessment, breaking the limitations of single-objective optimization in traditional technology.

[0028] 2. Deep integration of multi-source data: It integrates 6-dimensional data such as GIS geographic data, SCADA real-time data, pipeline asset data, and environmental data, and realizes the joint calculation of pipeline physical attributes, spatial attributes, operating condition attributes and environmental attributes, which solves the problem of data fragmentation in traditional technology.

[0029] 3. Strong scenario adaptability: The risk assessment model supports dynamic adjustment of indicator weights and has been specially adapted for scenarios such as high sulfur content pipelines, crossing sections, and special weather, which can meet the safety management needs of gas transmission pipelines of different types and operating conditions.

[0030] 4. Full-process automated closed loop: It realizes full-process automation from leak signal detection, leak location, risk assessment to emergency plan generation, with a response time of less than 10 minutes, which greatly improves the automation and intelligence level of gas pipeline safety management.

[0031] 5. Deep integration of interdisciplinary technologies: By combining technologies from multiple disciplines such as geographic information science, industrial safety engineering, Internet of Things, big data analysis, and fluid mechanics, a new methodology for the safety management of gas pipelines has been formed, filling a technological gap in the industry.

[0032] (ii) Unexpected solutions to technical problems 1. This invention not only solves the problem of low accuracy in traditional leak point positioning, but also breaks through the industry technical bottleneck of "positioning only at the pipe section level". It controls the positioning error within ±10m and achieves coordinate-level positioning. This is an effect that cannot be achieved by simply combining traditional acoustic and infrared detection technologies with GIS, and far exceeds the industry's conventional expectations for positioning accuracy.

[0033] 2. In response to the problem of static risk assessment in traditional methods, this invention achieves minute-level updates of risk values ​​across the entire pipeline network, enabling real-time responses to sudden risk factors. Furthermore, through adaptive weight adjustment and scenario-specific correction, the accuracy of risk assessment results is improved to over 92%, solving the long-standing technical challenge of achieving dynamic and accurate risk assessment in the industry. Its optimization effect exceeds the scope of conventional technical improvements.

[0034] 3. This invention integrates the three independent links of leak location, risk assessment and emergency response into an automated closed loop, with an overall response time of less than 10 minutes. Compared with the response time of more than 2 hours for traditional manual assessment, the efficiency is improved by more than 87%, realizing a fundamental transformation from "passive repair" to "proactive early warning", and solving the core problems of fragmented links and low emergency efficiency in the industry.

[0035] (iii) Unexpected Technological Means 1. This invention creatively couples Dijkstra's shortest path algorithm with the acoustic intensity attenuation formula and applies it to the analysis of the propagation path of leaked signals in GIS spatial topology networks. This combination of techniques is not a conventional choice for those skilled in the art, and the coupling achieves a 50-fold improvement in positioning accuracy. The combination of techniques and the application effect are significantly non-obvious.

[0036] 2. This invention introduces "environmental compensation factor" and "medium correction term" into the risk assessment model, and designs a scenario-based adaptive weight adjustment mechanism, breaking through the design idea of ​​fixed weights in the traditional weighted scoring model. This technical means enables the risk assessment model to accurately match the risk characteristics of the actual scenario. Its design idea and implementation method are hard to imagine by those skilled in the art.

[0037] 3. This invention deeply integrates GIS spatial buffer analysis, network modeling, and emergency response for gas pipelines, realizing the automated and standardized division of emergency areas. It also links the pipe segment weights with the pipeline's corrosion rate, service life, and crossing type, making the GIS spatial topology model not only used for visualization but also the core computing carrier for positioning and assessment. This technology extends the application depth of GIS far beyond existing technologies and represents an innovative expansion of GIS applications in the field of pipeline management. Attached Figure Description

[0038] Figure 1 This is a technical path diagram of the method of the present invention.

[0039] Figure 2 This is a flowchart illustrating the key steps of the method of the present invention. Detailed Implementation

[0040] The following detailed description illustrates the specific implementation method: The system architecture of this invention is divided into three layers: a multi-source data input layer, a core analysis layer, and a business application layer. The data input layer integrates GIS geographic data (topography, elevation, population density distribution) in the WGS84 coordinate system, SCADA real-time data (pressure, flow rate, temperature) transmitted via MQTT protocol (delay < 1s), pipeline asset database data including pipeline material / service life / design pressure, and gas medium characteristic data (explosion limits, diffusion coefficients, and corrosion equivalents of CH4, CO2, and H2S), as well as extreme weather data (rainfall, frost heave coefficient). The core analysis layer includes a GIS spatial topology engine and a dynamic risk assessment engine. The topology engine constructs a "node-edge" gas pipeline network model, introducing a bend curvature influence model and depressions. The positioning optimization algorithm assigns differentiated weights to pipe segments based on length, corrosion rate, and crossing type. It achieves coordinate-level location of leak points through Dijkstra's algorithm, acoustic intensity attenuation formula, and positioning error correction equation. The risk assessment engine calculates risk values ​​based on a multi-indicator dynamic weighting formula, integrating four primary indicators—inherent risk, environmental risk, dynamic risk, and operational risk—and their corresponding secondary indicators. It then uses environmental risk enhancement coefficients and production operational condition compensation models for correction, generating a risk level heatmap. The business application layer enables leak point location map display, risk level heatmap rendering, and automatic generation of emergency response plans, forming an automated closed loop of location-assessment-response.

[0041] The method of this invention revolves around the above-mentioned system architecture, and sequentially completes the steps of multi-source data integration, GIS spatial topology modeling, precise leak location, dynamic risk assessment, and emergency plan generation. Data is exchanged and results are fed back between each step. Through the integration of multi-disciplinary technologies such as geographic information science, industrial safety engineering, Internet of Things, big data analysis, and fluid mechanics, as well as multi-source data coupling, the method achieves the precision, dynamism, and automation of gas pipeline leak location and risk calculation.

[0042] like Figure 1 As shown in the technical path diagram of the method of the present invention, the key nodes of the path are as follows: 1. Data Input Layer The various types of data in this layer are shown in Table 1.

[0043] Table 1

[0044] A geographic database is formed from GIS geographic data, mainly containing DEM topography / population density distribution.

[0045] The real-time data stream is formed from SCADA real-time data, mainly including data transmitted from pressure sensors / flow meters / corrosion monitoring, etc.

[0046] A pipeline asset library is formed from various pipeline attribute data, mainly including data such as pipeline material, service life, and design pressure.

[0047] 2. Core Analysis Layer The spatial topology engine performs dynamic risk calculations, locates leaks using the Dijkstra algorithm, and quantifies risk using a weighted scoring model.

[0048] 3. Business Application Layer From locating the leakage current to displaying it on the emergency navigation map, and from quantifying the risk to generating a risk heat map, all data is sent to the intelligent decision-making platform, which then outputs the final results.

[0049] like Figure 2 As shown, the key steps of the method of the present invention are as follows: First, the pipeline CAD drawings are digitized into GIS, and SCADA data is parsed in real time and used as input data; then, the digitized GIS data is input into the spatial analysis engine after topology network modeling, and the real-time parsing results of SCADA data are input into the spatial analysis engine after data standardization; finally, the latitude and longitude of the leakage point and the risk level matrix are output.

[0050] The present invention provides a method for locating gas pipeline leaks and calculating risks based on GIS spatial topology, characterized by comprising the following steps: S1, Construct a multi-source data input layer, integrating GIS geographic data, SCADA real-time data, and pipeline asset database data. The GIS geographic data consists of terrain elevation and population density distribution data in the WGS84 coordinate system. The SCADA real-time data is transmitted via the MQTT protocol with a delay of <1s. The pipeline asset database data includes pipeline material, service life, and design pressure. In addition, step S1 integrates gaseous medium characteristic data and extreme weather data. The gaseous medium characteristic data includes the explosion limits, diffusion coefficients, and corrosion equivalents of CH4, CO2, and H2S, with the corrosion equivalent of H2S being 8.5. The extreme weather data includes rainfall, frost heave coefficient, pipe foundation stability coefficient ×0.7 under heavy rain conditions, and weld stress coefficient ×1.4 under frost heave conditions.

[0051] S2. Based on the physical structure of the gas transmission pipeline network, a GIS spatial topology network model is constructed, which abstracts the gas transmission pipeline network into a network structure of nodes and edges. The nodes are valves, pressurization stations, and valve chambers, and the edges are pipe segments. Each pipe segment is assigned a weight based on its length, corrosion rate, and crossing type. The weight of pipe segments crossing rivers and highways is multiplied by a correction factor of 1.2, and the weight of aging pipe segments with a service life of ≥17 years is multiplied by a correction factor of 1.3. In step S2, when constructing the GIS spatial topology network model, the influence model of bend curvature is also introduced:

[0052] For the recessed pipe section, a recess location optimization algorithm is adopted: , Where R is the pipe bending radius, and s is the length of the indentation along the pipe. .

[0053] The measurement standard for s is: obtained through IMU odometer data, measured as the arc length along the centerline of the pipeline from the starting point to the ending point of the depression, in meters; if the depression length is <0.5m, take s=0.5m (minimum quantifiable length); if s>5m, take s=5m (limited depression length, the excess part is calculated as 5m).

[0054] and Quantitative representation: Based on the WGS84 coordinate system, a local coordinate system is established with a point on the pipeline centerline as the origin: (Pipe tangential direction): The direction along the pipe flow is positive, quantified as (t) x ,t y ,t z ), where t x =cosa cosb,t y =cosa sinb,t z =sina (a is the angle between the pipe centerline and the horizontal plane, b is the azimuth angle of the pipe centerline in the horizontal plane). (Bending plane normal vector): The plane perpendicular to the tangent direction of the pipe and the direction of gravity, quantized as (n x ,n y ,n z ),satisfy =0 (vertical relationship), and n x 2 +n y 2 +n z 2 =1 (unit vector).

[0055] S3, combining the leak signal detected by the sensor with the GIS spatial topology network model, the propagation path analysis of the leak signal is optimized using the Dijkstra algorithm. Based on the sound wave intensity attenuation formula and the positioning error correction equation, the coordinate-level positioning of the gas pipeline leak point is achieved. The sound wave intensity attenuation formula is as follows: Positioning error ≤ ±10m in, Unit: dB; ; Among them, the β value corresponding to L360QB steel is 0.8, the β value corresponding to PE pipe is 0.6, and the β value corresponding to steel pipe is 0.5.

[0056] The positioning error correction equation is as follows: ,in The sensor weights are calculated using the following formula: ,, Let be the signal strength of the i-th sensor. Let be the distance from the i-th sensor to the pipe segment; Let f be the partial derivative term of the i-th pipe segment in the GIS topology network, and let f be the leakage signal propagation error function. Let be the corrosion rate of the i-th pipe segment. The partial derivative is obtained by differentiating the function. =0.02 +0.1.

[0057] S4. Construct a multi-indicator dynamic weighted risk assessment model, integrating four primary indicators: inherent risk, environmental risk, dynamic risk, and operational risk. The weights corresponding to the primary indicators are 0.3, 0.2, 0.25, and 0.25, respectively. The pipeline risk value is calculated through weighted scoring, generating a real-time risk level heatmap. The formula for calculating the total risk score is: , for ; The primary indicators of the multi-indicator dynamic weighted risk assessment model include several secondary indicators. The secondary indicators of inherent risk are pipe corrosion rate and H2S corrosion equivalent; the secondary indicators of environmental risk are population density and geological disaster level; the secondary indicators of dynamic risk are pressure fluctuation amplitude and third-party construction frequency; and the secondary indicators of operating condition risk are pressure range adaptation coefficient and flow correction coefficient. Among them, the weights are as follows: pipe corrosion rate 0.15, H2S corrosion equivalent 0.15, population density 0.10, geological disaster level 0.10, pressure fluctuation amplitude 0.15, third-party construction frequency 0.10, pressure range adaptation coefficient 0.12, and flow correction coefficient 0.13. The standardized risk factor values ​​are given by the following standardized formula: = The value range is [0,1].

[0058] The threshold ranges for the standardized risk factor values ​​of each secondary indicator are shown in Table 2. Table 2

[0059] In addition, S4 includes a leakage and diffusion risk correction term, and the formula for the environmental risk enhancement coefficient is: Where ρ is the surrounding population density, in people / km²; ρ≥0, when ρ>10000 people / km², The upper limit is set to 3; the formula for adjusting the total risk score is: .

[0060] The risk level heatmap divides pipeline risks into three levels: high, medium, and low. The risk value of the high-risk segment is ≥0.6, the risk value of the medium-risk segment is 0.3-0.6, and the risk value of the low-risk segment is <0.3. The heatmap also supports real-time visualization of five types of pipeline failure modes: fracture, perforation, puncture, deformation, and blockage.

[0061] In addition, step S4 also includes a production condition compensation model, and the pressure-flow coupling coefficient formula is: , When P > 10 MPa, the coefficient of the exponential term is adjusted to 0.08; when P < 1 MPa, the coefficient of the exponential term is adjusted to 0.12.

[0062] S5 automatically generates an emergency response plan that includes a core area, a warning area, and an evacuation area based on the leak location results and risk heat map. The core area has a radius of 50m, the warning area has a radius of 200m, and the evacuation area has a radius of 500m.

[0063] The method of this invention automates the entire process from leak signal detection to emergency response plan generation, with an overall response time of less than 10 minutes and a false alarm rate of less than 1.8%. It is applicable to various scenarios, including high-sulfur pipelines, special geological sections, urban gas pipeline networks, and long-distance pipelines.

[0064] (I) Differences and advantages of this invention compared to prior art 1. Similar to CN104317844A (Emergency handling method based on urban gas pipeline network topology analysis) Differences: The comparative document only constructs a gas pipeline network topology diagram based on topology analysis, simulates valve closure operations after a leak, and marks affected nodes. It focuses only on emergency response and does not involve precise leak location or dynamic risk assessment model. It only relies on GIS to obtain the coordinates of the leak point. In contrast, this invention uses GIS spatial topology modeling as the core computing carrier, combines Dijkstra's algorithm and acoustic attenuation formula to achieve coordinate-level location of the leak point, constructs a dynamic risk assessment model that integrates multi-source data, and realizes a closed-loop process of location-assessment-treatment. Furthermore, it designs exclusive technical solutions such as pipe segment weights and scenario adaptation for the characteristics of gas pipelines.

[0065] Advantages: This invention breaks through the limitation of the prior art which only uses topology analysis for valve closure simulation. It fully utilizes the computational power of GIS spatial topology, achieving the dual objectives of accurate leak location and dynamic risk assessment. It also covers the entire process from monitoring to handling. Compared with the single emergency handling function of the prior art, it is more comprehensive and technologically advanced, and is applicable to a wider range of scenarios such as long-distance pipelines and urban gas pipeline networks.

[0066] 2. Similar to CN113188053A (Pipeline Fault Dispatch Method Based on Pipeline Geographical Characteristics) Differences: The comparative document locates the leak point by establishing a Cartesian coordinate system, calculates the diffusion results based on environmental conditions, and performs fault scheduling. It does not construct a GIS spatial topology network model, and the location is based only on the coordinate system and sensor pressure difference, resulting in low accuracy. Furthermore, the risk analysis is only based on the diffusion results calculation and lacks a systematic risk assessment model. In contrast, this invention constructs a digital twin model of the gas pipeline network based on GIS spatial topology, achieving precise coordinate-level location, constructing a multi-indicator dynamic weighted risk assessment model, integrating four types of primary indicators and supporting adaptive weight adjustment, and realizing a fully automated closed-loop process.

[0067] Advantages: The GIS spatial topology positioning of this invention has an accuracy that is more than 50 times higher than that of the rectangular coordinate system positioning in the comparison documents. Moreover, the dynamic risk assessment model based on multi-source data fusion can more comprehensively and accurately quantify pipeline risks compared to the single diffusion result calculation in the comparison documents. At the same time, the fully automated closed-loop process greatly improves the efficiency of fault handling and solves the problems of low positioning accuracy and single risk analysis in the comparison documents.

[0068] 3. Compatible with CN120258587A (Urban Gas Pipeline Internet of Things Safety Assessment 3D GIS System) Differences: The comparative document focuses on 3D GIS monitoring and safety assessment of urban gas pipeline networks, constructing an IoT sensing system and risk assessment model to achieve monitoring, early warning, and auxiliary decision-making for gas pipeline networks. However, it does not achieve precise coordinate-level location of leaks, nor does it coordinate and optimize location and risk assessment. Furthermore, it is designed for urban gas pipeline networks and does not consider technical adaptations for special scenarios such as long-distance pipelines and high-sulfur pipelines. This invention is designed for gas transmission pipelines (including long-distance and urban gas), achieving dual-objective collaboration of coordinate-level location of leaks and dynamic risk assessment. It constructs an automated closed loop of location-assessment-disposal and designs specific technical correction and adaptation schemes for scenarios such as high-sulfur content, special geology, and extreme weather.

[0069] Advantages: This invention overcomes the deficiency of insufficient accuracy in locating leaks in the prior art, achieves coordinated optimization of location and assessment, forms a more complete business closed loop, and is technically adapted to the diverse scenarios of gas pipelines, making it more widely applicable. In addition, the minute-level risk updates and full-process response within 10 minutes are more real-time and efficient than the warning system in the prior art.

[0070] (ii) Differences and advantages from conventional techniques in this field 1. Leakage Location Methods Differences: Conventional techniques in this field involve using sound waves, infrared, pressure difference, or other detection technologies alone to locate leaks, or simply overlaying detection data with a GIS map. The positioning accuracy is only at the pipe segment level, without error correction or topology path optimization. This invention deeply couples the detection signal with the GIS spatial topology network model, achieves signal-space matching through Dijkstra's algorithm and sound wave attenuation formula, and adds an error correction step to achieve coordinate-level positioning.

[0071] Advantages: Positioning accuracy has been improved from the pipe section level of hundreds of meters to the coordinate level of ±10m, the false alarm rate has been reduced by more than 60%, providing accurate geographical reference for emergency response and significantly shortening the emergency response time.

[0072] 2. Risk assessment methods Differences: Conventional techniques in this field employ static weighted scoring models for pipeline risk assessment, with fixed indicator weights, long update cycles for assessment results, and integration of only a small amount of static data such as pipeline material and corrosion rate. This invention constructs a dynamic weighted risk assessment model that integrates four primary indicators, supports adaptive adjustment of weights based on the scenario, and incorporates multi-source data such as GIS geographic data, real-time SCADA data, and environmental dynamic data to achieve minute-level risk updates.

[0073] Advantages: The risk assessment results are more in line with the actual scenario, and can respond to dynamic risk factors in real time. The early warning accuracy rate has increased from 68% to 92%, realizing the transformation from static assessment to dynamic early warning, and can effectively carry out preventive maintenance.

[0074] 4. Pipeline management methods Differences: Conventional techniques in this field involve independent operation of each step, namely manual inspection to detect leaks, professional analysis and location, and offline development of risk assessment reports and emergency plans. Each step is fragmented and relies on manual labor. This invention achieves full-process automation from data collection, leak location, risk assessment to emergency plan generation, and forms an integrated technical system based on GIS spatial topology and multi-source data fusion.

[0075] Advantages: The entire process response time is less than 10 minutes, which is more than 87% more efficient than the traditional manual method. It also significantly reduces the subjectivity and error of manual operation, improves the automation and intelligence level of pipeline safety management, and reduces annual maintenance costs by more than 35%.

[0076] The specific implementation process is as follows: Example 1: Leak location and risk calculation for long-distance gas pipelines with high sulfur content 1. Implementation Scenario: Jinfu Line B section gas pipeline, with specifications of Φ273×8mm, length of 21km, material of L360QB steel (3PE anti-corrosion), service life of 17 years, design pressure of 8.5MPa, actual operating pressure of 5.41-5.75MPa, and daily gas transmission capacity of 103.75×10 4 m³; gas quality characteristics: H₂S content 19.2-22.2 g / m³ (corrosion equivalent 8.5); there are 19 crossing points (including rivers and highways) along the route, 3 geological hazard sensitive points (1 level IV collapse and 2 level III landslides), and the population density within 500 meters of Jinshan Station is 647 people / km²; a total of 28 acoustic, corrosion and pressure sensors are deployed, and sensor data is transmitted via MQTT protocol with a delay of <1s.

[0077] 2. Implementation Steps S1: Integrates multi-source data, including GIS geographic data in the WGS84 coordinate system (topography and elevation, population density of 647 people / km², distribution of geological hazard sensitive points), and SCADA real-time data (pressure 5.41-5.75 MPa, flow rate 103.75 × 10⁻⁶). 4 m³ / h), pipeline asset data (L360QB steel material, 17-year service life, 8.5MPa design pressure), gas medium characteristic data (H2S corrosion equivalent 8.5), extreme weather data (no heavy rain / freezing heave conditions).

[0078] S2: Construct a GIS spatial topology network model with nodes Wenquan 1-1 Well (starting point), Jinshan Station (ending point), and 2 valve chambers, and edges representing pipe segments; the pipe segment weight assignment rules are: river / highway crossing segment weight × 1.2, and 17-year service life segment weight × 1.3; introduce a bend curvature influence model to calculate the effective radius of bend pipe segments, and for pipe segments without depressions, there is no need to execute the depression positioning optimization algorithm.

[0079] S3: A leakage signal was detected in the Fangjiagou Village subsidence area (Level IV risk). The sound wave intensity attenuation formula was used. (β=0.8 for L360QB steel), the leakage signal propagation path is optimized using the Dijkstra algorithm; the positioning error correction equation is used. Correct the positioning results, among which Calculation based on sensor signal strength and distance =0.02 +0.1, The corrosion rate of the i-th pipe segment was used to determine the coordinates of the leak point, with a measured positioning error of 7.3m.

[0080] S4: Construct a multi-indicator dynamic weighted risk assessment model. The primary indicators are weighted as follows: inherent risk 0.3, environmental risk 0.4, dynamic risk 0.25, and operating condition risk 0.25. The secondary indicators are: pipe corrosion rate 0.15 (data source: ultrasonic testing), H2S corrosion equivalent 0.15 (data source: gas chromatography analysis report), population density 0.20 (data source: GIS spatial overlay), geological disaster level 0.20 (data source: disaster investigation report), pressure fluctuation amplitude 0.15 (data source: SCADA real-time data), third-party construction frequency 0.15 (data source: inspection records, 0 times), pressure range adaptation coefficient 0.12, and flow correction coefficient 0.13. The original data of each indicator are converted into Xi (value range [0,1]) using a standardized formula and substituted into the risk total score calculation formula. Combined with the environmental risk enhancement coefficient ≈1.405 (not exceeding the upper limit of 3), the risk value of the Jinshan Station entrance section after correction is 0.78, which is judged as a high-risk section. The risk value of the Jiangjiashan landslide area in Yongxing Town is 0.52 (medium risk), and the risk value of the uninhabited area of ​​Hanlin Village in Xintai Township is 0.21 (low risk). A risk level heat map is generated to visually display five types of failure modes, including fracture and perforation.

[0081] S5: Based on the leak location results and risk heat map, an emergency response plan is automatically generated, with a core area radius of 50m, a warning area of ​​200m, and an evacuation area of ​​500m. Supplementary measures for preventing hydrogen sulfide poisoning are provided to address the high sulfur content.

[0082] 3. Implementation Results / Advantages: This embodiment targets long-distance pipelines with high sulfur content, multiple crossings, and aging infrastructure. By integrating H2S corrosion equivalent correction with geological hazard data, it accurately quantifies pipeline risks under special circumstances. The leak location error is 7.3m, which is more than 68 times more accurate than the traditional method (±500m). The dynamic response time for risk assessment is ≤1 minute, and the early warning accuracy rate reaches 92%. The annual maintenance cost is reduced by 38%, and the false alarm rate is reduced to 1.5%. It effectively avoids the risk of accidents such as personnel poisoning and explosions caused by hydrogen sulfide leaks, verifying the applicability and superiority of this invention in long-distance pipeline scenarios with high sulfur content.

[0083] Example 2: Leak Location and Risk Calculation in Urban Gas Pipeline Networks 1. Implementation Scenario: A gas pipeline network in the main urban area of ​​a city, with a total length of 80km. The pipeline is made of PE pipe and steel pipe, and some sections have a service life of 10-15 years. The pipeline is surrounded by residential areas, commercial areas, and highway crossings, with the highest population density reaching 800 people / km². The operating pressure is 0.3-0.8MPa. 50 pressure sensors and 50 acoustic sensors are deployed. Sensor data is transmitted via MQTT protocol with a delay of <1s. Recently, a third party has been conducting manual excavation work near a commercial area, with a construction frequency of once a month and a proximity of 120m.

[0084] 2. Implementation Steps S1: Integrates multi-source data, including GIS geographic data in the WGS84 coordinate system (topography, population density distribution, up to 800 people / km²), SCADA real-time data (pressure 0.3-0.8MPa, flow rate), pipeline asset data (PE pipe / steel pipe material, 10-15 years of service life), gas medium characteristic data (CH4 explosion limit 5-15%, diffusion coefficient 0.196m² / s, corrosion equivalent 1.0), extreme weather data (no special extreme weather), and third-party construction data (manual excavation, once / month, 120m).

[0085] S2: Construct a GIS spatial topology network model, with nodes being pressure regulating stations and valve wells (32 in total), and edges being pipe segments; the pipe segment weight assignment rules are: highway crossings are weighted by 1.2, and steel pipe segments with a service life of ≥10 years are weighted by 1.2; introduce a bend curvature influence model to calculate the effective radius of bend pipe segments, and do not need to execute the bend positioning optimization algorithm for pipe segments without depressions.

[0086] S3: A certain acoustic sensor detected a leak signal. Based on the pipe material, the corresponding acoustic intensity attenuation formula was used (β=0.6 for PE pipe and β=0.5 for steel pipe). The propagation path was optimized using the Dijkstra algorithm. After correction by the positioning error correction equation, the coordinates of the leak point were determined to be (116.45°E, 39.92°N), with a positioning error of 6.8m.

[0087] S4: Construct a multi-indicator dynamic weighted risk assessment model. The primary indicators are weighted as follows: inherent risk 0.3, environmental risk 0.4, dynamic risk 0.25, and operating condition risk 0.25. Secondary indicators include: pipe corrosion rate 0.15, H2S corrosion equivalent 0.15 (0 if no H2S), population density 0.20 (800 people / km²), geological hazard level 0.20 (0 if no geological hazard), pressure fluctuation amplitude 0.15, third-party construction frequency 0.15 (manual excavation, 1 time / month, 120m, risk value 0.3), pressure range adaptation coefficient 0.12 (low-pressure operation, adaptation coefficient 0.8), and flow correction coefficient 0.13. X is converted using a standardized formula. iSubstitute the values ​​into the total risk score calculation formula, and combine them with the environmental risk enhancement coefficient. ≈1.587 (not exceeding the upper limit of 3), the calculated risk value around the leak point is corrected to 0.89, and it is judged as a high-risk segment; a risk level heat map is generated to visually display the 5 types of failure modes.

[0088] S5: Based on the leak location results and risk heat map, an emergency response plan is automatically generated, with a core area of ​​50m, a warning area of ​​200m, and an evacuation area of ​​500m. Additional personnel evacuation optimization measures are added to address the dense population characteristics of commercial areas.

[0089] 3. Implementation Results / Advantages: This implementation plan addresses scenarios involving densely populated urban gas pipeline networks with diverse materials and third-party construction. By integrating third-party construction data and correcting for population density, it accurately quantifies dynamic and environmental risks. The leak location error is 6.8m, which is more than 7 times more accurate than the traditional segment-level location of urban gas pipeline networks (error ±50m). Risk assessments are updated within 5 minutes, and emergency plans are automatically generated within 3 minutes, which is 20 times more efficient than the traditional manual plan formulation (which takes more than 1 hour). This effectively ensures public safety in the main urban area and avoids explosions and fires caused by gas leaks.

[0090] Example 3: Location and Risk Calculation of Gas Pipeline Leaks under Extreme Rainstorm Weather 1. Implementation Scenario: A gas pipeline in North China, with a total length of 50km, is constructed of steel and PE pipes. There are several sections of loess subsidence along the pipeline. The pipeline experienced heavy rain with a rainfall of 45mm / h. The pipeline operates at a pressure of 0.5-1.2MPa. There are farmland and villages nearby, with a population density of 200 people / km². 35 sensors were deployed to monitor the pipeline pressure, flow rate, and surrounding terrain subsidence in real time. There were no third-party construction activities.

[0091] 2. Implementation Steps S1: Integrates multi-source data, including GIS geographic data in the WGS84 coordinate system (topography, distribution of loess subsidence sections, population density of 200 people / km²), SCADA real-time data (pressure 0.5-1.2MPa, flow rate), pipeline asset data (steel pipe / PE pipe material, service life of 8 years), gas medium characteristic data (CH4, CO2 related parameters), and extreme weather data (rainfall of 45mm / h, rainstorm conditions).

[0092] S2: Construct a GIS spatial topology network model, with nodes being booster stations and valve wells (18 in total), and edges being pipe segments; the pipe segment weight assignment rule is: the weight of the pipe segment in the loess settlement section is ×1.3, and the weight of the steel pipe segment is ×1.1; introduce the elbow curvature influence model to calculate the effective radius of the elbow pipe segment, and the depression positioning optimization algorithm is not required for pipe segments without depressions; the pipe foundation stability coefficient under heavy rain conditions is ×0.7.

[0093] S3: A pressure sensor detected an abnormal flow rate. Based on the acoustic detection signal, the corresponding acoustic intensity attenuation formula was applied according to the pipe material (β=0.5 for steel pipe and β=0.6 for PE pipe). The propagation path was optimized using the Dijkstra algorithm. After correction by the positioning error correction equation, the leak point was determined to be located within the loess settlement section, with a positioning error of 8.9m.

[0094] S4: Construct a multi-indicator dynamic weighted risk assessment model. The primary indicators are weighted as follows: inherent risk 0.3, environmental risk 0.4, dynamic risk 0.25, and operating condition risk 0.25. The secondary indicators include: pipe corrosion rate 0.15, H2S corrosion equivalent 0.15 (0 if no H2S), population density 0.20 (200 people / km²), geological hazard level 0.20 (loess subsidence, level three), pressure fluctuation range 0.15 (pressure 0.5-1.2MPa), third-party construction frequency 0.15 (0 times), pressure range adaptation coefficient 0.12, and flow correction coefficient 0.13. The model is then converted to Xi using a standardized formula and substituted into the total risk score calculation formula, combined with the environmental risk enhancement coefficient. ≈1.182 (Not exceeding the upper limit of 3), while introducing the pipe foundation stability correction under heavy rain conditions, the calculated leakage risk value after correction is 0.82, which is judged as a high-risk section; generate a risk level heat map to visually display 5 types of failure modes.

[0095] S5: Based on the leak location results and risk heat map, an emergency response plan is automatically generated, with a core area of ​​50m, a warning area of ​​200m, and an evacuation area of ​​500m. For scenarios involving heavy rain and loess subsidence, supplementary measures are taken to reinforce pipe foundations and prevent water backflow.

[0096] 3. Implementation Results / Advantages: This embodiment addresses the complex scenarios of extreme rainstorms and loess subsidence. By fusing extreme weather data and correcting for pipeline foundation stability, it accurately quantifies pipeline risks under special weather and geological conditions. Even with sensor signal interference caused by heavy rain, it still achieves a leak location accuracy of 8.9m, which is more than 11 times higher than the location error (±100m or more) of traditional methods under extreme weather conditions. The emergency plan is specifically adapted to the characteristics of the scenario, effectively avoiding secondary accidents such as pipeline foundation collapse and gas diffusion caused by pipeline leaks, ensuring the personal safety of people in villages along the pipeline, and verifying the stability and applicability of the invention in extreme weather scenarios.

[0097] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements 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 method for locating leaks and calculating risks in gas pipelines based on GIS spatial topology, characterized in that, Includes the following steps: S1, Construct a multi-source data input layer, integrating GIS geographic data, SCADA real-time data, and pipeline asset database data. The GIS geographic data consists of terrain elevation and population density distribution data in the WGS84 coordinate system. The SCADA real-time data is transmitted via the MQTT protocol with a delay of <1s. The pipeline asset database data includes pipeline material, service life, and design pressure. S2. Based on the physical structure of the gas transmission pipeline network, a GIS spatial topology network model is constructed, which abstracts the gas transmission pipeline network into a network structure of nodes and edges. The nodes are valves, pressurization stations, and valve chambers, and the edges are pipe segments. Each pipe segment is assigned a weight based on its length, corrosion rate, and crossing type. The weight of pipe segments crossing rivers and highways is multiplied by a correction factor of 1.2, and the weight of aging pipe segments with a service life of ≥17 years is multiplied by a correction factor of 1.

3. S3, combining the leak signal detected by the sensor with the GIS spatial topology network model, the propagation path analysis of the leak signal is optimized using the Dijkstra algorithm. Based on the sound wave intensity attenuation formula and the positioning error correction equation, the coordinate-level positioning of the gas pipeline leak point is achieved. The sound wave intensity attenuation formula is as follows: Positioning error ≤ ±10m in, Unit: dB; ; S4. Construct a multi-indicator dynamic weighted risk assessment model, integrating four primary indicators: inherent risk, environmental risk, dynamic risk, and operational risk. The weights corresponding to the primary indicators are 0.3, 0.2, 0.25, and 0.25, respectively. The pipeline risk value is calculated through weighted scoring, generating a real-time risk level heatmap. The formula for calculating the total risk score is: ， for ; S5 automatically generates an emergency response plan that includes a core area, a warning area, and an evacuation area based on the leak location results and risk heat map. The core area has a radius of 50m, the warning area has a radius of 200m, and the evacuation area has a radius of 500m.

2. The method according to claim 1, characterized in that, In step S3, the β value corresponding to L360QB steel is 0.8, the β value corresponding to PE pipe is 0.6, and the β value corresponding to steel pipe is 0.

5.

3. The method according to claim 1, characterized in that, The positioning error correction equation in step S3 is: ,in The sensor weights are calculated using the following formula: ,, Let be the signal strength of the i-th sensor. Let be the distance from the i-th sensor to the pipe segment; Let f be the partial derivative term of the i-th pipe segment in the GIS topology network, and let f be the leakage signal propagation error function. Let be the corrosion rate of the i-th pipe segment. The partial derivative is obtained by differentiating the function. =0.02 +0.

1.

4. The method according to claim 3, characterized in that, The primary indicators of the multi-indicator dynamic weighted risk assessment model described in step S4 include several secondary indicators. The secondary indicators of inherent risk are pipe corrosion rate and H2S corrosion equivalent; the secondary indicators of environmental risk are population density and geological disaster level; the secondary indicators of dynamic risk are pressure fluctuation amplitude and third-party construction frequency; and the secondary indicators of operating condition risk are pressure range adaptation coefficient and flow correction coefficient. Among them, the weights are as follows: pipe corrosion rate 0.15, H2S corrosion equivalent 0.15, population density 0.10, geological disaster level 0.10, pressure fluctuation amplitude 0.15, third-party construction frequency 0.10, pressure range adaptation coefficient 0.12, and flow correction coefficient 0.

13. The standardized risk factor values ​​are given by the following standardized formula: = The value range is [0,1].

5. The method according to claim 1, characterized in that, Step S4 also includes a leakage and diffusion risk correction term, and the formula for the environmental risk enhancement coefficient is: Where ρ is the surrounding population density, in people / km²; ρ≥0, when ρ>10000 people / km², The upper limit is set to 3; The formula for adjusting the total risk score is: .

6. The method according to claim 1, characterized in that, The risk level heat map described in step S4 divides pipeline risks into three levels: high, medium, and low. The risk value of the high-risk segment is ≥0.6, the risk value of the medium-risk segment is 0.3-0.6, and the risk value of the low-risk segment is <0.

3. The heat map also supports real-time visualization of five types of pipeline failure modes: fracture, perforation, puncture, deformation, and blockage.

7. The method according to claim 1, characterized in that, Step S1 also integrates gas medium characteristic data and extreme weather data. The gas medium characteristic data includes the explosion limits, diffusion coefficients, and corrosion equivalents of CH4, CO2, and H2S, with the corrosion equivalent of H2S being 8.

5. The extreme weather data includes rainfall, frost heave coefficient, pipe foundation stability coefficient ×0.7 under heavy rain conditions, and weld stress coefficient ×1.4 under frost heave conditions.

8. The method according to claim 2, characterized in that, Step S4 also includes a production condition compensation model, and the pressure-flow coupling coefficient formula is: , ； When P > 10 MPa, the coefficient of the exponential term is adjusted to 0.08; when P < 1 MPa, the coefficient of the exponential term is adjusted to 0.

12.

9. The method according to claim 1, characterized in that, In step S2, when constructing the GIS spatial topology network model, the influence model of bend curvature is also introduced: For the recessed pipe section, a recess location optimization algorithm is adopted: , Where R is the pipe bending radius, and s is the length of the indentation along the pipe. .

10. The method according to claim 1, characterized in that, The method automates the entire process from leak signal detection to emergency response plan generation, with an overall response time of less than 10 minutes and a false alarm rate of less than 1.8%. It is applicable to various scenarios, including high-sulfur pipelines, special geological sections, urban gas pipeline networks, and long-distance pipelines.

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