Urban gas pipeline risk assessment method, storage medium and electronic equipment

Abstract

The invention discloses an urban gas pipeline risk assessment method, a storage medium and electronic equipment, and relates to the technical field of risk assessment. The urban gas pipeline risk assessment method comprises the steps that a multi-stage index system is constructed based on risk factors of an urban gas pipeline, and the risk factors comprise human factors, equipment factors, management factors and environmental factors; constructing a fault tree based on the multi-level index system, and performing quantification processing on the events in the fault tree according to risk factor categories corresponding to the events in the fault tree; mapping the fault tree to a Bayesian network in combination with a quantitative processing result, and introducing a time factor into the Bayesian network to obtain a dynamic risk assessment model of the urban gas pipeline; and performing risk assessment on the urban gas pipeline through the dynamic risk assessment model. According to the method, different quantification modes are adopted for the events corresponding to different risk factors, the accuracy of urban gas pipeline risk assessment can be improved, and therefore the safety of an urban gas pipeline network can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of risk assessment, and particularly relates to a city gas pipeline risk assessment method, a storage medium and an electronic device. BACKGROUND

[0002] City gas refers to a clean energy provided to urban residents, commercial users and industrial users through pipelines or other means, mainly including natural gas, liquefied petroleum gas and artificial gas. City gas plays an important role in urban energy supply and is widely used in many fields such as residential life, commercial activities and industrial production. With the acceleration of urbanization and the continuous advancement of infrastructure construction, the demand for city gas will continue to increase, and the huge market demand drives the rapid development of city gas production and supply industry in China. With the expansion of city gas pipeline coverage, gas accidents have become an important problem threatening public safety. Therefore, it is necessary to conduct risk assessment on city gas pipelines. SUMMARY

[0003] The present application provides a city gas pipeline risk assessment method to improve the accuracy of city gas pipeline risk assessment, thereby helping to improve the safety of city gas pipeline network.

[0004] In a first aspect, the present application provides a city gas pipeline risk assessment method, which comprises: based on the risk factors of city gas pipelines, a multi-level index system is constructed, wherein the risk factors include human factors, equipment factors, management factors and environmental factors; based on the multi-level index system, a fault tree is constructed, and the events in the fault tree are quantitatively processed according to the risk factor categories corresponding to the events in the fault tree; the fault tree is mapped to a Bayesian network combined with the quantitatively processed results, and a time factor is introduced into the Bayesian network to obtain a dynamic risk assessment model of the city gas pipeline; the city gas pipeline is risk assessed through the dynamic risk assessment model.

[0005] In some embodiments, for events corresponding to the human factors and the management factors, the quantitative processing process comprises: respectively performing fuzzy processing on a plurality of expert evaluation results of the events to obtain a plurality of fuzzy number parameters; and obtaining a probability value of the event based on the plurality of fuzzy number parameters.

[0006] In some embodiments, the evaluation levels of the events include a plurality of evaluation levels, and the membership function of each evaluation level is represented by the following formula:

[0007]

[0008] wherein, μR(x) represents the membership function, x represents the expert evaluation result, a, b, c and d respectively represent interval division fuzzy numbers.

[0009] In some embodiments, when the number of experts is 5, the obtaining the probability value of the event based on the plurality of fuzzy number parameters comprises:

[0010] The evaluation similarity of the experts is calculated by the following formula:

[0011]

[0012] wherein, S ij represents the evaluation similarity of expert i and expert j, x in , x jn respectively represent the nth fuzzy number in the fuzzy number parameters of expert i, expert j;

[0013] The comprehensive weight is calculated by the following formula:

[0014]

[0015] CCE i = 0.5 x WE i + 0.5 x RAE i

[0016] wherein, AAE i represents the average similarity of the ith expert, AAE k represents the average similarity of the kth expert, RAE i represents the normalized weight of the ith expert, WE i is the weight of the ith expert, w i is the weight score of the ith expert, WE is the weight score of all experts, CCE i represents the comprehensive weight of the ith expert, and M is the number of all experts.

[0017] The probability value is obtained by the following formula:

[0018]

[0019] k = 2.301 x m 1 / 3

[0020] FPr = 10 -k

[0021] wherein, X i represents the fuzzy number parameters of the ith expert, RAG represents the weighted aggregation result, m represents the uncertainty strength of the fuzzy number, k represents the conversion coefficient, and FPr represents the probability value.

[0022] In some embodiments, for the pipeline failure event corresponding to the equipment factor, the quantitative processing process comprises:

[0023] A probability density function of the remaining life of the pipeline is obtained by the following formula:

[0024]

[0025] Wherein, f(t) represents the probability density of the pipeline failure at time t, β represents the failure mode, η represents the characteristic life, corresponding to the time point of the failure probability of 63.2%, F(t) represents the probability of the pipeline failure at time t, f T (t) represents the probability density function of the remaining life T of the pipeline;

[0026] According to the probability density function, the relationship between the corrosion depth of the pipeline and the time, the failure probability of the pipeline is obtained.

[0027] In some embodiments, for the pipeline corrosion event corresponding to the environmental factor, the quantitative processing process comprises:

[0028] The pipeline corrosion rate is predicted by the following formula:

[0029] d(T)=d0+k(T-T0) α

[0030]

[0031] Wherein, d(T) represents the depth of the corrosion defect changing with time, d0 represents the initial corrosion depth, T and T0 respectively represent the exposure time and the corrosion initiation time of the gas pipeline, k and α are empirical constants of the soil-pipeline interaction, and v(T) represents the corrosion rate;

[0032] According to the corrosion rate, the failure probability of the pipeline is obtained.

[0033] In some embodiments, the combination of the quantitative processing result and the mapping of the fault tree to the Bayesian network comprises: corresponding mapping of the top event, the intermediate event, the basic event and the logic gate of the fault tree to the parent node, the intermediate node, the child node and the directed edge; obtaining a directed acyclic graph according to the parent node, the intermediate node, the child node and the directed edge; obtaining the Bayesian network according to the directed acyclic graph and the quantitative processing result.

[0034] In some embodiments, the risk assessment of the urban gas pipeline by the dynamic risk assessment model comprises: calculating the prior probability of each node in the dynamic risk assessment model, and performing risk assessment on the urban gas pipeline based on the prior probability and the directed acyclic graph.

[0035] In a second aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the urban gas pipeline risk assessment method of the first aspect.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor implements the method for risk assessment of urban gas pipeline according to the first aspect when executing the computer program.

[0037] The method for risk assessment of urban gas pipeline, the storage medium and the electronic device provided by the embodiments of the present application firstly construct a multi-level index system based on risk factors of urban gas pipeline, wherein the risk factors include human factors, equipment factors, management factors and environmental factors; then construct a fault tree based on the multi-level index system, and quantitatively process events in the fault tree according to different quantification methods of risk factors corresponding to the events; then map the fault tree to a Bayesian network combined with the quantitatively processed results, and introduce a time factor into the Bayesian network to obtain a dynamic risk assessment model of urban gas pipeline; and finally perform risk assessment on the urban gas pipeline through the dynamic risk assessment model. The method can improve the accuracy of risk assessment of urban gas pipeline by using different quantification methods for events corresponding to different risk factors, thereby helping to improve the safety of urban gas pipeline network.

[0038] Additional aspects and advantages of the present application will be made apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of the method for risk assessment of urban gas pipeline according to an embodiment of the present application;

[0040] Figure 2 is a schematic diagram of a fault tree according to an example of the present application;

[0041] Figure 3 is a schematic diagram of a membership function according to an example of the present application;

[0042] Figure 4 is a mapping relationship diagram of a fault tree to a Bayesian network according to an example of the present application;

[0043] Figure 5 is a schematic diagram of a Bayesian network according to an example of the present application;

[0044] Figure 6 is a schematic diagram of a dynamic risk assessment model according to an example of the present application;

[0045] Figure 7 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] Embodiments of the present application are described below in detail, examples of which are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0047] A city gas pipeline risk assessment method of an embodiment of the present application is described below with reference to the drawings.

[0048] Figure 1 is a flowchart of the city gas pipeline risk assessment method of an embodiment of the present application.

[0049] As shown in Figure 1 , the city gas pipeline risk assessment method comprises:

[0050] S1, based on risk factors of the city gas pipeline, a multi-level index system is constructed, wherein the risk factors include human factors, equipment factors, management factors and environmental factors.

[0051] Among them, the human factors can include operation errors, insufficient training, etc., the equipment factors can include pipeline corrosion, equipment aging, etc., the environmental factors can include geological disasters, surrounding construction, etc., and the management factors can include defects in operation and maintenance strategies, delay in emergency response, etc.

[0052] Specifically, based on the system safety theory and the practical needs of risk management, the present application considers the management factors in combination with the human factors, and this integration embodies the essential characteristics of the “human-management” interaction in the risk generation mechanism. There is a close interaction between people and management in the management system, that is, management is a “framework” and people are “executors”. The rationality of management design directly affects the behavior of people, and the behavior of people will also react to the management effect. In the dynamic risk assessment model, the separation modeling of human factors and management factors will weaken the probability dependence relationship between them. Taking the Bayesian network used in the following text as an example, if “operation procedure missing” (management) and “operation error” (human factor) are set as independent nodes, the amplification effect of management defects on behavior risk will be underestimated. Isolated analysis may lead to systematic blind spots, while collaborative thinking can more comprehensively identify risks and optimize processes.

[0053] Exemplarily, the human factors are operation layer factors, including illegal construction (such as rush construction, not protecting facilities), construction errors (such as insufficient corrosion protection measures), improper construction (such as avoiding geological disaster areas during construction), operator errors (such as improper valve maintenance), and others (such as cast iron pipe rupture, vehicle impact).

[0054] Management factors are divided into management layer factors and decision layer factors. The management layer factors include whether to perform risk assessment and update management processes for changes such as equipment upgrades and process adjustments; whether to perform personnel safety knowledge education and performance evaluation on time; and whether to reasonably command the construction site. The decision layer factors include whether to establish a management system in line with national standards; and whether to establish a safety production rules system with clear responsibilities.

[0055] The equipment factors can be analyzed mainly from the aspects of pipeline body, valve, flange, internal corrosion, corrosion protection system, etc., among which the internal corrosion is a key reason. The environmental factors can be mainly divided into three aspects of external corrosion environment, natural disaster damage and social environment damage, among which the external corrosion factor occupies a dominant position. Generally, external corrosion is an electrochemical process, usually accompanied by anode dissolution, in which electrons are transferred from steel to the surrounding environment. This happens when the pipeline steel reacts with its laying environment and deteriorates.

[0056] Based on the above analysis, a multi-level index system is constructed based on the risk factors of urban gas pipelines as shown in Table 1. Table 1 takes a three-level index system as an example, which not only conforms to the "trajectory intersection theory" in the accident causation theory, completely covers the "person-object-environment-pipe" four types of risk factors, but also meets the time sequence requirement of dynamic risk assessment, and provides a foundation for subsequent dynamic weighting of risk factors through the hierarchical design of three-level indexes. This hierarchical structure not only ensures the comprehensiveness of risk identification, but also provides a clear variable input framework for subsequent model construction.

[0057] Table 1

[0058]

[0059]

[0060] S2, based on the multi-level index system, a fault tree is constructed, and the events in the fault tree are quantitatively processed according to the risk factor categories corresponding to the events in the fault tree.

[0061] Exemplarily, the fault tree constructed based on the three-level index system shown in Table 1 is as shown in Figure 2 .

[0062] Among them, the fault tree can identify the root cause of system failure through logical reasoning and graphical modeling. It takes "top event" (system failure) as the starting point, and decomposes into intermediate events and basic events (component failure or human error) layer by layer, and uses logical gates to describe the causal relationship between events. Because the fault tree adopts "top-down" deductive logic, it forces the analyst to comprehensively analyze all potential causes of the accident, which can reveal the coupling effect of multiple factors, so the present application uses it for risk assessment of urban gas pipelines.

[0063] According to the risk factor category corresponding to the event in the fault tree, different quantification manners are adopted for quantification processing. Thus, the accuracy of risk assessment can be improved.

[0064] Specifically, in some embodiments of the present application, for the event corresponding to the human factor and the management factor, the quantification processing process comprises: respectively performing fuzzy processing on the multiple expert evaluation results of the event to obtain multiple fuzzy number parameters; and obtaining the probability value of the event based on the multiple fuzzy number parameters.

[0065] Exemplarily, the evaluation grades of the event include multiple, and the membership function of each evaluation grade is represented by the following formula:

[0066]

[0067] Wherein, μR(x) represents the membership function, x represents the expert evaluation result, a, b, c and d respectively represent the interval division fuzzy number.

[0068] Exemplarily, obtaining the probability value of the event based on the multiple fuzzy number parameters comprises:

[0069] The evaluation similarity of the expert is calculated by the following formula:

[0070]

[0071] Wherein, S ij represents the evaluation similarity of the expert i and the expert j, x in , x jn respectively represent the nth fuzzy number in the fuzzy number parameters of the expert i and the expert j;

[0072] The comprehensive weight is calculated by the following formula:

[0073]

[0074] CCE i = 0.5 × WE i + 0.5 × RAE i (6)

[0075] Wherein, AAE i represents the average similarity of the ith expert, AAE k represents the average similarity of the kth expert, RAE i represents the normalized weight of the ith expert, WE i is the weight of the ith expert, w i is the weight score of the ith expert (which is a preset value), WE is the weight score of all experts (i.e. the sum of the weight scores of all experts), CCE i represents the comprehensive weight of the ith expert;

[0076] The probability value is obtained by the following formula:

[0077]

[0078] k(i) = 2.301 x m 13 (9)

[0079] FPr = 10 -k (10)

[0080] Wherein, X i represents the fuzzy number parameter of the i-th expert, RAG represents the weighted aggregation result, m represents the uncertainty intensity of the fuzzy number, k represents the conversion coefficient, and FPr represents the probability value.

[0081] Specifically, the human factors and management factors are difficult to obtain the failure probability through the database, so the expert opinions can be processed by introducing the trapezoidal fuzzy number and the triangular fuzzy number to quantize the expert opinions, and the failure probability is obtained by defuzzification, and the specific steps are as follows:

[0082] Firstly, in order to facilitate the experts to give objective evaluation results, seven evaluation grades {very low (VL), low (L), slightly low (RL), medium (M), slightly high (RH), high (H), very high (VH)} are introduced as shown in Table 2, and the evaluation results of the experts are processed by trapezoidal (triangular) fuzzy number, and the corresponding relationship between each evaluation grade and membership function is shown in Table 2, wherein the trapezoidal (triangular) fuzzy number is represented as R = (a, b, c, d), and the membership function is shown in the above formula (1). In formula (1), a and d represent the lower limit and the upper limit of the estimation domain respectively, and the interval [b, c] represents the region with a membership degree of 1. When b = c, the membership function is a triangular fuzzy membership function. Figure 3

[0083] Table 2

[0084]

[0085] Secondly, M experts (such as 5 experts) including senior experts in the comprehensive pipe gallery industry, gas pipe network research or design personnel with senior titles, comprehensive pipe gallery research or design personnel, etc. are invited to form an expert group, and the events are analyzed and evaluated by the experts.

[0086] Firstly, the evaluation similarity of the experts is calculated by the above formula (2) to provide a basis for subsequent weight distribution. In formula (2), the value range of S XY is [0, 1], 1 represents complete consistency, and 0 represents complete inconsistency. The formula is calculated to identify the expert group with similar opinions and reduce the interference of extreme opinions.

[0087] ​Then, the comprehensive weight is generated by combining the preset weight score (such as expert authority) and the similarity weight through the above formulas (3)-(6).

[0088] In the third step, the fuzzy evaluation of multiple experts is aggregated into group opinions according to the weight, and then the group opinions are converted into precise probability values for decision-making. The precise probability values are calculated through the above formulas (7)-(10). The coefficient 2.031 in formula (9) is an empirical value for calibrating the probability range.

[0089] In some embodiments of the present application, for the pipeline failure event corresponding to the equipment factor, the quantitative processing process includes: obtaining the probability density function of the pipeline residual life through the following formulas (11)-(13), and obtaining the pipeline failure probability according to the relationship between the pipeline corrosion depth and time based on the probability density function.

[0090]

[0091] wherein f(t) represents the probability density of the pipeline failure at time t, β represents the failure mode (β<1 for early failure, β=1 for random failure, and β>1 for aging failure), η represents the characteristic life corresponding to the time point with a failure probability of 63.2%, and F(t) represents the probability of the pipeline failure within time t, f T (t) represents the probability density function of the pipeline residual life T.

[0092] Exemplarily, the relationship between the pipeline corrosion depth and time is obtained through in-line inspection (ILI) or spot thickness measurement. The relationship between the pipeline corrosion depth and time can be a linear relationship, as shown in the following formula (14), or a power law relationship, as shown in the following formula (15).

[0093]

[0094] wherein CR represents the average corrosion rate (mm / year), d final represents the pipeline depth at time t, d initial represents the initial pipeline depth.

[0095] d(t) = a*t b (15)

[0096] wherein a is a scale parameter, which is related to the concentration of the corrosion medium; and b is a shape parameter, which reflects the corrosion acceleration trend (b>1 for accelerated corrosion, and b<1 for decelerated corrosion).

[0097] The failure probability can be the probability that the corrosion depth d(t) exceeds the critical wall thickness dref. Taking the power law relationship as an example, the positioning failure condition is g(t)=dref-d(t)≤0, and the failure probability is: In combination with the above formula (13), the failure probability can be obtained as follows:

[0098]

[0099] In some embodiments of the present application, for the pipeline corrosion event corresponding to the environmental factors, the quantitative processing process comprises: predicting the pipeline corrosion rate by the following formula (17)-(18), and obtaining the pipeline failure probability according to the corrosion rate.

[0100] d(T) = d0 + k(T-T0) α (17)

[0101]

[0102] wherein d(T) represents the depth of the corrosion defect changing with time, d0 represents the initial corrosion depth, T and T0 represent the exposure time and the corrosion initiation time of the gas pipeline respectively, k and a are empirical constants of the soil-pipeline interaction, and v(T) represents the corrosion rate.

[0103] Exemplarily, the pipeline failure probability can be obtained according to the corrosion rate by using a pre-obtained failure probability model. Specifically, the corrosion rate is input into the failure probability model, and the output of the model is the pipeline failure probability.

[0104] wherein the failure probability model can be an exponential relationship function between the corrosion rate and the pipeline failure probability fitted by historical data: Pf is the failure probability, and λ and β are material characteristic coefficients.

[0105] S3, the fault tree is mapped to the Bayesian network in combination with the quantitative processing result, and the time factor is introduced into the Bayesian network to obtain the dynamic risk assessment model of the urban gas pipeline.

[0106] The fault tree can generally only process binary logic, such as failure or normal, occurrence or non-occurrence, and assumes that the events are completely independent or strictly determined, and it is difficult to explicitly model the dependency relationship between events, and it is difficult to fit the actual situation in application, and the fault tree can only deduce the top event from the basic event, and cannot be reversely inferred. Therefore, the fault tree is mapped to the Bayesian network, which not only can upgrade the static logic model to the dynamic probability model to process uncertainty, dependency relationship and multi-state problem, but also can bidirectional diagnosis, and through the understanding of part of information, the posterior probability is calculated by using reverse reasoning to identify the most possible root cause of the accident.

[0107] In some embodiments of the present application, mapping the fault tree to the Bayesian network in combination with the quantization processing result comprises: mapping the top event, the intermediate event, the basic event and the logic gate of the fault tree into the parent node, the intermediate node, the child node and the directed edge correspondingly; obtaining a directed acyclic graph according to the parent node, the intermediate node, the child node and the directed edge; and obtaining the Bayesian network according to the directed acyclic graph and the quantization processing result.

[0108] Specifically, the mapping process of the fault tree to the (static) Bayesian network is as shown in Figure 4 , and the obtained Bayesian network is as shown in Figure 5 . The core idea of the Bayesian network is to describe uncertainty through probability, and to constantly update the belief (posterior probability) of a hypothesis using known information (prior knowledge) and new data (likelihood). The Bayesian network is a directed acyclic graph composed of nodes and conditional probabilities, and each node in the Bayesian network corresponds to a random variable in the set. If there is a directed arc from one node to another node, and the conditional probability of any node variable is only related to its parent node variable, then the node is called the parent node of the other node. The probability formula of the Bayesian network is as shown in the following formula (19):

[0109]

[0110] wherein P(X|Y) represents the conditional probability of event X, i.e. the probability of event X occurring given that event Y occurs; P(Y|X) represents the conditional probability of event Y, i.e. the probability of event Y occurring given that event X occurs. P(X) and P(Y) represent the prior probability of events X and Y, i.e. the marginal probability.

[0111] The dynamic risk assessment model is a probability distribution model combining the (static) Bayesian network with the Markov model, combining the static probabilistic graph model with time series analysis, and modeling the dynamic evolution relationship between variables by introducing time factors. The dynamic risk assessment model is composed of a plurality of static Bayesian networks (usually referred to as time slices), as shown in Figure 6 . The horizontal arrow represents the relationship between the same variable at different time slices, and the vertical arrow represents the relationship between different variables at the same time slice. The dynamic Bayesian network can be defined as B0 represents the initial Bayesian network, which defines the prior probability distribution P(X t =0) of the variable, X t =(U t , H t , O t ) is the input, hidden and output variable of the model at time step t; is the transition network, which contains the Bayesian networks of two adjacent time slices, and determines the probability distribution as:

[0112]

[0113] wherein, Xi represents the ith node at the tth time slice, Xi-1 represents the parent node of Xi, N represents the number of variables in the network.

[0114] The dynamic risk assessment model satisfies the following assumptions:

[0115] 1) Markov assumption: the occurrence probability of each node at each time slice is only related to the occurrence probability of the node at the previous time slice, and is irrelevant to other time slices.

[0116] P(X t+1 |X1,X2,Λ,X t )=P(X t+1 |X t ) (21)

[0117] 2) Homogeneous assumption: in a limited time, the random process is stationary and invariant, that is, the conditional probability remains consistent for any time t.

[0118] P(X t1+1 |X t1 )=P(X t2 |X t2 ) (22)

[0119] According to the assumptions when establishing the dynamic Bayesian network, in a limited time, the conditional probability in the dynamic risk assessment model changes stably, and the system network topology structure is repeatedly, the structure of the dynamic risk assessment model is extended to any Tth network, so that the joint probability distribution of any node of the T network is obtained:

[0120]

[0121] wherein, Xi represents all nodes of the system random variable from the initial time to the Tth time, Xi represents the state of the system random variable at the initial time, Xi represents the state of the system random variable at the tth time, Xi represents the state probability distribution of the system at the initial time, Xi represents the state probability distribution of the system at the Tth time.

[0122] S4, risk assessment of the urban gas pipeline by the dynamic risk assessment model.

[0123] In some embodiments of the present application, the city gas pipeline is evaluated by a dynamic risk assessment model, comprising: calculating the prior probability of each node in the dynamic risk assessment model, and evaluating the risk of the city gas pipeline based on the prior probability and the directed acyclic graph.

[0124] Specifically, the traditional Bayesian network needs to define the conditional probability table for all parent node combinations of each node. If a node has n binary parent nodes, the conditional probability table needs 2n parameters, which is complex to calculate and difficult to learn. Taking gas pipeline failure analysis as an example, if only relying on the traditional Bayesian network, the combination probability of all corrosion, construction, temperature and other factors needs to be exhausted, and it is difficult to build a reliable model when data is insufficient. Therefore, the present application introduces the missing probability, and assumes that the causal effects of the parent nodes are independent and superimposed. The parent nodes X L and the rest are X ALL , and their corresponding connection probabilities are P L and P ALL , the probability of failure of the child node Y can be expressed as:

[0125] P(Y|X L )=1-(1-P L )(1-P ALL )=P L +P ALL -P L P ALL (24)

[0126]

[0127] By combining equation (24) and equation (25), the connection probability Pi of each root node is calculated as:

[0128]

[0129] After obtaining the connection probability Pi of each root node by equation (26), the uncertainty factor X L is combined with the connection probability P L , and the prior probability of the node Y is obtained as:

[0130]

[0131] Exemplarily, the prior probability of each event obtained according to the calculation results of the above calculation process is shown in Table 3 as follows:

[0132] Table 3

[0133]

[0134]

[0135] It should be noted that the prior probability of the intermediate events (i.e. intermediate nodes) M1-M26 can be calculated from the basic events (i.e. child nodes) F1-F40.

[0136] After obtaining the prior probability corresponding to the basic events, the possibility of event occurrence is ranked according to the probability of expert evaluation and is divided into low, medium, high and extra-high, as shown in Table 4:

[0137] Table 4

[0138] Probability rating Event likelihood Probability of occurrence 1 Low ＜0.001 2 Medium 0.0125 3 High 0.05 4 Very high ＞0.125

[0139] It can be seen from this standard that although the event occurrence probability of the basic events that may cause the city gas pipeline leakage is mostly in level 1 and level 2, it does not mean that the accident will not occur. The prior probability refers to the initial estimate of the possibility of an event occurring when there is a lack of observation data or new evidence, which is the core link of the Bayesian network from "theoretical framework" to "practical tool". The prior probability is only a quantitative index obtained by expert fuzzy scoring, experience rules and the like for the existing conditions. According to the constructed Bayesian network and the input prior probability of the basic events, the gas pipeline failure probability obtained after the model is run is about 0.2794, which is much greater than the extra-high bottom line in the evaluation standard, and has considerable potential risk hidden dangers. Therefore, the present application also performs reverse reasoning on the Bayesian network to obtain the posterior probability, and analyzes various risk factors affecting the system failure in detail.

[0140] Exemplarily, in the Bayesian network model, assuming that the probability of failure of the gas pipeline is 100%, reverse reasoning is performed to obtain the posterior probability. It is found that the probability of occurrence of small cathodic protection distance (Z3), poor pigging effect (Z10), flange failure (Z23), foundation subsidence (Z25), not timely discovery (Z36) and not timely treatment (Z37) is relatively large, the posterior probability of Z36 is the largest, about 11.31%, the posterior probability of Z37 is the second, about 8.98%, the posterior probabilities of Z25, Z3, Z23 and Z10 are successively smaller, and are 6.80%, 6.01%, 5.80% and 4.62% respectively. Therefore, in normal treatment, prevention should be focused on the above aspects, for example, for small cathodic protection distance, periodic evaluation of the efficiency of the cathodic protection system can be carried out, potential measurement and data checking are performed at least once every quarter, and according to the results of the periodic inspection, in the area where the soil resistivity is high, low-resistivity material (such as coke powder) is backfilled or the pipeline is wrapped with an insulating coating to reduce current loss; and for poor pigging effect, different pigging devices and pigging cycles should be adopted according to the material of the pipeline and the characteristics of the medium in the pipeline, and at the same time, according to the current technological development, in-wall pipeline sensor or acoustic monitoring equipment can be deployed to real-time feedback the running state of the pig and the pigging effect, so as to avoid “ineffective pigging”. For other dangerous factors with relatively small probability of occurrence, the potential consequences may be amplified due to coupling effect or extreme conditions, so these events cannot be completely ignored, and can be classified into monitoring range by classified measures, dynamic monitoring and systematic defense, so that the whole life cycle management and control of the urban gas pipeline risk can be truly realized.

[0141] It should be noted that when a dynamic risk assessment model is used to analyze a complex system (such as pipeline corrosion failure risk), key variables with relatively high possibility should be selectively focused on instead of all basic events, because in actual accident analysis, the possibility of occurrence of each basic event may change dynamically, i.e. change over time, which leads to exponential growth of the number of variables and state combinations, explosive consumption of computing resources for full modeling, and difficulty in covering all possible scenarios with actual data. For example, in the analysis of pipeline corrosion, the core variables that can directly affect the corrosion rate are selected, which can capture the main risk driving mechanism and avoid unnecessary analysis and redundancy introduced by secondary factors (such as soil color and installation year). This strategy selects key variables through expert experience or sensitivity analysis, which not only retains the core logic of dynamic evolution (such as the accumulation of corrosion depth over time), but also ensures the feasibility and interpretability of the model in real-time monitoring or long-term prediction, and finally realizes accurate risk decision-making with minimum computing cost.

[0142] The beneficial effects of the urban gas pipeline risk assessment method of the present application are illustrated below by a specific example of an explosion accident of an urban gas pipeline:

[0143] The direct cause of the accident is that the gas pipeline involved in the accident is corroded by soil and affected by a humid environment due to a lack of maintenance for a long time, resulting in pipe wall damage, continuous leakage of natural gas into a closed space, and the leakage point being located below the market, and the accumulated leakage gas in the poorly ventilated building structure reaches the explosion limit concentration. The indirect causes are (1) the market in the explosion area is a non-compliant covered building, and the ventilation system is not designed according to the specification, and the underground space is completely closed; (2) the market is densely populated, and personnel activities are frequent, but no gas leakage monitoring and alarm device is installed, and the danger cannot be discovered in time; (3) the gas supply unit does not perform the responsibility of regular detection, maintenance and replacement of old pipelines, and the daily inspection is formal; (4) the city planning management is chaotic, allowing the market to be built illegally above the gas pipeline, and the departments of housing construction, emergency, market supervision and others have been superficial in safety inspection of gas enterprises for a long time. The relevant information of the gas pipeline at the scene of the accident is shown in Table 5

[0144] Table 5

[0145] Node Parameter Node Parameter Resistivity 80.63339 Ω·m Moisture content 16.95％ pH 6.83 Oxidation-reduction potential 166.152 mV Sulfate content 0.02496％ Self-corrosion potential -688.72 mV Chloride content 0.01254％ Soil type Loam Emergency response Inadequate response Threatened personnel ＞50 Underground space distribution ≤ 3

[0146] As can be seen from Table 5, the pipeline at the scene of the accident has undergone considerable changes in its own properties and the properties of the soil in which it is buried after many years of operation. Among them, the parameters that have a greater impact on corrosion, such as moisture content, are in a very high state, the oxidation-reduction potential is high, and the self-corrosion potential is also very high, which has led to gradual deepening of corrosion under the combined action of various factors, and eventually led to pipeline damage, leakage accident and heavy casualties.

[0147] Based on the above information, a dynamic risk assessment model is constructed using the method of the present application. The probability of gas pipeline leakage is set to 100% for reverse reasoning, and the posterior probability of each event in the accident is shown in Table 6:

[0148] Table 6

[0149] Event Posterior probability Event Posterior probability Pipe perforation 0.414 Electrochemical corrosion environment 0.048 Pipe rupture 0.547 External coating failure 0.152 Pipe corrosion 0.317 Internal corrosion environment 0.072 Pipe defect 0.099 Internal coating failure 0.072 Valve failure 0.102 Weld defect 0.001 Natural disaster damage 0.172 Installation defect 0.038 Stress damage 0.317 Trench design defect 0.023 Third-party damage 0.040 Design error 0.009 Buried corrosion 0.171 Failure between packing and valve stem 0.014 Internal corrosion 0.148 Flange failure 0.084 Pipe quality defect 0.019 Valve body failure 0.009 Improper construction 0.075 Foundation subsidence 0.099 Severe pressure accumulation 0.028 Operator error 0.019 Rough third-party construction 0.063 Failure of anti-misoperation measures 0.003 Long-term pressure on pipeline 0.271 Inadequate operation specification 0.003 Vehicle rolling 0.015 Not found in time 0.164 External corrosion environment 0.014 Not handled in time 0.131 Chemical corrosion environment 0.005

[0150] As can be seen from Table 6, among the basic events considered, the posterior probabilities of chemical corrosion environment, electrochemical corrosion environment, and external corrosion failure are significantly greater than those of other events, which indicates that this accident is most likely caused by chemical corrosion environment and electrochemical corrosion environment, i.e., external corrosion and external corrosion failure. According to the accident causes mentioned above, the water content and soil salinity are significantly higher than usual due to the closed space and the influence of the adjacent river for a long time, which has led to gradual deepening of environmental corrosion of the gas pipeline and eventually caused the pipeline to perforate. The conclusion is consistent, so it can be verified that the method of the present application can accurately assess the risk of urban gas pipelines.

[0151] The city gas pipeline risk assessment method of the embodiment of the present application realizes accurate transition from qualitative description to quantitative analysis of risk identification by adopting different quantitative processing modes for different risk factors, and lays a foundation for dynamic risk assessment model construction. The dynamic risk assessment model of the city gas pipeline is constructed based on the fault tree and the Bayesian network, including: the bottom causes of the top event (such as pipeline failure) are identified through fault tree logical decomposition, and are mapped into the Bayesian network to process the multi-state dependency problem, and the time dimension is introduced to establish the dynamic risk assessment model, realize the time sequence evolution analysis of the risk probability, and solve the limitations of the static nature and independent assumption of the traditional model, and provide a theoretical tool for dynamic risk early warning.

[0152] Based on the city gas pipeline risk assessment method of the above embodiment, the present application provides a computer readable storage medium.

[0153] In the embodiment of the present application, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the city gas pipeline risk assessment method of the above embodiment.

[0154] Figure 7 is a structural block diagram of the electronic device of the embodiment of the present application.

[0155] As shown in Figure 7 , the electronic device 500 includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, such as through a bus 502. Optionally, the electronic device 500 can also include a transceiver 504. It should be noted that in actual application, the transceiver 504 is not limited to one, and the structure of the electronic device 500 does not constitute a limitation on the embodiments of the present application.

[0156] The processor 501 can be a CPU (Central Processing Unit, central processing unit), a general-purpose processor, a DSP (Digital Signal Processor, digital signal processor), an ASIC (Application Specific Integrated Circuit, application specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the present disclosure. The processor 501 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0157] The bus 502 can include a path that transmits information between the above-described components. The bus 502 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 502 can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 7 Only one thick line is used to represent the bus in the middle, but it does not mean that there is only one bus or one type of bus.

[0158] The memory 503 is configured to store a computer program corresponding to the urban gas pipeline risk assessment method of the above-embodiment of the present application. The processor 501 is configured to execute the computer program stored in the memory 503 to realize the content shown in the above-embodiment.

[0159] The electronic device 500 includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet PC), a PMP (Portable Multimedia Player), a car terminal (for example, a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. Figure 7 The electronic device 500 shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.

[0160] It is to be appreciated that the above description and the examples that follow are intended to be illustrative only and that changes can be made to the description, either functionally or chronologically, as well as changes being made concerning the order of implementation. The logic and / or steps represented in the flow diagrams and / or described herein can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus) or a propagation medium. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Note that the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0161] It is to be understood that the various parts of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following techniques, which are well known in the art of making integrated circuits, can be used alone or in any combination to implement the application: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth.

[0162] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The illustrative appearances of the above-mentioned terms in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0163] In the description of the application, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application.

[0164] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise explicitly specified and limited.

[0165] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements, unless otherwise explicitly limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0166] In the present application, unless otherwise explicitly specified and limited, the first feature "on" or "under" the second feature can be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature "above", "above" and "above" the second feature can be that the first feature is directly above or obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "below" and "below" the second feature can be that the first feature is directly below or obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0167] Although the embodiments of the application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as a limitation on the application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the application.

Claims

1. A method for risk assessment of a city gas pipeline, characterized by, The method comprises: Based on the risk factors of urban gas pipeline, a multi-level index system is constructed, wherein the risk factors include human factors, equipment factors, management factors and environmental factors; Based on the multi-level index system, a fault tree is constructed, and events in the fault tree are quantitatively processed according to the risk factor categories corresponding to the events in the fault tree; The fault tree is mapped to a Bayesian network combined with the quantitative processing results, and a time factor is introduced into the Bayesian network to obtain a dynamic risk assessment model of the urban gas pipeline; The dynamic risk assessment model is used to assess the risk of the urban gas pipeline.

2. The urban gas pipeline risk assessment method according to claim 1, characterized in that, For events corresponding to the human factors and the management factors, the quantitative processing process comprises: Fuzzy processing is performed on a plurality of expert evaluation results of the events respectively to obtain a plurality of fuzzy number parameters; The probability value of the event is obtained based on the plurality of fuzzy number parameters.

3. The urban gas pipeline risk assessment method according to claim 2, characterized in that, The evaluation levels of the event include a plurality of levels, and the membership function of each evaluation level is represented by the following formula: Wherein, μR(x) represents the membership function, x represents the expert evaluation result, a, b, c and d respectively represent interval division fuzzy numbers.

4. The urban gas pipeline risk assessment method according to claim 2, characterized by, The probability value of the event is obtained based on the plurality of fuzzy number parameters, comprising: The evaluation similarity of the experts is calculated by the following formula: where S ij represents the evaluation similarity between expert i and expert j, x in , x jn represent the nth fuzzy number in the fuzzy number parameters of expert i, expert j, respectively; The comprehensive weight is calculated by the following formula: CCE i = 0.5 x WE i + 0.5 x RAE i wherein AAE i represents the average similarity of the i-th expert, AAE k represents the average similarity of the k-th expert, RAE i represents the normalized weight of the i-th expert, WE i is the weight of the i-th expert, w i is the weight score of the i-th expert, WEis the weight score of all experts, CCE i represents the comprehensive weight of the i-th expert, M is the number of all experts; The probability value is obtained by the following formula: k = 2.301 x m 1 / 3 FPr = 10 -k wherein X i represents the fuzzy number parameter of the i-th expert, RAG represents the weighted aggregation result, m represents the uncertainty strength of the fuzzy number, k represents a conversion coefficient, and FPr represents the probability value.

5. The urban gas pipeline risk assessment method according to claim 1, characterized by, For pipeline failure events corresponding to the equipment factors, the quantitative processing process comprises: The probability density function of the remaining life of the pipeline is obtained by the following formula: Wherein, f(t) represents the probability density of the failure of the pipeline at time t, β represents the failure mode, η represents the characteristic life, the time point corresponding to the failure probability of 63.2%, F(t) represents the probability of the failure of the pipeline at time t, f T (t) represents the probability density function of the remaining life T of the pipeline; According to the relationship between the probability density function, the corrosion depth of the pipeline and the time, the pipeline failure probability is obtained.

6. The urban gas pipeline risk assessment method according to claim 1, characterized by, For pipeline corrosion events corresponding to the environmental factors, the quantitative processing process comprises: The pipeline corrosion rate is predicted by the following formula: d(T) = d0 + k(T - T0) α Wherein, d(T) represents the depth of the corrosion defect changing with time, d0 represents the initial corrosion depth, T and T0 respectively represent the exposure time and the corrosion initiation time of the gas pipeline, k and α are empirical constants of soil-pipeline interaction, and v(T) represents the corrosion rate; The pipeline failure probability is obtained according to the corrosion rate.

7. The urban gas pipeline risk assessment method according to any one of claims 1 to 6, characterized in that, The fault tree is mapped to the Bayesian network combined with the quantitative processing results, comprising: The top event, intermediate event, basic event and logic gate of the fault tree are correspondingly mapped to parent node, intermediate node, child node and directed edge; A directed acyclic graph is obtained according to the parent node, the intermediate node, the child node and the directed edge; The Bayesian network is obtained according to the directed acyclic graph and the quantitative processing results.

8. The urban gas pipeline risk assessment method according to claim 7, characterized in that, The dynamic risk assessment model is used to assess the risk of the urban gas pipeline, comprising: The prior probability of each node in the dynamic risk assessment model is calculated, and the risk of the urban gas pipeline is assessed based on the prior probability and the directed acyclic graph.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the urban gas pipeline risk assessment method according to any one of claims 1-8.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the urban gas pipeline risk assessment method according to any one of claims 1-8.

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