A gas pipeline unit failure identification method and system

Abstract

The present application relates to the technical field of gas pipeline detection processing, and particularly relates to a gas pipeline unit failure identification method and system, which comprises setting sectional points in a gas pipeline network and obtaining comprehensive failure factor data between adjacent sectional points; applying a reciprocal tabu breadth-first base ring search method to determine all base rings; based on the comprehensive failure factor data of the gas pipeline and the obtained base rings, using a fuzzy clustering method to cluster the gas pipeline into basic pipeline units; applying a spatial breadth-first search method and a fuzzy clustering method to cluster several basic pipeline units that are close in space into regional pipeline units; integrating the comprehensive failure factor data proportion of all pipelines in each regional pipeline unit into a factor set, and establishing an evaluation set, and using a fuzzy comprehensive evaluation method to evaluate the corresponding grade of all regional pipeline units and classify them into the evaluation set. Based on the evaluated gas pipeline failure grade, the present application can dynamically develop a risk reduction scheme to avoid safety accidents.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas pipeline detection processing, and particularly relates to a gas pipeline unit failure identification method and system. BACKGROUND

[0002] The safe and stable operation of a gas pipeline is of great significance to the protection of national economic development. Identifying gas pipelines prone to failure accidents and developing targeted risk reduction measures to avoid any accidents is an important measure to maintain the safety of gas pipelines. However, there are a large number of gas pipelines, with over a thousand gas pipelines in medium-sized cities, and they are intertwined into complex ring-branch structures, making it difficult to identify gas pipelines prone to failure.

[0003] In the prior art, a gas pipeline is divided into linear pipeline units, and then pipeline risk assessment is performed based on the obtained basic data to identify pipelines prone to failure.

[0004] In the process of implementing the present application, it is found that the prior art has at least the following problems:

[0005] The risk assessment method based on linear pipeline units is only suitable for long-distance oil and gas pipelines with a relatively simple surrounding social environment and a branch structure. For gas pipelines, the surrounding social environment factors are complex, and the gas pipeline is divided into multiple linear pipeline units. A 500-kilometer gas pipeline generally needs to be divided into more than 2000 linear pipeline units, and hundreds of basic data need to be obtained for each pipeline unit, resulting in a huge workload and a long identification period for identifying pipelines prone to failure, which greatly occupies the management resources of enterprises and cannot dynamically perceive the changes in failure risks. Therefore, there is an urgent need to develop an efficient pipeline failure identification method that adapts to the ring-branch structure characteristics of gas pipelines to support the formation and development of gas pipeline risk pre-evaluation technology and help with gas pipeline risk dynamic evaluation and risk control.

[0006] Currently, from the perspective of being well adapted to ring-branch structure gas pipelines, the complex ring-branch structure gas pipeline is divided into several ring structure regional pipeline units, and the improved fuzzy clustering analysis and fuzzy comprehensive evaluation method are used to efficiently and batch identify pipelines prone to failure. However, no such achievement has been found. A more reasonable technical solution should be proposed to solve the technical problems in the prior art. SUMMARY

[0007] To at least overcome one of the above-mentioned defects, the present application proposes a gas pipeline unit failure identification method and system. By dividing the gas pipeline into several base ring structure regional pipeline units, applying the opposite taboo breadth priority base ring search, improved fuzzy clustering and improved fuzzy comprehensive evaluation method, the failure susceptibility level evaluation of the regional pipeline unit (pipeline set) can be completed with less workload, and the long period, huge workload and risk evaluation of each pipeline are changed into dynamic, small workload and batch pipeline failure susceptibility identification of pipeline set, which can support managers to create risk pre-evaluation technology, dynamically master the failure susceptibility level of the gas pipeline and develop targeted control measures, and solve the problems of the prior art, such as not applicable to ring-branch urban gas pipeline, low identification efficiency and huge workload.

[0008] To achieve the above-mentioned purpose, the identification method disclosed by the present application can adopt the following technical solutions:

[0009] A gas pipeline unit failure identification method, comprising:

[0010] Setting a segmentation point in the gas pipeline network and obtaining comprehensive failure factor data between adjacent segmentation points;

[0011] Applying the opposite taboo breadth priority base ring search method to determine all base rings in the target ring-branch gas pipeline network;

[0012] Based on the comprehensive failure factor data of the gas pipeline and the obtained base rings, the fuzzy clustering method is used to cluster the gas pipeline into basic pipeline units;

[0013] Applying the spatial breadth priority search method and the fuzzy clustering method, several basic pipeline units that are close in space are clustered into regional pipeline units;

[0014] Integrating the comprehensive failure factor data proportion of all pipelines in each regional pipeline unit into a factor set, setting an evaluation level and establishing a corresponding evaluation set, and using the fuzzy comprehensive evaluation method to evaluate the corresponding level of all regional pipeline units and classify them into the evaluation set.

[0015] The above-identified method solves the problems of the prior art, such as not applicable to ring-branch gas pipeline network, low efficiency, huge workload and great occupation of management resources, breaks through the fixed mode of traditional failure susceptibility pipeline identification using linear pipeline units and one-by-one risk evaluation, considers the ring-branch structure characteristics of the gas pipeline network, creates regional pipeline units coupled with base ring structures, divides a large number of gas pipelines into several regional pipeline units, and then batch identifies the failure susceptibility pipelines, effectively reduces the workload of failure susceptibility pipeline identification, significantly shortens the identification period, and avoids the waste of urban gas pipeline management resources.

[0016] Further, the application aims at the demand for normalizing the identification of failure-prone gas pipelines, and proposes a contravariant tabu breadth-first base loop search algorithm, which can efficiently obtain base loops in loop-branch gas pipe networks. The contravariant tabu breadth-first base loop search algorithm is proved to reduce 7 / 9 of the calculation amount, and based on the algorithm, the calculation speed can be improved, and the identification result of the failure-prone pipeline can be quickly obtained. Specifically, determining all base loops in the target loop-branch gas pipe network includes the following processes:

[0017] S201: establishing an undirected graph G with the segment points in the target pipe network as vertices and the pipelines between the segment points as edges;

[0018] S202: selecting one of the vertices of an edge T in the undirected graph G as a starting point and the other vertex as a terminal point, selecting the starting point as a current point a and the terminal point as a current point b, and recording T as a search edge connecting the current point a and b;

[0019] S203: marking the current point a as a forwardly searched point and the current point b as a reversely searched point, obtaining a vertex set A connected with the current point a and not marked as a forwardly searched point by applying a breadth-first search method, synchronously obtaining a vertex set B connected with the current point b and not marked as a reversely searched point, recording a search edge connected with the current point a as a forward leading edge and a search edge connected with the current point b as a reverse leading edge;

[0020] S204: calculating the included angles of the connecting edges of the vertices in the vertex set A and the current point a and the forward leading edge, marking the vertex with the smallest included angle as a forward tabu point, and recording the connecting edge of the forward tabu point and the current point a as a search edge; synchronously calculating the included angles of the connecting edges of the vertices in the vertex set B and the current point b and the reverse leading edge, marking the vertex with the smallest included angle as a reverse tabu point, and recording the connecting edge of the reverse tabu point and the current point a as a search edge;

[0021] S205: sequentially judging whether the vertices in A are reversely searched, and synchronously sequentially judging whether the vertices in B are forwardly searched, if all the vertices in A or B are not marked, randomly selecting one vertex in A or B other than the forward tabu point or the reverse tabu point as a new current point a or a new current point b, and starting the processing from S203 again; if there are marked vertices in A or B, obtaining a pipeline path from the marked vertices to the starting point and the terminal point according to the search order, and forming a base loop with the selected edge T, and marking the pipelines forming the base loop as traversed;

[0022] S206: selecting other untraversed pipelines as the edge T, and starting the processing from S202 again until all the pipelines are marked as traversed.

[0023] Further, the present application is directed to the problem that the traditional clustering analysis method requires high accuracy of basic data, and the basic data collected manually is prone to inaccuracy, and an improved fuzzy clustering method is proposed, which can ensure the accuracy of the base ring pipeline unit clustering result under the condition of tolerating certain basic data deviation. Specifically, based on the comprehensive failure factor data of the gas pipeline and the obtained base ring, the fuzzy clustering method is used to cluster the gas pipeline into basic pipeline units, including the following processes:

[0024] S301: Based on all base rings in the target ring-branch town gas pipe network, all pipelines contained in one base ring are taken as clustering objects, the fuzzy similarity coefficient of any two pipelines is calculated based on the comprehensive failure factor data of the two pipelines, and a fuzzy similarity matrix is established;

[0025] S302: The cut matrix of the selected base ring is obtained based on the fuzzy similarity matrix, based on which it is judged whether all pipelines in the selected base ring can form a base ring pipeline unit, if yes, a new base ring is selected and the process is re-calculated from S301 until all base rings have completed clustering analysis and marking; if not, step S303 is performed;

[0026] S303: The pipelines in the base ring that cannot form a base ring pipeline unit, and the pipelines in the target ring-branch pipe network except the base ring are clustered and marked to obtain linear pipeline units;

[0027] S304: After the clustering analysis and marking are completed, the base ring pipeline units and the linear pipeline units jointly form the basic pipeline units.

[0028] Further, the fuzzy similarity matrix in step S301 is R=(r ij ) m×m , when calculating the fuzzy similarity coefficient of two pipelines, the following method is adopted:

[0029]

[0030] In the formula, r ij is the fuzzy similarity coefficient of pipeline i and pipeline j in the base ring, where i=2, 3, …, m, j=2, 3, …, m, m is the number of pipelines in the base ring, x ik , x jk are the scores of the kth failure factor of pipelines i and j respectively, n is the number of failure factors, β is a weight coefficient for balancing the average similarity and difference between pipelines, x i,max , x j,min are the maximum and minimum values in the corresponding scores of all failure factors of pipelines i and j, and α is a pipe material data consistency discrimination variable, which takes a value of 0 when consistent and a value of 1 when inconsistent.

[0031] Further, the application proposes a space breadth first search algorithm, which can efficiently obtain the base ring pipeline units with adjacent and consistent pipeline attributes and hazard factors in space, and provides an effective method for obtaining regional pipeline units. The space breadth first search method and the fuzzy clustering method are applied to cluster several basic pipeline units adjacent in space into regional pipeline units, including the following processes:

[0032] S401: Select a basic pipeline unit C that is not marked as clustered, and select all the basic pipeline units adjacent to C in space as a set N;

[0033] S402: Apply the fuzzy clustering method to the clustering analysis of C and the pipeline units S in set N in turn, and mark the pipeline units S as a fusion area when the clustering condition is met, otherwise mark them as a non-fusion area;

[0034] S403: After all the pipeline units S in set N are clustered, mark the selected pipeline unit C as clustered, select the basic pipeline unit marked as a fusion area, and go to step S401 to perform the processing again;

[0035] S404: After all the basic pipeline units are marked as clustered, merge the basic pipeline units marked as a fusion area into a regional pipeline unit.

[0036] Further, the comprehensive failure factor data proportion of all pipelines in each regional pipeline unit is integrated into a factor set, including the following processes:

[0037] S501: Establish a factor set U according to each failure factor in the comprehensive failure factor data of the urban gas pipeline;

[0038] S502: Select the pipe age failure factor, improper protection failure factor and natural environment failure factor of the regional pipeline unit and the pipeline in the unit one by one, and count the proportion of the selected evaluation factor item of all pipelines in the selected regional pipeline unit;

[0039] S503: Select the social environment failure factor of the regional pipeline unit and the pipeline in the unit one by one, and based on the actual social environment in the coverage area of the regional pipeline unit and the risk from heavy principle, determine that the highest score factor item of all pipelines corresponding to the selected failure factor is 1, and the proportion of other failure factors is 0.

[0040] Further, after setting the evaluation level, the corresponding evaluation set is established, the fuzzy comprehensive evaluation method is used to evaluate the corresponding level of all regional pipeline units and is classified into the evaluation set, including the following processes:

[0041] S504: Taking the proportion of all pipes in the regional pipe unit to the four factor items of the failure factor as the fuzzy membership degree, a fuzzy comprehensive evaluation matrix is established, an improved weight vector is obtained by applying the balanced translation method, and the failure-prone grade of the regional pipe unit is calculated;

[0042] S505: Based on the calculation result vector, the failure-prone grade of each regional pipe unit is determined according to the maximum membership degree principle, the failure-prone grade of each pipe in the regional pipe unit is consistent with the regional pipe unit, and the failure-prone grade of the pipe belonging to multiple regional pipe units is determined according to the risk priority principle.

[0043] Further, the improved fuzzy comprehensive evaluation method is provided, in order to solve the problem of inaccurate result caused by the subjective method of determining the weight by using the analytic hierarchy process in the ordinary fuzzy comprehensive evaluation method, the relatively objective weight is determined by applying the balanced translation method based on the statistical proportion of the comprehensive failure data of the pipe, so that the failure-prone unit grade evaluation is more accurate. The improved weight is obtained by the balanced translation method in the following way:

[0044]

[0045] Wherein, s j is the jth element of the weight vector, w i is the statistical proportion of the factor item, r it , r tj are the elements of the ith row and the tth column and the tth row and the jth column in the fuzzy comprehensive evaluation matrix respectively; ∧ is a minimum operator, and the result is equal to the smaller value of the two numbers; ∨ is a maximum operator, and the result is equal to the larger value of the two numbers.

[0046] The above content explains the content of the identification method, and the identification system is also provided, which is specifically as follows:

[0047] A gas pipe unit failure identification system, comprising:

[0048] A data acquisition module is used to acquire comprehensive failure factor data of the pipe between adjacent segment points of the target ring-branch gas pipe network;

[0049] A base ring search module is used to determine all base rings in the target ring-branch gas pipe network by applying the opposite taboo breadth priority base ring search method;

[0050] A pipe determination module is used to cluster the gas pipes into basic pipe units and regional pipe units according to the comprehensive failure factors of the gas pipes and the determined base rings;

[0051] A grade evaluation module is used to integrate the comprehensive failure factor data of the pipes covered in each regional pipe unit into a factor set, and to evaluate the failure-prone grades of all regional pipe units by using a fuzzy comprehensive evaluation method.

[0052] Furthermore, the pipeline determination module includes:

[0053] The basic pipeline unit determination module is used to cluster the gas pipeline into several basic pipeline units using the fuzzy clustering method based on the comprehensive failure factor data of the gas pipeline and the obtained base ring;

[0054] The regional pipeline unit determination module is used to cluster each basic pipeline unit and spatially adjacent basic pipeline units into a number of regional pipeline units.

[0055] Compared with the prior art, some of the beneficial effects of the technical solution disclosed in the present invention include:

[0056] Based on the assessed failure level of gas pipelines, the present invention allows managers to dynamically formulate risk reduction plans and develop targeted risk control measures for pipelines that are prone to failure or relatively prone to failure. For pipelines that are generally prone to failure or not prone to failure, only enhanced monitoring is required to avoid safety accidents caused by untimely risk reduction plans.

[0057] Supporting the creation of gas pipeline risk pre-assessment technology can transform traditional periodic risk assessment into dynamic risk pre-assessment, dynamically identify changes in social environment, natural environment, and improper protection failure factors, and provide a basis for the formulation of dynamic risk management measures.

[0058] It provides a new method for managers to quickly understand the current safety status of pipelines and control risks to improve quality and efficiency. It is of great significance for building a scientific and safe gas pipeline network. It can also be applied to the identification of pipelines prone to failure in other ring-branch pipeline networks such as water supply networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only represent some embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 Flowchart of the method for identifying failure-prone units provided in Example 1 of the present invention.

[0061] Figure 2 Flowchart of the base ring search method based on the opposite taboo breadth-first base ring search algorithm provided in Example 1 of the present invention.

[0062] Figure 3 Flowchart of the basic pipeline unit determination method based on the improved fuzzy clustering method provided in Example 1 of the present invention.

[0063] Figure 4 The flow chart of the region pipeline unit determination method based on the spatial breadth-first search algorithm provided in Example 1 of the present application.

[0064] Figure 5 The flow chart of the region pipeline unit failure-prone grade evaluation method based on the improved fuzzy comprehensive evaluation method provided in Example 1 of the present application.

[0065] Figure 6 The identified failure-prone pipeline distribution map provided in Example 1 of the present application.

[0066] Figure 7 The module structure schematic diagram of the failure-prone unit identification system provided in Example 2 of the present application. DETAILED DESCRIPTION

[0067] The present application will be further explained in conjunction with the accompanying drawings and specific embodiments.

[0068] The following embodiments are optimized and overcome the defects in the prior art in view of the defects in the gas pipeline network in the prior art.

[0069] Embodiment 1

[0070] The present embodiment provides a gas pipeline unit failure identification method, comprising:

[0071] S1: setting a segmentation point in the gas pipeline network and obtaining comprehensive failure factor data between adjacent segmentation points;

[0072] S2: applying the opposite tabu breadth-first base ring search method to determine all base rings in the target ring-branching gas pipeline network;

[0073] S3: based on the gas pipeline comprehensive failure factor data and the obtained base ring, using a fuzzy clustering method to cluster the gas pipeline into basic pipeline units;

[0074] S4: applying the spatial breadth-first search method and the fuzzy clustering method to cluster several basic pipeline units that are close in space into region pipeline units;

[0075] S5: integrating the comprehensive failure factor data proportion of all pipelines in each region pipeline unit into a factor set, setting an evaluation grade, and then establishing a corresponding evaluation set, using a fuzzy comprehensive evaluation method to evaluate the corresponding grade of all region pipeline units and classifying them into the evaluation set.

[0076] The identification method disclosed in the embodiment breaks the fixed mode of using linear pipeline units and one-by-one risk evaluation for traditional invalid pipeline identification, considers the ring-branch structure characteristics of the gas pipeline network, creates regional pipeline units coupled with the base ring structure, divides the large number of gas pipelines into several regional pipeline units, and then identifies the invalid pipelines in batches, effectively reducing the workload of invalid pipeline identification, significantly shortening the identification period, and avoiding the waste of urban gas pipeline management resources.

[0077] The embodiment proposes a reciprocal taboo breadth priority base ring search algorithm for the demand of normalizing invalid gas pipeline identification, which can efficiently obtain the base ring in the ring-branch gas pipeline network. The reciprocal taboo breadth priority base ring search algorithm is proved to reduce 7 / 9 of the calculation amount, and based on this algorithm, the calculation speed can be improved to quickly obtain the identification result of the invalid pipeline. Specifically, determining all base rings in the target ring-branch gas pipeline network includes the following processes:

[0078] S201: Establishing an undirected graph G with the segmented points in the target pipeline network as vertices and the pipelines between the segmented points as edges;

[0079] S202: Selecting one of the vertices of an edge T in the undirected graph G as the starting point and the other vertex as the terminal point, selecting the starting point as the current point a and the terminal point as the current point b, and recording T as the search edge connecting the current points a and b;

[0080] S203: Marking the current point a as forward searched and the current point b as reverse searched, applying the breadth-first search method to obtain a vertex set A connected with the current point a and not marked as forward searched, synchronously obtaining a vertex set B connected with the current point b and not marked as reverse searched, recording the search edge connected with the current point a as the forward leading edge and the search edge connected with the current point b as the reverse leading edge;

[0081] S204: Calculating the included angle of the connection edge of the vertex set A and the current point a and the forward leading edge, marking the vertex with the smallest included angle as the forward taboo point, and recording the connection edge between the forward taboo point and the current point a as the search edge; synchronously calculating the included angle of the connection edge of the vertex set B and the current point b and the reverse leading edge, marking the vertex with the smallest included angle as the reverse taboo point, and recording the connection edge between the reverse taboo point and the current point a as the search edge;

[0082] S205: judging whether the vertex in A is reverse searched in turn, judging whether the vertex in B is forward searched in turn synchronously, if all the vertices in A or B are not marked, randomly selecting a vertex in A or B other than the non-forward taboo point or the non-reverse taboo point as a new current point a or current point b, and starting the processing from S203 again; if there is a marked vertex in A or B, obtaining the pipeline path from the marked vertex to the starting point and the terminal point according to the search order, and forming a base ring with the selected edge T, and marking the pipeline constituting the base ring as traversed;

[0083] S206: selecting other untraversed pipelines as the edge T, and starting the processing from S202 again until all the pipelines are marked as traversed.

[0084] The embodiment aims at the problem that the traditional clustering analysis method has high accuracy requirement for basic data, and the basic data collected manually is prone to inaccuracy, and proposes an improved fuzzy clustering method, which can ensure the accuracy of the base ring pipeline unit clustering result under the condition of compatibility of certain basic data deviation. Specifically, based on the comprehensive failure factor data of the gas pipeline and the obtained base ring, the fuzzy clustering method is used to cluster the gas pipeline into basic pipeline units, including the following processes:

[0085] S301: based on all the base rings in the target ring-branch town gas pipeline network, taking all the pipelines contained in one base ring as the clustering object, calculating the fuzzy similarity coefficient of any two pipelines based on the comprehensive failure factor data of the two pipelines, and establishing a fuzzy similarity matrix;

[0086] S302: obtaining the cut matrix of the selected base ring based on the fuzzy similarity matrix, judging whether all the pipelines in the selected base ring can constitute a base ring pipeline unit according to the cut matrix, if yes, selecting a new base ring and re-computing and processing from S301 until all the base rings have completed the clustering analysis and are marked; if no, performing step S303;

[0087] S303: clustering analyzing and marking the pipelines in the base ring which cannot constitute a base ring pipeline unit and the pipelines in the target ring-branch pipeline network except the base ring, and obtaining a linear pipeline unit;

[0088] S304: after the clustering analysis and marking are completed, the base ring pipeline unit and the linear pipeline unit jointly constitute a basic pipeline unit.

[0089] Preferably, the fuzzy similarity matrix in step S301 is R=(r ij ) m×m When calculating the fuzzy similarity coefficient of the two pipelines, the following method is adopted:

[0090]

[0091] wherein r ij is the fuzzy similarity coefficient of pipe i and pipe j in the base ring, wherein i = 2, 3, …, m, j = 2, 3, …, m, m is the number of pipes in the base ring, x ik , x jk are the scores of the kth failure factor of pipe i and j respectively, n is the number of failure factors, β is the weight coefficient for balancing the average similarity and difference between pipes, x i,max , x j,min are the maximum and minimum values of the scores of all failure factors of pipe i and j, and a is the pipe data consistency discrimination variable of pipe i and j, which takes the value of 0 when consistent and takes the value of 1 when inconsistent.

[0092] The spatial breadth-first search algorithm is proposed in the embodiment, and the base ring pipe unit adjacent in space and having relatively consistent pipe properties and hazard factors can be efficiently obtained by applying the algorithm, thereby providing an effective method for obtaining the regional pipe unit. The spatial breadth-first search method and the fuzzy clustering method are applied to cluster a plurality of base pipe units adjacent in space into a regional pipe unit, including the following processes.

[0093] S401: selecting a base pipe unit C which is not marked as clustered, and taking all base pipe units adjacent to C in space as a set N;

[0094] S402: applying the fuzzy clustering method to the pipe unit S in set N and C in sequence for clustering analysis, and marking the pipe unit S as a fusion region when the clustering condition is met, or marking the pipe unit S as a non-fusion region;

[0095] S403: after all pipe units S in set N are clustered, marking the selected pipe unit C as clustered, selecting the base pipe unit marked as a fusion region, and returning to step S401 for processing again;

[0096] S404: after all base pipe units are marked as clustered, merging the base pipe units marked as a fusion region into a regional pipe unit.

[0097] Preferably, the comprehensive failure factor data of all pipes in each regional pipe unit is integrated into a factor set, including the following processes.

[0098] S501: establishing a factor set U according to each failure factor in the comprehensive failure factor data of the urban gas pipe;

[0099] S502: selecting the pipe age failure factor, the improper protection failure factor and the natural environment failure factor of the regional pipe unit and the pipe in the unit in sequence, and calculating the proportion of the evaluation factor item of the selected failure factor in all pipes in the selected regional pipe unit;

[0100] For example, pipe age, improper protection, and natural environment are three types of factors, and factor items are specific evaluation items of factors, such as pipe age including four factor items, which are 0-5 years, 5-10 years, 10-15 years, and 15-20 years. If one of the three pipes has a pipe age of 5-10 years, and two have a pipe age of 10-15 years, then the proportions of the four evaluation factor items are 0, 0.3333, 0.6667, and 0.

[0101] S503: Select the social environment failure factors of the regional pipeline unit and the in-unit pipeline one by one, and determine the highest score factor item proportion of all pipelines corresponding to the selected failure factors as 1 and the proportions of other failure factors as 0 based on the actual social environment in the coverage area of the regional pipeline unit and the risk from heavy principle.

[0102] Preferably, after setting the evaluation levels, a corresponding evaluation set is established, and a fuzzy comprehensive evaluation method is used to evaluate the corresponding levels of all regional pipeline units and classify them into the evaluation set, which includes the following process:

[0103] S504: Taking the proportions of all pipelines in the regional pipeline unit for the four factor items of failure factors as fuzzy membership degrees, a fuzzy comprehensive evaluation matrix is established, an improved weight vector is obtained by using the balanced translation method, and the failure-prone level of the regional pipeline unit is calculated;

[0104] S505: Based on the calculation result vector, the failure-prone level of each regional pipeline unit is determined according to the maximum membership degree principle. The failure-prone levels of the pipelines in the regional pipeline unit are consistent with the regional pipeline unit. For pipelines belonging to multiple regional pipeline units, the failure-prone level is determined according to the risk from heavy principle.

[0105] The improved fuzzy comprehensive evaluation method is proposed in this embodiment. In view of the problem that the result is inaccurate due to the subjective method of determining the weight by using the analytic hierarchy process in the ordinary fuzzy comprehensive evaluation method, the relatively objective weight is determined by using the balanced translation method based on the statistical proportion of the pipeline comprehensive failure data, so that the failure-prone unit level evaluation is more accurate. The improved weight is obtained by the balanced translation method in the following manner:

[0106]

[0107] wherein, s j is the jth element of the weight vector, w i is the statistical proportion of the factor item, r it and r tj are the elements of i row t column and t row j column in the fuzzy comprehensive evaluation matrix respectively; ∧ is a minimum operator, and the result is equal to the smaller value of the two numbers; ∨ is a maximum operator, and the result is equal to the larger value of the two numbers.

[0108] The method disclosed in the present example is illustrated by the following example.

[0109] Example 1

[0110] A small town gas pipe network is adjacent to a commercial district under construction and a wholesale market, and six residential areas, namely residential areas A to E. The pipe network presents a ring-branch structure, and all the pipes are steel pipes. The purpose is to identify the vulnerable town gas pipes, so as to formulate targeted risk control measures.

[0111] Example problem solving:

[0112] Using the method of the present application to identify the vulnerable pipes of the pipe network, first, according to step S1, based on the segmentation points in the pipe network, the pipe network can be divided into 24 town gas pipes, then the pipe self attributes and the pipe protection improper, natural environment and social environment failure factor data of each pipe are obtained, and the grade and score corresponding to each failure factor of each pipe are determined;

[0113] Secondly, according to step S2, the breadth-first taboo base ring search algorithm is used to search to obtain three base rings in the pipe network, which are base ring I around residential area A, including pipes (5), (7), (8), (9), (10), base ring II around residential area B, including pipes (8), (15), (16), (17), and base ring III around the commercial district under construction, including pipes (1), (2), (3), (4), (5), (6);

[0114] Thirdly, according to step S3, the intercept λ = 0.5 and the weight coefficient β = 0.5 are set, based on the obtained fuzzy similarity matrix and λ intercept matrix, it is judged that the pipes covered by base rings I, II and III can be clustered into the same class to form base pipe units I, II and III, and linear pipes are clustered into linear pipe units L1, L2 and L3, L1 includes pipes (11), (12), (13), (14), L2 includes pipes (18), (19), (20), (21), (22), (23), and L3 includes pipe (24);

[0115] After that, according to step S4, the spatial breadth-first search algorithm and the improved fuzzy clustering method are applied to cluster base pipe units I, II and III, respectively, I and II are clustered into one class, but due to the significant difference in construction activity and heavy vehicle rolling failure factors, I and II cannot be clustered into one class with III, then the new base pipe unit formed by I and II is clustered and analyzed with the adjacent linear pipe units L1 and L2 to become one class, which is regional pipe unit G1, and base ring III and L3 are clustered to become regional pipe unit G2;

[0116] Finally, based on step S5, the fuzzy comprehensive evaluation method is applied, based on the comprehensive failure factor data of all pipelines in the regional pipeline unit, the proportion of the highest score factor item of the social failure factor corresponding to the four factor items is determined by counting the proportion of the pipeline age, the failure factor of improper protection, and the failure factor of the natural environment, and the fuzzy comprehensive evaluation matrix H1 and H2 can be obtained as follows:

[0117]

[0118]

[0119] The weight vector W obtained by using the proposed balanced translation method is as follows:

[0120] W = (0.024 0.037 0.061 0.039 0.13 0.2 0.15 0.177 0.098 0.085)

[0121] The calculation result vectors C1 and C2 of the two regional pipeline units can be obtained according to the weight calculation formula of the balanced translation method, as follows:

[0122] C1 = (0 0.303 0.677 0.021)

[0123] C2 = (0.005 0.79 0.21 0.006)

[0124] The elements in vectors C1 and C2 are the membership degrees of the regional pipeline units to each level of the evaluation set. According to the maximum membership degree principle, the failure-prone level of regional pipeline unit G1 is general failure-prone, and the failure-prone level of regional pipeline unit G2 is relatively failure-prone, as shown in Figure 6 According to the risk priority principle, pipelines (1), (2), (3), (4), (5), (6), and (24) are relatively failure-prone pipelines, and targeted risk control measures should be taken. Pipelines (7)-(23) are general failure-prone pipelines, and inspection should be strengthened.

[0125] In the embodiment application of the method and system, because the opposite taboo breadth priority base ring search algorithm and the space breadth priority search algorithm are applied, 7 / 9 of the calculation amount can be saved in the failure-prone evaluation calculation stage; because a small amount of comprehensive failure data can be used, the data amount is only about 20% of that of the traditional risk evaluation method, so 80% of the work amount can be saved in the data collection stage.

[0126] Embodiment 2

[0127] The content of the above embodiment 1 describes the content of the identification method, and the identification system is proposed in this embodiment, as follows:

[0128] A gas pipeline unit failure identification system, comprising:

[0129] a data acquisition module configured to acquire comprehensive failure factor data of the pipes between adjacent segment points of the target ring-branch gas pipe network;

[0130] a base ring search module configured to determine all base rings in the target ring-branch gas pipe network by applying a breadth-first base ring search method with opposite taboos;

[0131] a pipe determination module configured to cluster the gas pipes into basic pipe units and regional pipe units according to the comprehensive failure factor data of the gas pipes and the determined base rings;

[0132] a grade evaluation module configured to integrate the comprehensive failure factor data of the pipes covered in each regional pipe unit into a factor set, and to evaluate the failure-prone grades of all the regional pipe units by using a fuzzy comprehensive evaluation method.

[0133] Preferably, the pipe determination module comprises:

[0134] a basic pipe unit determination module configured to cluster the gas pipes into a plurality of basic pipe units by using a fuzzy clustering method according to the comprehensive failure factor data of the gas pipes and the determined base rings;

[0135] a regional pipe unit determination module configured to cluster each basic pipe unit and the spatially adjacent basic pipe units into a plurality of regional pipe units.

[0136] The above is the embodiment of the present application, but the present application is not limited to the above-mentioned optional embodiments, and those skilled in the art can obtain other various embodiments by arbitrarily combining the above-mentioned embodiments. Any person can obtain other various forms of embodiments under the inspiration of the present application. The above specific embodiments should not be understood as limiting the protection scope of the present application, and the protection scope of the present application should be defined by the claims.

Claims

1. A method for identifying failure of a gas pipeline unit, characterized in that: include: Set segmentation points in the gas pipeline network and obtain comprehensive failure factor data between adjacent segmentation points; The breadth-first base ring search method with opposite taboo is used to determine all base rings in the target ring-branch gas pipeline network. Based on the comprehensive failure factor data of gas pipelines and the obtained base ring, the fuzzy clustering method is used to cluster the gas pipelines into basic pipeline units; Applying the spatial breadth-first search method and fuzzy clustering method, several spatially adjacent basic pipeline units are clustered into regional pipeline units. The proportion of comprehensive failure factor data of all pipelines in each regional pipeline unit is integrated into a factor set, and the corresponding evaluation set is established after setting the evaluation level. The corresponding level of all regional pipeline units is assessed using the fuzzy comprehensive evaluation method and classified into the evaluation set; Based on the comprehensive failure factor data of gas pipelines and the obtained base ring, the fuzzy clustering method is used to cluster the gas pipelines into basic pipeline units, including the following process: S301: Based on all base rings in the target ring-branch town gas network, all pipelines included in one base ring are clustered, and based on the comprehensive failure factor data of two of the pipelines, the fuzzy similarity coefficients of any two pipelines are calculated to establish a fuzzy similarity matrix; S302: Based on the fuzzy similarity matrix, a truncation matrix of the selected base ring is obtained. Based on this matrix, it is determined whether all the pipes in the selected base ring can form a base ring pipe unit. If so, a new base ring is selected and the calculation process is repeated from S301 until all base rings have completed cluster analysis and are marked. If not, step S303 is performed. S303: performing cluster analysis and marking on the pipes in the base ring that cannot form the base ring pipe unit and the pipes in the target ring-branch pipe network excluding the base ring to obtain linear pipe units; S304: After cluster analysis and labeling are completed, the base ring pipeline unit and the linear pipeline unit together constitute the basic pipeline unit; The fuzzy similarity matrix in step S301 is ,When calculating the fuzzy similarity coefficient of two pipelines, the following method is used: Where, For the pipeline in the base ring and pipelines The fuzzy similarity coefficient of , , m is the number of pipes in the base ring, 、 Pipeline and No. The score of the failure factor, is the number of failure factors, is the weight coefficient, which is used to balance the average similarity and difference between pipelines. 、 For pipelines and The maximum and minimum values ​​of the corresponding scores of all failure factors, For pipelines and Pipe data consistency judgment variable, when consistent The value is 0, when inconsistent The value is 1; Applying the spatial breadth-first search method and fuzzy clustering method, several spatially adjacent basic pipeline units are clustered into regional pipeline units, including the following process: S401: Select a basic pipeline unit C that is not marked as clustered, and take all basic pipeline units S that are spatially adjacent to the basic pipeline unit C as a set N; S402: Applying a fuzzy clustering method to perform cluster analysis on the basic pipe unit C and the basic pipe units S in the set N in sequence, and marking the basic pipe unit S as a fusible area if it meets the clustering conditions, otherwise marking it as a non-fusible area; S403: After all the basic pipeline units S in the set N are clustered, the selected basic pipeline unit C is marked as clustered, and the basic pipeline unit S marked as a fusible area is selected, and the process goes to step S401 and is performed again; S404: After all basic pipeline units C are marked as clustered, the basic pipeline units S marked as meltable areas are merged into regional pipeline units.

2. The method for identifying failure of a gas pipeline unit according to claim 1, characterized in that: Determining all base rings in the target ring-branch gas network includes the following process: S201: Create an undirected graph G with segmentation points in the target pipe network as vertices and pipelines between the segmentation points as edges; S202: Taking one vertex of an edge T in the undirected graph G as the starting point and another vertex as the ending point, select the starting point as the current point a and the ending point as the current point b, and record T as the search edge connecting the current points a and b; S203: Mark the current point a as forward searched and the current point b as reverse searched, apply a breadth-first search method to obtain a set of vertices A that are not marked as forward searched and connected to the current point a, and simultaneously obtain a set of vertices B that are not marked as reverse searched and connected to the current point b, record the search edge connected to the current point a as the forward leader edge, and the search edge connected to the current point b as the reverse leader edge; S204: Calculate the included angle between the connecting edge and the forward leading edge of the vertex in the vertex set A and the current point a, mark the vertex with the smallest included angle as the forward taboo point, and record the connecting edge between the forward taboo point and the current point a as the search edge; simultaneously calculate the included angle between the connecting edge and the reverse leading edge of the vertex in the vertex set B and the current point b, mark the vertex with the smallest included angle as the reverse taboo point, and record the connecting edge between the reverse taboo point and the current point b as the search edge; S205: Determine in sequence whether the vertices in A have been searched in the reverse direction, and simultaneously determine in sequence whether the vertices in B have been searched in the forward direction. If all vertices in A or B are not marked, randomly select a vertex in A or B other than a non-forward taboo point or a non-reverse taboo point as the new current point a or current point b, and restart the process from S203; if a vertex in A or B is marked, obtain the pipeline path from the marked vertex to the starting point and the end point according to the search order, and form a base ring with the selected edge T, and mark the pipeline forming the base ring as traversed; S206: Select other untraversed pipelines as edges T, and start processing again from S202 until all pipelines are marked as traversed.

3. The method for identifying failure of a gas pipeline unit according to claim 1, characterized in that: Integrating the comprehensive failure factor data proportions of all pipelines in each regional pipeline unit into a factor set includes the following process: S501: Establish a factor set U based on various failure factors in the comprehensive failure factor data of urban gas pipelines; S502: Selecting regional pipeline units and the failure factors of pipe age, improper protection, and natural environment for pipelines within the units one by one, and calculating the proportions of several evaluation factors of the selected failure factors for all pipelines in the selected regional pipeline units; S503: Select the social environment failure factors of the regional pipeline units and pipelines within the units one by one. Based on the actual social environment in the area covered by the regional pipeline units and the principle of prioritizing risks, determine that the highest-scoring factor item corresponding to the selected failure factors for all pipelines accounts for 1, and the other failure factors account for 0.

4. The method for identifying failure of a gas pipeline unit according to claim 3, characterized in that: After setting the evaluation level, the corresponding evaluation set is established. The corresponding levels of all regional pipeline units are assessed using the fuzzy comprehensive evaluation method and classified into the evaluation set, which includes the following process: S504: Using the proportions of all pipelines in the regional pipeline unit to the four failure factor items as fuzzy membership, a fuzzy comprehensive evaluation matrix is ​​established, and an improved weight vector is obtained by applying the balanced translation method to calculate the failure susceptibility level of the regional pipeline unit; S505: Based on the calculation result vector, the failure susceptibility level of each regional pipeline unit is determined according to the maximum membership principle. The failure susceptibility level of each pipeline in the regional pipeline unit is consistent with the regional pipeline unit. For pipelines belonging to multiple regional pipeline units, the failure susceptibility level is determined according to the risk severity principle.

5. The method for identifying failure of a gas pipeline unit according to claim 4, characterized in that: The improved weights are obtained by the balanced translation method as follows: in, is the weight vector elements, is the statistical proportion of the factor items, 、 They are respectively OK Column and OK Column elements; For the smaller operator, the result is equal to the smaller of the two numbers; The greater operator equals the larger of the two numbers.

6. A gas pipeline unit failure identification system for implementing the method according to any one of claims 1 to 5, characterized in that: include: A data acquisition module is used to obtain comprehensive failure factor data of pipelines between adjacent segment points of the target ring-branch gas pipeline network; A base ring search module is used to determine all base rings in the target ring-branch gas pipeline network by applying a relative taboo breadth-first base ring search method; The pipeline identification module is used to cluster gas pipelines into basic pipeline units and regional pipeline units based on the comprehensive failure factors of gas pipelines and the determined base rings; The grade assessment module is used to integrate the comprehensive failure factor data of the pipelines covered in each regional pipeline unit into a factor set, and use the fuzzy comprehensive evaluation method to assess the failure susceptibility grade of all regional pipeline units.

7. The gas pipeline unit failure identification system according to claim 6, characterized in that: The pipeline determination module includes: The basic pipeline unit determination module is used to cluster the gas pipeline into several basic pipeline units using the fuzzy clustering method based on the comprehensive failure factor data of the gas pipeline and the obtained base ring; The regional pipeline unit determination module is used to cluster each basic pipeline unit and spatially adjacent basic pipeline units into a number of regional pipeline units.

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