CN116911594A - Method and device for evaluating leakage emergency repair risk of gas pipeline - Google Patents
Method and device for evaluating leakage emergency repair risk of gas pipeline Download PDFInfo
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Abstract
The invention provides a gas pipeline leakage emergency repair risk evaluation method and device, wherein the method comprises the following steps: acquiring risk factors existing in a basic stage; taking the risk factors as root nodes, taking the gas pipeline leakage accident emergency repair as top nodes, and establishing a Bayesian network model; assigning scores to the risk factors to obtain corresponding scores, describing the scores as corresponding trapezoidal fuzzy numbers, and processing the trapezoidal fuzzy numbers to obtain prior probability of the root node; reverse reasoning is carried out on the Bayesian network model, so that posterior probability of the root node is obtained; calculating the probability change rate of the root node according to the prior probability and the posterior probability; and determining a target risk factor according to the calculated probability change rate. The probability change rate is larger, the risk value is higher, and the risk factor with the greatest influence on the gas pipeline leakage repair operation is obtained, so that the prevention is facilitated in advance, and the smooth implementation of the emergency repair operation is ensured.
Description
Technical Field
The invention relates to the technical field of risk evaluation, in particular to a gas pipeline leakage emergency repair risk evaluation method and device.
Background
The natural gas resources in China are abundant, and the natural gas drives related industries to develop rapidly by virtue of the advantages of cleanness, high efficiency and wide application, so that the application range is expanded from civil cooking to various fields of heating, refrigeration, power generation, industry and fuel automobiles. Meanwhile, the rapid development of the urban gas industry leads to a great increase in gas demand, and in recent years, the consumption of urban gas and the pipe network scale are in a positive growth proportion, the production time of underground pipelines is long, and the urban gas is influenced by engineering construction, geological settlement, stray current interference and environmental corrosion factors, so that gas pipeline leakage accidents occur. The leakage of the gas pipeline not only causes a great deal of gas loss, but also directly affects the economic benefit of enterprises, and even when the gas quantity in the space reaches the explosion limit, the gas explosion occurs when the gas quantity meets open fire, thereby causing huge loss of lives and properties of personnel. In the emergency repair process of the gas pipeline leakage accident, risk factors are different under different conditions, and the smooth progress of the emergency repair process is hindered by improper personnel operation, surrounding environment and weather problems, so that the risk is increased, and even the life safety of emergency repair workers is threatened. Therefore, in order to avoid the adverse consequences caused by the leakage of the gas pipeline, the safety of the repair operation is ensured, and the risk evaluation of the emergency repair process of the gas pipeline leakage accident is particularly important.
The evaluation method applied to the leakage accident of the gas pipeline at present comprises the following steps: accident tree analysis, hierarchical analysis, bow-tie model and bayesian network analysis. Among these methods, the accident tree analysis method (FaultTree Analysis, abbreviated as FTA) is also called as a fault tree analysis method, is a tree-like deduction method from top to bottom, and as a multi-factor analysis method, the FTA can systematically and comprehensively perform risk identification and evaluation to describe causal relationships of occurrence of problems, but basic events of the accident tree are usually determined, and it is difficult to obtain correct results for constructing the accident tree for complex problems. The analytic hierarchy process decomposes elements related to decision into a target layer, a criterion layer and an index layer, performs qualitative and quantitative analysis, but cannot analyze the mutual influence degree among events, and the like, and the analytic hierarchy process is applied to the leakage emergency repair process of the long-distance natural gas pipeline by using the analytic hierarchy process such as sewei flat, fang Jianping and the like to perform risk analysis, but compared with the leakage of a community gas pipeline, the emergency repair risk factors are not comprehensive enough, and the emergency treatment process is different. The Bow-tie model integrates an accident tree analysis method and an event tree analysis method, comprehensively and systematically analyzes target risk factors and accident results, and can provide preventive measures for the target risk factors to form management control, but the uncertain logic relationship cannot be analyzed, related historical data are needed during analysis and calculation, and the accident risk grade can be obtained only under the condition that the occurrence probability of basic events is sufficient.
Aiming at the research of the emergency repair process of the gas pipeline leakage accident, the existing achievements are concentrated on the emergency repair decision system, the reliability of emergency repair personnel, the applicability of emergency repair equipment and material distribution and an emergency repair method, and the risk evaluation research in the leakage emergency repair process is few.
The Bayesian network is based on a probability theory, and can graphically represent the relation between variables, wherein the input nodes and the output nodes are uncertain and mutually influence, any node change has influence on other nodes, and the influence can be estimated and predicted through Bayesian network reasoning. However, in terms of gas pipeline leakage risk evaluation, the current Bayesian network is limited to the identification of risk factors causing leakage, and no risk evaluation in the rush repair process of community gas pipeline leakage accidents exists.
As shown in fig. 1-3, fang Jianping performs risk evaluation on the construction stage by applying the GeNIe software in the emergency repair process of the long-distance natural gas pipeline leakage accident, but does not specifically describe how to obtain the prior probability of the root node, and the risk factors are different from the urban gas pipeline, and the ratio of the posterior probability to the prior probability is used as a judgment basis in the determination of the target risk factors.
Disclosure of Invention
In view of the above problems, the present invention has been made to provide a method and apparatus for evaluating risk of gas pipeline leakage repair that overcomes or at least partially solves the above problems.
The invention provides a gas pipeline leakage rush repair risk evaluation method, which comprises the following steps:
acquiring risk factors existing in a basic stage of emergency repair of a gas pipeline leakage accident, wherein the basic stage comprises the following steps: a warning receiving and disposing stage, a job preparing stage, an implementation repairing stage and a later recovery stage;
taking the risk factors as root nodes, taking the alarm receiving treatment stage, the operation preparation stage, the implementation restoration stage and the later recovery stage as intermediate layer nodes, taking the gas pipeline leakage accident emergency repair as top layer nodes, and establishing a Bayesian network model;
assigning scores to the risk factors to obtain corresponding scores, describing the scores as corresponding trapezoidal fuzzy numbers, and processing the trapezoidal fuzzy numbers to obtain prior probabilities of root nodes;
reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained;
calculating the probability change rate of the root node according to the prior probability and the posterior probability;
and determining a target risk factor according to the calculated probability change rate so as to realize the risk evaluation of the leakage emergency repair of the gas pipeline.
Further, the risk factors include: the method has the advantages of incomplete knowledge of dangerous situations, delay of reaching the scene, inaccurate information transmission, incomplete configuration of emergency detection equipment, insufficient experience of an expert group, traffic accidents, unclear division of workers, unsmooth matching of a plurality of units, unsophisticated emergency resource calling, incomplete warning facilities, incomplete evacuation of surrounding workers, unsmooth detection of gas leakage concentration, disordered placement of field equipment, too high gas diffusing speed, difficult discovery of leakage points, incomplete temporary disposal of leakage points, improper operation of operators, failure of emergency equipment, improper on-site command, imperfect regulation and system, unstable operation pressure control, welding quality problems, health problems of operators, inappropriately protective measures and air leakage caused by too fast re-compression.
Further, the assigning the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, and processing the ladder ambiguity numbers to obtain prior probabilities of root nodes, including:
dividing the risk level of the risk factors, and assigning a score to the divided risk factors to obtain corresponding scores;
the trapezoidal fuzzy number is used as a membership function of fuzzy language, the trapezoidal fuzzy number is expressed by a, b, c and d parameters, the fuzzy set is A= (a, b, c, d), and the membership function expression is as follows:
the comment set S= { S with preset granularity and generated by the score 0 ,s 1 ,s 2 ,…,s g Ith element s in } i Described as corresponding trapezoidal blur number A i ；
Ladder ambiguity number A i The expression is as follows:
wherein g is a positive integer, i=0, 1, …, g;
and processing the trapezoidal fuzzy number by using an arithmetic average method to obtain the probability of the trapezoidal fuzzy number, wherein the expression is as follows:
wherein n is a positive integer;
processing the trapezoidal fuzzy number probability to obtain a weight, wherein the weight P i The expression is as follows:
and multiplying the weight by a preset coefficient to obtain the prior probability of the root node.
Further, the assigning the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, and processing the ladder ambiguity numbers to obtain prior probabilities of root nodes, further includes:
utilizing directed edges to connect nodes with causal relation, and establishing a directed acyclic graph;
and establishing a conditional probability table among the nodes according to the pre-acquired OR logic gates of the fault tree.
Further, the method further comprises the following steps:
inputting the prior probability of the root node, the conditional probability of the middle layer node obtained by the conditional probability table and the conditional probability of the top layer node obtained by the conditional probability table into the Bayesian network model through Bayesian software to obtain a forward reasoning result, and predicting the occurrence probability of the risk event according to the forward reasoning result.
Further, the reverse reasoning is performed on the bayesian network model through bayesian software to obtain posterior probability of the root node, including:
the posterior probability of the root node is obtained by setting evidence for the top node in the Bayesian network model based on Bayesian software;
further, the calculating the probability change rate of the root node according to the prior probability and the posterior probability includes:
wherein P is 2 For posterior probability and P 1 Is a priori probability.
In a second aspect of the present invention, there is provided a gas pipeline leakage rush repair risk assessment device, the device comprising:
the acquisition module is used for acquiring risk factors existing in a basic stage of the emergency repair of the gas pipeline leakage accident, and the basic stage comprises the following steps: a warning receiving and disposing stage, a job preparing stage, an implementation repairing stage and a later recovery stage;
the establishing module is used for taking the risk factors as root nodes, taking the alarm receiving treatment stage, the operation preparation stage, the implementation restoration stage and the later restoration stage as intermediate layer nodes, taking the gas pipeline leakage accident emergency repair as top layer nodes and establishing a Bayesian network model;
the scoring module is used for scoring the risk factors to obtain corresponding scores, describing the scores as corresponding trapezoidal fuzzy numbers, and processing the trapezoidal fuzzy numbers to obtain the prior probability of the root node;
the reverse reasoning module is used for carrying out reverse reasoning on the Bayesian network model through Bayesian software to obtain posterior probability of the root node;
the calculation module is used for calculating the probability change rate of the root node according to the prior probability and the posterior probability;
the determining module is used for determining target risk factors according to the calculated probability change rate so as to realize the risk evaluation of the leakage emergency repair of the gas pipeline.
In another aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above gas pipeline leakage emergency repair risk assessment method.
In yet another aspect of the present invention, there is also provided an electronic device comprising a storage controller including a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above gas pipeline leakage emergency repair risk assessment method when executing the computer program.
According to the method and the device for evaluating the risk of the leakage repair of the gas pipeline, provided by the embodiment of the invention, the risk factors are assigned, the score is described as the corresponding trapezoidal fuzzy number, and the trapezoidal fuzzy number is processed to obtain the prior probability of the root node; reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained; calculating the probability change rate of the root node according to the prior probability and the posterior probability; according to the probability change rate obtained by calculation, a target risk factor is determined so as to realize the risk evaluation of the gas pipeline leakage repair.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 schematically illustrates a Bayesian network diagram at a construction stage;
FIG. 2 schematically illustrates a Bayesian network update diagram at a construction stage;
FIG. 3 schematically illustrates a posterior/prior ratio plot of nodes at a construction stage;
FIG. 4 is a flowchart of a method for evaluating risk of gas pipeline leakage rush repair provided by an embodiment of the invention;
FIG. 5 is a diagram of risk factors existing in a basic stage of emergency repair of a gas pipeline leakage accident according to an embodiment of the present invention;
FIG. 6 is a Bayesian network model diagram of a gas pipeline leakage repair risk provided by an embodiment of the invention;
FIG. 7 is a Bayesian network forward reasoning diagram of the risk of gas pipeline leakage repair provided by the embodiment of the invention;
FIG. 8 is a Bayesian network update chart of the risk of gas pipeline leakage repair provided by the embodiment of the invention;
FIG. 9 is a graph of probability change rate of risk of gas pipeline leakage repair provided by an embodiment of the invention;
fig. 10 is a schematic structural diagram of a risk evaluation device for gas pipeline leakage emergency repair according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Fig. 4 schematically shows a flowchart of a gas pipeline leakage emergency repair risk evaluation method according to an embodiment of the present invention. Referring to fig. 4, the method for evaluating the risk of gas pipeline leakage repair according to the embodiment of the invention specifically includes the following steps:
s41, acquiring risk factors existing in a basic stage of emergency repair of a gas pipeline leakage accident, wherein the basic stage comprises the following steps: a warning receiving and disposing stage, a job preparing stage, an implementation repairing stage and a later recovery stage;
s42, taking the risk factors as root nodes, taking the alarm receiving treatment stage, the operation preparation stage, the implementation restoration stage and the later restoration stage as intermediate layer nodes, taking the gas pipeline leakage accident emergency repair as top layer nodes, and establishing a Bayesian network model;
s43, assigning scores to the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, and processing the ladder ambiguity numbers to obtain prior probabilities of root nodes;
s44, reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained;
s45, calculating the probability change rate of the root node according to the prior probability and the posterior probability;
s46, determining target risk factors according to the calculated probability change rate so as to realize gas pipeline leakage emergency repair risk evaluation.
Further, the risk factors include: the method has the advantages of incomplete knowledge of dangerous situations, delay of reaching the scene, inaccurate information transmission, incomplete configuration of emergency detection equipment, insufficient experience of an expert group, traffic accidents, unclear division of workers, unsmooth matching of a plurality of units, unsophisticated emergency resource calling, incomplete warning facilities, incomplete evacuation of surrounding workers, unsmooth detection of gas leakage concentration, disordered placement of field equipment, too high gas diffusing speed, difficult discovery of leakage points, incomplete temporary disposal of leakage points, improper operation of operators, failure of emergency equipment, improper on-site command, imperfect regulation and system, unstable operation pressure control, welding quality problems, health problems of operators, inappropriately protective measures and air leakage caused by too fast re-compression.
In this embodiment, the emergency repair process of the gas pipeline leakage accident is divided into four stages, namely, an alarm receiving treatment stage, an operation preparation stage, an implementation repair stage and a later recovery stage, and risks existing in each stage are analyzed.
The method comprises the steps that in the alarm receiving treatment stage, specific condition information of an accident scene is uploaded to an upper level in the first time as a first step of gas pipeline leakage accident scene treatment, a dispatching center expert carries out alarm condition analysis on the basic condition of an emergency event according to information provided by reporting, a pre-plan is preliminarily formulated, and risk factors in the alarm receiving treatment stage specifically comprise: alarm response, verification judgment, information report and field emergency treatment.
The job preparation phase includes: coordination of on-site command, personnel evacuation and warning, pipeline excavation, gas pipeline diffusing and pressure control related treatment; common operation modes of the urban gas pipeline comprise: the operation of pressure reduction is not carried out, and the operation of pressure reduction and gas stopping are carried out; the low-pressure pipeline is not depressurized generally, the medium-pressure pipeline is depressurized for repairing the gas pipeline, so that the pressure of the leakage point meets the operation requirement, the pressure cannot be too high, the pipeline cannot be negative pressure, and the gas stopping mode is basically not selected.
The repair stage is implemented by determining the position of the leakage point, performing temporary treatment and repair operation on the leakage point. After the pipeline is dug out, operators go into the operation pit to observe the exposed pipeline anticorrosive coating, wind or air-out sound can be perceived near the dug-out leakage point, or dry soil appears obviously on surrounding soil, and the position of the leakage point can be accurately judged through checking, brushing leakage and stripping the anticorrosive coating; in order to prevent a great deal of waste caused by continuous leakage of fuel gas, the leakage points can be treated first, so that the leakage quantity is reduced.
The later recovery stage is to comprehensively review the quality problem of the repaired pipe section, and the pipe section needs to be subjected to the repair operation again if bubbles appear on the pipe wall and wind sounds can be obviously heard by operators, so that the repair operation is not thorough, the pipe section is not completely repaired, and the repair operation needs to be performed again; if no bubbles are generated, the leakage port is completely repaired. After meeting the requirements, the construction site needs to be restored after the emergency repair work is completed, and the operation pit is backfilled.
By analyzing the rush-repair stage, the risk factors are determined, and the risk factors are shown in fig. 5.
In this embodiment, the gas pipeline leakage repair operation flow is divided into four basic stages, namely, an alarm receiving treatment stage, an operation preparation stage, an implementation repair stage and a later recovery stage, and each stage is deeply analyzed to identify risk factors which may affect the repair operation in the stage, and the risk factors are used as basic events (root nodes). By analysis, 36 risk factors were determined, the corresponding numbers are shown in table 1, and a bayesian network model was established by the genie3.0 software, as shown in fig. 6.
TABLE 1 risk factors and numbering thereof
Further, the assigning the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, and processing the ladder ambiguity numbers to obtain prior probabilities of root nodes, including:
dividing the risk level of the risk factors, and assigning a score to the divided risk factors to obtain corresponding scores;
the trapezoidal fuzzy number is used as a membership function of fuzzy language, the trapezoidal fuzzy number is expressed by a, b, c and d parameters, the fuzzy set is A= (a, b, c, d), and the membership function expression is as follows:
the comment set S= { S with preset granularity and generated by the score 0 ,s 1 ,s 2 ,…,s g Ith element s in } i Described as corresponding trapezoidal blur number A i ；
Ladder ambiguity number A i The expression is as follows:
wherein g is a positive integer, i=0, 1, …, g;
and processing the trapezoidal fuzzy number by using an arithmetic average method to obtain the probability of the trapezoidal fuzzy number, wherein the expression is as follows:
wherein n is a positive integer;
processing the trapezoidal fuzzy number probability to obtain a weight, wherein the weight P i The expression is as follows:
and multiplying the weight by a preset coefficient to obtain the prior probability of the root node.
In this embodiment, the trapezoidal blur number is used as a membership function of the blur language, and the trapezoidal blur number is paired with the expert score, so as to determine the trapezoidal blur number under 5 risk levels. And scoring by an expert to obtain scores of all risk factors, and carrying out average calculation on the obtained scores corresponding to the trapezoidal fuzzy numbers.
In this embodiment, fuzzy data of the root nodes are obtained in the form of industry expert scores, scores of all the experts are integrated, and calculation is performed based on the trapezoidal fuzzy numbers to obtain prior probabilities of all the root nodes.
The risk level comments are divided into 5 levels, low, general, high, respectively, with corresponding scores of 1, 2, 3, 4, 5. Using the trapezoidal blur number as a membership function of the blur language, wherein the trapezoidal blur number is expressed by 4 parameters, and the blur set is marked as A= (a, b, c, d);
the score is presented in the form of a trapezoidal fuzzy number, conversion between the fuzzy number and a language variable is realized, and the trapezoidal fuzzy number is determined by the following steps:
comment set S= { S with preset granularity of g+1 0 ,s 1 ,s 2 ,…,s g I-th element S in S i (i=0, 1, …, g) is expressed as a trapezoidal blur number a i ；
Dividing the probability of node occurrence into 5 grades, and calculating a corresponding trapezoidal fuzzy number based on a trapezoidal fuzzy number expression according to a 5-granularity comment set generated by the score:
A 0 ＝(0,0,0.11,0.22)
A 1 ＝(0.11,0.22,0.33,0.44)
A 2 ＝(0.33,0.44,0.56,0.67)
A 3 ＝(0.56,0.67,0.78,0.89)
A 4 ＝(0.78,0.89,1,1)
conversion of the score to a trapezoidal blur number is achieved, and the corresponding class name and corresponding blur number are shown in table 2.
TABLE 2 fuzzy probability language sets
| Risk level comment | Assignment of value | Ladder ambiguity number |
| Low and low | 1 | (0,0,0.11,0.22) |
| Lower level | 2 | (0.11,0.22,0.33,0.44) |
| In general | 3 | (0.33,0.44,0.56,0.67) |
| Higher height | 4 | (0.56,0.67,0.78,0.89) |
| High height | 5 | (0.78,0.89,1,1) |
Processing the trapezoidal fuzzy number by using an arithmetic average method so as to obtain a reasonable trapezoidal fuzzy number probability;
processing the fuzzy probability, and further converting the obtained trapezoidal fuzzy number probability into weight;
when the prior probability of the root node is determined, the scoring result is selected, and the language set assigns the risk level of the basic event, so that the risk and the occurrence probability of the basic event are compared, and the occurrence probability of the basic event is 5% according to experience.
P＝5％×P i
In this embodiment, after the scores of multiple experts are obtained, the scores are converted into the ladder-shaped fuzzy numbers, all the ladder-shaped fuzzy numbers of the same risk factors are added by different experts to obtain an average value, the fuzzy numbers after the average value are converted into weights of event risk degrees, the weights are multiplied by 5% to serve as prior probabilities of root nodes, and the weights are calculated through GeNIe.
And determining the prior probability of all the root nodes according to the probability value calculation method. The probability of the root node of the bayesian network is processed, and the probability values of all the root nodes are obtained after calculation, as shown in table 3.
Table 3 root node prior probability statistics table
Further, the assigning the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, and processing the ladder ambiguity numbers to obtain prior probabilities of root nodes, further includes:
utilizing directed edges to connect nodes with causal relation, and establishing a directed acyclic graph;
and establishing a conditional probability table among the nodes according to the pre-acquired OR logic gates of the fault tree.
Further, the method further comprises the following steps:
inputting the prior probability of the root node, the conditional probability of the middle layer node obtained by the conditional probability table and the conditional probability of the top layer node obtained by the conditional probability table into the Bayesian network model through Bayesian software to obtain a forward reasoning result, and predicting the occurrence probability of the risk event according to the forward reasoning result.
In this embodiment, quantitative association is established between bayesian network nodes, and conditional probability distribution of each node is collected. The fault tree method is used as a modeling basis, the fault tree is mapped into a Bayesian network, besides the structure mapping, the conditional probability is mapped, and the conditional probability distribution is another expression form of an OR gate.
Let X be 1 And X 2 For two basic events in the fault tree, T is an input event, and 0 and 1 represent two states of normal and fault, respectively:
if X 1 And X 2 Is an and gate input, then the conditional probability distribution in the bayesian network for T is:
P(T＝1|X 1 ＝1,X 2 ＝1)＝1,P(T＝1|else)＝0
if X 1 And X 2 Is an or gate input, then T corresponds to a conditional probability distribution in the bayesian network as:
P(T＝1|X 1 ＝0,X 2 ＝0)＝0,P(T＝1|else)＝1
in the whole emergency repair process of the community gas pipeline leakage accident, the system, the flow and the complexity are realized, the whole emergency repair process is regarded as a system, only the whole emergency repair process is safely and smoothly completed in a short time, otherwise, any operation is wrong or is not normal, the risk upgrading or the emergency repair time extension of the whole emergency repair process is possibly caused, the risk in the emergency repair process is enlarged, and the logic of an OR gate is adopted between the emergency repair process and the risk factors.
Further, the reverse reasoning is performed on the bayesian network model through bayesian software to obtain posterior probability of the root node, including:
the posterior probability of the root node is obtained by setting evidence for the top node in the Bayesian network model based on Bayesian software;
further, the calculating the probability change rate of the root node according to the prior probability and the posterior probability includes:
wherein P is 2 For posterior probability and P 1 Is a priori probability.
In the embodiment, forward reasoning of the Bayesian network for the gas pipeline leakage repair is performed through the Bayesian software GeNIe3.0, the probability of occurrence of risk events is predicted in advance according to forward reasoning results, posterior probabilities of all risk factors are obtained through reverse reasoning of the Bayesian network, and the probability change rate of a root node is judged, so that target risk factors are determined, prevention is facilitated in advance, and smooth implementation of emergency repair operation is guaranteed.
And (3) inputting prior probabilities in the table 4 for all root nodes in the Bayesian network, and determining conditional probability table distribution of intermediate nodes to obtain a forward reasoning result of the community gas pipeline leakage repair risk Bayesian network, as shown in fig. 7.
In this embodiment, the reverse reasoning function of bayesian sets evidence on top events in the network model to obtain posterior probabilities of all nodes, where the posterior probabilities reflect factors of maximum occurrence probability of the top events. In the bayesian network model of the gas pipeline leakage repair risk, the occurrence probability of the top event is set to be 1, namely, P (with) =1, the model is updated, and the posterior probability of all nodes is calculated again, as shown in fig. 8.
In the embodiment, after posterior probabilities of all root nodes are obtained, risk factors with the greatest influence on the gas pipeline leakage repair operation are analyzed and determined by taking the probability change rate as a judgment basis;
the probability change rate was calculated for all root nodes based on the probability change rate expression, and the results are shown in table 4.
Table 4 probability change Rate of all root nodes
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FIG. 9 is a graph of probability change rate of risk of gas pipeline leakage repair provided by an embodiment of the invention;
in this embodiment, as shown by the bayesian network reverse reasoning result, if the gas pipeline leakage accident repair operation cannot be performed smoothly, the point with the maximum posterior probability is the key point, and the key points are respectively incomplete configuration of the A4 emergency detection equipment, inaccurate closing of the valve of the B12 equipment, incorrect operation of the C3 operator, unstable control of the C7 operation pressure and air leakage caused by too fast D1 repression.
According to the result analysis of the probability change rate, the A5 expert group has insufficient experience, inaccurate A3 information transmission, damage to pipelines caused by B10 construction, improper C9 welding process and inadequate C11 protective measures, and is a target risk factor. In the gas pipeline leakage repair operation process, the accident is reduced as much as possible, and precautions are needed to be taken in advance for ensuring the smooth progress of the emergency repair operation aiming at the target risk factors.
According to the gas pipeline leakage rush repair risk evaluation method provided by the embodiment of the invention, the risk factors are assigned, the score is described as the corresponding trapezoidal fuzzy number, and the trapezoidal fuzzy number is processed to obtain the prior probability of the root node; reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained; calculating the probability change rate of the root node according to the prior probability and the posterior probability; according to the probability change rate obtained by calculation, a target risk factor is determined so as to realize the risk evaluation of the gas pipeline leakage repair.
For the purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated by one of ordinary skill in the art that the methodologies are not limited by the order of acts, as some acts may, in accordance with the methodologies, take place in other order or concurrently. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.
Fig. 10 schematically shows a structural diagram of a gas pipeline leakage emergency repair risk evaluation device according to an embodiment of the present invention. Referring to fig. 10, the gas pipeline leakage emergency repair risk evaluation device according to the embodiment of the present invention specifically includes:
an obtaining module 101, configured to obtain risk factors existing in a basic stage of emergency repair of a gas pipeline leakage accident, where the basic stage includes: a warning receiving and disposing stage, a job preparing stage, an implementation repairing stage and a later recovery stage;
the establishing module 102 is configured to take the risk factor as a root node, the alarm receiving and disposing stage, the job preparing stage, the implementing and repairing stage and the later recovering stage as intermediate layer nodes, and the gas pipeline leakage accident emergency repair as a top layer node, and establish a bayesian network model;
a scoring module 103, configured to score the risk factors to obtain corresponding scores, describe the scores as corresponding ladder ambiguity numbers, and process the ladder ambiguity numbers to obtain a priori probability of a root node;
the reverse reasoning module 104 is configured to reverse reason the bayesian network model through bayesian software to obtain posterior probability of the root node;
a calculating module 105, configured to calculate a probability change rate of a root node according to the prior probability and the posterior probability;
and the determining module 106 is used for determining a target risk factor according to the calculated probability change rate so as to realize the risk evaluation of the gas pipeline leakage emergency repair.
According to the gas pipeline leakage rush repair risk evaluation device provided by the embodiment of the invention, the risk factors are assigned, the score is described as the corresponding trapezoidal fuzzy number, and the trapezoidal fuzzy number is processed to obtain the prior probability of the root node; reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained; calculating the probability change rate of the root node according to the prior probability and the posterior probability; according to the probability change rate obtained by calculation, a target risk factor is determined so as to realize the risk evaluation of the gas pipeline leakage repair.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
In addition, the embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, and the program is executed by a processor to realize the steps of the fuel gas pipeline leakage emergency repair risk evaluation method.
In this embodiment, the module/unit integrated with the gas pipeline leakage emergency repair risk assessment device may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
In addition, the embodiment of the invention also provides electronic equipment, which comprises a storage controller, wherein the storage controller comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor realizes the steps of the fuel gas pipeline leakage emergency repair risk evaluation method when executing the program. For example, steps S41 to S46 shown in fig. 4. Or, the processor performs the functions of each module/unit in the embodiment of the device for evaluating the risk of repairing the leakage of the gas pipeline when executing the computer program, for example, an acquisition module 101, an establishment module 102, a score assignment module 103, a reverse reasoning module 104, a calculation module 105 and a determination module 106 shown in fig. 10.
According to the method and the device for evaluating the risk of the leakage repair of the gas pipeline, provided by the embodiment of the invention, the risk factors are assigned, the score is described as the corresponding trapezoidal fuzzy number, and the trapezoidal fuzzy number is processed to obtain the prior probability of the root node; reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained; calculating the probability change rate of the root node according to the prior probability and the posterior probability; according to the probability change rate obtained by calculation, a target risk factor is determined so as to realize the risk evaluation of the gas pipeline leakage repair.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, any of the claimed embodiments can be used in any combination.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for evaluating the risk of gas pipeline leakage rush repair is characterized by comprising the following steps:
acquiring risk factors existing in a basic stage of emergency repair of a gas pipeline leakage accident, wherein the basic stage comprises the following steps: a warning receiving and disposing stage, a job preparing stage, an implementation repairing stage and a later recovery stage;
taking the risk factors as root nodes, taking the alarm receiving treatment stage, the operation preparation stage, the implementation restoration stage and the later recovery stage as intermediate layer nodes, taking the gas pipeline leakage accident emergency repair as top layer nodes, and establishing a Bayesian network model;
assigning scores to the risk factors to obtain corresponding scores, describing the scores as corresponding trapezoidal fuzzy numbers, and processing the trapezoidal fuzzy numbers to obtain prior probabilities of root nodes;
reverse reasoning is carried out on the Bayesian network model through Bayesian software, so that posterior probability of the root node is obtained;
calculating the probability change rate of the root node according to the prior probability and the posterior probability;
and determining a target risk factor according to the calculated probability change rate so as to realize the risk evaluation of the leakage emergency repair of the gas pipeline.
2. The method of claim 1, wherein the risk factors include: the method has the advantages of incomplete knowledge of dangerous situations, delay of reaching the scene, inaccurate information transmission, incomplete configuration of emergency detection equipment, insufficient experience of an expert group, traffic accidents, unclear division of workers, unsmooth matching of a plurality of units, unsophisticated emergency resource calling, incomplete warning facilities, incomplete evacuation of surrounding workers, unsmooth detection of gas leakage concentration, disordered placement of field equipment, too high gas diffusing speed, difficult discovery of leakage points, incomplete temporary disposal of leakage points, improper operation of operators, failure of emergency equipment, improper on-site command, imperfect regulation and system, unstable operation pressure control, welding quality problems, health problems of operators, inappropriately protective measures and air leakage caused by too fast re-compression.
3. The method of claim 1, wherein assigning the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, and processing the ladder ambiguity numbers to obtain a priori probabilities of root nodes comprises:
dividing the risk level of the risk factors, and assigning a score to the divided risk factors to obtain corresponding scores;
the trapezoidal fuzzy number is used as a membership function of fuzzy language, the trapezoidal fuzzy number is expressed by a, b, c and d parameters, the fuzzy set is A= (a, b, c, d), and the membership function expression is as follows:
the comment set S= { S with preset granularity and generated by the score 0 ,s 1 ,s 2 ,…,s g Ith element s in } i Described as corresponding trapezoidal blur number A i ；
Ladder ambiguity number A i The expression is as follows:
wherein g is a positive integer, i=0, 1, …, g;
and processing the trapezoidal fuzzy number by using an arithmetic average method to obtain the probability of the trapezoidal fuzzy number, wherein the expression is as follows:
wherein n is a positive integer;
processing the trapezoidal fuzzy number probability to obtain a weight, wherein the weight P i The expression is as follows:
and multiplying the weight by a preset coefficient to obtain the prior probability of the root node.
4. The method of claim 1, wherein the assigning the risk factors to obtain corresponding scores, describing the scores as corresponding ladder ambiguity numbers, processing the ladder ambiguity numbers to obtain a priori probabilities of root nodes, and further comprising:
utilizing directed edges to connect nodes with causal relation, and establishing a directed acyclic graph;
and establishing a conditional probability table among the nodes according to the pre-acquired OR logic gates of the fault tree.
5. The method as recited in claim 4, further comprising:
inputting the prior probability of the root node, the conditional probability of the middle layer node obtained by the conditional probability table and the conditional probability of the top layer node obtained by the conditional probability table into the Bayesian network model through Bayesian software to obtain a forward reasoning result, and predicting the occurrence probability of the risk event according to the forward reasoning result.
6. The method according to claim 1, wherein the reverse reasoning on the bayesian network model by bayesian software to obtain the posterior probability of the root node includes:
and obtaining posterior probability of the root node by setting evidence for the top node in the Bayesian network model based on Bayesian software.
7. The method of claim 1, wherein said calculating a probability rate of change of a root node based on said prior probability and said posterior probability comprises:
wherein P is 2 For posterior probability and P 1 Is a priori probability.
8. A gas pipeline leakage rush repair risk assessment device, the device comprising:
the acquisition module is used for acquiring risk factors existing in a basic stage of the emergency repair of the gas pipeline leakage accident, and the basic stage comprises the following steps: a warning receiving and disposing stage, a job preparing stage, an implementation repairing stage and a later recovery stage;
the establishing module is used for taking the risk factors as root nodes, taking the alarm receiving treatment stage, the operation preparation stage, the implementation restoration stage and the later restoration stage as intermediate layer nodes, taking the gas pipeline leakage accident emergency repair as top layer nodes and establishing a Bayesian network model;
the scoring module is used for scoring the risk factors to obtain corresponding scores, describing the scores as corresponding trapezoidal fuzzy numbers, and processing the trapezoidal fuzzy numbers to obtain the prior probability of the root node;
the reverse reasoning module is used for carrying out reverse reasoning on the Bayesian network model through Bayesian software to obtain posterior probability of the root node;
the calculation module is used for calculating the probability change rate of the root node according to the prior probability and the posterior probability;
the determining module is used for determining target risk factors according to the calculated probability change rate so as to realize the risk evaluation of the leakage emergency repair of the gas pipeline.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
10. An electronic device comprising a memory controller, the memory controller comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1-7 when the computer program is executed.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118037064A (en) * | 2024-04-10 | 2024-05-14 | 中国神华能源股份有限公司 | Targeted security authentication method, device and system based on risk pre-control |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106846155A (en) * | 2017-03-29 | 2017-06-13 | 哈尔滨理工大学 | Submarine pipeline leakage accident methods of risk assessment based on fuzzy Bayesian network |
| CN109657907A (en) * | 2018-11-13 | 2019-04-19 | 香港理工大学深圳研究院 | Method of quality control, device and the terminal device of geographical national conditions monitoring data |
| CN110490433A (en) * | 2019-07-30 | 2019-11-22 | 同济大学 | A kind of train control system methods of risk assessment |
| CN114564881A (en) * | 2022-01-26 | 2022-05-31 | 天津大学 | Drilling riser fracture failure risk evaluation method based on fuzzy Bayesian network |
| CN114648211A (en) * | 2022-03-02 | 2022-06-21 | 天津大学 | Oil storage tank failure assessment method and related device |
| CN114819384A (en) * | 2021-12-14 | 2022-07-29 | 浙江海洋大学 | Natural gas pipeline third-party damage accident early warning method based on dynamic Bayesian network |
| CN115018334A (en) * | 2022-06-15 | 2022-09-06 | 湖南科技大学 | Fuzzy Bayesian network-based gas explosion risk assessment method |
| CN115099603A (en) * | 2022-06-20 | 2022-09-23 | 哈尔滨工程大学 | Rocket offshore recovery platform dynamic positioning system fault risk assessment method, computer equipment and readable storage medium |
| CN116167620A (en) * | 2023-02-28 | 2023-05-26 | 北京石油化工学院 | Multi-dimensional risk dynamic evaluation method and system for large-scale vehicle exhibition fire disaster |
-
2023
- 2023-06-05 CN CN202310656407.9A patent/CN116911594A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106846155A (en) * | 2017-03-29 | 2017-06-13 | 哈尔滨理工大学 | Submarine pipeline leakage accident methods of risk assessment based on fuzzy Bayesian network |
| CN109657907A (en) * | 2018-11-13 | 2019-04-19 | 香港理工大学深圳研究院 | Method of quality control, device and the terminal device of geographical national conditions monitoring data |
| CN110490433A (en) * | 2019-07-30 | 2019-11-22 | 同济大学 | A kind of train control system methods of risk assessment |
| CN114819384A (en) * | 2021-12-14 | 2022-07-29 | 浙江海洋大学 | Natural gas pipeline third-party damage accident early warning method based on dynamic Bayesian network |
| CN114564881A (en) * | 2022-01-26 | 2022-05-31 | 天津大学 | Drilling riser fracture failure risk evaluation method based on fuzzy Bayesian network |
| CN114648211A (en) * | 2022-03-02 | 2022-06-21 | 天津大学 | Oil storage tank failure assessment method and related device |
| CN115018334A (en) * | 2022-06-15 | 2022-09-06 | 湖南科技大学 | Fuzzy Bayesian network-based gas explosion risk assessment method |
| CN115099603A (en) * | 2022-06-20 | 2022-09-23 | 哈尔滨工程大学 | Rocket offshore recovery platform dynamic positioning system fault risk assessment method, computer equipment and readable storage medium |
| CN116167620A (en) * | 2023-02-28 | 2023-05-26 | 北京石油化工学院 | Multi-dimensional risk dynamic evaluation method and system for large-scale vehicle exhibition fire disaster |
Non-Patent Citations (1)
| Title |
|---|
| 章浩然等: ""基于FMECA 和模糊评判的数控机床可靠性分析方法"", 《检测与质量》, no. 11, pages 127 - 129 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118037064A (en) * | 2024-04-10 | 2024-05-14 | 中国神华能源股份有限公司 | Targeted security authentication method, device and system based on risk pre-control |
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