Risk evaluation method and device for gas pipeline leakage first-aid repair

Abstract

The invention provides a risk evaluation method and device for gas pipeline leakage first-aid repair, and belongs to the technical field of risk evaluation, and the method comprises the steps: determining a first-level risk index and a second-level risk index of a to-be-evaluated object; determining a first risk weight of the first-level risk index and a second risk weight of the second-level risk index; determining a membership subset of the risk evaluation level of the second-level risk index, and determining a fuzzy evaluation matrix of the first-level risk index according to the membership subset; determining a first membership degree vector of a risk evaluation level of the first-level risk index according to the second risk weight and a fuzzy evaluation matrix of the first-level risk index, and determining a total evaluation matrix of the to-be-evaluated object according to the first membership degree vector; and according to the first risk weight and a total evaluation matrix of the to-be-evaluated object, determining a second membership degree vector of a risk evaluation level of the to-be-evaluated object, and according to a risk level assignment corresponding to the risk evaluation level, determining a comprehensive risk score and a first-aid repair risk level of the to-be-evaluated object.

Description

Technical Field

[0001] The present application relates to the field of risk assessment technology and can also be used in the field of gas transportation safety technology, and in particular to a risk assessment method and device for emergency repair of gas pipeline leakage. Background Art

[0002] In recent years, the consumption of urban gas has been growing in proportion to the scale of the pipeline network. The underground pipeline has been in operation for a long time and is affected by engineering construction, geological subsidence, stray current interference and environmental corrosion factors, resulting in frequent gas pipeline leakage accidents. Gas pipeline leakage not only causes a large amount of gas loss, but also directly affects the economic benefits of the enterprise. Even when the amount of gas in the space reaches the explosion limit, it will explode when it encounters an open flame, causing huge losses of life and property. Therefore, in order to avoid the adverse consequences caused by gas pipeline leakage and ensure the safety of emergency repair operations, it is particularly important to conduct risk assessment on the emergency repair process of gas pipeline leakage accidents.

[0003] In the prior art, the evaluation methods for gas leakage accidents mainly include: accident tree analysis, Bayesian network analysis, Bow-tie (Bow-tie Analysis) model and hierarchical analysis method. However, these methods have certain drawbacks. It is often difficult to obtain correct results for complex problems when constructing accident trees; in the absence of accident data, it is difficult for the Bayesian network to determine the probability of occurrence of basic events, resulting in inaccurate subsequent evaluation results; the Bow-tie model can use accident tree analysis and event tree analysis to analyze key risk factors in the leakage process, but it cannot analyze problems that lack historical data and logical relationships. Furthermore, in the process of repairing gas pipeline leaks, the uncertainty of various risks can easily lead to an increase in the risk of repair operations. It is particularly important to identify and analyze the risk factors in the emergency repair process. The existing gas pipeline leak repair risk analysis mainly focuses on the repair decision-making system, repair equipment and material distribution, and the applicability of repair methods, while there is little research on risk evaluation in the leak repair process. Summary of the invention

[0004] In view of the problems existing in the prior art, the present application provides a risk assessment method and device for emergency repair of gas pipeline leakage, which can realize risk warning of emergency repair of gas pipeline leakage and improve the comprehensiveness and accuracy of risk assessment of emergency repair of gas pipeline leakage.

[0005] According to a first aspect of the present application, a risk assessment method for emergency repair of gas pipeline leakage is provided, comprising:

[0006] Determine the primary risk indicators of the object to be evaluated and the secondary risk indicators included in each of the primary risk indicators;

[0007] Determine the first risk weight of the first-level risk indicators and the second risk weight of the second-level risk indicators relative to the first-level risk indicators;

[0008] Determine the membership degree subset of the risk evaluation level of the second-level risk indicators, and determine the fuzzy evaluation matrix of the first-level risk indicators according to the membership degree subset;

[0009] According to the second risk weight and the fuzzy evaluation matrix of the first-level risk indicators, determine the first membership degree vector of the risk evaluation level of the first-level risk indicators, and determine the total evaluation matrix of the object to be evaluated according to the first membership degree vector;

[0010] According to the first risk weight and the total evaluation matrix of the object to be evaluated, determine the second membership degree vector of the risk evaluation level of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership degree vector and the risk level assignment corresponding to the risk evaluation level;

[0011] Determine the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk evaluation level.

[0012] In some alternative ways of this embodiment, the determining the first risk weight of the first-level risk indicators includes:

[0013] Determine the first risk score of the first-level risk indicators according to the importance degree after pairwise comparison of the first-level risk indicators;

[0014] Construct the first judgment matrix corresponding to the first-level risk indicators according to the first risk score, and perform a consistency test on the first judgment matrix;

[0015] In response to determining that the consistency test of the first judgment matrix passes, determine the first eigenvector of the first judgment matrix, and perform normalization processing on the first eigenvector;

[0016] Determine the first risk weight of the first-level risk indicators according to the first eigenvector after normalization processing.

[0017] In some alternative ways of this embodiment, determining the second risk weight of the second-level risk indicators relative to the first-level risk indicators includes:

[0018] Determine the second risk score of the second-level risk indicators according to the importance degree after pairwise comparison of the second-level risk indicators in the first-level risk indicators;

[0019] Construct a second judgment matrix corresponding to the secondary risk indicators according to the second risk score, and perform a consistency test on the second judgment matrix;

[0020] In response to determining that the consistency test of the second judgment matrix passes, determine the second eigenvector of the second judgment matrix, and perform normalization processing on the second eigenvector;

[0021] Determine the second risk weight of the secondary risk indicators according to the second eigenvector after normalization processing.

[0022] In some alternative embodiments of the present embodiment, determining the membership subset of the risk evaluation level of the secondary risk indicators includes:

[0023] Determine the third risk score of the secondary risk indicators according to the determination of the risk evaluation levels of the secondary risk indicators in the primary risk indicators, where the risk evaluation levels include high risk level, relatively high risk level, medium risk level, relatively low risk level, and low risk level from high to low;

[0024] Determine the membership subset of the risk evaluation level of the secondary risk indicators according to the third risk score.

[0025] In some alternative embodiments of the present embodiment, the determining the fuzzy evaluation matrix of the primary risk indicators according to the membership subset includes:

[0026] Determine the membership subsets of the secondary risk indicators included in the primary risk indicators respectively;

[0027] Use the membership subsets of the secondary risk indicators as the row vectors of the fuzzy evaluation matrix of the primary risk indicators, where the number of rows of the fuzzy evaluation matrix is the number of secondary risk indicators included in the primary risk indicators.

[0028] In some alternative embodiments of the present embodiment, the determining the first membership vector of the risk evaluation level of the primary risk indicators according to the second risk weight and the fuzzy evaluation matrix of the primary risk indicators includes:

[0029] Determine the first product result of the second risk weight and the fuzzy evaluation matrix of the primary risk indicators;

[0030] Determine the first membership vector of the risk evaluation level of the primary risk indicators according to the first product result.

[0031] In some alternative embodiments of the present embodiment, the determining the total evaluation matrix of the object to be evaluated according to the first membership vector includes:

[0032] Determine the first membership degree vector of each of the first-level risk indicators respectively;

[0033] Take the first membership degree vector of each of the first-level risk indicators as the row vector of the total evaluation matrix of the object to be evaluated, where the number of rows of the total evaluation matrix is the number of the first-level risk indicators.

[0034] In some alternative embodiments of the present embodiment, the determining the second membership degree vector of the risk evaluation level of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated includes:

[0035] Determine the second product result of the first risk weight and the total evaluation matrix of the object to be evaluated;

[0036] Determine the second membership degree vector of the risk evaluation level of the object to be evaluated according to the second product result.

[0037] In some alternative embodiments of the present embodiment, the determining the comprehensive risk score of the object to be evaluated according to the second membership degree vector and the risk level assignment corresponding to the risk evaluation level includes:

[0038] Use the weighted average method to determine the third product result of the second membership degree vector and the risk level assignment corresponding to each of the risk evaluation levels;

[0039] Determine the comprehensive risk score of the object to be evaluated according to the third product result.

[0040] In some alternative embodiments of the present embodiment, the determining the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk evaluation level includes:

[0041] Determine the risk score interval corresponding to the comprehensive risk score according to the risk level assignment corresponding to the risk evaluation level;

[0042] Determine the emergency repair risk level of the object to be evaluated according to the risk score interval.

[0043] According to the second aspect of the present application, there is also provided a risk evaluation device for gas pipeline leakage emergency repair, including:

[0044] A risk indicator determination module, configured to determine the first-level risk indicators of the object to be evaluated and the second-level risk indicators included in each of the first-level risk indicators;

[0045] A risk weight determination module, configured to determine the first risk weight of the first-level risk indicators and the second risk weight of the second-level risk indicators relative to the first-level risk indicators;

[0046] The fuzzy evaluation matrix determination module is configured to determine the membership subset of the risk evaluation level of the secondary risk indicators, and determine the fuzzy evaluation matrix of the primary risk indicators according to the membership subset;

[0047] The total evaluation matrix determination module is configured to determine the first membership vector of the risk evaluation level of the primary risk indicators according to the second risk weight and the fuzzy evaluation matrix of the primary risk indicators, and determine the total evaluation matrix of the object to be evaluated according to the first membership vector;

[0048] The comprehensive risk score determination module is configured to determine the second membership vector of the risk evaluation level of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership vector and the risk level assignment corresponding to the risk evaluation level;

[0049] The emergency repair risk level determination module is configured to determine the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk evaluation level.

[0050] According to the third aspect of the present application, an electronic device is further provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the risk evaluation method for gas pipeline leakage emergency repair are implemented.

[0051] According to the fourth aspect of the present application, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the risk evaluation method for gas pipeline leakage emergency repair are implemented.

[0052] The risk evaluation method and device for gas pipeline leakage emergency repair provided by the present application comprehensively consider key risk factors by determining the primary risk indicators of the object to be evaluated and the secondary risk indicators included in each primary risk indicator, establish a risk evaluation system for gas pipeline leakage emergency repair, and finally determine the emergency repair risk level based on the comprehensive risk score, realizing the risk early warning of gas leakage emergency repair operations, proposing risk response measures, thereby improving the comprehensiveness and accuracy of the risk evaluation of gas pipeline leakage emergency repair and effectively improving the safety of emergency repair personnel. Description of the Drawings

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 One of the flowcharts of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0055] Figure 2 Schematic diagram of the primary risk indicators and secondary risk indicators according to an embodiment of the present application;

[0056] Figure 3 Another flowchart of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0057] Figure 4 Another flowchart of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application

[0058] Figure 5 Another flowchart of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0059] Figure 6 Another flowchart of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0060] Figure 7 Another flowchart of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0061] Figure 8 Another flowchart of the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0062] Figure 9 Schematic diagram of the structure of the risk assessment device for gas pipeline leakage emergency repair according to an embodiment of the present application;

[0063] Figure 10 Block diagram of the electronic device for implementing the risk assessment method for gas pipeline leakage emergency repair according to an embodiment of the present application. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0065] An embodiment of the present application provides a risk assessment method for gas pipeline leakage emergency repair, as Figure 1 shown, the method includes:

[0066] Step 101: Determine the primary risk indicators of the object to be evaluated and the secondary risk indicators included in each of the primary risk indicators;

[0067] Step 102: Determine the first risk weight of the primary risk indicators and the second risk weight of the secondary risk indicators relative to the primary risk indicators;

[0068] Step 103: Determine the membership degree subset of the risk assessment levels of the secondary risk indicators, and determine the fuzzy evaluation matrix of the primary risk indicators according to the membership degree subset;

[0069] Step 104: Determine the first membership degree vector of the risk assessment level of the primary risk indicators according to the second risk weight and the fuzzy evaluation matrix of the primary risk indicators, and determine the total evaluation matrix of the object to be evaluated according to the first membership degree vector;

[0070] Step 105: Determine the second membership degree vector of the risk assessment level of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership degree vector and the risk level assignment corresponding to the risk assessment level;

[0071] Step 106: Determine the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk assessment level.

[0072] A risk assessment method for gas pipeline leakage emergency repair provided by the present application, by determining the primary risk indicators of the object to be evaluated and the secondary risk indicators included in each primary risk indicator, comprehensively considering key risk factors, establishing a risk assessment system for gas pipeline leakage emergency repair, and finally determining the emergency repair risk level based on the comprehensive risk score, realizing the risk early warning of gas leakage emergency repair operations, proposing risk response measures, thereby improving the comprehensiveness and accuracy of the risk assessment of gas pipeline leakage emergency repair, and effectively improving the safety of emergency repair personnel.

[0073] Next, for Figure 1Each step will be introduced as follows:

[0074] Step 101: Determine the first-level risk indicators of the object to be evaluated and the second-level risk indicators included in each of the first-level risk indicators.

[0075] In this embodiment, the object to be evaluated is the risk in the process of gas leakage emergency repair. It should be understood that after a gas pipeline leaks, it is necessary to quickly carry out emergency repair operations. However, the risk factors involved in the repair process are too complex. Therefore, this application proposes six major risks in the process of gas pipeline leakage emergency repair as the first-level risk indicators; the 28 risk factors after the six major risks are subdivided are used as the second-level risk indicators, and the analytic hierarchy process is used to establish a risk evaluation system for gas pipeline leakage emergency repair.

[0076] Among them, the analytic hierarchy process structure model consists of three levels: the goal layer, the criterion layer, and the index layer. Among them, the goal layer is the goal to be achieved, the criterion layer is each link to achieve the goal, and the index layer is various solutions or measures proposed to achieve the goal. In this embodiment, based on the analytic hierarchy process, a risk evaluation system for gas pipeline leakage emergency repair is established, as Figure 2 shown. Set "the risk in the process of gas pipeline leakage emergency repair" as the goal layer A, the criterion layer B is the six major risks in the process of gas pipeline leakage emergency repair. The index layer C is the 28 risk factors after the six major risks are subdivided. Specifically:

[0077] The first-level risk indicator B includes: personnel risk B 1 , equipment risk B 2 , material risk B 3 , technical risk B 4 , environmental risk B 5 and management risk B 6 ; the second-level risk indicators included in personnel risk B 1 are: personnel physical fitness C 1 , personnel safety responsibility awareness C 2 , personnel skill level C 3 , personnel psychological quality C 4 and personnel teamwork ability C 5 ; the second-level risk indicators included in equipment risk B 2 are: equipment configuration C 6 , equipment reliability detection C 7 and equipment repair and maintenance C 8 ; the second-level risk indicators included in material risk B 3 are: material quality C 9 , material use C 10 and material storage C 11 ; the second-level risk indicators included in technical risk B 4 are: technical scheme selection C12 、Technical disclosure C 13 、Operation Procedure C 14 And supervision and acceptance C 15 ; Environmental risk B 5 The secondary risk indicators included are: climate, geological conditions, C 16 , On-site risk control (warning, evacuation) C 17 、Site layout C 18 、Interaction between pipelines C 19 , Gas users and government requirements C 20 And repair time C 21 ; Manage Risk B 6 The secondary risk indicators included are: Organizational structure and responsibilities C 22 、Rules and Regulations C 23 , emergency drills and plans C 24 , On-site command and supervision C 25 , knowledge and skills training C 26 , Staffing C 27 And data completeness C 28 .

[0078] Step 102: Determine a first risk weight of the primary risk indicator and a second risk weight of the secondary risk indicator relative to the primary risk indicator.

[0079] In this embodiment, the aforementioned analytic hierarchy process is explained. The basic principle of the analytic hierarchy process is: regard the problem being studied as a system, analyze the various factors within the system, find out the levels of interrelationships between the factors, and then ask industry experts to compare the relative importance of the factors at each level, construct a corresponding mathematical model, calculate the weights of the factors at each level, and sort them; finally, according to the results of the weight sorting, propose measures to solve the problem.

[0080] To this end, in some optional embodiments of this embodiment, such as Figure 3 As shown, determining the first risk weight of the primary risk indicator includes:

[0081] Step 1021A: Determine the first risk score of the first-level risk indicator according to the importance of each first-level risk indicator after pairwise comparison.

[0082] In this embodiment, before constructing the judgment matrix, it is necessary to compare the importance of each risk indicator at the same level, and the comparison results construct the judgment matrix. Among them, the importance of each risk indicator is determined according to the nine-level scaling method, as shown in Table 1:

[0083] Table 1

[0084] Scale Definition (comparison factors i and j) 1 i is equally important as j 3 i is slightly more important than j 5 i is significantly more important than j 7 i is much more important than j 9 i is a lot more important than j 2，4，6，8 Indicates taking the intermediate value of two adjacent judgments <![CDATA[Reciprocal(1 / B n )]]> <![CDATA[(1 / B n ) represents the result of comparing j with i]]>

[0085] If the sub - element A of A k The next - level influencing factor is B 1 , B 2 ..., B n , then A k 's judgment matrix can be expressed as b ij represents B i relative to B j 's importance degree, where, b ii = 1, b ij = 1 / b ji , (i, j, k = 1, 2, 3...n).

[0086] In some alternative ways of this embodiment, the first - level indicators include personnel risk B 1 , equipment risk B 2 , material risk B 3 , technology risk B 4 , environmental risk B 5 , management risk B 6 . For the importance degrees between pairs of the first - level risk indicators, the first risk scores of the first - level risk indicators determined are shown in Table 2 as follows:

[0087] Table 2

[0088] <![CDATA[B 1 > <![CDATA[B 2 > <![CDATA[B 3 > <![CDATA[B 4 > <![CDATA[B 5 > <![CDATA[B 6 > <![CDATA[B 1 > 1 3 4 3 2 1 / 2 <![CDATA[B 2 > 1 / 3 1 3 2 1 / 2 1 / 3 <![CDATA[B 3 > 1 / 4 1 / 3 1 1 / 2 1 / 3 1 / 5 <![CDATA[B 4 > 1 / 3 1 / 2 2 1 1 / 2 1 / 3 <![CDATA[B 5 > 1 / 2 2 3 2 1 1 / 3 <![CDATA[B 6 > 2 3 5 3 3 1

[0089] For example, the importance degree of B1 is 3 times that of B2, and the importance degree of B2 is 1 / 3 times that of B1. This application will not elaborate on Table 2 anymore.

[0090] Step 1022A: According to the first risk scores, construct the first judgment matrix corresponding to the first - level risk indicators, and perform a consistency test on the first judgment matrix.

[0091] In this embodiment, perform a consistency test on this first judgment matrix. Among them, use the square - root method to solve the judgment matrix to obtain the maximum eigenvalue, calculate the test rate for the maximum eigenvalue to obtain the consistency test rate; determine whether the consistency test rate is not greater than the preset threshold. If the consistency test rate is not greater than the preset threshold, the consistency test passes and the eigenvector is generated; if the consistency test rate is greater than the preset threshold, the consistency test fails, and the judgment matrix is corrected until the consistency test rate is not greater than the preset threshold, such as 0.1.

[0092] Specifically, the consistency test is achieved through the consistency ratio formula, which is used to check whether the constructed judgment matrix is reasonable. The calculation method follows formula (1); the judgment criterion follows formula (2). When CR ≤ 0.1, the consistency of the judgment matrix is acceptable. Otherwise, the judgment matrix needs to be corrected until the calculation result after correction meets the standard of the consistency test.

[0093] CI = (λ max - n) / (n - 1) Formula (1)

[0094] CR = CI / RI Formula (2)

[0095] Among them, CI is the consistency index; CR is the consistency test rate; RI is the random consistency index, which has corresponding fixed values according to the value of n; n is the number of rows of the judgment matrix.

[0096] In some alternative ways of this embodiment, based on the first risk score shown in the foregoing Table 2, a first judgment matrix corresponding to the first-level risk indicators can be constructed, that is:

[0097]

[0098] Through matrix calculation, the following parameters are obtained:

[0099] The maximum eigenvalue λ max = 6.2192; the consistency ratio CR = 0.0348; since CR < 0.1, it meets the requirements of the consistency test.

[0100] Step 1023A, in response to determining that the consistency test of the first judgment matrix passes, determine the first eigenvector of the first judgment matrix and perform normalization processing on the first eigenvector.

[0101] In this embodiment, when the requirements of the consistency test are met, determine the first eigenvector of the first judgment matrix and normalize it to W A = (0.2469 0.1150 0.0480 0.0911 0.1524 0.3466).

[0102] Step 1024A, according to the first eigenvector after normalization processing, determine the first risk weight of the first-level risk indicators.

[0103] In this embodiment, according to the first eigenvector after normalization processing, for example, the first eigenvector after the above normalization processing is W A = (0.2469 0.1150 0.0480 0.0911 0.1524 0.3466), determine the first risk weight of the first-level risk indicators as: Personnel risk B1 is 24.69%, equipment risk B 2 is 11.5%, material risk B 3 is 4.8%, technology risk B 4 is 9.11%, environmental risk B 5 is 15.24% and management risk B 6 is 34.66%. It is observed that among the first-level risk indicators, the management risk has the largest risk weight ratio, reaching about 35%, followed by the personnel risk, and the material risk has the smallest risk, only accounting for about 5%.

[0104] In another alternative of this embodiment, as Figure 4 shown, determining the second risk weight of the secondary risk indicator relative to the primary risk indicator includes:

[0105] Step 1021B: Determine the second risk score of the secondary risk indicator according to the importance degree after pairwise comparison of each secondary risk indicator in the primary risk indicator.

[0106] Similar to the previous embodiment, first, determine the importance degree after pairwise comparison of each secondary risk indicator in the primary risk indicator, and determine the second risk score of the secondary risk indicator. For example, equipment risk B 2 includes equipment configuration C 6 and equipment reliability detection C 7 and equipment repair and maintenance C 8 , and the second risk score is shown in Table 3:

[0107] Table 3

[0108] <![CDATA[C 6 > <![CDATA[C 7 > <![CDATA[C 8 > <![CDATA[C 6 > 1 2 1 <![CDATA[C 7 > 1 / 2 1 1 <![CDATA[C 8 > 1 1 1

[0109] Step 1022B: Construct the second judgment matrix corresponding to the secondary risk indicator according to the second risk score, and perform a consistency test on the second judgment matrix.

[0110] In this embodiment, according to the second risk score in Table 3, the constructed second judgment matrix is:

[0111]

[0112] Through matrix calculation, the following parameters are obtained: the largest eigenvalue λ max = 3.0536, and the consistency ratio CR = 0.0516.

[0113] Step 1023B: In response to determining that the consistency test of the second judgment matrix passes, determine the second eigenvector of the second judgment matrix and perform normalization processing on the second eigenvector.

[0114] In this embodiment, since CR < 0.1, which meets the requirements of the consistency test, it is determined that the consistency test of the second judgment matrix passes. Further, the second eigenvector of the second judgment matrix is determined, and after normalizing this second eigenvector, it is

[0115] Step 1024B: Determine the second risk weight of the secondary risk indicators according to the normalized second eigenvector.

[0116] In this embodiment, according to the normalized it is possible to determine that the second risk weight of equipment configuration C in equipment risk is 41.26%, the second risk weight of equipment reliability detection C 6 is 25.99%, and the second risk weight of equipment repair and maintenance C 7 is 32.75. It is observed that in equipment risk, the risk weight with the largest proportion is equipment configuration, reaching 41%, followed by equipment repair and maintenance, and the one with the smallest risk is reliability detection, accounting for about 26%. 8 In some other alternative ways of this embodiment, the calculation of the second risk weights of the remaining secondary risk indicators in the primary risk indicators is similar to that in the foregoing embodiments. Specifically:

[0117] In some other alternative ways of this embodiment, the calculation of the second risk weights of the remaining secondary risk indicators in the primary risk indicators is similar to that in the foregoing embodiments. Specifically:

[0118] (1) Personnel risk B 1 includes physical fitness of personnel C 1 , awareness of safety responsibility of personnel C 2 , skill level of personnel C 3 , psychological quality of personnel C 4 , and teamwork ability of personnel C 5 . The importance degrees of each indicator for personnel risk are shown in Table 4:

[0119] Table 4

[0120] <![CDATA[C 1 > <![CDATA[C 2 > <![CDATA[C 3 > <![CDATA[C 4 > <![CDATA[C 5 > <![CDATA[C 1 > 1 1 / 3 1 / 2 1 1 / 3 <![CDATA[C 2 > 3 1 2 3 1 / 2 <![CDATA[C 3 > 2 1 / 2 1 2 1 / 2 <![CDATA[C 4 > 1 1 / 3 1 / 2 1 1 / 3 <![CDATA[C 5 > 3 2 2 3 1

[0121] Through matrix calculation, the following parameters are obtained:

[0122] Eigenvector Maximum eigenvalue λ max = 5.0719, consistency ratio CR = 0.0161; since CR < 0.1, it meets the requirements of the consistency test.

[0123] It is observed that in personnel risk, the risk weight with the largest proportion is teamwork ability of personnel, reaching 36%, followed by awareness of safety responsibility of personnel, and the ones with the smallest risk are psychological quality and physical fitness of personnel, both accounting for about 10%.

[0124] (2) Material Risk B 3 including the quality of materials C 9 , the use of materials C 10 , the storage of materials C 11 . The importance of each indicator for material risk is shown in Table 5 below.

[0125] Table 5

[0126] <![CDATA[C 9 > <![CDATA[C 10 > <![CDATA[C 11 > <![CDATA[C 9 > 1 2 2 <![CDATA[C 10 > 1 / 2 1 1 / 2 <![CDATA[C 11 > 1 / 2 2 1

[0127] Through matrix calculation, the following parameters are obtained:

[0128] Eigenvector The maximum eigenvalue λ max = 3.0536, Consistency Ratio CR = 0.0516; Since CR < 0.1, it meets the requirements of the consistency test. It is observed that among the material risks, the largest risk weight ratio is for material quality, reaching 49%, followed by material storage, and the smallest risk is for the use of materials, accounting for only about 20%.

[0129] (3) Technical Risk B 4 including the selection of technical solutions C 12 , technical disclosure C 13 , operating procedures C 14 , supervision and acceptance C 15 . The importance of each indicator for technical risk is shown in Table 6 below.

[0130] Table 6

[0131]

[0132]

[0133] Through calculation, the following parameters are obtained:

[0134] Eigenvector The maximum eigenvalue λ max = 4.0606, Consistency Ratio CR = 0.0227; Since CR < 0.1, it meets the requirements of the consistency test. It is observed that among the technical risks, the largest risk weight ratio is for operating procedures, reaching 39%, followed by supervision and acceptance, and the smallest risk is for technical disclosure, accounting for only about 17%.

[0135] (4) Environmental Risk B 5 including climate and geological conditions C 16 , on-site risk control (warning, evacuation) C 17 , site layout C 18 , interactive influence between pipelines 19, Gas users and government requirements C 20 , Repair time C 21 . The importance levels of each index to environmental risk are shown in Table 7.

[0136] Table 7

[0137] <![CDATA[C 16 > <![CDATA[C 17 > <![CDATA[C 18 > <![CDATA[C 19 > <![CDATA[C 20 > <![CDATA[C 21 > <![CDATA[C 16 > 1 1 / 2 2 2 2 1 / 2 <![CDATA[C 17 > 2 1 3 3 3 2 <![CDATA[C 18 > 1 / 2 1 / 3 1 1 / 2 2 1 / 2 <![CDATA[C 19 > 1 / 2 1 / 3 2 1 2 1 / 2 <![CDATA[C 20 > 1 / 2 1 / 3 1 / 2 1 / 2 1 1 / 3 <![CDATA[C 21 > 2 1 / 2 2 2 3 1

[0138] Through matrix calculation, the following parameters are obtained:

[0139] Eigenvector Largest eigenvalue λ max = 6.1810, consistency ratio CR = 0.0287; Since CR < 0.1, it meets the requirements of consistency test. It is observed that among environmental risks, the one with the largest risk weight ratio is on-site risk control, reaching 32%, followed by repair time, and the one with the smallest risk is gas users and government requirements, only accounting for about 7%.

[0140] (5) Management risk B 6 Including institutional setup and responsibilities C 22 , Rules and regulations C 23 , Emergency drills and plans C 24 , On-site command and supervision C 25 , Knowledge and skill training C 26 , Personnel allocation C 27 , Completeness of materials C 28 . The importance levels of each index to management risk are shown in Table 8.

[0141] Table 8

[0142] <![CDATA[C 22 > <![CDATA[C 23 > <![CDATA[C 24 > <![CDATA[C 25 > <![CDATA[C 26 > <![CDATA[C 27 > <![CDATA[C 28 > <![CDATA[C 22 > 1 1 / 3 1 1 / 2 1 1 / 2 2 <![CDATA[C 23 > 3 1 3 2 3 2 3 <![CDATA[C 24 > 1 1 / 3 1 1 / 2 2 1 / 2 2 <![CDATA[C 25 > 2 1 / 2 2 1 2 1 / 2 3 <![CDATA[C 26 > 1 1 / 3 1 / 2 1 / 2 1 1 / 2 2 <![CDATA[C 27 > 2 1 / 2 2 2 2 1 3 <![CDATA[C 28 > 1 / 2 1 / 3 1 / 2 1 / 3 1 / 2 1 / 3 1

[0143] Through matrix calculation, the following parameters are obtained:

[0144] Eigenvector Largest eigenvalue λ max = 7.1702, consistency ratio CR = 0.0902. Since CR < 0.1, it meets the requirements of consistency test. It is observed that among management risks, the one with the largest risk weight ratio is rules and regulations, reaching 29%, followed by on-site command and supervision, and the one with the smallest risk is completeness of materials, only accounting for about 6%.

[0145] In this embodiment, on the one hand, the steps for constructing the risk evaluation index system are elaborated. According to the principles and steps for constructing the risk factor index system in the emergency repair process, through the methods of expert questionnaire surveys and literature review, the risk factors are decomposed and refined from six main aspects, namely, personnel risk, equipment risk, material risk, technical risk, environmental risk, and management risk. On the other hand, the steps of the Analytic Hierarchy Process (AHP) are elaborated, the risk evaluation index system for gas pipeline leakage emergency repair is determined, and a two-level risk evaluation index system is formed. Among them, there are 6 first-level indicators, namely, personnel risk, equipment risk, material risk, technical risk, environmental risk, and management risk. In addition, 28 second-level indicators are established under the first-level indicators. The judgment matrix is constructed using the Analytic Hierarchy Process to determine the weights of each risk factor index in the gas pipeline leakage emergency repair process. Finally, the results are summarized and analyzed. Among the first-level indicators, the weights in descending order are: personnel risk is 0.2469, equipment risk is 0.1150, material risk is 0.0480, technical risk is 0.0911, environmental risk is 0.1524, and management risk is 0.3466.

[0146] It should also be noted that the risk evaluation method in this embodiment further includes: determining the second-level risk indicator corresponding to the maximum value among the second-level risk weights in each first-level risk indicator, and taking this second-level risk indicator as the target risk indicator. Based on the target risk indicator, countermeasures for gas pipeline leakage emergency repair are proposed. That is to say, according to the above risk evaluation results, for the second-level risk indicators with relatively large risk weights in each of the six first-level risk indicators, this embodiment can also specifically propose risk countermeasures in the gas pipeline leakage emergency repair process, aiming to enable the gas pipeline leakage emergency repair process to proceed safely and smoothly.

[0147] For example, among the personnel risk weights, the largest proportion is the personnel team cooperation ability, reaching 36%, followed by the personnel safety responsibility awareness. The least risky ones are the personnel psychological quality and physical quality, both accounting for about 10%. For the personnel risk factors, the emergency repair company should pay attention to aspects such as the qualification and professional ability of emergency repair personnel, let the emergency repair personnel be familiar with the operation characteristics, safety precautions, and personal protection requirements in the gas pipeline leakage emergency repair process, master the emergency repair skills, and standardize their own behaviors.

[0148] Step 103: Determine the membership degree subset of the risk evaluation level of the second-level risk indicator, and based on the membership degree subset, determine the fuzzy evaluation matrix of the first-level risk indicator.

[0149] In this embodiment, the fuzzy comprehensive evaluation method is introduced first. The basis of the fuzzy comprehensive evaluation method is fuzzy mathematics. The object to be evaluated and the fuzzy concepts that can reflect the evaluation object are regarded as fuzzy sets, and the corresponding membership functions are constructed. By using the relevant operations and transformations of fuzzy sets, the quantitative analysis of fuzzy objects is realized. The fuzzy comprehensive evaluation has the following advantages: the modeling method is simple and easy to learn, and it can evaluate complex multi-factor problems.

[0150] In the risk assessment of the gas pipeline leakage repair process, each risk factor has a certain degree of fuzziness, and the accurate value of the risk cannot be given. However, the fuzzy comprehensive evaluation can, in the evaluation process, through the use of fuzzy operation rules, realize the quantitative risk assessment of uncertain factors. Using the fuzzy comprehensive evaluation method to evaluate the risk of the gas pipeline leakage repair process can comprehensively summarize the suggestions of each repair expert and comprehensively reflect the risk level in the repair process.

[0151] Among them, the relevant steps of the fuzzy comprehensive evaluation method are as follows:

[0152] (1) Determine the evaluation index C of the object to be evaluated

[0153] C = (c 1 , c 2 ,..., c n ), where n is the number of evaluation object indicators.

[0154] (2) Determine the comment set V of the evaluation object

[0155] V = (u 1 , u 2 ... u m ), where m is the number of evaluation levels. Generally, the evaluation levels are divided into 3 to 5 levels, and each evaluation level can correspond to a fuzzy subset.

[0156] (3) Construct the fuzzy relation matrix R

[0157] Determine the membership degree of each evaluation index to the level fuzzy subset of the evaluation object to obtain the fuzzy relation matrix R:

[0158]

[0159] Among them, r ij represents the membership degree of the evaluation index c i to the level fuzzy subset of the evaluation

[0160] (4) Determine the weight vector W of the evaluation index

[0161] Use the analytic hierarchy process to obtain the weight vector of the evaluation index: W = (w 1 , w 2,..., w n ), and then normalize W to obtain the weight of the evaluation index at the next level relative to a certain factor at the previous level.

[0162] (5) Obtain the result matrix S of the final fuzzy comprehensive evaluation

[0163] The result matrix S is the product of the weight vector W of each evaluation index and the fuzzy relation matrix R.

[0164] Among them, S i represents the membership degree of the final evaluation object to each fuzzy subset of the evaluation level. (6) Analyze the result vector S of the fuzzy comprehensive evaluation

[0165] Finally, it is necessary to convert the fuzzy comprehensive evaluation result into a real value. The common conversion methods are the maximum membership degree method and the weighted average method. If the maximum membership degree method is adopted, the final evaluation level is the maximum value of S i ; but when there is more than one maximum value of S i , it is difficult to obtain the evaluation level. The weighted average method is to multiply the membership degree of each fuzzy subset of the evaluation level by the corresponding membership degree value and then add them up to obtain the final evaluation result.

[0166] In some alternative embodiments of this example, still taking the risk evaluation of gas pipeline leakage repair as an example, on the basis of having established the risk evaluation index of gas pipeline leakage repair and obtaining the weight coefficients of each level of indicators by the AHP method, this example further needs to determine the membership degree subset of the risk evaluation level of the secondary risk indicators, and determine the fuzzy evaluation matrix of the primary risk indicators according to the membership degree subset.

[0167] First, establish the influence factor set, comment set and weight set. Specifically:

[0168] (1) According to the previously established risk index system, obtain the following influence factor set:

[0169] B 1 ={C 1 C 2 C 3 C 4 C 5}, B 2 ={C 6 C 7 C 8},

[0170] B 3 ={C 9 C 10C 11}, B 4 = {C 12 C 13 C 14 C 15},

[0171] B 5 = {C 16 C 17 C 18 C 19 C 20 C 21}, B 6 = {C 22 C 23 C 24 C 25 C 26 C 27 C 28}

[0172] (2) Construct the comment set, that is, determine the risk evaluation levels and assign values to different risk evaluation levels. In a specific example, as shown in Table 9:

[0173] Table 9

[0174] Risk level High Relatively high Average Relatively low Low Assignment 5 4 3 2 1

[0175] The constructed comment set is as follows: V = {v 1 , v 2 , v 3 , v 4 , v 5} = {High, Relatively high, General, Relatively low, Low}.

[0176] (3) Establish the weight set

[0177] According to the risk weights of each level of indicators in the risk evaluation of the leakage emergency repair process determined by the analytic hierarchy process as mentioned above, the weight set can be obtained, which is equivalent to the second eigenvector after the aforementioned normalization process, that is:

[0178] W B1 = [0.0969 0.2720 0.1729 0.0969 0.3613]

[0179] W B2 = [0.4126 0.2599 0.3275]W B3 = [0.4934 0.1958 0.3108]

[0180] W B4 = [0.1976 0.1682 0.3952 0.2390]

[0181] W B5 = [0.1655 0.3194 0.0977 0.1235 0.0721 0.2219]

[0182] W B6 = [0.0957 0.2877 0.1080 0.1626 0.0882 0.1992 0.0587]

[0183] Further, construct the membership degree set. In this embodiment, as Figure 5 shown, determine the membership degree subset of the risk evaluation level of the secondary risk indicators, including:

[0184] Step 1031: According to the determination of the risk evaluation levels of the secondary risk indicators in the primary risk indicators, determine the third risk score of the secondary risk indicators, where the risk evaluation levels include high risk level, relatively high risk level, general risk level, relatively low risk level, and low risk level in descending order.

[0185] In some alternative ways of this embodiment, invite experts in gas pipeline leakage repair to conduct influence factor scoring. For example, invite 10 repair experts to determine the risk evaluation levels of each secondary risk indicator from high risk level, relatively high risk level, general risk level, relatively low risk level, and low risk level. The third risk score is shown in Table 10, where the values in the table represent the number of people who select this risk evaluation level.

[0186] Table 10

[0187]

[0188]

[0189] Referring to Table 10, taking the physical fitness C of personnel 1 in the primary risk indicator of personnel risk B 1 as an example, 5 experts determine that it belongs to the high risk level, 3 experts determine that it belongs to the relatively high risk level, 2 experts determine that it is at the general risk level, no expert determines that it is at the low risk level and relatively low risk level, and the determination of the remaining secondary risk indicators can also be obtained from Table 10. This application will not elaborate further here.

[0190] Step 1032: According to the third risk score, determine the membership degree subset of the risk evaluation level of the secondary risk indicators.

[0191] In this embodiment, after determining the third risk score shown in Table 10, the membership degree subset of the risk evaluation level of the secondary risk indicators can be further determined.

[0192] Specifically: membership degree subset: R i =(r i1 r i2 ...r i5 )

[0193] R i refers to the membership degree of each v 1 v 2 ...v 5 in the risk comment set corresponding to the i-th risk indicator.

[0194]

[0195] where j = (1, 2,..., 5).

[0196] In a specific example, still taking the above-mentioned primary risk indicator of personnel risk B 1 and the physical fitness of personnel C 1 as an example, if 5 experts determine that it belongs to the high-risk level, the membership degree of the high-risk level is 0.5; if 3 experts determine that it belongs to the relatively high-risk level, the membership degree of the relatively high-risk level is 0.3; if 2 experts determine that it is at the general risk level, the membership degree of the general risk level is 0.2; if no expert determines that it is at the low-risk level and the relatively low-risk level, the membership degrees of the low-risk level and the relatively low-risk level are 0. Thus, the membership degree subset of the physical fitness of personnel C 1 is

[0197] In this embodiment, after determining the membership degree subset, the fuzzy evaluation matrix of the primary risk indicator can be further determined. As Figure 5 shown, determining the fuzzy evaluation matrix of the primary risk indicator includes:

[0198] Step 1033: Determine the membership degree subsets of the secondary risk indicators included in the primary risk indicator respectively.

[0199] In this embodiment, for example, the secondary risk indicators in the personnel risk B 1 also include the safety responsibility awareness of personnel C 2 , the skill level of personnel C 3 , the psychological quality of personnel C 4 , and the teamwork ability of personnel C 5 . The determination of the risk levels of these secondary risk indicators can also be obtained from Table 10, and the corresponding membership degrees can also be determined through Table 10. The detailed determination process is not elaborated in this application. Specifically, the membership degree subsets of the secondary risk indicators of the personnel risk B 1 are as follows:

[0200]

[0201]

[0202]

[0203] In addition, equipment risk B 2 The membership subsets of each secondary risk index are as follows:

[0204]

[0205]

[0206] Material risk B 3 The membership subsets of each secondary risk index are as follows:

[0207]

[0208]

[0209] Technical risk B 4 The membership subsets of each secondary risk index are as follows:

[0210]

[0211]

[0212] Environmental risk B 5 The membership subsets of each secondary risk index are as follows:

[0213]

[0214]

[0215]

[0216] Management risk B 6 The membership subsets of each secondary risk index are as follows:

[0217]

[0218]

[0219]

[0220]

[0221] Step 1034: Take the membership subsets of the secondary risk indicators as the row vectors of the fuzzy evaluation matrix of the primary risk indicator, where the number of rows of the fuzzy evaluation matrix is the number of secondary risk indicators included in the primary risk indicator.

[0222] In some alternative embodiments of this embodiment, after determining the membership subsets of the secondary risk indicators, it is also possible to determine the fuzzy evaluation matrix of the primary risk indicator corresponding to each secondary risk indicator according to the membership subsets of the secondary risk indicators. Specifically, take the membership subsets of the secondary risk indicators as the row vectors of the fuzzy evaluation matrix of the primary risk indicator, and the number of rows of the fuzzy evaluation matrix is the number of secondary risk indicators included in the primary risk indicator.

[0223] Taking the membership subsets of the secondary risk indicators of the aforementioned personnel risk B 1 as an example for illustration, that is, take and as row vectors, and the number of rows is 5, which is the number of secondary risk indicators included in the personnel risk B 1 Finally, obtain the fuzzy evaluation matrix R 1 of the personnel risk B B1 as follows:

[0224]

[0225] The determination process of the fuzzy evaluation matrices of the remaining primary risk indicators is similar, and this application will not elaborate here. The fuzzy evaluation matrices of the remaining primary risk indicators are as follows:

[0226] Equipment risk fuzzy matrix

[0227] Material risk fuzzy matrix

[0228] Technology risk fuzzy matrix

[0229] Environmental risk fuzzy matrix

[0230] Management risk fuzzy matrix

[0231] Step 104: Determine the first membership degree vector of the risk evaluation level of the primary risk indicator according to the second risk weight and the fuzzy evaluation matrix of the primary risk indicator, and determine the total evaluation matrix of the object to be evaluated according to the first membership degree vector.

[0232] In this embodiment, as Figure 6As shown, according to the second risk weight and the fuzzy evaluation matrix of the first-level risk indicators, determining the first membership degree vector of the risk evaluation level of the first-level risk indicators includes:

[0233] Step 1041: Determine the first product result of the second risk weight and the fuzzy evaluation matrix of the first-level risk indicators;

[0234] Step 1042: Determine the first membership degree vector of the risk evaluation level of the first-level risk indicators according to the first product result.

[0235] In some alternative ways of this embodiment, determining the first product result of the second risk weight and the fuzzy evaluation matrix of the first-level risk indicators, that is the first membership degree vector S of each first-level indicator B i for the risk evaluation level can be obtained. Bi .

[0236] In a specific example, the second risk weight of the secondary risk indicators and the fuzzy evaluation matrix of the first-level risk indicators have been determined through the foregoing embodiments. Taking the personnel risk B 1 in the first-level risk indicators as an example, its first membership degree vector is:

[0237]

[0238] In this embodiment, as Figure 6 shown, according to the first membership degree vector, determining the total evaluation matrix of the object to be evaluated includes:

[0239] Step 1043: Determine the first membership degree vectors of each of the first-level risk indicators respectively.

[0240] In some alternative ways of this embodiment, based on the method of the foregoing step 1042, the first membership degree vectors of other first-level risk indicators can be determined in the same way, as follows:

[0241] S B2 = W B2 × R B2 = [0.5587 0.3 0.1413 0 0]

[0242] S B3 = W B3 × R B3 = [0.6183 0.2311 0.1 0.0506 0]

[0243] S B4 = W B4 × R B4 = [0.6395 0.2366 0.1 0.0239 0]

[0244] S B5 = W B5 ×R B5 = [0.5248 0.3166 0.1170 0.0416 0]

[0245] S B6 = W B6 ×R B6 = [0.5960 0.2780 0.1 0.0260]

[0246] Step 1044: Take the first membership degree vectors of the first-level risk indicators as the row vectors of the total evaluation matrix of the object to be evaluated, where the number of rows of the total evaluation matrix is the number of the first-level risk indicators.

[0247] In some alternative embodiments of the present embodiment, after determining the first membership degree vectors of the first-level risk indicators, the total evaluation matrix of the object to be evaluated can also be determined according to the first membership degree vectors of the first-level risk indicators. Specifically, take the first membership degree vectors of the first-level risk indicators as the row vectors of the total evaluation matrix of the object to be evaluated, and the number of rows of the total evaluation matrix is the number of the first-level risk indicators.

[0248] In a specific example, taking the foregoing example for illustration, the total evaluation matrix is as follows:

[0249]

[0250] Step 105: Determine the second membership degree vector of the risk evaluation level of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership degree vector and the risk level assignment corresponding to the risk evaluation level.

[0251] In the present embodiment, as Figure 7 shown, determining the second membership degree vector of the risk evaluation level of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated includes:

[0252] Step 1051: Determine the second product result of the first risk weight and the total evaluation matrix of the object to be evaluated;

[0253] Step 1052: Determine the second membership degree vector of the risk evaluation level of the object to be evaluated according to the second product result.

[0254] In some alternative embodiments of the present embodiment, determining the second product result of the first risk weight and the total evaluation matrix of the object to be evaluated, that is, S A = WA ×R A , the second membership degree vector S of the risk evaluation level of the object to be evaluated can be obtained A , specifically:

[0255]

[0256] In this embodiment, as Figure 7 shown, according to the second membership degree vector and the risk level assignment corresponding to the risk evaluation level, determining the comprehensive risk score of the object to be evaluated includes:

[0257] Step 1053: Use the weighted average method to determine the third product result of the second membership degree vector and the risk level assignments corresponding to each risk evaluation level.

[0258] In this embodiment, according to Table 9 of the foregoing embodiment, the assignment for the high-risk level is 5 points, the assignment for the relatively high-risk level is 4 points, the assignment for the general-risk level is 3 points, the assignment for the relatively low-risk level is 2 points, and the assignment for the low-risk level is 1 point. It should be understood that the above risk level assignments are exemplary, and the present application does not make any limitations thereto.

[0259] Among them, the third product result of the second membership degree vector and the risk level assignments corresponding to each risk evaluation level is: 0.5823×5, 0.2767×4, 0.1144×3, 0.0266×2.

[0260] Step 1054: Determine the comprehensive risk score of the object to be evaluated according to the third product result.

[0261] Using the weighted average method, the comprehensive evaluation of the gas pipeline leakage emergency repair process is:

[0262]

[0263] In the formula, S i represents the second membership degree vector of the risk evaluation level of the object to be evaluated, V i represents the magnitude of the risk level assignment, and S is the comprehensive risk score of the object to be evaluated.

[0264] Step 106: Determine the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk evaluation level.

[0265] In this embodiment, as Figure 8 shown, Step 106 further includes:

[0266] Step 1061: Determine the risk score interval corresponding to the comprehensive risk score according to the risk level assignment corresponding to the risk evaluation level;

[0267] Step 1062: Determine the emergency repair risk level of the object to be evaluated according to the risk score range.

[0268] In this embodiment, the fuzzy comprehensive evaluation method is used to evaluate the risk of the gas pipeline leakage emergency repair process, and the comprehensive risk score S of the gas pipeline leakage emergency repair process is obtained as 4.4147. After determining the comprehensive risk score, further according to the risk level assignment corresponding to the risk evaluation level, the corresponding risk score range is determined. For example, the aforementioned comprehensive score is S = 4.4147, which is in the risk score range of [4, 5], that is, between the "higher risk" and "high risk" levels. Further determine that 4.4147 is closer to 4, so the emergency repair risk level of the gas pipeline leakage emergency repair process belongs to the higher risk.

[0269] It should be noted that in the technical solution of this application, the acquisition, storage, use, and processing of data all comply with the relevant regulations of laws and regulations.

[0270] Based on the same inventive concept, the embodiment of this application also provides a risk evaluation device for gas pipeline leakage emergency repair, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of solving the problem by this risk evaluation device for gas pipeline leakage emergency repair is similar to that of a risk evaluation method for gas pipeline leakage emergency repair, the implementation of a risk evaluation device for gas pipeline leakage emergency repair can refer to the implementation of a risk evaluation method for gas pipeline leakage emergency repair, and the repeated parts will not be described again. As used below, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and contemplated.

[0271] As Figure 9 shown, this risk evaluation device for gas pipeline leakage emergency repair includes:

[0272] A risk index determination module 701, configured to determine the first-level risk indicators of the object to be evaluated and the second-level risk indicators included in each of the first-level risk indicators;

[0273] A risk weight determination module 702, configured to determine the first risk weight of the first-level risk indicators and the second risk weight of the second-level risk indicators relative to the first-level risk indicators;

[0274] A fuzzy evaluation matrix determination module 703, configured to determine the membership subset of the risk evaluation level of the second-level risk indicators, and determine the fuzzy evaluation matrix of the first-level risk indicators according to the membership subset;

[0275] The overall evaluation matrix determination module 704 is configured to determine a first membership degree vector of the risk evaluation level of the first-level risk indicators according to the second risk weights and the fuzzy evaluation matrix of the first-level risk indicators, and determine the overall evaluation matrix of the object to be evaluated according to the first membership degree vector;

[0276] The comprehensive risk score determination module 705 is configured to determine a second membership degree vector of the risk evaluation level of the object to be evaluated according to the first risk weights and the overall evaluation matrix of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership degree vector and the risk level assignment corresponding to the risk evaluation level;

[0277] The emergency repair risk level determination module 706 is configured to determine the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk evaluation level.

[0278] In some alternative embodiments of the present embodiment, the risk weight determination module includes a first risk weight determination unit, wherein the first risk weight unit is configured to:

[0279] Determine a first risk score of the first-level risk indicators according to the importance degree after pairwise comparison of the first-level risk indicators;

[0280] Construct a first judgment matrix corresponding to the first-level risk indicators according to the first risk score, and perform a consistency test on the first judgment matrix;

[0281] In response to determining that the consistency test of the first judgment matrix passes, determine a first eigenvector of the first judgment matrix, and perform normalization processing on the first eigenvector;

[0282] Determine the first risk weights of the first-level risk indicators according to the first eigenvector after normalization processing.

[0283] In some alternative embodiments of the present embodiment, the risk weight determination module includes a second risk weight determination unit, wherein the second risk weight unit is configured to:

[0284] Determine a second risk score of the second-level risk indicators according to the importance degree after pairwise comparison of the second-level risk indicators in the first-level risk indicators;

[0285] Construct a second judgment matrix corresponding to the second-level risk indicators according to the second risk score, and perform a consistency test on the second judgment matrix;

[0286] In response to determining that the consistency check of the second judgment matrix passes, determine the second eigenvector of the second judgment matrix, and perform normalization processing on the second eigenvector;

[0287] According to the normalized second eigenvector, determine the second risk weight of the secondary risk indicator.

[0288] In some alternative embodiments of the present embodiment, the fuzzy evaluation matrix determination module includes a membership subset determination unit, wherein the membership subset determination unit is configured to:

[0289] According to the determination of the risk evaluation levels of the secondary risk indicators in the primary risk indicator, determine the third risk score of the secondary risk indicator, where the risk evaluation levels include high risk level, relatively high risk level, medium risk level, relatively low risk level, and low risk level from high to low;

[0290] According to the third risk score, determine the membership subset of the risk evaluation level of the secondary risk indicator.

[0291] In some alternative embodiments of the present embodiment, the fuzzy evaluation matrix determination module includes a fuzzy evaluation matrix determination unit, wherein the fuzzy evaluation matrix determination unit is configured to:

[0292] Respectively determine the membership subsets of the secondary risk indicators included in the primary risk indicator;

[0293] Use the membership subsets of the secondary risk indicators as the row vectors of the fuzzy evaluation matrix of the primary risk indicator, where the number of rows of the fuzzy evaluation matrix is the number of secondary risk indicators included in the primary risk indicator.

[0294] In some alternative embodiments of the present embodiment, the overall evaluation matrix determination module includes a first membership vector determination unit, wherein the first membership vector determination unit is configured to:

[0295] Determine the first product result of the second risk weight and the fuzzy evaluation matrix of the primary risk indicator;

[0296] According to the first product result, determine the first membership vector of the risk evaluation level of the primary risk indicator.

[0297] In some alternative embodiments of the present embodiment, the overall evaluation matrix determination module includes an overall evaluation matrix determination unit, wherein the overall evaluation matrix determination unit is configured to:

[0298] Respectively determine the first membership vectors of the primary risk indicators;

[0299] Take the first membership degree vector of each of the first-level risk indicators as the row vector of the total evaluation matrix of the object to be evaluated, where the number of rows of the total evaluation matrix is the number of the first-level risk indicators.

[0300] In some alternative embodiments of the present embodiment, the comprehensive risk score determination module includes a second membership degree vector determination unit, where the second membership degree determination unit is configured to:

[0301] Determine the second product result of the first risk weight and the total evaluation matrix of the object to be evaluated;

[0302] According to the second product result, determine the second membership degree vector of the risk evaluation level of the object to be evaluated.

[0303] In some alternative embodiments of the present embodiment, the comprehensive risk score determination module includes a comprehensive risk score determination unit, where the comprehensive risk score determination unit is configured to:

[0304] Adopt the weighted average method to determine the third product result of the second membership degree vector and the risk level assignment corresponding to each risk evaluation level;

[0305] According to the third product result, determine the comprehensive risk score of the object to be evaluated.

[0306] In some alternative embodiments of the present embodiment, the emergency repair risk level determination module is further configured to:

[0307] According to the risk level assignment corresponding to the risk evaluation level, determine the risk score interval corresponding to the comprehensive risk score;

[0308] According to the risk score interval, determine the emergency repair risk level of the object to be evaluated.

[0309] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0310] An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of a risk evaluation method for gas pipeline leakage emergency repair in the foregoing embodiments.

[0311] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the steps of a risk evaluation method for gas pipeline leakage emergency repair in the foregoing embodiments.

[0312] Figure 10FIG. shows a schematic block diagram of an exemplary electronic device 800 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0313] As Figure 10 shown, the device 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0314] A plurality of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0315] The computing unit 801 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above, such as a risk assessment method for emergency repair of gas pipeline leaks.

[0316] For example, in some embodiments, a risk assessment method for emergency repair of gas pipeline leaks can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed onto device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by computing unit 801, one or more steps of a risk assessment method for emergency repair of gas pipeline leaks described above can be performed. Alternatively, in other embodiments, computing unit 801 may be configured to perform a risk assessment method for emergency repair of gas pipeline leaks by any other suitable means (e.g., by means of firmware).

[0317] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0318] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0319] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0320] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0321] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0322] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating a blockchain.

[0323] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and no limitation is imposed herein.

[0324] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A risk assessment method for emergency repair of gas pipeline leaks, characterized in that, it includes: Determine the first-level risk indicators of the object to be evaluated and the second-level risk indicators included in each of the first-level risk indicators; Determine the first risk weight of the first-level risk indicators and the second risk weight of the second-level risk indicators relative to the first-level risk indicators; Determine the membership subset of the risk assessment level of the second-level risk indicators, and determine the fuzzy evaluation matrix of the first-level risk indicators according to the membership subset; According to the second risk weight and the fuzzy evaluation matrix of the first-level risk indicators, determine the first membership vector of the risk assessment level of the first-level risk indicators, and determine the total evaluation matrix of the object to be evaluated according to the first membership vector; According to the first risk weight and the total evaluation matrix of the object to be evaluated, determine the second membership vector of the risk assessment level of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership vector and the risk level assignment corresponding to the risk assessment level; According to the comprehensive risk score and the risk level assignment corresponding to the risk assessment level, determine the emergency repair risk level of the object to be evaluated.

2. The method according to claim 1, characterized in that, The determination of the first risk weight of the first-level risk indicators includes: Determine the first risk score of the first-level risk indicators according to the importance degree after pairwise comparison of the first-level risk indicators; Construct a first judgment matrix corresponding to the first-level risk indicators according to the first risk score, and perform a consistency test on the first judgment matrix; In response to determining that the consistency test of the first judgment matrix passes, determine the first eigenvector of the first judgment matrix, and perform normalization processing on the first eigenvector; Determine the first risk weight of the first-level risk indicators according to the first eigenvector after normalization processing.

3. The method according to claim 1, characterized in that, Determining the second risk weight of the second-level risk indicators relative to the first-level risk indicators includes: Determine the second risk score of the second-level risk indicators according to the importance degree after pairwise comparison of the second-level risk indicators in the first-level risk indicators; Construct a second judgment matrix corresponding to the second-level risk indicators according to the second risk score, and perform a consistency test on the second judgment matrix; In response to determining that the consistency test of the second judgment matrix passes, determine the second eigenvector of the second judgment matrix, and perform normalization processing on the second eigenvector; Determine the second risk weight of the second-level risk indicators according to the second eigenvector after normalization processing.

4. The method according to claim 1, characterized in that, Determining the membership subset of the risk assessment level of the second-level risk indicators includes: Determine the third risk score of the secondary risk indicators according to the determination of the risk evaluation levels of the secondary risk indicators in the primary risk indicators, where the risk evaluation levels include high risk level, relatively high risk level, general risk level, relatively low risk level, and low risk level in descending order; Determine the membership subset of the risk evaluation level of the secondary risk indicators according to the third risk score.

5. The method according to claim 1, wherein, the determining the fuzzy evaluation matrix of the primary risk indicators according to the membership subset includes: respectively determine the membership subsets of the secondary risk indicators included in the primary risk indicators; take the membership subsets of the secondary risk indicators as the row vectors of the fuzzy evaluation matrix of the primary risk indicators, where the number of rows of the fuzzy evaluation matrix is the number of secondary risk indicators included in the primary risk indicators.

6. The method according to claim 1, wherein, the determining the first membership vector of the risk evaluation level of the primary risk indicators according to the second risk weight and the fuzzy evaluation matrix of the primary risk indicators includes: determine the first product result of the second risk weight and the fuzzy evaluation matrix of the primary risk indicators; determine the first membership vector of the risk evaluation level of the primary risk indicators according to the first product result.

7. The method according to claim 1, wherein, the determining the total evaluation matrix of the object to be evaluated according to the first membership vector includes: respectively determine the first membership vectors of the primary risk indicators; take the first membership vectors of the primary risk indicators as the row vectors of the total evaluation matrix of the object to be evaluated, where the number of rows of the total evaluation matrix is the number of primary risk indicators.

8. The method according to claim 1, wherein, the determining the second membership vector of the risk evaluation level of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated includes: determine the second product result of the first risk weight and the total evaluation matrix of the object to be evaluated; determine the second membership vector of the risk evaluation level of the object to be evaluated according to the second product result.

9. The method according to claim 1, wherein, the determining the comprehensive risk score of the object to be evaluated according to the second membership vector and the risk level assignment corresponding to the risk evaluation level includes: using the weighted average method to determine the third product result of the second membership vector and the risk level assignment corresponding to each risk evaluation level; determine the comprehensive risk score of the object to be evaluated according to the third product result.

10. The method according to claim 1, wherein, the determining the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk evaluation level includes: determine the risk score interval corresponding to the comprehensive risk score according to the risk level assignment corresponding to the risk evaluation level; Determine the emergency repair risk level of the object to be evaluated according to the risk score range.

11. A risk assessment device for emergency repair of gas pipeline leakage, characterized in that, it includes: A risk index determination module configured to determine the primary risk indices of the object to be evaluated and the secondary risk indices included in each of the primary risk indices; A risk weight determination module configured to determine the first risk weight of the primary risk indices and the second risk weight of the secondary risk indices relative to the primary risk indices; A fuzzy evaluation matrix determination module configured to determine the membership degree subset of the risk assessment levels of the secondary risk indices, and determine the fuzzy evaluation matrix of the primary risk indices according to the membership degree subset; A total evaluation matrix determination module configured to determine the first membership degree vector of the risk assessment levels of the primary risk indices according to the second risk weight and the fuzzy evaluation matrix of the primary risk indices, and determine the total evaluation matrix of the object to be evaluated according to the first membership degree vector; A comprehensive risk score determination module configured to determine the second membership degree vector of the risk assessment levels of the object to be evaluated according to the first risk weight and the total evaluation matrix of the object to be evaluated, and determine the comprehensive risk score of the object to be evaluated according to the second membership degree vector and the risk level assignment corresponding to the risk assessment level; An emergency repair risk level determination module configured to determine the emergency repair risk level of the object to be evaluated according to the comprehensive risk score and the risk level assignment corresponding to the risk assessment level.

12. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps of a risk assessment method for emergency repair of gas pipeline leakage according to any one of claims 1 to 10.

13. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the steps of a risk assessment method for emergency repair of gas pipeline leakage according to any one of claims 1 to 10.

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