Fuel gas pipeline unit failure identification method and system

Abstract

The invention relates to the technical field of gas pipeline detection processing, in particular to a gas pipeline unit failure identification method and system, and the method comprises the steps: setting segmentation points in a gas pipe network, and obtaining the comprehensive failure factor data between the adjacent segmentation points; determining all base rings by applying an opposite tabu breadth-first base ring search method; based on the comprehensive failure factor data of the gas pipeline and the obtained base ring, clustering the gas pipeline into a basic pipeline unit by adopting a fuzzy clustering method; clustering a plurality of spatially adjacent basic pipeline units into regional pipeline units by applying a spatial breadth-first search method and a fuzzy clustering method; and the proportions of the comprehensive failure factor data of all the pipelines in each regional pipeline unit are integrated into a factor set, an evaluation set is established, and corresponding grades of all the regional pipeline units are evaluated by using a fuzzy comprehensive evaluation method and are classified into the evaluation set. According to the method, a risk reduction scheme can be dynamically formulated based on the evaluated easy failure grade of the gas pipeline, and safety accidents are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of gas pipeline detection and processing, and in particular to a method and system for identifying failure of a gas pipeline unit. Background Art

[0002] The safe and stable operation of gas pipelines is of great significance to the development of the national economy. It is an important measure to maintain the safety of gas pipelines to identify gas pipelines that are prone to failure accidents and formulate targeted risk reduction measures to avoid any accidents. However, there are a large number of gas pipelines. There are thousands of gas pipelines in a medium-sized city, and they are intertwined in a complex ring-branch structure. It is extremely difficult to identify gas pipelines that are prone to failure.

[0003] In the existing related technology, by dividing the gas pipeline into linear pipeline units, and then performing pipeline risk assessment one by one based on the acquired basic data, pipelines that are prone to failure can be identified.

[0004] In the process of implementing the present invention, it is found that the existing related technologies have at least the following problems:

[0005] The method of risk assessment based on linear pipeline units is only applicable to long-distance oil and gas pipelines with relatively simple surrounding social environments and branch-like structures. For gas pipelines, the social environment factors around the pipelines are complex, and gas pipelines will be divided into multiple linear pipeline units. A 500-kilometer gas pipeline generally needs to be divided into more than 2,000 linear pipeline units, and each pipeline unit needs to obtain hundreds of basic data, resulting in a huge workload and long identification cycle for identifying pipelines prone to failure, which greatly occupies the company's management resources and cannot dynamically perceive changes in failure risks. Therefore, it is urgent to develop an efficient and easy-to-fail pipeline identification method that is adapted to the ring-branch structure characteristics of gas pipelines, support the formation and development of gas pipeline risk pre-evaluation technology, and assist in the dynamic evaluation and risk control of gas pipeline risks.

[0006] At present, from the perspective of good applicability to ring-branch structure gas pipelines, the complex ring-branch structure gas pipelines are divided into several ring structure regional pipeline units, and the improved fuzzy clustering analysis and fuzzy comprehensive evaluation methods are used in coordination to efficiently batch identify the failure-prone pipelines. A more reasonable technical solution should be proposed to solve the technical problems existing in the existing technology. Summary of the invention

[0007] In order to overcome at least one of the defects mentioned above, the present invention proposes a method and system for identifying failure of a gas pipeline unit. By dividing the gas pipeline into several regional pipeline units of base ring structures, the relative taboo breadth-first base ring search, improved fuzzy clustering and improved fuzzy comprehensive evaluation methods are applied. The failure level evaluation of the regional pipeline unit ("pipeline set") can be completed with a smaller workload, and the long-term, huge-workload, pipeline-by-pipeline risk evaluation is transformed into a dynamic, small-workload, "pipeline set" batch pipeline failure identification. It can support managers to establish risk pre-evaluation technology, dynamically grasp the failure level of gas pipelines and formulate targeted control measures, and can solve the problems that the existing technology is not suitable for ring-branch urban gas pipelines and has low identification efficiency and huge workload.

[0008] In order to achieve the above purpose, the identification method disclosed in the present invention can adopt the following technical solutions:

[0009] A method for identifying failure of a gas pipeline unit, comprising:

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

[0011] The relative taboo breadth-first base ring search method is applied to determine all base rings in the target ring-branch gas pipeline network;

[0012] 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.

[0013] The spatial breadth-first search method and fuzzy clustering method are used to cluster several spatially adjacent basic pipeline units into regional pipeline units.

[0014] 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 fuzzy comprehensive evaluation method is used to assess the corresponding levels of all regional pipeline units and classify them into the evaluation set.

[0015] The above-disclosed identification method addresses the problems that the existing relevant technologies are not applicable to ring-branch gas pipeline networks, are inefficient, have a large workload, and occupy a large amount of management resources. It breaks through the fixed mode of traditional easy-to-fail pipeline identification using linear pipeline units and one-by-one risk assessment, considers the ring-branch structure characteristics of the gas pipeline network, creates regional pipeline units with a coupled base ring structure, divides a large number of gas pipelines into several regional pipeline units, and then identifies easy-to-fail pipelines in batches, effectively reducing the workload of easy-to-fail pipeline identification, significantly shortening the identification cycle, and avoiding the waste of urban gas pipeline management resources.

[0016] Furthermore, in response to the need for normalization of the identification of easily failed gas pipelines, the present invention proposes a mutually taboo breadth-first base ring search algorithm, which can efficiently obtain base rings in the ring-branch gas pipeline network. The mutually taboo breadth-first base ring search algorithm has been proven to reduce the amount of calculation by 7 / 9. Based on this algorithm, the calculation speed can be improved, and the identification results of easily failed pipelines can be quickly obtained. Specifically, determining all base rings in the target ring-branch gas pipeline network includes the following process:

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

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

[0019] S203: Mark the current point a as searched in the forward direction, and the current point b as searched in the reverse direction, apply the breadth-first search method to obtain the vertex set A that is not marked as searched in the forward direction and connected to the current point a, and simultaneously obtain the vertex set B that is not marked as searched in the reverse direction and connected to the current point b, record the search edge connected to the current point a as the forward leading edge, and the search edge connected to the current point b as the reverse leading edge;

[0020] S204: Calculate the 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 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; synchronously calculate the 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 angle as the reverse taboo point, and record the connecting edge between the reverse taboo point and the current point a as the search edge;

[0021] S205: sequentially determine whether the vertices in A have been searched in the reverse direction, and synchronously determine whether the vertices in B have been searched in the forward direction. If all the vertices in A or B are not marked, randomly select a vertex other than the non-forward taboo point or the non-reverse taboo point in A or B as the new current point a or current point b, and restart the process from S203; if there is a mark in the vertex in A or B, 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;

[0022] S206: Select other untraversed pipelines as edges T, and start processing again from S202 until all pipelines are marked as traversed.

[0023] Furthermore, the present invention proposes an improved fuzzy clustering method to solve the problem that the traditional clustering analysis method has high requirements for the accuracy of basic data and is prone to inaccurate input when manually collecting basic data. This method can ensure the accuracy of the clustering results of the base ring pipeline unit under the condition of being compatible with certain basic data deviations. 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 process:

[0024] S301: Based on all base rings in the target ring-branch town gas pipeline network, all pipelines included in one of the base rings 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;

[0025] S302: Based on the fuzzy similarity matrix, the truncation matrix of the selected base ring is obtained, and it is judged whether all the pipes in the selected base ring can constitute a base ring pipe unit. If so, a new base ring is selected and recalculated from S301 until all base rings have completed cluster analysis and marked; if not, step S303 is performed;

[0026] S303: performing cluster analysis and marking on the pipes in the base ring that cannot constitute the base ring pipe unit and the pipes in the target ring-branch pipe network excluding the base ring, to obtain a linear pipe unit;

[0027] S304: After cluster analysis and labeling are completed, the base ring pipeline unit and the linear pipeline unit together constitute a basic pipeline unit.

[0028] Furthermore, 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 used:

[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 the weight coefficient used to balance the average similarity and difference between pipelines. i,max 、x j,min is the maximum and minimum value of the corresponding scores of all failure factors of pipelines i and j, and α is the consistency discriminant variable of the pipe material data of pipelines i and j. When they are consistent, α takes the value of 0, and when they are inconsistent, α takes the value of 1.

[0031] Furthermore, the present invention proposes a spatial breadth-first search algorithm, which can be used to efficiently obtain spatially adjacent base ring pipeline units with relatively consistent pipeline attributes and hazard factors, providing an effective method for obtaining regional pipeline units. The spatial breadth-first search method and the fuzzy clustering method are used to cluster several spatially adjacent basic pipeline units into regional pipeline units, including the following process:

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

[0033] S402: applying fuzzy clustering method to perform cluster analysis on C and the pipeline units S in the set N in turn, and marking the pipeline unit S as a fusible area if it meets the clustering conditions, otherwise marking it as a non-fusible area;

[0034] S403: After all the pipeline units S in the set N are clustered, the selected pipeline unit C is marked as clustered, and the basic pipeline unit marked as a meltable area is selected, and the process goes to step S401 to be executed again;

[0035] S404: After all basic pipeline units are marked as clustered, basic pipeline units marked as meltable areas are merged into regional pipeline units.

[0036] Furthermore, the comprehensive failure factor data proportions of all pipelines in each regional pipeline unit are integrated into a factor set, including the following process:

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

[0038] S502: selecting the regional pipeline units and the failure factors of pipe age, improper protection failure factors and natural environment failure factors of the pipelines in the units one by one, and counting the proportions of several evaluation factor items of the selected failure factors for all pipelines in the selected regional pipeline units;

[0039] S503: Select the social environment failure factors of the regional pipeline units and pipelines within the units one by one, and based on the actual social environment in the area covered by the regional pipeline units and the principle of heavier risk, determine that the highest score factor item corresponding to the selected failure factor for all pipelines accounts for 1, and the other failure factors account for 0.

[0040] Furthermore, after setting the evaluation level, the corresponding evaluation set is established, and the corresponding levels of all regional pipeline units are evaluated using the fuzzy comprehensive evaluation method and classified into the evaluation set, which includes the following process:

[0041] S504: Taking the proportion of all pipelines in the regional pipeline unit to the four factor items of the failure factor as the 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;

[0042] 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.

[0043] Furthermore, the present invention proposes an improved fuzzy comprehensive evaluation method. In view of the inaccurate results caused by the subjective method of determining weights in ordinary fuzzy comprehensive evaluation methods, based on the statistical proportion of pipeline comprehensive failure data, the balanced translation method is used to determine relatively objective weights, making the level assessment of the prone to failure units more accurate. The improved weights are obtained by the balanced translation method in the following manner:

[0044]

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

[0046] The above content describes the content of the identification method. The present invention also proposes an identification system, which is specifically as follows:

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

[0048] 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;

[0049] A base ring search module, used for applying a mutually taboo breadth-first base ring search method to determine all base rings in a target ring-branch gas pipeline network;

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

[0051] 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 level of all regional pipeline units.

[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 a 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 plurality of regional pipeline units.

[0055] Compared with the prior art, some 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, formulate targeted risk control measures for pipelines that are prone to failure and relatively prone to failure, and only need to strengthen monitoring for pipelines that are generally prone to failure and not prone to failure, so as 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, and can dynamically identify changes in social environment, natural environment, and improper protection failure factors, providing 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 easily failed pipelines 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 drawings required for use in the embodiments are briefly introduced below. 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 related drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 Flow chart of the method for identifying a failure-prone unit provided in Example 1 of the present invention.

[0061] Figure 2 A flow chart of a base ring search method based on a breadth-first base ring search algorithm with opposite-direction taboo provided in Example 1 of the present invention.

[0062] Figure 3 Flow chart 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 A flow chart of a method for determining regional pipeline units based on a spatial breadth-first search algorithm provided in Example 1 of the present invention.

[0064] Figure 5 A flow chart of a method for assessing the failure level of a regional pipeline unit based on an improved fuzzy comprehensive evaluation method provided in Example 1 of the present invention.

[0065] Figure 6 Example 1 of the present invention provides a distribution map of pipelines that are prone to failure and are identified.

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

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

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

[0069] Example 1

[0070] This embodiment provides a method for identifying failure of a gas pipeline unit, including:

[0071] S1: Set segmentation points in the gas pipeline network and obtain comprehensive failure factor data between adjacent segmentation points;

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

[0073] S3: 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;

[0074] S4: Apply the spatial breadth-first search method and fuzzy clustering method to cluster several spatially adjacent basic pipeline units into regional pipeline units;

[0075] S5: 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 evaluated by using the fuzzy comprehensive evaluation method and classified into the evaluation set.

[0076] The identification method disclosed in this embodiment addresses the problems that the existing related technologies are not applicable to ring-branch gas pipeline networks, are inefficient, have a large workload, and occupy a large amount of management resources. It breaks through the fixed mode of traditional easy-to-fail pipeline identification using linear pipeline units and one-by-one risk assessment, considers the ring-branch structure characteristics of the gas pipeline network, creates regional pipeline units with a coupled base ring structure, divides a large number of gas pipelines into several regional pipeline units, and then identifies easy-to-fail pipelines in batches, effectively reducing the workload of easy-to-fail pipeline identification, significantly shortening the identification cycle, and avoiding the waste of urban gas pipeline management resources.

[0077] In response to the need for normalization of the identification of easily failed gas pipelines, this embodiment proposes a mutually taboo breadth-first base ring search algorithm, which can efficiently obtain base rings in the ring-branch gas pipeline network. The mutually taboo breadth-first base ring search algorithm has been proven to reduce the amount of calculation by 7 / 9. Based on this algorithm, the calculation speed can be improved and the identification results of easily failed pipelines can be obtained quickly. Specifically, determining all base rings in the target ring-branch gas pipeline network includes the following process:

[0078] S201: Establish an undirected graph G with segmentation points in the target pipe network as vertices and pipelines between the segmentation points as edges;

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

[0080] S203: Mark the current point a as searched in the forward direction, and the current point b as searched in the reverse direction, apply the breadth-first search method to obtain the vertex set A that is not marked as searched in the forward direction and connected to the current point a, and simultaneously obtain the vertex set B that is not marked as searched in the reverse direction and connected to the current point b, record the search edge connected to the current point a as the forward leading edge, and the search edge connected to the current point b as the reverse leading edge;

[0081] S204: Calculate the 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 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; synchronously calculate the 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 angle as the reverse taboo point, and record the connecting edge between the reverse taboo point and the current point a as the search edge;

[0082] S205: sequentially determine whether the vertices in A have been searched in the reverse direction, and synchronously determine whether the vertices in B have been searched in the forward direction. If all the vertices in A or B are not marked, randomly select a vertex other than the non-forward taboo point or the non-reverse taboo point in A or B as the new current point a or current point b, and restart the process from S203; if there is a mark in the vertex in A or B, 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;

[0083] S206: Select other untraversed pipelines as edges T, and start processing again from S202 until all pipelines are marked as traversed.

[0084] In view of the problem that the traditional clustering analysis method has high requirements for the accuracy of basic data and is prone to inaccurate input when manually collecting basic data, this embodiment proposes an improved fuzzy clustering method, which can ensure the accuracy of the clustering results of the base ring pipeline unit under the condition of being compatible with certain basic data deviations. 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 process:

[0085] S301: Based on all base rings in the target ring-branch town gas pipeline network, all pipelines included in one of the base rings 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;

[0086] S302: Based on the fuzzy similarity matrix, the truncation matrix of the selected base ring is obtained, and it is judged whether all the pipes in the selected base ring can constitute a base ring pipe unit. If so, a new base ring is selected and recalculated from S301 until all base rings have completed cluster analysis and marked; if not, step S303 is performed;

[0087] S303: performing cluster analysis and marking on the pipes in the base ring that cannot constitute the base ring pipe unit and the pipes in the target ring-branch pipe network excluding the base ring, to obtain a linear pipe unit;

[0088] S304: After cluster analysis and labeling are completed, the base ring pipeline unit and the linear pipeline unit together 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 two pipelines, the following method is used:

[0090]

[0091] 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 the weight coefficient used to balance the average similarity and difference between pipelines. i,max 、x j,min is the maximum and minimum value of the corresponding scores of all failure factors of pipelines i and j, and α is the consistency discriminant variable of the pipe material data of pipelines i and j. When they are consistent, α takes the value of 0, and when they are inconsistent, α takes the value of 1.

[0092] This embodiment proposes a spatial breadth-first search algorithm, which can be used to efficiently obtain spatially adjacent base ring pipeline units with relatively consistent pipeline attributes and hazard factors, providing an effective method for obtaining regional pipeline units. The spatial breadth-first search method and the fuzzy clustering method are used to cluster several spatially adjacent basic pipeline units into regional pipeline units, including the following process:

[0093] S401: Select a basic pipeline unit C that is not marked as clustered, and take all basic pipeline units adjacent to C in space as a set N;

[0094] S402: applying fuzzy clustering method to perform cluster analysis on C and the pipeline units S in the set N in turn, and marking the pipeline unit S as a fusible area if it meets the clustering conditions, otherwise marking it as a non-fusible area;

[0095] S403: After all the pipeline units S in the set N are clustered, the selected pipeline unit C is marked as clustered, and the basic pipeline unit marked as a meltable area is selected, and the process goes to step S401 to be executed again;

[0096] S404: After all basic pipeline units are marked as clustered, basic pipeline units marked as meltable areas are merged into regional pipeline units.

[0097] Preferably, integrating the comprehensive failure factor data proportions of all pipelines in each regional pipeline unit into a factor set includes the following process:

[0098] S501: Establish a factor set U according to various failure factors in the comprehensive failure factor data of urban gas pipelines;

[0099] S502: selecting the regional pipeline units and the failure factors of pipe age, improper protection failure factors and natural environment failure factors of the pipelines in the units one by one, and counting the proportions of several evaluation factor items of the selected failure factors for all pipelines in the selected regional pipeline units;

[0100] For example, pipe age, improper protection, and natural environment are three types of factors, and factor items are specific evaluation items of the factors. For example, pipe age includes four factor items, namely 0-5 years, 5-10 years, 10-15 years, and 15-20 years. One of the three pipelines is 5-10 years old, and two are 10-15 years old. Then the corresponding 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 units and pipelines within the units one by one, and based on the actual social environment in the area covered by the regional pipeline units and the principle of heavier risk, determine that the highest score factor item corresponding to the selected failure factor for all pipelines accounts for 1, and the other failure factors account for 0.

[0102] Preferably, after setting the evaluation level, a corresponding evaluation set is established, and the corresponding levels of all regional pipeline units are evaluated by using the fuzzy comprehensive evaluation method and classified into the evaluation set, which includes the following process:

[0103] S504: Taking the proportion of all pipelines in the regional pipeline unit to the four factor items of the failure factor as the 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;

[0104] 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.

[0105] This embodiment proposes an improved fuzzy comprehensive evaluation method. In order to solve the problem of inaccurate results caused by the subjective method of determining weights in ordinary fuzzy comprehensive evaluation methods, such as hierarchical analysis, a relatively objective weight is determined based on the statistical proportion of pipeline comprehensive failure data by using the balanced translation method, so that the level assessment of the prone-to-failure unit is more accurate. The improved weight is obtained by the balanced translation method in the following way:

[0106]

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

[0108] The method disclosed in this embodiment is described by the following example.

[0109] Example 1

[0110] A small town gas pipeline network is surrounded by a commercial area under construction, a wholesale market, and six residential areas, namely residential areas A to E. This pipeline network presents a ring-branch structure, and all pipelines are steel pipelines. It is necessary to identify the town gas pipelines that are prone to failure so as to formulate targeted risk control measures.

[0111] Example problem solving:

[0112] The method of the present invention is used to identify the easily failed pipelines in the pipeline network. First, according to step S1, based on the segmentation points in the pipeline network, the pipeline network can be divided into 24 urban gas pipelines, and then the pipeline attributes and failure factor data of improper pipeline protection, natural environment and social environment of each pipeline are obtained to determine the level and score corresponding to each failure factor of each pipeline;

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

[0114] Again, 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 the λ intercept matrix, it is determined that the pipelines covered by the base rings Ⅰ, Ⅱ, and Ⅲ can be clustered into the same category to form base ring pipeline units Ⅰ, Ⅱ, and Ⅲ. At the same time, the linear pipelines are clustered into linear pipeline units L1, L2, and L3. L1 includes pipelines (11), (12), (13), and (14). L2 includes pipelines (18), (19), (20), (21), (22), and (23). L3 includes pipeline (24).

[0115] Afterwards, according to step S4, the spatial breadth-first search algorithm and the improved fuzzy clustering method are applied to cluster the base ring pipeline unit Ⅰ with Ⅱ and Ⅲ respectively. Ⅰ and Ⅱ are clustered into one category, but due to the significant differences in failure factors such as construction activity and heavy vehicle rolling, Ⅰ, Ⅱ and Ⅲ cannot be clustered into one category. Then, the new base ring pipeline unit formed by Ⅰ and Ⅱ and the adjacent linear pipeline units L1 and L2 can be clustered into one category after cluster analysis, becoming the regional pipeline unit G1, and the base ring III and L3 are clustered into the regional pipeline 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 proportions of the four factor items corresponding to the pipe age, improper protection failure factor, and natural environment failure factor are counted, and the proportion of the highest score factor item of the social failure factor is determined. The fuzzy comprehensive evaluation matrices H1 and H2 can be obtained as follows:

[0117]

[0118]

[0119] The proposed balanced translation method can be used to obtain the weight vector W as follows:

[0120] W=(0.0240.0370.0610.0390.130.20.150.1770.0980.085)

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

[0122] C1=(00.3030.6770.021)

[0123] C2=(0.0050.790.210.006)

[0124] The elements in vectors C1 and C2 are the membership of the regional pipeline unit to each level of the evaluation set. According to the maximum membership principle, it can be obtained that the failure level of the regional pipeline unit G1 is generally prone to failure, and the failure level of the regional pipeline unit G2 is relatively prone to failure, such as Figure 6 As shown, according to the principle of prioritizing risk, pipelines (1), (2), (3), (4), (5), (6), and (24) are pipelines that are more prone to failure and targeted risk control measures should be taken. Pipelines (7) to (23) are pipelines that are generally prone to failure and only require enhanced inspections.

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

[0126] Example 2

[0127] The content of the above embodiment 1 illustrates the content of the identification method. This embodiment proposes an identification system, which is as follows:

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

[0129] 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;

[0130] A base ring search module, used for applying a mutually taboo breadth-first base ring search method to determine all base rings in a target ring-branch gas pipeline network;

[0131] A pipeline determination module is used to cluster gas pipelines into basic pipeline units and regional pipeline units according to comprehensive failure factors of gas pipelines and determined base rings;

[0132] 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 level of all regional pipeline units.

[0133] Preferably, the pipeline determination module includes:

[0134] The basic pipeline unit determination module is used to cluster the gas pipeline into several basic pipeline units using a fuzzy clustering method based on the comprehensive failure factor data of the gas pipeline and the obtained base ring;

[0135] The regional pipeline unit determination module is used to cluster each basic pipeline unit and spatially adjacent basic pipeline units into a plurality of regional pipeline units.

[0136] The above are the implementation methods listed in this embodiment, but this embodiment is not limited to the above optional implementation methods. Those skilled in the art can arbitrarily combine the above methods to obtain other various implementation methods. Anyone can derive other various implementation methods under the inspiration of this embodiment. The above specific implementation methods should not be understood as limiting the protection scope of this embodiment. The protection scope of this embodiment should be based on the definition in the claims.

Claims

1. A method for identifying failure of a gas pipeline unit, characterized in that: include: Setting segmentation points in the gas pipeline network and obtaining comprehensive failure factor data between adjacent segmentation points; The relative taboo breadth-first base ring search method is applied 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 gas pipelines are clustered into basic pipeline units using fuzzy clustering method; The spatial breadth-first search method and fuzzy clustering method are used to cluster several spatially adjacent basic pipeline units 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 fuzzy comprehensive evaluation method is used to assess the corresponding levels of all regional pipeline units and classify them into the evaluation set.

2. The method for identifying failure of a gas pipeline unit according to claim 1, characterized in that: Determining the target ring-all base rings in the branched gas network includes the following process: S201: Establish 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, selecting the starting point as the current point a and the ending point as the current point b, and recording T as the search edge connecting the current points a and b; S203: Mark the current point a as searched in the forward direction, and the current point b as searched in the reverse direction, apply the breadth-first search method to obtain the vertex set A that is not marked as searched in the forward direction and connected to the current point a, and simultaneously obtain the vertex set B that is not marked as searched in the reverse direction and connected to the current point b, record the search edge connected to the current point a as the forward leading edge, and the search edge connected to the current point b as the reverse leading edge; S204: Calculate the 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 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; synchronously calculate the 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 angle as the reverse taboo point, and record the connecting edge between the reverse taboo point and the current point a as the search edge; S205: sequentially determine whether the vertices in A have been searched in the reverse direction, and synchronously determine whether the vertices in B have been searched in the forward direction. If all the vertices in A or B are not marked, randomly select a vertex other than the non-forward taboo point or the non-reverse taboo point in A or B as the new current point a or current point b, and restart the process from S203; if there is a mark in the vertex in A or B, 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: 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 pipeline network, all pipelines included in one of the base rings 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, the truncation matrix of the selected base ring is obtained, and it is judged whether all the pipes in the selected base ring can constitute a base ring pipe unit. If so, a new base ring is selected and recalculated from S301 until all base rings have completed cluster analysis and marked; if not, step S303 is performed; S303: performing cluster analysis and marking on the pipes in the base ring that cannot constitute the base ring pipe unit and the pipes in the target ring-branch pipe network excluding the base ring, to obtain a linear pipe unit; S304: After cluster analysis and labeling are completed, the base ring pipeline unit and the linear pipeline unit together constitute a basic pipeline unit.

4. The method for identifying failure of a gas pipeline unit according to claim 3, characterized in that: 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 used: 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 the weight coefficient used to balance the average similarity and difference between pipelines. i,max 、x j,min is the maximum and minimum value of the corresponding scores of all failure factors of pipelines i and j, and α is the consistency discriminant variable of the pipe material data of pipelines i and j. When they are consistent, α takes the value of 0, and when they are inconsistent, α takes the value of 1.

5. The method for identifying failure of a gas pipeline unit according to claim 3 or 4, characterized in that: The spatial breadth-first search method and fuzzy clustering method are applied to cluster several spatially adjacent basic pipeline units 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 adjacent to C in space as a set N; S402: applying fuzzy clustering method to perform cluster analysis on C and the pipeline units S in the set N in turn, marking the pipeline unit S as a fusible area if it meets the clustering conditions, otherwise marking it as a non-fusible area; S403: After all the pipeline units S in the set N are clustered, the selected pipeline unit C is marked as clustered, and the basic pipeline unit marked as a meltable area is selected, and the process goes to step S401 to be executed again; S404: After all basic pipeline units are marked as clustered, basic pipeline units marked as meltable areas are merged into regional pipeline units.

6. 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 according to various failure factors in the comprehensive failure factor data of urban gas pipelines; S502: selecting the regional pipeline units and the failure factors of pipe age, improper protection failure factors and natural environment failure factors of the pipelines in the units one by one, and counting the proportions of several evaluation factor items 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, and based on the actual social environment in the area covered by the regional pipeline units and the principle of heavier risk, determine that the highest score factor item corresponding to the selected failure factor for all pipelines accounts for 1, and the other failure factors account for 0.

7. The method for identifying failure of a gas pipeline unit according to claim 1 or 6, characterized in that: After setting the evaluation level, the corresponding evaluation set is established. The corresponding levels of all regional pipeline units are evaluated using the fuzzy comprehensive evaluation method and classified into the evaluation set, which includes the following process: S504: Taking the proportion of all pipelines in the regional pipeline unit to the four factor items of the failure factor as the 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.

8. The method for identifying failure of a gas pipeline unit according to claim 7, characterized in that: The improved weights are obtained by the balanced shift method as follows: Among them, s j is the jth element of the weight vector, w i is the statistical proportion of the factor item, r it 、r tj They are the elements in row i and column t and row t and column j in the fuzzy comprehensive evaluation matrix respectively; ∧ is a smaller operator, and the result is equal to the smaller value of the two numbers; ∨ is a larger operator, and the result is equal to the larger value of the two numbers.

9. A gas pipeline unit failure identification system, 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, used for applying a mutually taboo breadth-first base ring search method to determine all base rings in a target ring-branch gas pipeline network; A pipeline determination module is used to cluster gas pipelines into basic pipeline units and regional pipeline units according to comprehensive failure factors of gas pipelines and 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 level of all regional pipeline units.

10. The gas pipeline unit failure identification system according to claim 9, 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 a 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 plurality of regional pipeline units.

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