Polyethylene gas pipeline leakage risk assessment method and system

Abstract

The invention belongs to the technical field of gas pipeline leakage risk evaluation, and discloses a polyethylene gas pipeline leakage risk evaluation method and system.The method comprises the steps that leakage failure factors are graded based on a pipeline failure accident tree, and the failure grade of a sample pipeline is judged based on the maximum membership degree principle; establishing a potential influence radius calculation model suitable for the nonmetal polyethylene pipeline, and determining a heat radiation influence radius model; obtaining a leakage rate curve, and dividing a thermal radiation influence area; determining a consequence severity level according to the loss number; and in combination with the failure level and the consequence severity level, performing leakage risk assessment on the polyethylene gas pipeline based on the risk matrix. According to the method, the pipeline failure grade is calculated based on the cloud model parameters, the membership value and the weight value, the leakage rate attenuation factor suitable for the nonmetal polyethylene pipeline is obtained based on computational fluid mechanics simulation, artificial subjective interference such as expert scoring is not needed, and the risk assessment accuracy and objectivity are greatly improved.

Description

Technical Field

[0001] This invention belongs to the technical field of gas pipeline leakage risk assessment, and particularly relates to a method and system for assessing leakage risk in polyethylene gas pipelines. Background Technology

[0002] Polyethylene (PE) possesses advantages such as good corrosion resistance, ductility, light weight, and ease of construction, making it widely used in urban gas transmission. However, with increasing service life, problems such as aging and third-party damage in PE pipelines gradually intensify. Furthermore, the transport of flammable and explosive media, concentrated human labor, and complex environments pose significant risks, seriously threatening the safety of people's lives and property. Therefore, assessing the risks present in pipeline operation using scientific methods and taking timely measures to mitigate these risks is crucial for ensuring the safe operation of urban gas pipelines.

[0003] Domestic and international scholars have conducted a series of studies on pipeline leakage risk assessment. Currently, commonly used pipeline risk assessment methods include qualitative assessment methods, semi-quantitative assessment methods, and quantitative assessment methods. However, most existing mature risk assessment models and methodologies are primarily designed for the characteristics of metallic pipelines. Although there are a few studies on non-metallic pipelines such as polyethylene, these methods share a significant common flaw: they fail to fully consider the substantial changes in the calculation parameters for leakage consequences resulting from the change of pipeline materials from metal to polyethylene. This leads to systematic biases in key consequence calculation aspects (such as leakage rate, diffusion range, ignition probability, and affected area) when assessing the leakage risk of non-metallic pipelines, making it impossible to accurately and objectively reflect the actual risk level of polyethylene pipeline failure.

[0004] Based on the above analysis, the existing technologies have the following problems and shortcomings: The existing pipeline risk assessment system has certain limitations at the methodological level. The weight calculation process based on expert scoring is highly subjective, leading to poor stability of the assessment results and making it impossible to objectively conduct pipeline risk assessments. Furthermore, the existing evaluation system mainly targets metal pipeline design and does not consider the changes in calculation parameters for leakage consequences caused by differences in pipeline materials, thus failing to accurately and objectively assess the leakage risk of polyethylene pipelines. Summary of the Invention

[0005] To overcome the problems existing in related technologies, the present invention discloses a method and system for assessing leakage risks in polyethylene gas pipelines, the technical solution of which is as follows:

[0006] This invention is implemented as follows: a method for assessing the leakage risk of polyethylene gas pipelines, comprising the following steps:

[0007] S1. Establish a pipeline failure accident tree and classify the leakage failure factor level based on the pipeline failure accident tree; calculate the characteristic parameters of the cloud model and collect the actual values ​​of the field pipeline samples.

[0008] S2, calculate the membership degree and combined weight value of the risk indicators; calculate the comprehensive membership degree based on the membership degree value and weight value, and determine the failure level of the sample pipeline based on the principle of maximum membership degree;

[0009] S3. Establish a potential influence radius calculation model applicable to non-metallic polyethylene pipes, and obtain the leakage rate curve of buried pipes through Fluent simulation.

[0010] S4. By determining the relationship curves between the initial peak leakage rate and the leakage rate at 60s and the pipe diameter and internal pressure, a leakage rate attenuation factor applicable to non-metallic polyethylene pipes is obtained.

[0011] S5, determine the thermal radiation influence radius model, and divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model;

[0012] S6. Determine the number of losses based on the loss calculation model, and determine the severity level of the consequences based on the amount of losses caused by pipeline failure;

[0013] S7, combining the failure level and the severity of consequences, uses a risk matrix to assess the leakage risk of polyethylene gas pipelines.

[0014] In step S1, a pipeline failure accident tree is established, including: establishing a pipeline failure accident tree through on-site investigation of the pipeline to be analyzed; dividing the failure factors of buried gas pipeline leakage into 4 primary indicators and 22 secondary indicators based on the accident tree; and dividing the failure level of buried gas pipeline leakage into 5 evaluation levels, IV.

[0015] Calculate the characteristic parameters of the cloud model, including: the characteristic parameter E of the cloud model. x E n H e The calculation formula is:

[0016] H e =kE n

[0017] In the formula, E x E represents the expected value of the cloud droplet distribution area and is the central value of the qualitative concept in the quantitative domain. n Entropy represents the randomness of the index's existence, reflecting the range of values ​​of cloud droplets acceptable for qualitative concepts in the domain space and the degree of dispersion of the cloud droplets; H e Hyperentropy characterizes the discreteness and randomness of entropy itself, intuitively reflecting the degree of condensation of cloud droplets in the domain space; Cmax C represents the maximum value of the risk indicator boundary. min is the minimum value of the risk indicator boundary; k is the uncertainty characteristic constant of the failure factor, which is adjusted according to the uncertainty of the variable itself.

[0018] In step S2, the membership degree of the risk indicator is calculated, including: combining the actual sample values, using a positive cloud generator to calculate the membership degree of the risk indicator, the specific steps of which are as follows:

[0019] (1) Generate a value with En as the desired value. Gaussian random numbers with variance En' is a random implementation value of entropy;

[0020] (2) Generate a random number x with expectation Ex and variance En';

[0021] (3) Define the random number x as a cloud droplet, representing the specific quantitative data of the qualitative concept, and calculate the membership value; the expression is:

[0022]

[0023] In the formula, μ i (xi) represents the membership value of the indicator;

[0024] (4) Forming the interval (x, μ) i (x i Cloud droplets;

[0025] (5) Repeat the above steps to generate N cloud droplets.

[0026] In step S2, the combined weight value is calculated, including: combining the actual sample values, calculating the combined weight value based on CRITIC and entropy weight methods, the specific steps of which are as follows:

[0027] (1) CRITIC method

[0028] ① Establish the decision matrix R;

[0029]

[0030] In the formula, x ij Let x be the sample value of the j-th indicator for the i-th object, m be the number of objects being evaluated, n be the number of evaluation indicators, and x be the sample value of the j-th indicator for the i-th object. mn This represents the sample value of the m-th evaluation object under the n-th evaluation index.

[0031] ② Standardize the values ​​of each index in the decision matrix R, as shown in the expression:

[0032]

[0033] In the formula, rij These are the standardized sample values;

[0034] The standardized decision matrix is ​​represented as follows:

[0035]

[0036] In the formula, For the standardized decision matrix, r mn Let m be the standardized value of the m-th evaluation object under the n-th evaluation index;

[0037] ③ Calculate the correlation coefficient matrix ρ jt The expression is:

[0038]

[0039] In the formula, ρ jt The correlation coefficient is the index. Let j be the average value of all evaluation objects under evaluation index j. r is the average value of all evaluated objects under evaluation index t. it Let be the standardized value of the i-th evaluation object under the t-th evaluation indicator, where t is the t-th evaluation indicator;

[0040] ④ Calculation C j coefficient of variation σ j The expression is:

[0041]

[0042] In the formula, σ j The coefficient of variation of the index. Let be the mean of the j-th evaluation index;

[0043] ⑤ Calculation criteria c j Information content G j The expression is:

[0044]

[0045] In the formula, G j For the amount of information in the indicator;

[0046] ⑥ Calculation criteria c j weight ω j ;

[0047]

[0048] In the formula, ω j The indicator weight value;

[0049] (2) Entropy weight method

[0050] ① Let the value of the j-th indicator of the i-th object be x out of m evaluation objects and n evaluation indicators. ij Construct the original evaluation index data matrix R;

[0051]

[0052] In the formula, x ij This represents the sample value of the j-th indicator for the i-th object;

[0053] ② Normalize the decision matrix to obtain

[0054]

[0055] In the formula, max(x) ij ) represents the maximum value in the original decision matrix R, min(x) ij ) represents the minimum value in the original decision matrix R;

[0056] The normalized decision matrix is ​​represented as follows:

[0057]

[0058] In the formula, r ij These are the standardized sample values;

[0059] ③ Calculation criterion condition c j Information entropy E j ;

[0060]

[0061] If r ij =0, then

[0062] In the formula, E j For indicator information entropy;

[0063] ④ Calculation C j weight ω j ;

[0064]

[0065] In the formula, ω j The indicator weight value;

[0066] (3) Combination weights

[0067] ① Combine and optimize the weights obtained by the CRITIC method and the entropy weight method to minimize the deviation of the weights, which is equivalent to transforming it into the optimization first derivative form of a system of linear equations:

[0068]

[0069] In the formula, α1 and α2 are linear combination coefficients, w1 and w2 are CRITIC weights and entropy weights, and T represents the transpose matrix;

[0070] ② Normalize the calculated optimal linear combination coefficients α1 and α2 to obtain the combination weight allocation coefficients β1 and β2;

[0071]

[0072] In the formula, α i To optimize the linear combination coefficients, β i Assign coefficients to the combined weights;

[0073] ③ Calculate the combined weights of the evaluation indicators;

[0074] W = β1w1 + β2s2

[0075] In the formula, W is the combined weight, s1 is the CRITIC method weight, and s2 is the entropy weight method weight.

[0076] In step S3, a potential influence radius calculation model suitable for non-metallic polyethylene pipes is established, and the expression is:

[0077]

[0078] In the formula, I is the thermal radiation intensity; η is the combustion efficiency coefficient; H c The heat of combustion of the gas; X g Q is the emissivity coefficient; r is the distance from the calculation point to the center of the fire source; eff Q represents the effective gas release rate. in λ is the initial release rate; λ is the leakage rate decay factor; C d The flow coefficient is used to simulate the leakage diffusion of buried gas pipelines, obtaining the peak leakage rate and the leakage rate at 60 seconds; d is the pipeline diameter; p is the pipeline internal pressure. is the flow coefficient; a0 is the gas sound velocity;

[0079]

[0080] In the formula, Q 60 Q is the pipeline leakage rate 60 seconds after the leak occurs. peak This represents the initial peak leakage rate.

[0081] In step S5, the model for the radius of influence of thermal radiation is as follows:

[0082]

[0083] In the formula, r is the distance from the calculation point to the center of the fire source; Xg λ is the emissivity coefficient; λ is the leakage rate attenuation factor; p is the internal pressure of the pipe; d is the pipe diameter; I is the thermal radiation intensity.

[0084] The thermal radiation influence region is divided based on the thermal radiation intensity threshold and thermal radiation influence radius model, expressed as follows:

[0085]

[0086] In the formula, A1 represents a high-risk area with a 100% mortality rate; r1 is the radius of the high-risk area; A 0.5 This is a medium-risk area with a 50% fatality rate; R 0.5 Radius of the medium-risk zone; A 0.01 Low-risk area, 1% fatality rate, R 0.01 The radius of the low-risk zone.

[0087] In step S6, the loss number is determined according to the loss calculation model, and the expression is:

[0088] N = f i ρ(P in A1+P out A1+0.5×P out A 0.5 +0.01×P out A 0.01 )

[0089] In the formula, ρ is the population density; P in ,P out These represent the proportion of people living indoors and outdoors along the pipeline route, respectively, with recommended values ​​of 0.93 and 0.07; f i The ignition probability is 1 under the condition of full-diameter fracture; N is the loss number.

[0090] The severity level of the consequences is determined as follows: if the number of losses is less than 1, the failure consequence level is low; if the number of losses is between [1,3), the failure consequence level is relatively low; if the number of losses is between [3,6), the failure consequence level is medium; if the number of losses is between [6,9), the failure consequence level is relatively high; if the number of losses is ≥9, the failure consequence level is high.

[0091] Another object of the present invention is to provide a polyethylene gas pipeline leakage risk assessment system, which is used to regulate the aforementioned polyethylene gas pipeline leakage risk assessment method. The system includes:

[0092] The buried gas pipeline leakage failure factor classification module is used to establish a pipeline failure accident tree and classify the leakage failure factor level based on the pipeline failure accident tree.

[0093] The sample pipeline failure level judgment module is used to calculate the characteristic parameters of the cloud model, collect the actual values ​​of the field pipeline samples, calculate the membership degree and combined weight value of the risk indicators, calculate the comprehensive membership degree based on the membership degree value and weight value, and judge the failure level of the sample pipeline based on the maximum membership degree principle.

[0094] The leakage rate curve simulation module is used to establish a potential influence radius calculation model applicable to non-metallic polyethylene pipes, and obtain the leakage rate curve of buried pipes through Fluent simulation.

[0095] The parameter determination module is used to obtain the leakage rate attenuation factor applicable to non-metallic polyethylene pipes by determining the relationship curve between the initial leakage peak rate and the leakage rate at 60s and the pipe diameter and internal pressure.

[0096] The thermal radiation influence area division module is used to determine the thermal radiation influence radius model and divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model.

[0097] The consequences severity level determination module is used to determine the number of losses based on the loss calculation model and to determine the severity level of the consequences based on the number of losses caused by pipeline failure.

[0098] The gas pipeline leakage risk assessment module is used to assess the leakage risk of polyethylene gas pipelines based on a risk matrix, combining the failure level and the severity level of the consequences.

[0099] Combining all the above technical solutions, the beneficial effects of this invention are as follows:

[0100] First, this invention addresses the risk assessment of leaks in non-metallic polyethylene gas pipelines. It includes calculating pipeline failure levels based on cloud model parameters, membership values, and weight values, and simulating a leakage rate attenuation factor suitable for non-metallic polyethylene pipelines based on computational fluid dynamics. This eliminates the need for subjective human interference such as expert scoring, greatly improving the accuracy and objectivity of risk assessment.

[0101] Secondly, the method for assessing the leakage risk of polyethylene gas pipelines in this invention includes calculating the pipeline failure probability and the severity of consequences, enabling accurate evaluation of leakage risks in urban gas polyethylene pipelines. This invention significantly improves the accuracy and precision of risk identification by constructing a quantitative risk assessment model tailored to the characteristics of non-metallic polyethylene (PE) gas pipelines. After technology transfer, it can be widely applied in areas such as gas company pipeline integrity management, government safety supervision, and insurance risk assessment. This effectively reduces accident risks and economic losses, optimizes operation and maintenance costs, and improves safety management and compliance.

[0102] Third, this invention effectively fills the technological gap in the field of quantitative and refined risk assessment of non-metallic gas pipelines both domestically and internationally. This invention systematically applies cloud model theory combined with a specific weighting system to the quantitative calculation of pipeline failure probability, fully considering the fuzziness and randomness of pipeline failure factors. Using computational fluid dynamics (CFD) simulation technology, a "leakage rate attenuation factor" suitable for non-metallic pipelines is constructed and quantified, thereby achieving a more realistic assessment of the severity of leakage consequences in non-metallic pipelines. Integrating the above-mentioned specialized assessment methods for the probability and severity of failure of non-metallic pipelines forms a complete quantitative risk level assessment system for non-metallic gas pipelines. This system is original in its model principles, key parameter acquisition (such as the attenuation factor), and assessment process.

[0103] Fourth, this invention successfully solves a long-standing key problem in the safety management of urban gas pipeline networks: how to conduct scientific, accurate, and quantitative risk assessments of the increasingly widespread but lacking risk assessment methods for non-metallic gas pipelines. It overcomes two common technical biases in the field of pipeline risk assessment: the bias that "metallic pipeline models can be easily modified for non-metallic pipelines" and the bias that "the consequences of leaks in non-metallic pipelines can be fully assessed through constant leakage models or empirical formulas for metallic pipelines." Attached Figure Description

[0104] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0105] Figure 1 This is a flowchart of the polyethylene gas pipeline leakage risk assessment method provided in the embodiments of the present invention;

[0106] Figure 2 This is a schematic diagram of the polyethylene gas pipeline leakage risk assessment method provided in this embodiment of the invention;

[0107] Figure 3 This is a pipeline risk assessment fault tree structure diagram provided in an embodiment of the present invention;

[0108] Figure 4 This is a leakage rate curve of a 100mm pipe diameter under different pressures provided in an embodiment of the present invention.

[0109] Figure 5 This is a leakage rate curve at different pressures under a 150mm pipe diameter provided in an embodiment of the present invention;

[0110] Figure 6 This is a leakage rate curve of a 200mm pipe diameter under different pressures provided in an embodiment of the present invention;

[0111] Figure 7This is a leakage rate curve at different pressures under a 250mm pipe diameter provided in an embodiment of the present invention;

[0112] Figure 8 This is a leakage rate curve of a 300mm pipe diameter under different pressures provided in an embodiment of the present invention;

[0113] Figure 9 This is a schematic diagram of the thermal radiation hazard zone division provided in an embodiment of the present invention;

[0114] Figure 10 This is a schematic diagram illustrating the risk assessment results of a pipeline based on a risk matrix, combining failure level and severity level of consequences, as provided in an embodiment of the present invention. Detailed Implementation

[0115] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0116] The innovation of this invention lies in the following: It establishes an intelligent evaluation system of "cloud model-membership-weight" for calculating the failure probability of non-metallic pipelines, effectively avoiding the influence of subjective factors such as expert scoring and objectively quantifying the uncertainty of pipeline failure. Based on CFD simulation methods, it quantitatively characterizes the pipeline leakage rate attenuation factor and establishes a dynamic physical model of the non-metallic pipeline leakage process. Ultimately, it achieves a fundamental separation between risk assessment methods for metallic and non-metallic pipelines, solving the problem of inaccurate assessments caused by neglecting material differences in existing technologies.

[0117] Example 1, as Figure 1 As shown, the polyethylene gas pipeline leakage risk assessment method provided in this embodiment of the invention specifically includes the following steps:

[0118] S1. Establish a pipeline failure accident tree and classify the leakage failure factor level based on the pipeline failure accident tree; calculate the characteristic parameters of the cloud model and collect the actual values ​​of the field pipeline samples.

[0119] By conducting on-site investigations of the pipeline to be analyzed, a pipeline failure accident tree was established. Based on the accident tree, the failure factors of buried gas pipeline leakage were divided into 4 primary indicators and 22 secondary indicators, and the failure level of buried gas pipeline leakage was divided into 5 evaluation levels, IV.

[0120] Calculate the characteristic parameters of the cloud model, including: the characteristic parameter E of the cloud model. x E nH e The calculation formula is:

[0121] H e =kE n (1)

[0122] In the formula, E x E represents the expected value of the cloud droplet distribution area and is the central value of the qualitative concept in the quantitative domain. n Entropy represents the randomness of the index's existence, reflecting the range of values ​​of cloud droplets acceptable for qualitative concepts in the domain space and the degree of dispersion of the cloud droplets; H e Hyperentropy characterizes the discreteness and randomness of entropy itself, intuitively reflecting the degree of condensation of cloud droplets in the domain space; C max C represents the maximum value of the risk indicator boundary. min is the minimum value of the risk indicator boundary; k is the uncertainty characteristic constant of the failure factor, which is adjusted according to the uncertainty of the variable itself.

[0123] S2, calculate the membership degree and combined weight value of the risk indicators; calculate the comprehensive membership degree based on the membership degree value and weight value, and determine the failure level of the sample pipeline based on the principle of maximum membership degree;

[0124] Calculating the membership degree of risk indicators includes: using a positive cloud generator to calculate the membership degree of risk indicators based on actual sample values. The specific steps are as follows:

[0125] (1) Generate a value with En as the desired value. Gaussian random numbers with variance En' is the random realization value of entropy; (2) Generate a random number x with Ex as expectation and En' as variance; (3) Define the random number x as a cloud droplet, representing the specific quantitative data of the qualitative concept, and calculate the membership value using formula (2); the expression is:

[0126]

[0127] In the formula, μ i (x) represents the membership value of the indicator;

[0128] (4) Forming the interval (x, μ) i (x ) (5) Repeat the above steps to generate N cloud droplets.

[0129] The calculation of the combined weight value includes: combining the actual sample values, calculating the combined weight value based on CRITIC and entropy weight methods, and the specific steps are as follows:

[0130] (1) CRITIC method

[0131] ① Establish the decision matrix R;

[0132]

[0133] In the formula, x ij Let x be the sample value of the j-th indicator for the i-th object, m be the number of objects being evaluated, n be the number of evaluation indicators, and x be the sample value of the j-th indicator for the i-th object. mm This represents the sample value of the m-th evaluation object under the n-th evaluation index.

[0134] ② Standardize the values ​​of each index in the decision matrix R, as shown in the expression:

[0135]

[0136] In the formula, r ij These are the standardized sample values;

[0137] The standardized decision matrix is ​​represented as follows:

[0138]

[0139] In the formula, For the standardized decision matrix, r mm Let m be the standardized value of the m-th evaluation object under the n-th evaluation index;

[0140] ③ Calculate the correlation coefficient matrix ρ jt The expression is:

[0141]

[0142]

[0143] In the formula, ρ jt The correlation coefficient is the index. Let j be the average value of all evaluation objects under evaluation index j. r is the average value of all evaluated objects under evaluation index t. it Let be the standardized value of the i-th evaluation object under the t-th evaluation indicator, where t is the t-th evaluation indicator;

[0144] ④ Calculation C j coefficient of variation σ j The expression is:

[0145]

[0146] In the formula, σ j The coefficient of variation of the index. Let be the mean of the j-th evaluation index;

[0147] ⑤ Calculation criteria c j Information content Gj The expression is:

[0148]

[0149] In the formula, G j For the amount of information in the indicator;

[0150] ⑥ Calculation criteria c j weight ω j ;

[0151]

[0152] In the formula, ω j The indicator weight value;

[0153] (2) Entropy weight method

[0154] ① Let the value of the j-th indicator of the i-th object be x out of m evaluation objects and n evaluation indicators. ij Construct the original evaluation index data matrix R;

[0155]

[0156] In the formula, x ij This represents the sample value of the j-th indicator for the i-th object;

[0157] ② Normalize the decision matrix to obtain

[0158]

[0159] In the formula, max(x) ij ) represents the maximum value in the original decision matrix R, max(x) ij ) represents the minimum value in the original decision matrix R;

[0160] The normalized decision matrix is ​​represented as follows:

[0161]

[0162] In the formula, r ij These are the standardized sample values;

[0163] ③ Calculation criterion condition c j Information entropy E j ;

[0164]

[0165] If r ij =0, then

[0166] In the formula, E jFor indicator information entropy;

[0167] ④ Calculation C j weight ω j ;

[0168]

[0169] In the formula, ω j The indicator weight value;

[0170] (3) Combination weights

[0171] ① Combine and optimize the weights obtained by the CRITIC method and the entropy weight method to minimize the deviation of the weights, which is equivalent to the first derivative form of the linear equation system, as shown in formula (15):

[0172]

[0173] In the formula, α1 and α2 are linear combination coefficients, w1 and w2 are CRITIC weights and entropy weights, and T represents the transpose matrix;

[0174] ② The calculated optimal linear combination coefficients α1 and α2 are normalized to obtain the combination weight allocation coefficients β1 and β2, as shown in equation (16);

[0175]

[0176] In the formula, α i To optimize the linear combination coefficients, β i Assign coefficients to the combined weights;

[0177] ③ Calculate the combined weights of the evaluation indicators;

[0178] W = β1w1 + β2w2 (17)

[0179] In the formula, W is the combined weight, w1 is the CRITIC method weight, and w2 is the entropy weight method weight.

[0180] S3. Establish a potential influence radius calculation model applicable to non-metallic polyethylene pipes, and obtain the leakage rate curve of buried pipes through Fluent simulation.

[0181] One of the innovative aspects of this invention is: calculating the comprehensive membership degree based on membership and weight values, and determining the failure level of the sample pipeline based on the principle of maximum membership. A potential influence radius calculation model suitable for non-metallic polyethylene pipelines is established, referencing the thermal radiation intensity model proposed in a research report by the Gas Research Institute (GRI).

[0182]

[0183] In the formula, I represents the thermal radiation intensity, with units of kW / m². 2 η is the combustion efficiency coefficient; H c X is the heat of combustion of a gas, expressed in kJ / kg. g Emissivity coefficient, which is related to the pipe material; r is the distance from the calculation point to the center of the fire source, in meters; Q eff Q represents the effective gas release rate. in λ is the initial release rate; λ is the leakage rate decay factor; C d is the flow coefficient, which is calculated by simulating the peak leakage rate and the leakage rate at 60s in the pipeline, and then using formula (19); d is the pipe diameter in mm; p is the internal pressure of the pipe in MPa; is the flow coefficient; a0 is the gas sound velocity, in m / s;

[0184]

[0185] In the formula, Q 60 Q is the pipeline leakage rate 60 seconds after the leak occurs, expressed in kg / s. peak为 Initial peak leakage rate, in kg / s.

[0186] The leakage rate curve of the buried pipeline was obtained through Fluent simulation. A three-dimensional geometric model of the leakage diffusion of the buried gas pipeline was established, and structured mesh generation was performed using ICEM software.

[0187] S4. By determining the relationship curves between the initial peak leakage rate and the leakage rate at 60s and the pipe diameter and internal pressure, a leakage rate attenuation factor applicable to non-metallic polyethylene pipes is obtained.

[0188] The complex fluid dynamics of PE pipeline leakage is condensed into an engineering-applicable "attenuation factor-operating condition" mapping relationship, fundamentally solving the consequence assessment bias caused by the neglect of time-varying material properties in traditional models. At the same time, through parametric design, high-precision CFD results can be applied to actual pipeline network risk management, combining innovation and practicality.

[0189] ASME B31.8S uses the instantaneous leakage rate 60 seconds after the pipeline leak ignites as the equivalent leakage rate for calculating the potential radius of influence of the jet fire. The leakage attenuation factor is defined as the ratio of the instantaneous leakage rate at 60 seconds to the initial peak leakage rate. A multivariate nonlinear regression analysis method was used to establish a natural gas pipeline leakage rate prediction model. Through fitting simulation data, quantitative relationship curves between the initial peak leakage rate and the leakage rate at 60 seconds with pipe diameter and internal pressure were obtained. Finally, the leakage rate attenuation factor was calculated based on the actual pipe diameter and internal pressure data of non-metallic pipelines.

[0190] S5, determine the thermal radiation influence radius model, and divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model;

[0191] The second innovation of this invention is: based on simulation, a leakage rate attenuation factor applicable to non-metallic polyethylene pipes is obtained, leading to a model of the thermal radiation influence radius.

[0192]

[0193] In the formula, r is the distance from the calculation point to the center of the fire source; X g λ is the emissivity coefficient; λ is the leakage rate attenuation factor; p is the internal pressure of the pipe; d is the pipe diameter; I is the thermal radiation intensity.

[0194] The thermal radiation influence area was delineated based on a thermal radiation intensity threshold and thermal radiation influence radius model. The data used conforms to API 521 requirements, and the expression is as follows:

[0195]

[0196] In the formula, A1 represents the high-risk area with a 100% mortality rate; R1 is the radius of the high-risk area; A 0.5 This is a medium-risk area with a 50% fatality rate; R 0.5 Radius of the medium-risk zone; A 0.01 Low-risk area, 1% fatality rate, R 0.01 The radius of the low-risk zone.

[0197] First, the area affected by thermal radiation is approximated as a circle, and its area is related to the radius of thermal radiation influence. The radius of thermal radiation influence is characterized by the formula for thermal radiation intensity. Under different thermal radiation intensity thresholds, the corresponding radius of thermal radiation influence is calculated, thereby calculating the area of ​​the area affected by thermal radiation.

[0198] S6. Determine the number of losses based on the loss calculation model, and determine the severity level of the consequences based on the amount of losses caused by pipeline failure;

[0199] If the number of losses is less than 1, the failure consequence level is low; if the number of losses is between [1,3), the failure consequence level is relatively low; if the number of losses is between [3,6), the failure consequence level is medium; if the number of losses is between [6,9), the failure consequence level is relatively high; if the number of losses is ≥9, the failure consequence level is high.

[0200] Based on the loss calculation model proposed by the Dutch Disaster Committee (CPR), the expression for calculating the loss is as follows:

[0201] N = f i ρ(P in A1+P out A1+0.5×P outA 0.5 +0.01×P out A 0.01 )(twenty four)

[0202] In the formula, ρ represents population density, with units of people / m². 2 ;P in ,P out These represent the proportion of people living indoors and outdoors along the pipeline route, respectively, with recommended values ​​of 0.93 and 0.07; f i The ignition probability is 1 under the condition of full-diameter fracture; N is the loss number.

[0203] The extent of loss caused by pipeline failure is calculated to determine the severity of the consequences, as shown in Table 1.

[0204] Table 1 Classification of Failure Consequence Levels

[0205] Loss Failure Consequence Level ＜1 Low [1,3) lower [3,6) middle [6,9) higher ≥9 high

[0206] S7, combining the failure level and the severity of consequences, uses a risk matrix to assess the leakage risk of polyethylene gas pipelines.

[0207] Example 2: The polyethylene gas pipeline leakage risk assessment system provided in this embodiment of the invention includes:

[0208] The buried gas pipeline leakage failure factor classification module is used to establish a pipeline failure accident tree and classify the leakage failure factor level based on the pipeline failure accident tree.

[0209] The sample pipeline failure level judgment module is used to calculate the characteristic parameters of the cloud model, collect the actual values ​​of the field pipeline samples, calculate the membership degree and combined weight value of the risk indicators, calculate the comprehensive membership degree based on the membership degree value and weight value, and judge the failure level of the sample pipeline based on the maximum membership degree principle.

[0210] The leakage rate curve simulation module is used to establish a potential influence radius calculation model applicable to non-metallic polyethylene pipes, and obtain the leakage rate curve of buried pipes through Fluent simulation.

[0211] The parameter determination module is used to obtain the leakage rate attenuation factor applicable to non-metallic polyethylene pipes by determining the relationship curve between the initial leakage peak rate and the leakage rate at 60s and the pipe diameter and internal pressure.

[0212] The thermal radiation influence area division module is used to determine the thermal radiation influence radius model and divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model.

[0213] The consequences severity level determination module is used to determine the number of losses based on the loss calculation model and to determine the severity level of the consequences based on the number of losses caused by pipeline failure.

[0214] The gas pipeline leakage risk assessment module is used to assess the leakage risk of polyethylene gas pipelines based on a risk matrix, combining the failure level and the severity level of the consequences.

[0215] To further demonstrate the positive effects of the above embodiments, the present invention conducted the following experiments based on the above technical solution. The results show that the calculation results of the traditional model exhibit obvious conservative characteristics.

[0216] This experiment assesses the risk of a section of a gas pipeline in Suzhou, Jiangsu Province; specifically:

[0217] Step 1: Establish a pipeline failure fault tree based on on-site surveys and local pipeline historical data, such as... Figure 3 As shown.

[0218] Step two involves classifying the leakage failure factors based on the established fault tree. The leakage failure factors of buried gas pipelines are divided into 4 primary indicators and 22 secondary indicators, and the probability of leakage failure of buried gas pipelines is further divided into 5 evaluation levels (IV), as shown in Table 2.

[0219] Table 2 Classification of Leakage Failure Factors in Buried Gas Pipelines

[0220]

[0221]

[0222] Step 3: Calculate the feature parameters (E) of the cloud model. x E n H e As shown in Table 3.

[0223] Table 3 Characteristic parameters of leakage failure factors of buried gas pipelines

[0224]

[0225]

[0226] Step 4: Collect the actual values ​​of the pipeline samples, as shown in Table 4.

[0227] Table 4 Values ​​of Leakage Failure Factors for Buried Gas Pipelines

[0228]

[0229]

[0230] Step 5: Calculate the membership degree of the risk indicators based on the actual sample values, as shown in Table 5-8.

[0231] Table 5 Risk Membership Degree of Sample 1

[0232]

[0233]

[0234] Table 6 Risk Membership Degree of Sample 2

[0235]

[0236] Table 7 Risk Membership Degree of Sample 3

[0237]

[0238]

[0239] Table 8 Risk Membership Degree of Sample 4

[0240]

[0241]

[0242] Step 6: Based on the actual sample values, calculate the combined weight values ​​using the CRITIC and entropy weight methods, as shown in Table 9. To overcome the limitations of a single weighting method, game theory-based combined weighting theory is introduced, and the optimal combination coefficients of the CRITIC and entropy weight methods are found to be 0.71 and 0.29, respectively.

[0243] Table 9 Weighting values ​​for buried gas pipeline indicators

[0244] Risk indicators CRITIC weight Entropy weight Combined weight values Gas pipeline diameter X11 0.0727 0.0555 0.0707 Gas pipeline wall thickness X12 0.0118 0.0458 0.0225 Gas pipeline burial depth X13 0.0296 0.0436 0.0325 Pipeline inspection frequency X14 0.0679 0.0549 0.0684 Line patrol system perfection X15 0.0128 0.0396 0.0208 Pipeline movement horizontal X16 0.0726 0.0396 0.057 Pipeline safety sign X17 0.0031 0.0385 0.0137 18 above-ground facilities 0.017 0.0733 0.0337 illegal occupation x19 0.0764 0.0455 0.0714 Pipeline maintenance error X21 0.1466 0.0305 0.1051 Pipeline accident emergency mishap X22 0.1311 0.0338 0.1049 SCADA System X23 0.029 0.0367 0.033 Employee safety education and training X24 0.0633 0.0335 0.0516 Pipe spacing X31 0.0341 0.0369 0.0368 Pipeline area traffic conditions X32 0.0123 0.0407 0.0213 Natural disaster intensity frequency X33 0.0346 0.0793 0.0479 Natural Geological Disaster Prevention and Control X34 0.0103 0.047 0.0215 Pipe material X41 0.0178 0.0733 0.0337 Pipeline service life x 42 0.0398 0.0417 0.0422 System security factor X43 0.0107 0.035 0.0176 Pipeline safety factor X44 0.0126 0.0396 0.0201 Testing equipment maintenance cycle X45 0.0938 0.0357 0.0736

[0245] Step 7: Calculate the comprehensive membership degree based on the membership degree value and weight value, and determine the failure level of the sample pipeline based on the principle of maximum membership degree, as shown in Table 10-13.

[0246] Table 10 Risk Level of Sample 1

[0247]

[0248] Table 11 Risk Levels of Sample 2

[0249]

[0250] Table 12 Risk Levels of Sample 3

[0251]

[0252] Table 13 Risk Levels of Sample 4

[0253]

[0254]

[0255] Confidence factor verification was used to calculate the membership results of the four risks. The results are shown in Table 14.

[0256] Table 14 Confidence Factor Verification Results

[0257] sample Confidence factor 1 0.423 2 0.672 3 0.907 4 0.397

[0258] The results show that Sample 3 passed the confidence factor verification. For the other three groups of samples, directly using a higher risk level would result in overly conservative results. Therefore, the maximum membership principle is still used to quantitatively characterize the failure level of the sample pipelines.

[0259] Step 8: Establish a potential influence radius calculation model based on the thermal radiation intensity model proposed in the research report of the Gas Research Institute (GRI).

[0260]

[0261] Step nine: Obtain the leakage rate curve of the buried pipeline using Fluent simulation, such as... Figure 4-8 As shown, a three-dimensional geometric model of the leakage and diffusion of buried gas pipelines was established, and a structured mesh was generated using ICEM software. Computational fluid dynamics was employed to numerically simulate and analyze the gas leakage and diffusion process in the soil. The finite volume method was used to spatially discretize the governing equations for numerical solutions. The DES model, which has high solution accuracy for leakage models in porous media, was selected for simulating the leakage and diffusion behavior of buried gas pipelines. Since the gas diffusion process in the soil domain is a transient problem, the PISO algorithm was used for pressure-velocity coupled solution. The boundary condition at the leak hole was set as a pressure inlet, the boundary condition around the soil was set as a pressure outlet, and the remaining boundaries were set as walls. The peak leakage rate and the leakage rate at 60 s were calculated.

[0262] Step 10: The relationship curves between the initial peak leakage rate and the leakage rate at 60s and the pipe diameter and internal pressure are obtained by using the multivariate nonlinear regression method. The calculation equation is shown in Equation (26).

[0263] Q peak =0.07674×(p-0.03943) 0.3834 ×(D+141.7) -0.0001937

[0264] Q 30s = 0.009277 × (x - 0.09506)0.99 ×(D+0.3808) -0.06711 (26)

[0265] Step 11: Based on the leakage rate attenuation factor obtained from the simulation, the thermal radiation influence radius model is obtained:

[0266]

[0267]

[0268] Step 12: Divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model, such as... Figure 9 As shown.

[0269] In the formula, A1 represents the high-risk zone (100% lethality); R1 is the radius of the high-risk zone; A 0.5 Medium-risk area (50% fatality rate); R 0.5 Radius of the medium-risk zone; A 0.01 For low-risk areas (1% mortality rate), R 0.01 The radius of the low-risk zone.

[0270] Step 13: Combining the loss calculation model proposed by the Dutch Disaster Committee (CPR), the expression for calculating the loss is obtained:

[0271] N = 0.1985X g ρλpD 2 (30)

[0272] In the formula, ρ represents population density, people / m² 2 ;P in ,P out These represent the proportion of people living indoors and outdoors along the pipeline route, respectively, with recommended values ​​of 0.93 and 0.07; f i This represents the ignition probability, which is set to 1 under the condition of full-diameter failure, and N is the loss number.

[0273] Step fourteen: Calculate the amount of loss caused by the pipeline failure to determine the severity of the consequences.

[0274] Table 15 Pipeline Failure Consequence Levels

[0275] sample Losses per person before improvement Failure Consequence Level Before Improvement Losses per person after improvement Improved failure consequence level Pipeline Section 1 4.7 middle 1.4 lower Pipeline Section 2 32.2 high 11 high Pipeline Section 3 5.6 middle 1.1 lower Pipeline Section 4 0.98 Low 0.16 Low

[0276] Step 15: Combining the failure level and the severity of the consequences, conduct a risk assessment of the pipeline based on the risk matrix, such as... Figure 10As shown, the failure probability levels of the four pipeline segments are low, relatively low, relatively low, and relatively high, respectively. The failure consequence levels are relatively low, high, relatively low, and low, respectively. Therefore, the risk levels of the four pipeline segments are low risk, medium risk, low risk, and medium risk, respectively. Among them, the risk levels of pipelines 1 and 3 are acceptable, while effective measures need to be taken to reduce the risk of pipelines 2 and 4.

[0277] Table 16 Pipeline Risk Levels

[0278] sample Failure probability level Failure Consequence Level Pipeline risk level Sample 1 Low lower Low risk Sample 2 lower high Medium risk Sample 3 lower lower Low risk Sample 4 higher Low Medium risk

[0279] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for assessing the leakage risk of polyethylene gas pipelines, characterized in that, The method includes the following steps: S1. Establish a pipeline failure accident tree and classify the leakage failure factor level based on the pipeline failure accident tree; calculate the characteristic parameters of the cloud model and collect the actual values ​​of the field pipeline samples. S2, calculate the membership degree and combined weight value of the risk indicators; calculate the comprehensive membership degree based on the membership degree value and weight value, and determine the failure level of the sample pipeline based on the principle of maximum membership degree; S3. Establish a potential influence radius calculation model applicable to non-metallic polyethylene pipes, and obtain the leakage rate curve of buried pipes through Fluent simulation. S4. By determining the relationship curves between the initial peak leakage rate and the leakage rate at 60s and the pipe diameter and internal pressure, a leakage rate attenuation factor applicable to non-metallic polyethylene pipes is obtained. S5, determine the thermal radiation influence radius model, and divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model; S6. Determine the number of losses based on the loss calculation model, and determine the severity level of the consequences based on the amount of losses caused by pipeline failure; S7, combining the failure level and the severity of consequences, uses a risk matrix to assess the leakage risk of polyethylene gas pipelines.

2. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 1, characterized in that, In step S1, a pipeline failure accident tree is established, including: establishing a pipeline failure accident tree through on-site investigation of the pipeline to be analyzed; dividing the failure factors of buried gas pipeline leakage into 4 primary indicators and 22 secondary indicators based on the accident tree; and dividing the failure level of buried gas pipeline leakage into 5 evaluation levels, IV.

3. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 1, characterized in that, In step S1, the feature parameters of the cloud model are calculated, including: the feature parameter E of the cloud model. x E n H e The calculation formula is: H e =kE n In the formula, E x E represents the expected value of the cloud droplet distribution area. n For entropy, H e For hyperentropy, C max C represents the maximum value of the risk indicator boundary. min is the minimum value of the risk index boundary; k is the uncertainty characteristic constant of the failure factor.

4. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 3, characterized in that, In step S2, the membership degree of the risk indicator is calculated, including: combining the actual sample values, using a positive cloud generator to calculate the membership degree of the risk indicator, the specific steps of which are as follows: (1) Generate a value with En as the desired value. Gaussian random numbers with variance En' is a random implementation value of entropy; (2) Generate a random number x with expectation Ex and variance En'; (3) Define the random number x as a cloud droplet, representing the specific quantitative data of the qualitative concept, and calculate the membership value; the expression is: In the formula, μ i (x) represents the membership value of the indicator; (4) Forming the interval (x, μ) i (x i Cloud droplets; (5) Repeat the above steps to generate N cloud droplets.

5. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 1, characterized in that, In step S2, the combined weight value is calculated, including: combining the actual sample values, calculating the combined weight value based on CRITIC and entropy weight methods, the specific steps of which are as follows: (1) CRITIC method ① Establish the decision matrix R; In the formula, x ij Let x be the sample value of the j-th indicator for the i-th object, m be the number of objects being evaluated, n be the number of evaluation indicators, and x be the sample value of the j-th indicator for the i-th object. mn This represents the sample value of the m-th evaluation object under the n-th evaluation index. ② Standardize the values ​​of each index in the decision matrix R, as shown in the expression: In the formula, r ij These are the standardized sample values; The standardized decision matrix is ​​represented as follows: In the formula, For the standardized decision matrix, r mn Let m be the standardized value of the m-th evaluation object under the n-th evaluation index; ③ Calculate the correlation coefficient matrix ρ jt The expression is: In the formula, ρ jt The correlation coefficient is the index. Let j be the average value of all evaluation objects under evaluation index j. r is the average value of all evaluated objects under evaluation index t. it Let be the standardized value of the i-th evaluation object under the t-th evaluation index, where t is the t-th evaluation index; ④ Calculate the coefficient of variation σ j The expression is: In the formula, σ j The coefficient of variation of the index. Let be the mean of the j-th evaluation index; ⑤ Calculation criteria c j Information content G j The expression is: In the formula, G j For the amount of information in the indicator; ⑥ Calculation criteria c j weight ω j ; In the formula, ω j The indicator weight value; (2) Entropy weight method ① Let the value of the j-th indicator of the i-th object be x out of m evaluation objects and n evaluation indicators. ij Construct the original evaluation index data matrix R; In the formula, x ij This represents the sample value of the j-th indicator for the i-th object; ② Normalize the decision matrix to obtain In the formula, max(x) ij ) represents the maximum value in the original decision matrix R, min(x) ij ) represents the minimum value in the original decision matrix R; The normalized decision matrix is ​​represented as follows: In the formula, r ij These are the standardized sample values; ③ Calculation criterion condition c j Information entropy E j ; If r ij =0, then In the formula, E j For indicator information entropy; ④ Calculation C j weight ω j ; In the formula, ω j The indicator weight value; (3) Combination weights ① Combine and optimize the weights obtained by the CRITIC method and the entropy weight method to minimize the deviation of the weights, which is equivalent to transforming it into the optimization first derivative form of a system of linear equations: In the formula, α1 and α2 are linear combination coefficients, w1 and w2 are CRITIC weights and entropy weights, and T represents the transpose matrix; ② Normalize the calculated optimal linear combination coefficients α1 and α2 to obtain the combination weight allocation coefficients β1 and β2; In the formula, α i To optimize the linear combination coefficients, β i Assign coefficients to the combined weights; ③ Calculate the combined weights of the evaluation indicators; W = β1w1 + β2w2 In the formula, W is the combined weight, w1 is the CRITIC method weight, and w2 is the entropy weight method weight.

6. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 1, characterized in that, In step S3, a potential influence radius calculation model suitable for non-metallic polyethylene pipes is established, and the expression is: Q eff =2λQ in In the formula, I represents the thermal radiation intensity; η is the combustion efficiency coefficient; H c X is the heat of combustion of the gas; g Q is the emissivity coefficient; r is the distance from the calculation point to the center of the fire source; eff Q represents the effective gas release rate. in λ is the initial release rate; λ is the leakage rate decay factor; C d The flow coefficient is used to obtain the peak leakage rate and the leakage rate at 60 seconds through leakage diffusion simulation of buried gas pipelines; d is the pipeline diameter; p is the pipeline internal pressure. is the flow coefficient; a0 is the gas sound velocity; In the formula, Q 60 Q is the pipeline leakage rate 60 seconds after the leak occurs. peak This represents the initial peak leakage rate.

7. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 1, characterized in that, In step S5, the model for the radius of influence of thermal radiation is as follows: In the formula, r is the distance from the calculation point to the center of the fire source; X g λ is the emissivity coefficient; λ is the leakage rate attenuation factor; p is the internal pressure of the pipe; d is the pipe diameter; I is the thermal radiation intensity. The thermal radiation influence region is divided based on the thermal radiation intensity threshold and thermal radiation influence radius model, expressed as follows: In the formula, A1 represents the high-risk area with a 100% mortality rate; R1 is the radius of the high-risk area; A 0.5 This is a medium-risk area with a 50% fatality rate; R 0.5 Radius of the medium-risk zone; A 0.01 Low-risk area, 1% fatality rate, R 0.01 The radius of the low-risk zone.

8. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 7, characterized in that, In step S6, the loss number is determined according to the loss calculation model, and the expression is: N=f t ρ(P in A1+P out A1+0.5×P out A 0.5 +0.01×P out A 0.01 ) In the formula, ρ is the population density; P in ,P out These represent the proportion of people living indoors and outdoors along the pipeline route, respectively, with recommended values ​​of 0.93 and 0.07; f i The ignition probability is 1 under the condition of full-diameter fracture; N is the loss number.

9. The method for assessing the leakage risk of polyethylene gas pipelines according to claim 1, characterized in that, In step S6, the severity level of the consequences is determined, including: If the number of losses is less than 1, the failure consequence level is low; if the number of losses is between [1,3), the failure consequence level is relatively low; if the number of losses is between [3,6), the failure consequence level is medium; if the number of losses is between [6,9), the failure consequence level is relatively high; if the number of losses is ≥9, the failure consequence level is high.

10. A polyethylene gas pipeline leakage risk assessment system, characterized in that, This system is used to regulate the polyethylene gas pipeline leakage risk assessment method according to any one of claims 1-9, and the system includes: The buried gas pipeline leakage failure factor classification module is used to establish a pipeline failure accident tree and classify the leakage failure factor level based on the pipeline failure accident tree. The sample pipeline failure level judgment module is used to calculate the characteristic parameters of the cloud model, collect the actual values ​​of the field pipeline samples, calculate the membership degree and combined weight value of the risk indicators, calculate the comprehensive membership degree based on the membership degree value and weight value, and judge the failure level of the sample pipeline based on the maximum membership degree principle. The leakage rate curve simulation module is used to establish a potential influence radius calculation model applicable to non-metallic polyethylene pipes, and obtain the leakage rate curve of buried pipes through Fluent simulation. The parameter determination module is used to obtain the leakage rate attenuation factor applicable to non-metallic polyethylene pipes by determining the relationship curve between the initial leakage peak rate and the leakage rate at 60s and the pipe diameter and internal pressure. The thermal radiation influence area division module is used to determine the thermal radiation influence radius model and divide the thermal radiation influence area based on the thermal radiation intensity threshold and the thermal radiation influence radius model. The consequences severity level determination module is used to determine the number of losses based on the loss calculation model and to determine the severity level of the consequences based on the number of losses caused by pipeline failure. The gas pipeline leakage risk assessment module is used to assess the leakage risk of polyethylene gas pipelines based on a risk matrix, combining the failure level and the severity level of the consequences.

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