CN113418146B - Leakage auxiliary positioning control method for water supply network - Google Patents
Leakage auxiliary positioning control method for water supply network Download PDFInfo
- Publication number
- CN113418146B CN113418146B CN202110883606.4A CN202110883606A CN113418146B CN 113418146 B CN113418146 B CN 113418146B CN 202110883606 A CN202110883606 A CN 202110883606A CN 113418146 B CN113418146 B CN 113418146B
- Authority
- CN
- China
- Prior art keywords
- leakage
- pressure
- water
- water supply
- supply network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 93
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 28
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 238000012163 sequencing technique Methods 0.000 claims abstract description 16
- 239000013598 vector Substances 0.000 claims abstract description 16
- 230000035945 sensitivity Effects 0.000 claims description 23
- 230000008859 change Effects 0.000 claims description 9
- 238000003064 k means clustering Methods 0.000 claims description 9
- 238000005192 partition Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000002238 attenuated effect Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000000638 solvent extraction Methods 0.000 claims description 3
- 239000002689 soil Substances 0.000 claims 1
- 230000008439 repair process Effects 0.000 abstract description 2
- 238000004904 shortening Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 7
- 239000011159 matrix material Substances 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000008399 tap water Substances 0.000 description 1
- 235000020679 tap water Nutrition 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Examining Or Testing Airtightness (AREA)
Abstract
The invention relates to a leakage auxiliary positioning control method for a water supply network, which specifically comprises the following steps: s1, acquiring the number of pressure gauges to be arranged, and setting the pressure gauges distributed in corresponding numbers in a water supply network according to the number of the pressure gauges; s2, collecting real-time monitoring data of a water supply network, and determining a suspicious pressure gauge according to the real-time monitoring data; s3, acquiring all nodes on a monitoring path where the suspicious pressure gauge is located, calculating fitting indexes of all the nodes, and sequencing to generate a suspicious leak point list; s4, checking the suspicious leakage point list through a multidimensional high-order vector analysis algorithm, and outputting the detected leakage points and the corresponding leakage water quantity. Compared with the prior art, the invention has the advantages of ensuring the accuracy and high timeliness of system data, perfecting the pressure monitoring surface of the pipe network, effectively shortening the discovery time of leakage points, improving the rush repair efficiency and the like.
Description
Technical Field
The invention relates to the technical field of water supply management, in particular to a leakage auxiliary positioning control method for a water supply network.
Background
Due to the expansion of the city scale and unpredictable human factors or natural factors, the water supply network of the city is inevitably damaged to a certain extent, so that tap water is lost, and the problem that the water supply network management is necessary to solve is how to locate the lost area with the minimum cost. The auxiliary positioning of the leakage of the water supply network depends on the subarea metering, so that the subarea metering management system is finished at present, but the analysis is not carried out according to the data of the subarea metering, the leakage area cannot be positioned in time, the approximate water leakage position and the approximate water leakage amount can be known only through on-site feedback, the flexibility is poor in response scheme and response time, and the efficiency is low.
Disclosure of Invention
The invention aims to overcome the defect that the leakage area cannot be positioned in time in the prior art, and provides a leakage auxiliary positioning control method for a water supply network.
The aim of the invention can be achieved by the following technical scheme:
the leakage auxiliary positioning control method for the water supply network specifically comprises the following steps:
S1, acquiring the number of pressure gauges to be arranged, and setting the pressure gauges distributed in corresponding numbers in a water supply network according to the number of the pressure gauges;
s2, collecting real-time monitoring data of a water supply network, and determining a suspicious pressure gauge according to the real-time monitoring data;
s3, acquiring all nodes on a monitoring path where the suspicious pressure gauge is located, calculating fitting indexes of all the nodes, and sequencing to generate a suspicious leak point list;
S4, checking the suspicious leakage point list through a multidimensional high-order vector analysis algorithm, and outputting the detected leakage points and the corresponding leakage water quantity.
The process of setting the pressure gauges distributed with the corresponding number in the step S1 includes the following steps:
S11, partitioning the water supply network to enable the number of the partitions to be equal to the number of pressure gauges to be arranged;
S12, adding a smaller flow to each node in each partition, calculating the sensitivity of pressure change of each node, and sequencing according to the sensitivity;
And S13, placing the manometer on the node with the highest sensitivity in each area according to the sequencing result of the sensitivity.
Further, in the step S12, the sensitivity of the node pressure change is calculated through a k-means clustering point distribution algorithm.
Further, the node with the highest sensitivity in the step S13 is specifically the centroid of a plurality of clusters in the k-means cluster point distribution algorithm.
And in the step S1, a manometer is arranged in the water supply pipe network through a searching and dotting algorithm.
The real-time monitoring data comprise pressure data and flow data of the water supply pipe network, and are collected through a hydraulic model system and an SCADA system established by the water supply pipe network.
The process of determining the suspicious pressure gauge according to the real-time monitoring data in the step S2 comprises the following steps:
s21, calculating the difference value of the actual measurement value and the analog value of each pressure gauge according to the real-time monitoring data;
s22, sorting the pressure gauges according to the calculated difference value;
S23, taking the pressure gauges with the preset number which are ranked ahead in the ranking result as suspicious pressure gauges.
The process of generating the suspicious leak point list in the step S3 includes the following steps:
S31, acquiring all nodes passing through a suspicious pressure gauge;
s32, calculating fitting indexes of water leakage probabilities of all nodes and sequencing;
S33, generating a suspicious leak point list according to the sequencing result of the fitting indexes.
Further, the nodes in the suspicious leak point list are ordered according to the water leakage probability from high to low.
The calculation formula of the lost water amount in the step S4 is as follows:
Wherein, Q L is leakage point flow (m 3/s),C1 is the influence of earth covering on leakage outflow, which is converted into a correction coefficient, C 2 is flow coefficient, A is leakage hole area (m 2), H is leakage hole pressure (m), g is gravity acceleration.
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, through the real-time monitoring data of the associated hydraulic model and the SCADA system, the accuracy and high timeliness of the system data are ensured; the installation position of the pressure gauge is automatically given through a point distribution algorithm, the deployment of the pressure measurement points of the pipe network is guided, and the pressure monitoring surface of the pipe network is perfected; by establishing a multidimensional high-order vector analysis algorithm, the pressure change conditions of each node under different water leakage amounts are fitted in advance, and are subjected to correlation analysis with actual pressure flow data, so that the most suspicious water leakage points are judged, a suspicious water leakage point list is provided, the discovery time of the water leakage points is effectively shortened, and the rush repair efficiency is improved.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic flow chart of a k-means clustering and point distribution algorithm in an embodiment of the invention;
FIG. 3 is a schematic flow chart of a search and distribution algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of modeling of a multidimensional high-order vector analysis algorithm in an embodiment of the present invention;
Fig. 5 is a schematic flow chart of leak detection of a multidimensional high-order vector analysis algorithm according to an embodiment of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
Examples
As shown in fig. 1, the leakage auxiliary positioning control method for the water supply network specifically comprises the following steps:
s1, acquiring the number of pressure gauges to be arranged, and setting the pressure gauges distributed in corresponding numbers in a water supply network according to the number of the pressure gauges;
s2, collecting real-time monitoring data of a water supply network, and determining a suspicious pressure gauge according to the real-time monitoring data;
s3, acquiring all nodes on a monitoring path where the suspicious pressure gauge is located, calculating fitting indexes of all the nodes, and sequencing to generate a suspicious leak point list;
S4, checking the suspicious leakage point list through a multidimensional high-order vector analysis algorithm, and outputting the detected leakage points and the corresponding leakage water quantity.
In this embodiment, the distribution point of the manometer may be adjusted according to the weight, or a node that is not allowed to arrange the manometer may be designated, and the node information may be read by the program in the form of an Excel list, where the node information includes a node number, a node name, and a node coordinate.
The process of setting the pressure gauges distributed by the corresponding number in step S1 includes the steps of:
S11, partitioning the water supply network to enable the number of the partitions to be equal to the number of pressure gauges to be arranged;
S12, adding a smaller flow to each node in each partition, calculating the sensitivity of pressure change of each node, and sequencing according to the sensitivity;
And S13, placing the manometer on the node with the highest sensitivity in each area according to the sequencing result of the sensitivity.
In step S12, the sensitivity of the node pressure change is calculated through a k-means clustering point distribution algorithm.
The node with the highest sensitivity in the step S13 is specifically the mass center of a plurality of clusters in the k-means clustering point distribution algorithm.
In this embodiment, as shown in fig. 2, a pressure sensor is arranged for a water supply network system through a k-means clustering and point distribution algorithm. K-means clustering is a clustering algorithm based on division, and n data objects are divided into K clusters by taking K as a parameter, so that the clusters have higher similarity and the similarity among the clusters is lower. For pressure sensitivity, the sensitivity of node1 to other nodes refers to the degree to which node1 water pressure changes such that the pressure of other nodes is affected, represented by vectors, and assuming that there are n nodes in the network, represented by nodes 1, node2, …, noden, respectively, the sensitivity vector of node1 to other nodes is represented as follows:
where Δ Pnode1 to Δ Pnoden are pressure differences of the corresponding nodes, and the sensitivity matrix of the network can be expressed as m= (x 1, x2, …, xn) T.
In specific implementation, the following steps are realized:
(1) Firstly, dividing the whole pipe network area into a plurality of areas (namely m areas) according to distance by clustering the position points;
(2) Then, selecting n points for each partition, and placing pressure gauges, wherein the n points are obtained by carrying out k-means clustering on row vectors of a pressure sensitivity matrix of the partition, the k-means algorithm can cluster vectors with nearest Euclidean distances in x1, x2, x3, … and xn into a cluster, and if the Euclidean vectors are close, the influence on the pressure of other nodes is similar when water leakage occurs to the vectors;
(3) Finally, through k-means clustering, the nodes which affect other nodes similarly are clustered to obtain n clusters, and then the mass centers of the n clusters are found to be used as the positions for placing the pressure gauges.
And in step S1, laying out pressure gauges in the water supply pipe network through a searching and dotting algorithm.
In this embodiment, as shown in fig. 3, the distributed pressure gauges are gradually pushed to the whole water supply pipe network through a searching and dotting algorithm, one node with the highest profit is selected from the periphery of the distributed pressure gauges each time, the pressure gauges are distributed for the node, and the operation is repeated until the pressure gauges are distributed on the whole pipe network.
In the searching and point setting algorithm, the observed value of a certain node refers to the observed capability of the arranged pressure gauge on the node, the larger the observed value is, the larger the influence of the water pressure change of the node on the pressure gauge is, the observed value is attenuated towards surrounding nodes by taking the pressure gauge point as 100%, and the calculation formula of the attenuation is as follows: observed value delta = elevation delta + tube length + tube diameter.
In the concrete implementation, assuming a total of n nodes of the water supply network, m pressure gauges are required to be arranged, and the searching and dotting algorithm realizes the following steps:
(1) A first pressure gauge is arranged at a water outlet of the water plant;
(2) Setting the point observation value of the distributed manometer to be 100%;
(3) Updating the observed values of the remaining n-1 points of the whole pipe network according to an attenuation formula;
(4) Selecting a point set P with an observed value attenuated to 1 to 20 percent;
(5) For each point P in P, calculating how much increment of an observation value is generated for the whole pipe network if a pressure gauge is arranged at the point P, and selecting a point x with the largest increment;
(6) The manometer is arranged at point x and returns to 2 until the arranged manometer reaches the required number m.
The real-time monitoring data comprise pressure data and flow data of the water supply pipe network, and are collected through a hydraulic model system and an SCADA system established by the water supply pipe network.
The process of determining the suspicious pressure gauge according to the real-time monitoring data in the step S2 comprises the following steps:
s21, calculating the difference value of the actual measurement value and the analog value of each pressure gauge according to the real-time monitoring data;
s22, sorting the pressure gauges according to the calculated difference value;
S23, taking the pressure gauges with the preset number which are ranked ahead in the ranking result as suspicious pressure gauges.
The process of generating the suspicious leak point list in the step S3 includes the following steps:
S31, acquiring all nodes passing through a suspicious pressure gauge;
s32, calculating fitting indexes of water leakage probabilities of all nodes and sequencing;
S33, generating a suspicious leak point list according to the sequencing result of the fitting indexes.
And ordering the nodes in the suspicious leak point list according to the water leakage probability from high to low.
In this embodiment, as shown in fig. 4, a model for leak detection is trained by using simulated pressure of a manometer through a multidimensional high-order vector analysis algorithm, and the following steps are specifically implemented:
(1) Increasing the water demand of the node x1 by 50, running a pipe network simulation, and forming the water pressure of all pressure gauges and 50 into a point p1= (P1, P2, P3,., 50);
(2) The water demand of the node x1 is increased by 60, a pipe network simulation is operated, and the water pressure of all pressure gauges and 60 form a point p2= (P1, P2, P3,., 60);
(3) The water demand of the node x1 is increased by 70, a pipe network simulation is operated, and the water pressure and 70 of all pressure gauges form a point p3= (P1, P2, P3,., 70);
(4) Vectors P1, P2, P3 of node x1 are connected into a line segment L1.
Repeating the operations of (1) - (4) for all nodes, the set of line segments formed being the Model = { L1, L2, L3,..ln }.
Then inputting the real pressure value into the constructed leakage point detection model, and judging water leakage and detecting leakage points of the current water supply network, as shown in fig. 5, specifically implementing the following steps:
(1) Assuming a total of n points in the pipe network, m pressure gauges, taking pressure gauge readings, forming a straight line p= (P1, P2, P3,) for the water pressure of all pressure gauges and the uncertainty leak z;
(2) Calculate the distance of the straight line P to all line segments in model= { L1, L2, L3,..ln.;
(3) According to the calculation result, the closer L is to P, the greater the possibility of water leakage.
The calculation formula of the lost water amount in step S4 is as follows:
wherein, Q L is the leakage point flow (m 3/s),C1 is the influence of the earth on the leakage outflow, which is converted into a correction coefficient, according to the pipe diameter, C 1＝1,C2 is the flow coefficient in this embodiment, C 2 =0.6 in this embodiment, a is the leakage hole area (m 2), H is the leakage hole pressure (m), g is the gravity acceleration, and 9.8m/s 2 in this embodiment.
Furthermore, the particular embodiments described herein may vary from one embodiment to another, and the above description is merely illustrative of the structure of the present invention. Equivalent or simple changes of the structure, characteristics and principle of the present invention are included in the protection scope of the present invention. Various modifications or additions to the described embodiments or similar methods may be made by those skilled in the art without departing from the structure of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (9)
1. The leakage auxiliary positioning control method for the water supply network is characterized by comprising the following steps of:
S1, acquiring the number of pressure gauges to be arranged, and setting the pressure gauges distributed in corresponding numbers in a water supply network according to the number of the pressure gauges;
s2, collecting real-time monitoring data of a water supply network, and determining a suspicious pressure gauge according to the real-time monitoring data;
s3, acquiring all nodes on a monitoring path where the suspicious pressure gauge is located, calculating fitting indexes of all the nodes, and sequencing to generate a suspicious leak point list;
s4, checking the suspicious leakage point list through a multidimensional high-order vector analysis algorithm, and outputting the detected leakage points and the corresponding leakage water quantity;
the calculation formula of the lost water amount in the step S4 is as follows:
,
Wherein, Q L is leakage flow, the unit is: m 3/s,C1 is the influence of the covering soil on the water leakage outflow, the influence is converted into a correction coefficient, C 2 is a flow coefficient, A is the area of a water leakage hole, the unit is m 2, H is the pressure of the water leakage hole, the unit is m, and g is gravity acceleration;
Through a multidimensional high-order vector analysis algorithm, a model for detecting leakage points is trained by using the simulated pressure of the pressure gauge, and the following steps are specifically realized:
step S4-1-1: increasing the water demand of the node x1 by 50, running a pipe network simulation, and forming the water pressure of all pressure gauges and 50 into a point p1= (P1, P2, P3,., 50);
step S4-1-2: the water demand of the node x1 is increased by 60, a pipe network simulation is operated, and the water pressure of all pressure gauges and 60 form a point p2= (P1, P2, P3,., 60);
Step S4-1-3: the water demand of the node x1 is increased by 70, a pipe network simulation is operated, and the water pressure and 70 of all pressure gauges form a point p3= (P1, P2, P3,., 70);
step S4-1-4: connecting points P1, P2 and P3 of the node x1 into a high-dimensional curve L1;
repeating the operations from step S4-1-1 to step S4-1-4 for all nodes, wherein the formed set of high-dimensional curves is the Model = { L1, L2, L3,..ln };
Then inputting the real pressure value into a constructed leakage point detection model, and judging water leakage and detecting leakage points of the current water supply network, wherein the method comprises the following steps of:
Step S4-2-1: a total of n points in the pipe network, m pressure gauges, reading the pressure gauges, forming a high-dimensional curve p= (P1, P2, P3,) of the water pressure of all pressure gauges and the uncertain water leakage z;
Step S4-2-2: calculating the distance of the high-dimensional curve P to all the high-dimensional curves in the model= { L1, L2, L3,..ln };
Step S4-2-3: according to the calculation result, the closer L is to P, the greater the possibility of water leakage.
2. The method for auxiliary positioning of leakage loss for water supply network according to claim 1, wherein the step S1 of setting the corresponding number of pressure gauges comprises the steps of:
S11, partitioning the water supply network to enable the number of the partitions to be equal to the number of pressure gauges to be arranged;
S12, adding a smaller flow to each node in each partition, calculating the sensitivity of pressure change of each node, and sequencing according to the sensitivity;
And S13, placing the manometer on the node with the highest sensitivity in each area according to the sequencing result of the sensitivity.
3. The method for auxiliary positioning of leakage loss for water supply network according to claim 2, wherein the sensitivity of node pressure variation is calculated by k-means clustering and point distribution algorithm in step S12.
4. The method for auxiliary positioning and controlling leakage loss of water supply network according to claim 3, wherein the node with highest sensitivity in the step S13 is specifically a centroid of a plurality of clusters in a k-means cluster distribution algorithm.
5. The method for auxiliary positioning of leakage loss for water supply network according to claim 1, wherein in step S1, a pressure gauge is arranged in the water supply network by searching a point distribution algorithm;
The search and distribution algorithm comprises the following steps:
Step S1-2-1: a first pressure gauge is arranged at a water outlet of the water plant;
step S1-2-2: setting the point observation value of the distributed pressure gauge as 100%, wherein the observation value is the observation capability of the distributed pressure gauge on the point, and the larger the observation value is, the larger the influence of the water pressure change of the point on the pressure gauge is;
Step S1-2-3: updating the observed values of the remaining n-1 points of the whole pipe network according to an attenuation formula, wherein the attenuation formula specifically comprises: observed value attenuation = elevation change factor + tube length factor + tube diameter factor;
step S1-2-4: selecting a set of points with an observed value attenuated to 1% to 20%;
step S1-2-5: for each point in the point set, calculating the increment of an observation value generated by laying a pressure gauge at each point, and selecting one point x with the largest increment;
Step S1-2-6: the manometer is arranged at the point x and returns to the step S1-2-2 until the arranged manometer reaches the required number m.
6. The method for auxiliary positioning of leakage loss in a water supply network according to claim 1, wherein the real-time monitoring data includes pressure data and flow data of the water supply network.
7. The method for auxiliary positioning of leakage loss for water supply network according to claim 1, wherein the step S2 of determining the suspicious pressure gauge according to the real-time monitoring data comprises the steps of:
s21, calculating the difference value of the actual measurement value and the analog value of each pressure gauge according to the real-time monitoring data;
s22, sorting the pressure gauges according to the calculated difference value;
S23, taking the pressure gauges with the preset number which are ranked ahead in the ranking result as suspicious pressure gauges.
8. The method for auxiliary positioning of leakage loss in water supply network according to claim 1, wherein the step of generating the suspicious leakage point list in the step S3 comprises the steps of:
S31, acquiring all nodes passing through a suspicious pressure gauge;
s32, calculating fitting indexes of water leakage probabilities of all nodes and sequencing;
S33, generating a suspicious leak point list according to the sequencing result of the fitting indexes.
9. The method for auxiliary positioning of leakage loss for water supply network according to claim 8, wherein nodes in the suspicious leakage point list are ordered according to the probability of water leakage from high to low.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110883606.4A CN113418146B (en) | 2021-08-03 | 2021-08-03 | Leakage auxiliary positioning control method for water supply network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110883606.4A CN113418146B (en) | 2021-08-03 | 2021-08-03 | Leakage auxiliary positioning control method for water supply network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113418146A CN113418146A (en) | 2021-09-21 |
| CN113418146B true CN113418146B (en) | 2024-08-13 |
Family
ID=77718761
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110883606.4A Active CN113418146B (en) | 2021-08-03 | 2021-08-03 | Leakage auxiliary positioning control method for water supply network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113418146B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114659037B (en) * | 2022-03-30 | 2022-11-29 | 浙江大学 | A location method for pipe burst in urban water supply network |
| CN115289403B (en) * | 2022-06-30 | 2025-01-07 | 中控技术股份有限公司 | A method for automatically monitoring closed paths within a pipe network section |
| CN118365140B (en) * | 2024-06-18 | 2024-09-17 | 中山大学 | Urban security risk assessment method and system based on data fusion |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866899A (en) * | 2015-06-17 | 2015-08-26 | 山东省环境保护科学研究设计院 | Leakage detection method based on hydraulic model calibration of urban water supply network |
| CN106090621A (en) * | 2016-06-02 | 2016-11-09 | 东北电力大学 | A kind of water supply network leakage, plugging fault diagnosis and localization method analyzed based on pressure signal |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE7209732U (en) * | 1971-03-24 | |||
| CN106844814B (en) * | 2016-09-30 | 2021-02-02 | 西安石油大学 | Large-scale complex natural gas pipe network system leakage detection method |
| CN107143750B (en) * | 2017-04-13 | 2019-05-31 | 浙江大学 | A kind of space layout method of pipe burst monitoring network |
| CN107657074B (en) * | 2017-08-18 | 2021-04-09 | 昆明理工大学 | A Stochastic Simulation Method for Evaluating the Reliability of Urban Water Supply Networks |
| CN108122074B (en) * | 2017-12-21 | 2021-09-17 | 北京工业大学 | Distribution method of water leakage amount in water supply pipe network model |
| CN108647470B (en) * | 2018-05-29 | 2022-05-31 | 杭州电子科技大学 | An initial leak location method based on clustering and deep belief network |
| CN109869638B (en) * | 2019-03-25 | 2021-03-09 | 杭州电子科技大学 | Method for initial positioning of water supply pipe network pipe burst leakage |
| CN111141653B (en) * | 2019-12-30 | 2022-08-09 | 上海地铁维护保障有限公司 | Tunnel leakage rate prediction method based on neural network |
| CN111853555B (en) * | 2020-07-07 | 2022-03-25 | 杭州电子科技大学 | Water supply pipe network blind leakage identification method based on dynamic process |
| CN113094853B (en) * | 2021-04-01 | 2024-07-02 | 上海天麦能源科技有限公司 | Optimized layout method and system for gas pipe network leakage positioning |
-
2021
- 2021-08-03 CN CN202110883606.4A patent/CN113418146B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866899A (en) * | 2015-06-17 | 2015-08-26 | 山东省环境保护科学研究设计院 | Leakage detection method based on hydraulic model calibration of urban water supply network |
| CN106090621A (en) * | 2016-06-02 | 2016-11-09 | 东北电力大学 | A kind of water supply network leakage, plugging fault diagnosis and localization method analyzed based on pressure signal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113418146A (en) | 2021-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113418146B (en) | Leakage auxiliary positioning control method for water supply network | |
| CN110108328B (en) | Method for acquiring water leakage amount of leakage area of water supply pipe network | |
| CN108197744B (en) | A method and system for determining photovoltaic power generation power | |
| CN107886182B (en) | Optimal design method and device for oil field gathering and transportation system | |
| CN114580786B (en) | A method for optimizing the layout of monitoring points in urban water supply networks | |
| CN105046352B (en) | Water supply network leakage loss calculation method based on blood vessel bionic principle | |
| CN115685787B (en) | Modeling simulation method, modeling simulation system and medium for urban gas pipe network | |
| CN106195646A (en) | Leak and be distributed estimating device, deduction system and presumption method | |
| CN107016622B (en) | Inversion method for urban water supply network node water demand containing large user water consumption information | |
| CN107633146B (en) | Method for high-precision conversion of crustal stress calculation results among different scale models | |
| CN112503400A (en) | Multi-objective optimization arrangement method for pressure measuring points of water supply pipe network | |
| CN113222351B (en) | Urban emergency water supply fine scheduling method based on water evolution change | |
| CN111022932A (en) | Sensor point distribution system and method for water supply pipe network | |
| CN117469603A (en) | Multi-water-plant water supply system pressure optimal control method based on big data learning | |
| CN117521548A (en) | Real-time back calculation method for wind resistance of mine tunnel section | |
| CN118998624A (en) | Urban water supply network leakage monitoring method | |
| CN118644015A (en) | Pipeline network optimization dispatching system and method based on hydraulic model and neural network algorithm | |
| Wang et al. | An Improved Grey Prediction Model Based on Matrix Representations of the Optimized Initial Value. | |
| CN109116300B (en) | Extreme learning positioning method based on insufficient fingerprint information | |
| CN114912364A (en) | Natural gas well flow prediction method, device, equipment and computer readable medium | |
| CN119358952A (en) | Wind farm digital integrated planning platform and method | |
| CN102567606A (en) | Method for drawing equal-water-pressure surface of water supply pipeline network in real time | |
| CN107688702A (en) | A Simulation Method of River Flood Discharge Evolution Law Based on Wolf Pack Algorithm | |
| CN100478650C (en) | Method and apparatus for improved fault detection in power generation equipment | |
| Li et al. | A stepwise fast leakage localization method applying the strategy of dynamic area narrowing down for large-scale water distribution network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |