A method for searching detection paths of water supply network

Abstract

The present invention relates to a method for searching a water supply network detection path, comprising dividing the water supply network in the area to be detected into a plurality of grids; evaluating the risk value of the water supply pipeline corresponding to each grid; taking the grid with the highest risk value among all grids as the starting point of the current search path; taking the starting point as the center, searching in the grids of the first adjacent outer circle around the periphery to obtain the grid with the highest risk value around the starting point; taking the grid with the highest risk value as the next step grid of the current search path; taking the next step grid of the current search path as the center, searching for the grid with the highest risk value in the grids of the first adjacent outer circle around the periphery; determining whether the set global factor condition is met, and if so, ending and saving the current search path. The present invention has clear steps and concise logic, can quickly provide an accurate detection sequence for large-area leakage detection, and is of great significance to the leakage detection work of urban water supply network.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal engineering, and in particular to a method for searching a detection path of a water supply network. Background Art

[0002] Leakage is the most common disease of water supply pipes and the source of major accidents such as pipe bursts and road collapses. Due to its great harm, strong concealment, and difficulty in detection, existing detection technologies are difficult to implement accurately. At present, the main means of leak detection for water supply pipes in cities in my country is manual detection, that is, the detection personnel use a leak detector to listen to the sound caused by leakage along the pipe section by section. This method requires manual traversal of the entire pipe network in the leakage area, which makes the detection process very time-consuming and inefficient, and it is difficult to meet the needs of today's large cities for pipe network leakage surveys. For the actual needs of leak detection, we need to set corresponding detection routes for the distribution of pipes in an area. For example, high-risk pipelines usually need to invest more manpower and material resources to be inspected first. The route setting can give priority to passing through such nodes. Low-risk pipelines generally do not need to be inspected or temporarily do not need to be inspected. The route setting considers ignoring such nodes.

[0003] In fact, the existing urban water supply network is complexly distributed, and most of them are tree-like networks and ring networks combined. In the central areas of cities and densely populated areas, the distribution of pipelines is characterized by high density and short spacing. Therefore, how to give the optimal detection path for complex areas of the water supply network can be transformed into a study of the method for solving the shortest path in the pipe network. The current methods for the shortest path can be basically divided into two types: breadth-first search method and depth-first search method. Breadth-first search is also called hierarchical traversal, which can obtain all the shortest paths between the initial point and the target point. However, for complex network problems such as water supply networks, data redundancy will be generated, which is not conducive to practical application. For depth-first search, it does not retain all node paths, avoiding unnecessary repeated search problems. While being able to obtain the shortest path, the search efficiency is greatly improved, which can be applied to the optimal detection path search problem of urban water supply networks. To this end, the present invention proposes a search method for water supply network detection paths based on depth-first search. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention proposes a method for searching detection paths for water supply pipe networks to solve the problem in the prior art that it is difficult to determine the optimal detection route for manual leak detection, and to provide a reference for the actual operation, maintenance and updating of water supply pipe networks.

[0005] On the one hand, the present invention discloses a method for searching a water supply network detection path, which comprises the following steps:

[0006] Step S1, dividing the water supply network of the area to be detected into multiple grids, and establishing a corresponding relationship between each grid and the water supply pipeline;

[0007] Step S2, evaluating the risk value of the water supply pipeline corresponding to each grid, and taking the risk value of the water supply pipeline corresponding to each grid as the risk value of the grid;

[0008] Step S3, taking the grid with the highest risk value among all grids in the water supply network of the area to be detected as the starting point of the current search path;

[0009] Step S4, taking the starting point as the center, searching in the grids of the first adjacent outer circle around the starting point to obtain the grid with the highest risk value around the starting point;

[0010] Step S5, taking the grid with the highest risk value as the next grid of the current search path;

[0011] Step S6, taking the next step grid of the current search path as the center, searching for the grid with the highest risk value in the first adjacent outer circle grids around;

[0012] Step S7, determine whether the set global factor condition is met, if so, end and save the current search path, otherwise, return to execute steps S5 to S7;

[0013] In some embodiments, step S1 includes the following steps:

[0014] Step S101, determining the boundaries of the water supply network in the area to be detected in two directions according to the plane layout of the water supply network in the area to be detected;

[0015] Step S102, determining the required interval in each direction of the water supply network in the area to be detected according to the size of the required grid division;

[0016] Step S103, dividing the water supply network of the area to be detected into a plurality of grids in two directions according to the intervals required in each direction of the water supply network of the area to be detected determined in step S102;

[0017] Step S104, associating the pipeline in each grid with the grid number to establish a corresponding relationship between each grid and the water supply pipeline.

[0018] In some of the embodiments, in step S1, the water supply network in the area to be inspected is gridded in the GIS platform to divide the water supply network in the area to be inspected into a plurality of grids.

[0019] In some embodiments, step S2 specifically includes: establishing a regional pipe network risk assessment model, and assessing the risk value of the water supply pipe corresponding to each grid according to the regional pipe network risk assessment model, and using the risk value of the water supply pipe corresponding to each grid as the risk value of the grid;

[0020] Among them, the water supply pipeline corresponding to each grid includes several pipeline sections, and the regional pipeline network risk assessment model includes the corresponding relationship between the risk value of the water supply pipeline corresponding to any grid and the state value of the secondary indicator of each pipeline section in the grid and the weight of the secondary indicator.

[0021] In some embodiments, the step of evaluating the risk value of the water supply pipeline corresponding to each grid according to the regional pipe network risk assessment model specifically includes:

[0022] Step S201, extracting attribute parameters from the GIS platform, initializing the pipe network data and estimating environmental parameters, so as to obtain the status value of the secondary indicator of each pipe section in each grid, and importing the status value of the secondary indicator of each pipe section into the regional pipe network risk assessment model;

[0023] Step S202, determining the weight of the secondary index of each pipeline section in each grid according to the consistency matrix method, and checking whether the consistency requirement is met;

[0024] Step S203, importing the weight of the secondary indicator of each pipeline section into the regional pipe network risk assessment model to obtain the risk value of each pipeline section in each grid, and then obtain the risk value of the water supply pipeline corresponding to each grid.

[0025] In some of the embodiments, each pipeline section includes 12 secondary indicators, and the 12 secondary indicators are: index layer cover thickness, index layer pipe diameter, index layer pipe pressure, index layer pipe material, index layer temperature difference, index layer road load, index layer pipe age, index layer soil corrosiveness, index layer regional activity, index layer pipeline network density, index layer pipeline network leakage rate, and index layer pipeline network accident rate.

[0026] In some of the embodiments, in step S4, when the search path reaches the boundary of the water supply network of the area to be detected or there is no way to go, the search range is expanded by one circle to continue searching for the grid with the highest risk value in the grid of the second adjacent outer circle around the starting point.

[0027] In some of the embodiments, in step S6, when the search path reaches the boundary of the water supply network of the area to be detected or there is no way to go, the search range is expanded by one circle to continue searching for the grid with the highest risk value in the second adjacent outer circle of grids around the next step grid of the current search path.

[0028] In some embodiments, the global factors are the base area S and tolerance Tl of the search path;

[0029] The set global factor condition is that the lower limit of the length of the search path is Lmin and the upper limit is Lmax;

[0030] in,

[0031] Lmin = S·(1-Tl·100%);

[0032] Lmax=S·(1+Tl·100%).

[0033] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0034] (1) The present invention has clear steps and concise logic, and can quickly provide an accurate detection sequence for large-area leakage detection, which is of great significance to the leakage detection of urban water supply pipe networks.

[0035] (2) Based on the existing manual section-by-section inspection of the water supply network, the present invention introduces a depth-first search method, which simplifies the number of traversal searches while ensuring that the search path will not be repeated, thereby reducing the workload during on-site inspection.

[0036] (3) Based on the existing water supply network risk analysis, the present invention provides a regional pipeline network risk assessment model based on pipeline structure attributes and pipeline network operation attributes. By inputting 12 pipeline indicator data, the two factors of pipeline network structure attribute SP and pipeline network operation SO are comprehensively scored, thereby improving the accuracy of water supply pipeline risk value assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention and its features, appearance and advantages will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings. The same reference numerals indicate the same parts in all the drawings. The drawings are not drawn to scale, and emphasis is placed on illustrating the subject matter of the present invention.

[0038] Figure 1 It is a flow chart of a method for searching a water supply network detection path in an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of a water supply network in a certain area in an embodiment of the present invention;

[0040] Figure 3 It is a schematic diagram of risk assessment of each grid in the water supply network in the area to be detected in an embodiment of the present invention.

[0041] Figure 4 Schematic diagram of detecting path search results in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention is further described below with reference to the accompanying drawings and specific embodiments, but is not intended to be a limitation of the present invention.

[0043] like Figure 1 As shown, the present invention discloses a method for searching a water supply network detection path. Specifically, it is known that the water supply area of ​​a certain region is 23.45 km2, and 88 historical accidents in the region are collected. Based on the GIS information obtained from the water supply management department, according to the GIS platform (i.e., GIS geographic information system), the network composition is displayed, such as Figure 2 This embodiment takes the water supply network in this area as an example for specific description.

[0044] Specifically, the method for searching the water supply network detection path in this embodiment includes the following steps:

[0045] Step S1, divide the water supply network in the area to be detected into multiple grids, and establish a corresponding relationship between each grid and the water supply pipe; specifically, grid processing is performed on the water supply network in the area to be detected in the GIS platform to divide the water supply network in the area to be detected into multiple grids.

[0046] In a preferred embodiment of the present invention, the above step 1 specifically includes the following steps:

[0047] Step S101 : determining the boundaries of the water supply network in the area to be detected in two directions (the two directions are horizontal and vertical respectively) according to the plane layout of the water supply network in the area to be detected.

[0048] Step S102, determining the required interval in each direction of the water supply network in the area to be detected according to the size of the required grid division;

[0049] Step S103, dividing the water supply network of the area to be inspected into a plurality of grids in two directions according to the required intervals in each direction of the water supply network of the area to be inspected determined in step 102;

[0050] Step S104, associating the pipeline in each grid with the grid number to establish a corresponding relationship between each grid and the water supply pipeline.

[0051] Step S2, evaluating the risk value of the water supply pipeline corresponding to each grid, and taking the risk value of the water supply pipeline corresponding to each grid as the risk value of the grid.

[0052] Specifically, step S2 is as follows: establishing a regional pipeline network risk assessment model, and evaluating the risk value of the water supply pipeline corresponding to each grid according to the regional pipeline network risk assessment model, and taking the risk value of the water supply pipeline corresponding to each grid as the risk value of the grid; wherein the water supply pipeline corresponding to each grid includes a number of pipeline segments, and the regional pipeline network risk assessment model includes the corresponding relationship between the risk value of the water supply pipeline corresponding to any grid and the state value of the secondary indicator of each pipeline segment in the grid and the weight of the secondary indicator.

[0053] Specifically, the regional pipeline network risk assessment model includes:

[0054]

[0055] Where R is the risk value of the water supply pipeline corresponding to a certain grid, l i is the length of the i-th pipe segment in the grid, m is the total number of pipe segments in the grid, G i is the risk value of the i-th pipeline segment in the grid; specifically, each pipeline segment includes 12 secondary indicators, which are: index layer soil thickness, index layer pipe diameter, index layer pipe pressure, index layer pipe material, index layer temperature difference, index layer road load, index layer pipe age, index layer soil corrosivity, index layer regional activity, index layer pipe network density, index layer pipe network leakage rate, index layer pipe network accident rate. i It is obtained by the status values ​​of 12 secondary indicators (i.e., secondary indicator status values), and a score value G is obtained with a score system of 0 to 10 points. i， Specific:

[0056]

[0057] In the formula, ω P S is the factor layer structure attribute P The weight, ω O For factor-layer pipeline network operation O The weight of the factor layer structure attribute S P and factor-layer pipeline network operation O The calculation formula is as follows:

[0058]

[0059] S O =ω O1 De+ω O2 Le+ω O3 Ac (2-4)

[0060] In the formula, ω P1 is the weight of the index layer cover thickness h; ω P2 is the weight of the pipe diameter D of the index layer; ω P3is the weight of the index layer management pressure P; ω P4 is the weight of the index layer pipe M; ω P5 is the weight of the index layer temperature difference T; ω P6 is the weight of the index layer road load Rl; P7 is the weight of the index layer pipe age A; ω P8 is the weight of soil corrosivity E of the index layer; ω P9 is the weight of the regional activity Ra in the indicator layer; ω O1 is the weight of the pipe network density De of the index layer; ω O2 is the weight of the leakage rate Le of the index layer pipeline network; ω O3 is the weight of the accident rate Ac of the index layer pipeline network; and satisfies Σω Pi =1,Σω Oi =1.

[0061] In an embodiment of the present invention, the step of evaluating the risk value of the water supply pipeline corresponding to each grid according to the regional pipe network risk assessment model specifically includes:

[0062] Step S201, extracting attribute parameters from the GIS platform, initializing the pipeline network data and estimated environmental parameters to obtain the secondary indicator status value of each pipeline section in each grid, and importing the secondary indicator status value of each pipeline section into the regional pipeline network risk assessment model.

[0063] Step S202, determining the weights of the 12 secondary indicators of each pipeline section in each grid according to the consistency matrix method, and checking whether the consistency requirements are met.

[0064] Specifically, according to the above-mentioned weight determination method, the weights of each risk assessment system indicator item are obtained as shown in the table.

[0065] Table 1: Weights of the 12 risk assessment indicators

[0066]

[0067] Step S203, the weights of the 12 secondary indicators of each pipeline segment are imported into the regional pipeline network risk assessment model to obtain the risk value of each pipeline segment in each grid (i.e., the risk value of each pipeline segment in each grid is calculated for the pipeline network structure attribute S P and Pipeline Network Operations O The comprehensive score of the two factors is used to obtain the risk value of the water supply pipeline corresponding to each grid.

[0068] like Figure 3 As shown in Figure 2, the area is divided into 100m×150m grids and the results of regional risk assessment are shown in Figure 2. Figure 3 , the lighter the color, the higher the risk assessment level.

[0069] Step S3, taking the grid with the highest risk value among all the grids in the water supply network of the area to be detected as the starting point of the current search path (all the grids in the water supply network of the area to be detected in step S3 refer to all grids in the water supply network of the area to be detected that are not used as search paths, that is, grids that have not been used).

[0070] Step S4, taking the starting point as the center, search in the grids of the first adjacent outer circle around the starting point (generally the 8 grids closest to the starting point around the starting point, or the grids in the grid circle closest to the starting point in the grid circle set around the starting point) to obtain the grid with the highest risk value around the starting point; when the search path reaches the boundary of the water supply network in the area to be detected or there is no way to go (all the surrounding grids have been used), expand the search range by one circle to continue searching for the grid with the highest risk value in the grids of the second adjacent outer circle around the starting point (the grids in the grid circle set around the starting point that is second adjacent to the starting point).

[0071] Step S5: taking the grid with the highest risk value as the next grid of the current search path.

[0072] Step S6, taking the next step grid of the current search path as the center, search for the grid with the highest risk value in the first adjacent outer circle of grids around the next step grid of the current search path (generally, the 8 grids closest to the next step grid of the current search path around the next step grid of the current search path, or the grids in a grid circle closest to the next step grid of the current search path in the grid circle set around the next step grid of the current search path); when the search path reaches the boundary of the water supply network of the area to be detected or there is no way to go (all the surrounding grids have been used), expand the search range by one circle to continue searching for the grid with the highest risk value in the second adjacent outer circle of grids around the next step grid of the current search path (i.e., the grids in a grid circle second adjacent to the next step grid of the current search path in the grid circle set around the next step grid of the current search path).

[0073] Step S7, determine whether the set global factor condition is met. If not, return to step S3 to step S7; if satisfied, end and save the current search path, and return to step S3 to step S7 for the remaining grids not used as search paths (i.e., grids that have not been used) until the water supply network of the entire area to be detected is traversed.

[0074] Specifically, the global factors are the reference area S and the tolerance Tl. The reference area S refers to the average area of ​​a search path in a physical sense, and the tolerance Tl is the range within which the reference area can float. The combination of these two parameters determines the upper and lower limits of the search path length L. The lower limit Lmin of the length of the search path is:

[0075] Lmin=S·(1-Tl·100%) (2-5)

[0076] The upper limit Lmax of the search path length is:

[0077] Lmax=S·(1+Tl·100%) (2-6)

[0078] It should be noted that the global factor condition set in this embodiment is

[0079] The lower limit of the length of the search path is Lmin, and the upper limit is Lmax.

[0080] In the formula, the reference area S and tolerance Tl can be changed according to the needs of the actual detection task. If the detection task is heavy and the time is tight, a smaller reference area S and a larger tolerance Tl are required to detect the key areas more quickly. The search path after gridding is between its upper and lower limits in length. Within the range of the upper and lower limits, the grid with the highest risk value is found as the interruption point, that is, the current search path ends and a new search path starts.

[0081] like Figure 4 As shown in the figure, through the evaluation of the night working ability in the actual inspection work, it is estimated that under normal working intensity conditions, each inspection team can work 8 hours per night and can inspect the area within the range of 1.5km path length and 5m path width per hour. Therefore, each inspection team can inspect 0.06km per night. 2 Therefore, the reference area S is 0.06km 2 The results of the first five paths found under the global factor of 25% are shown in Figure 4 .

[0082] In summary, the water supply network detection path search method disclosed in the present invention is a scientific and reliable auxiliary detection path identification method. It does not rely on the empirical analysis and general planning of conventional detection work. It combines the gridding of regional pipeline network GIS data with the identification of pipeline network risk status. On the basis of determining the sub-grid interval, it adopts a depth-first search algorithm combined with global factor conditions to achieve accurate division of the optimal detection path, which has great reference value for actual water supply network detection work.

[0083] Those skilled in the art should understand that those skilled in the art can implement variations by combining the prior art and the above embodiments, which will not be described in detail here. Such variations do not affect the essential content of the present invention, and will not be described in detail here.

[0084] The above describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above-mentioned specific embodiments, and the devices and structures that are not described in detail should be understood to be implemented in a common manner in the art; any technician familiar with the art can use the above-disclosed methods and technical contents to make many possible changes and modifications to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, or modify them into equivalent embodiments of equivalent changes, which does not affect the essential content of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention are still within the scope of protection of the technical solutions of the present invention.

Claims

1. A method for searching a water supply network detection path, characterized in that: The following steps are involved: Step S1, dividing the water supply network of the area to be detected into multiple grids, and establishing a corresponding relationship between each grid and the water supply pipeline; Step S2, establishing a regional pipe network risk assessment model, and assessing the risk value of the water supply pipe corresponding to each grid according to the regional pipe network risk assessment model, and taking the risk value of the water supply pipe corresponding to each grid as the risk value of the grid; The water supply pipeline corresponding to each grid includes a number of pipeline sections, and the regional pipe network risk assessment model includes a corresponding relationship between the risk value of the water supply pipeline corresponding to any grid and the state value of the secondary indicator of each pipeline section in the grid and the weight of the secondary indicator; The regional pipeline network risk assessment model includes: Where R is the risk value of the water supply pipeline corresponding to a certain grid; i is the length of the i-th pipe segment in the grid; m is the total number of pipe segments in the grid; G i is the risk value of the i-th pipeline segment in the grid; In the formula, ω P S is the factor layer structure attribute P The weight, ω O For factor-layer pipeline network operation O Step S3, taking the grid with the highest risk value among all the grids in the water supply network of the area to be detected as the starting point of the current search path; Step S4, taking the starting point as the center, searching in the grids of the first adjacent outer circle around the starting point to obtain the grid with the highest risk value around the starting point; Step S5, taking the grid with the highest risk value as the next grid of the current search path; Step S6, taking the next step grid of the current search path as the center, searching for the grid with the highest risk value in the first adjacent outer circle grids around; Step S7, determine whether the set global factor condition is met, if not, return to step S3 to step S7; if satisfied, end and save the current search path, and return to step S3 to step S7 for the remaining grids not used as the search path until the water supply network of the entire area to be detected is traversed; The global factors are the base area S and tolerance Tl of the search path; The set global factor condition is that the lower limit of the length of the search path is Lmin and the upper limit is Lmax; in, Lmin = S·(1-Tl·100%); Lmax=S·(1+Tl·100%).

2. The method for searching a water supply network detection path according to claim 1, characterized in that: The step S1 comprises the following steps: Step S101, determining the boundaries of the water supply network in the area to be detected in two directions according to the plane layout of the water supply network in the area to be detected; Step S102, determining the required interval in each direction of the water supply network in the area to be detected according to the size of the required grid division; Step S103, dividing the water supply network of the area to be detected into a plurality of grids in two directions according to the intervals required in each direction of the water supply network of the area to be detected determined in step S102; Step S104, associating the pipeline in each grid with the grid number to establish a corresponding relationship between each grid and the water supply pipeline.

3. The method for searching a water supply network detection path according to claim 1, characterized in that: In the step S1, the water supply network in the area to be inspected is gridded in the GIS platform to divide the water supply network in the area to be inspected into a plurality of grids.

4. The method for searching a water supply network detection path according to claim 1, characterized in that: The step of evaluating the risk value of the water supply pipeline corresponding to each grid according to the regional pipe network risk assessment model specifically includes: Step S201, extracting attribute parameters from the GIS platform, initializing the pipe network data and estimating environmental parameters, so as to obtain the status value of the secondary indicator of each pipe section in each grid, and importing the status value of the secondary indicator of each pipe section into the regional pipe network risk assessment model; Step S202, determining the weight of the secondary index of each pipeline section in each grid according to the consistency matrix method, and checking whether the consistency requirement is met; Step S203, importing the weight of the secondary indicator of each pipeline section into the regional pipe network risk assessment model to obtain the risk value of each pipeline section in each grid, and then obtain the risk value of the water supply pipeline corresponding to each grid.

5. The method for searching the detection path of a water supply network according to claim 4, characterized in that: Each pipeline section includes 12 secondary indicators, which are: index layer cover thickness, index layer pipe diameter, index layer pipe pressure, index layer pipe material, index layer temperature difference, index layer road load, index layer pipe age, index layer soil corrosiveness, index layer regional activity, index layer pipeline network density, index layer pipeline network leakage rate, and index layer pipeline network accident rate.

6. The method for searching a water supply network detection path according to claim 1, characterized in that: In step S4, when the search path reaches the boundary of the water supply network in the area to be detected or there is no way to go, the search range is expanded to continue searching for the grid with the highest risk value in the grid of the second adjacent outer circle around the starting point.

7. The method for searching a water supply network detection path according to claim 1, characterized in that: In step S6, when the search path reaches the boundary of the water supply network in the area to be detected or there is no way to go, the search range is expanded to continue searching for the grid with the highest risk value in the second adjacent outer circle of grids around the next step grid of the current search path.

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