A method, device and system for detecting and locating leakage in multiple pipes of a water supply network

Abstract

A method, device and system for detecting and locating multi-pipeline leakage in a water supply network, comprising: constructing a network model and sensor deployment to create a complex hydraulic network model, screening optimal monitoring points, and generating pressure and flow sensor layout plans; outputting a network topology diagram and sensor deployment results; performing scenario approximation and constructing an optimization model so that hydraulic data under normal operating conditions fit hydraulic data under leakage scenarios, and finally introducing hydraulic data correction to output possible leakage nodes and corresponding hydraulic parameters; narrowing the scope of the leaking network, and based on preliminary inference of the leakage nodes, continuously updating and screening the scope of pipelines that may have leakage; after iterative optimization, outputting the local network area where leakage is most likely to occur; establishing an observer and locating specific leakage points, dynamically estimating leakage parameters in suspected leaking pipe sections, comprehensively analyzing filtering results, and outputting the specific leaking pipe sections and leakage location coordinates.

Description

Technical Field

[0001] The present invention belongs to the field of multi-point leakage detection in urban water supply pipe networks, and specifically relates to a detection and positioning method, device and system for multi-pipeline leakage in water supply pipe networks. Background Art

[0002] Currently, water supply networks are still struggling to address the challenges of operational length and multiple leaks. Leaks in complex networks, with multiple pipes, are not only difficult to detect and locate, impacting daily water consumption for urban residents, but also leading to waste of natural resources and losses for water utilities. Improving the accuracy of locating multiple leaks in complex water supply networks has become a pressing social issue.

[0003] In the field of water supply network leakage detection, the equipment method is a relatively advanced technology. This technology mainly uses various detection instruments to detect leakage in the pipeline network. However, the accuracy of its detection results depends largely on the experience of the operator. Moreover, when dealing with leakage detection in large pipeline networks, this method often requires a lot of time and manpower. With the development of artificial intelligence and sensors, data-driven methods have emerged. Chinese patent publication CN115994487A, "A water supply network leakage location method based on correlation coefficient," proposes a water supply network leakage location method based on an improved gray wolf optimization algorithm. Under the conditions of annular and complex pipeline networks, a large number of flow meters are required, which is economically costly. Chinese patent publication CN119123342A, "A method and system for monitoring leakage in urban water supply pipeline networks," proposes a method and system for monitoring leakage in urban water supply pipeline networks that predicts water consumption through a neural network. However, this model requires a large amount of historical hydraulic data for training, has poor applicability to complex pipeline networks in reality, and incurs a large amount of computational cost. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method, device and system for detecting and locating leakage in multiple pipes of a water supply network.

[0005] A first aspect of the present invention relates to a method for detecting and locating leakage in multiple pipes of a water supply network, comprising the following specific steps:

[0006] S1: Build pipe network model and sensor deployment;

[0007] The system receives hydraulic data input by the user, analyzes it using mass and energy conservation equations, and combines node and pipeline information to create a complex hydraulic network model. It then calculates the correlation matrix between nodes, combines a greedy algorithm, and the degree of information redundancy to screen the optimal monitoring points and generate a layout plan for pressure and flow sensors. The system then outputs a network topology diagram and sensor deployment results.

[0008] S2: Perform scenario approximation;

[0009] A basic judgment is made on whether the pipeline network is leaking. If there is a large difference between the sensor measurement value and the pipeline model simulation value, the pipeline network is considered to have a leak. The flow and pressure sensors that are sensitive to changes in the measurement data are determined based on the relative fluctuation values ​​of the arranged sensors. The optimization variables are then determined by combining the correlation matrix and information redundancy. An optimization model is constructed to make the hydraulic data under the original normal working conditions fit the hydraulic data under the leakage scenario. Finally, hydraulic data correction is introduced. The possible leakage nodes and corresponding hydraulic parameters are output.

[0010] S3: Reduce the scope of the leaking pipe network;

[0011] Using the node expansion strategy and depth-first search algorithm, based on the preliminary inference of leaking nodes, the range of pipelines that may have leakages is continuously updated and screened; after iterative optimization, the local pipeline network area where leakage is most likely to occur is output.

[0012] S4: Establish an observer and locate the specific leakage point;

[0013] The extended Kalman filter is used to construct an observer model, fuse multi-source observation information, and dynamically estimate the leakage parameters in the suspected leaking pipe section. The filtering results are comprehensively analyzed to output the specific leaking pipe section and the coordinates of the leakage location.

[0014] Furthermore, in step S1, building a pipe network model and sensor deployment specifically includes:

[0015] S11: Based on the hydraulic data input by the system and the principles of conservation of mass and energy, the node continuity equations and pipe segment energy equations in the pipe network are constructed to accurately describe the hydraulic behavior of the system;

[0016] S12: Establish a finite-dimensional mathematical model for each pipe segment in the pipe network and discretize the continuous hydraulic process to support subsequent sensitivity analysis and monitoring point optimization calculation;

[0017] S13: Based on the perturbation analysis method, the pressure sensitivity matrix between nodes is calculated, and after normalization and threshold processing, a binary correlation matrix is ​​generated. , used to measure the degree of pressure correlation between nodes;

[0018] S14: Initialize the sensor selection process, set the selected node set to empty, and the node set to be covered to the correlation matrix All columns in ;

[0019] S15: Under the condition that the set of columns to be covered is not empty, perform the following operations: traverse all rows of the correlation matrix and find the row that can cover the most unselected columns; add the node corresponding to the row to the selected set, and update the set of columns to be covered accordingly, until all columns are covered or there is no feasible selection;

[0020] S16: Return the final selected row index. The corresponding node is the recommended location for installing pressure sensors. Flow sensors are installed on the pipes connected to these nodes to achieve accurate perception of leakage or abnormal working conditions. The data measured by the sensors will form a joint observation vector ,in Indicates flow meter reading, Indicates the pressure gauge reading, and are the number of flow and pressure sensors respectively, and the total observation dimension is .

[0021] Furthermore, in step S2, determining the leakage area and performing scenario approximation specifically include:

[0022] S21: After determining the sensor layout using the sensor deployment algorithm, the residual between the hydraulic characteristics of the pipe network simulated by the model and the actual measurement values ​​of the sensors can be calculated. If there is a large difference, it is considered that the pipe network has leakage, and the following scenario approximation method is used;

[0023] S22: Calculate the relative fluctuation value based on the residual in S21, select the two pressure sensors and flow sensors with larger relative fluctuation values, calculate the information redundancy of the sensor-related nodes, change the selected sensor, and finally calculate the information redundancy of the sensor-related nodes and the correlation matrix. , determine the optimization variables;

[0024] S23: Initialize the extra flow vector. Initialize the extra flow vector of each node represented by the optimization variable to a 0 vector;

[0025] S24: Calculate the characteristics of the pipe network and estimate the observation vector under the current state ,in represents the time step of the model operation, Indicates that the observation vector is The model estimate after time steps, Function represents the hydraulic calculation function based on Todini gradient algorithm, Represents the optimal additional flow list of the node. The observation vector is estimated With node index Matching, the updated pressure estimate is ;

[0026] S25: define the optimization objective function;

[0027] S26: Use the CSA Chameleon Swarm Optimization algorithm to solve the optimization objective function and obtain the optimal additional flow list ;

[0028] S27: Use the best Re-simulate and update for several steps to obtain an estimate of the observation vector that approximates the leakage state And the optimal additional water demand list for each relevant node ;

[0029] S28: Hydraulic characteristic adjustment: After identifying the leaking pipe section, the upstream and downstream flows are weighted to fully approximate the leakage scenario;

[0030] S29: Output the final observation vector estimate of the approximate leakage state and the optimal additional water demand for each relevant node ;

[0031] Furthermore, in step S3, reducing the scope of the leaking pipe network specifically includes:

[0032] S31: Initialize variables; leakage flow list The calculation results of the S2 method, the leakage start and end nodes list of the leakage pipe section Initialized to an empty list, the marked visited node list is initialized to .

[0033] S32: Traverse the additional traffic list The flow value of each node in the , then add the flow value of the node to the leakage flow list , indicating that the node may be leaked;

[0034] S33: Traverse the leakage flow list For each node in, a depth-first search (DFS) is performed on the unvisited leaky nodes to determine the possible leaky nodes connected to it;

[0035] S34: Returns a list of both end nodes of all leaking pipe segments.

[0036] Furthermore, in step S4, establishing an observer and locating a specific leakage point specifically includes:

[0037] S41: Determine the state space expression for a single pipe segment with a unique leakage;

[0038] First, determine the hydraulic boundary conditions of the target pipe section, and use the pressure of the nodes at both ends of the pipe section as the input signal. and ; The flow at both ends is used as the system observation output signal, which is recorded as and . Set the system state variables to:

[0039]

[0040] in, Indicates the inlet flow rate of the pipe section, unit: m³ / s; Indicates the pressure at the intermediate node, unit: m (meter water column); Indicates the outlet flow rate of the pipe section, unit: m³ / s; Indicates the relative position of the leakage in the pipe section, with a value range of 0 to 1 and no unit; Indicates the leakage flow rate per unit time, unit: m³ / s.

[0041] Define the system input variables as:

[0042]

[0043] Define the system observation output as:

[0044]

[0045] The continuous-time state equation of the system is as follows:

[0046]

[0047] in, Function represents the current state and input Next, the derivative of the state variable.

[0048] S42: Discretized system variables;

[0049] The continuous-time state equation is discretized using Heun's method (an improved trapezoidal method). Assume the time step is , No. The state update for each time step is:

[0050]

[0051] This formula can approximately describe the state changes of nonlinear systems in a short period of time. The discrete state transfer function of the system is defined as follows:

[0052]

[0053] S43: Initialize state variable estimates and covariance matrix;

[0054] Set the initial state estimate to:

[0055]

[0056] in Denotes expectation. The error covariance matrix is ​​initialized as:

[0057]

[0058] S44: state prediction;

[0059] For every moment , use the prediction model to estimate the current state:

[0060]

[0061] Simultaneous forecast error covariance matrix:

[0062]

[0063] in, The Jacobian matrix representing the system state function; represents the process noise covariance matrix.

[0064] S45: measurement update, residual calculation;

[0065] Compute the observation residuals:

[0066]

[0067] in is the observation matrix, which is:

[0068]

[0069] S46: state estimation correction;

[0070] Correct the state estimate based on the residuals:

[0071]

[0072] The Kalman gain matrix is:

[0073]

[0074] And update the error covariance:

[0075]

[0076] in is the observation noise covariance matrix, is the identity matrix.

[0077] S47: Estimation result output;

[0078] Through the continuous iteration of the above state prediction and correction, the state variables are finally obtained. and The convergence estimate of , that is, the location and leakage intensity of the only leakage point in the pipe section.

[0079] A second aspect of the present invention relates to a water supply pipe leakage detection and positioning device, configured in a data processing and analysis device, comprising:

[0080] Pipeline monitoring data acquisition module: used to obtain pipeline-related data sent by the monitoring equipment, wherein the monitoring data includes water level data, water flow data and water pressure data of each node unit basin in the current monitoring area;

[0081] Pipeline leakage analysis result determination module: used to perform forecast evolution calculation and pipeline leakage analysis on monitoring point data to obtain leakage analysis results of the current monitoring area;

[0082] The early warning module generates early warning information and relevant data about the leaking pipeline, including leak point pipeline data, water flow data, water pressure data, and location data. The early warning information is then sent to the early warning device, which then notifies maintenance personnel based on the information.

[0083] A third aspect of the present invention relates to a water supply pipe leakage detection and positioning system, characterized in that it includes a pipe network monitoring device, a data acquisition device, a data processing and analysis device, a terminal device, and an early warning device, wherein the data acquisition device, the early warning device, and the terminal device are connected to the data processing and analysis device via a communication device;

[0084] Pipeline network monitoring equipment: used to monitor the flow and pressure status of the pipeline network;

[0085] Data acquisition equipment: used to collect water pressure data, water level data, and water flow data of each node unit basin in the area;

[0086] Data processing and analysis equipment: used to obtain data sent by the pipe network data acquisition equipment, estimate water demand based on the scenario approximation method, use the depth-first algorithm to narrow the pipe network leakage area, and use the extended Kalman observer to perform specific leakage location analysis to obtain leakage detection results in the corresponding area;

[0087] Early warning device: if the leakage detection analysis result obtained by the data processing and analysis device is confirmed to be a leakage state, it generates early warning information, including pipeline data, water flow data, water pressure data and location data of the leakage point, and sends the early warning information to the early warning device, so that the early warning device notifies relevant maintenance personnel according to the early warning information;

[0088] Terminal device: used to receive the analysis data and pipeline leakage analysis results sent by the data processing and analysis device, and visualize the analysis data and the pipeline leakage analysis results.

[0089] The innovative features of the present invention are:

[0090] This method, based on observer technology (extended Kalman filter), proceeds in three steps. First, sensors are arranged in the pipe network using a pressure sensitivity matrix. Then, threshold and scenario approximation methods are used to identify the leaking area and leaking pipe segment, respectively. A depth-first search algorithm is used to narrow down the size of the potentially leaking pipe network. Finally, an observer is designed for the leaking pipe segment to determine the leakage parameters. This gradually narrows the leakage range to the specific leaking pipe segment, significantly reducing the scale of the observer problem. This detection method improves leak detection accuracy and efficiency, reduces labor costs, and helps personnel promptly detect and repair leaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 This is a schematic diagram of the overall process of a method for detecting and locating multiple leaks in a water supply network in Example 1 of the present invention.

[0092] Figure 2 This is a schematic diagram of specific implementation steps of a method for detecting and locating multiple leaks in a water supply network in Example 1 of the present invention.

[0093] Figure 3 It is a schematic diagram of pipeline leakage detection simulation and sensor layout in Example 1 of the present invention.

[0094] Figure 4 It is a structural schematic diagram of a water supply pipe leakage detection and positioning device provided in the second embodiment of the present invention.

[0095] Figure 5 This is a structural diagram of a water supply pipe leakage detection and positioning system provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0096] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0097] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with exemplary embodiments.

[0098] Example 1

[0099] refer to Figure 1 、 Figure 2 、 Figure 3 The present invention provides a method for detecting and locating leakage in multiple pipes of a water supply network, comprising the following steps:

[0100] S1: Constructing a pipe network model and sensor deployment. The system receives hydraulic data input by the user, analyzes it using mass and energy conservation equations, and combines node and pipeline information to create a complex hydraulic pipe network model. By calculating the correlation matrix between nodes, combined with a greedy algorithm and the degree of information redundancy, the optimal monitoring points are selected and a pressure and flow sensor layout plan is generated. The pipe network topology and sensor deployment results are output.

[0101] Specifically, step S1 includes:

[0102] S11: Based on the hydraulic data input by the system and the principles of conservation of mass and energy, the node continuity equations and pipe segment energy equations in the pipe network are constructed to accurately describe the hydraulic behavior of the system;

[0103] This example uses the Hazen-Williams (HW) formula to calculate the head loss. The effects of the pipe parameters in this formula are summarized in the HW resistance coefficient of each pipe. In the equation, the coefficient is a The pipeline parameter function is defined as Then, the pipeline is calculated by the HW formula The head loss is as follows:

[0104]

[0105] in is a constant index, It's a pipeline of traffic.

[0106] Pumps are another important element in a water distribution network. They are characterized by a pressure-flow curve, which is used to relate the pump flow rate to the pump head gain according to the specifications of each pump. This is described by the following equation:

[0107]

[0108] in , and It is the coefficient of the pump's pressure-flow curve. The negative sign indicates that the pump's head is a gain rather than a loss.

[0109] Therefore, the energy conservation equation in the water supply network can be written as follows:

[0110]

[0111] in is the hydraulic head at the unknown node, , , a matrix representing the water flow direction relationship between nodes, if , indicating that the water flow is from the node Flow Node , otherwise if It means and The water flow direction relationship between nodes is from Flow direction .if This indicates the node and There is no direct connection. In each equation The sum of the known heads that appear in . In vector notation, the vector of the known heads is represented by given.

[0112] The mass conservation equation in the water supply network can be written as follows:

[0113]

[0114] S12: Establish a finite-dimensional mathematical model for each pipe segment in the pipe network and discretize the continuous hydraulic process to support subsequent sensitivity analysis and monitoring point optimization calculation;

[0115] Will Pipeline node at the moment Traffic in Recorded as . Similarly, Pipeline node at the moment Pressure in Recorded as .use and represent and The difference between two adjacent pipeline nodes is recorded as . is the local acceleration due to gravity. is the cross-sectional area of ​​the pipe. is the inner diameter of the pipe. For any number of pipe segments, a finite-dimensional model can be obtained:

[0116]

[0117]

[0118] in 、 、 and Corresponding to the boundary conditions 、 、 and Friction coefficient itself depends on the flow rate, which will be of interest when considering subsequent leakage situations. The model is:

[0119]

[0120] in Indicates the pipe roughness, is the Reynolds number, and the calculation formula is as follows: ,in is the kinematic viscosity of water, For the The flow rate of the pipe section.

[0121] S13: Based on the perturbation analysis method, the pressure sensitivity matrix between nodes is calculated, and after normalization and threshold processing, a binary correlation matrix is ​​generated. , used to measure the degree of pressure correlation between nodes;

[0122] Based on the microscopic analysis method of the pipe network, calculation Node Pair The sensitivity of the nodes is as follows:

[0123]

[0124] in , The water demand node under the baseline condition and Water pressure, , for After the node traffic changes , Water pressure at the node.

[0125] For the first The element values ​​of the column are normalized to the minimum and maximum values, and the values ​​of all nodes for Dimensionless sensitivity matrix when node water demand changes , No. Rank The column elements are:

[0126]

[0127] in and Represents the sensitivity matrix No. The minimum and maximum values ​​of the column. In order to express the strength of the correlation, set the correlation threshold

[0128]

[0129] S14: Initialize the sensor selection process, set the selected node set to empty, and the node set to be covered to the correlation matrix All columns in ;

[0130] S15: Under the condition that the set of columns to be covered is not empty, perform the following operations: traverse all rows of the correlation matrix and find the row that can cover the most unselected columns; add the node corresponding to the row to the selected set, and update the set of columns to be covered accordingly, until all columns are covered or there is no feasible selection;

[0131] S16: Return the final selected row index. The corresponding node is the recommended location for installing pressure sensors. Flow sensors are installed on the pipes connected to these nodes to achieve accurate perception of leakage or abnormal working conditions. The data measured by the sensors will form a joint observation vector ,in Indicates flow meter reading, Indicates the pressure gauge reading, and are the number of flow and pressure sensors respectively, and the total observation dimension is .

[0132] S2: Perform scenario approximation to determine if the network is leaking. If there is a significant discrepancy between the measured values ​​of the sensors deployed in S1 and the simulated values ​​of the network model, the network is considered to be leaking. Therefore, a scenario approximation method is introduced. Two flow and two pressure sensors are selected based on the relative fluctuations of the deployed sensors. The optimization variables are then determined by combining the correlation matrix and information redundancy. An optimization model is constructed to fit the hydraulic data under normal operating conditions to the hydraulic data under the leakage scenario. Finally, hydraulic data correction is introduced to output the nodes with potential leakage and the corresponding hydraulic parameters.

[0133] Specifically, step S2 includes:

[0134] S21: After determining the sensor layout using the sensor deployment algorithm, the residual between the hydraulic characteristics of the pipe network simulated by the model and the actual measurement values ​​of the sensors can be calculated. If there is a large difference, it is considered that the pipe network has leakage, and the following scenario approximation method is used;

[0135] S22: Calculate the relative fluctuation value based on the residual in S21, select the two pressure sensors and flow sensors with larger relative fluctuation values, calculate the information redundancy of the sensor-related nodes, change the selected sensor, and finally calculate the information redundancy of the sensor-related nodes and the correlation matrix. , determine the optimization variables;

[0136] S23: Initialize the extra flow vector. Initialize the extra flow vector of each node represented by the optimization variable to a 0 vector;

[0137] S24: Calculate the characteristics of the pipe network and estimate the observation vector under the current state ,in represents the time step of the model operation, Indicates that the observation vector is The model estimate after time steps, Function represents the hydraulic calculation function based on Todini gradient algorithm, Represents the optimal additional flow list of the node. The observation vector is estimated With node index Matching, the updated pressure estimate is ;

[0138] S25: Define the optimization objective function; the scenario under normal working conditions of the water supply network is defined as The scenario where leakage occurs in the pipeline network is defined as leakage scenario The purpose of scenario approximation is to change the water demand of relevant water demand nodes under normal circumstances so that the node pressure and pipe flow characteristics in the pipe network can be consistent with the leakage scenario. Approximately equal.

[0139] The sensor is recorded as , suppose these and The nodes with strong sensor correlation are , .set up The additional water demand at the node is , and define The scene approximation problem can be described by the following optimization model:

[0140]

[0141]

[0142]

[0143] in for The measured values ​​of flow and pressure sensors in the pipe network at all times, for Apply at the corresponding pipe network node at all times Then the pipe network flow and pressure characteristics are obtained by using the pipe network characteristic calculation method The flow rate and node pressure of the pipe section where the sensor is installed are recorded as . is the sensor weight matrix. Assuming that the measurements between each sensor are independent, then the matrix It is a diagonal matrix, and the larger the value of the diagonal element, the higher the accuracy of the corresponding sensor. is the total water leakage in the water supply network area, because There is uncertainty, so the fluctuation value of the leakage is set .

[0144] S26: Use CSA (Chameleon Swarm Optimization Algorithm) to solve the optimization objective function and obtain the best additional flow list ;

[0145] S27: Use the best Re-simulate and update for several steps to obtain an estimate of the observation vector that approximates the leakage state and the optimal additional water demand for each relevant node ;

[0146] S28: hydraulic characteristics adjustment;

[0147] Approximating the flow and pressure characteristics of the leakage scenario by simply changing the node water demand cannot approximate the upstream and downstream flows in the leakage pipe section. Therefore, it is necessary to weight the upstream and downstream flows after identifying the leakage pipe section to fully approximate the leakage scenario.

[0148] set up yes The pressure difference between the nodes at both ends of the pipe segment, yes The diameter of the pipe section, This is the number of leaks involving the same node, excluding the number of pipe sections with flowmeters installed. According to the Darcy-Weisbach equation, the flow rate in a pipeline is related to the pressure difference at the nodes at both ends of the pipe section and the pipe diameter. The distribution weights are set as follows:

[0149]

[0150] Related pipe sections after weight assignment The flow calculation form is as follows:

[0151]

[0152] S29: Output the final observation vector estimate of the approximate leakage state and the optimal additional water demand for each relevant node ;

[0153] S3: Narrow the scope of the leaking pipeline network; adopt a node expansion strategy and a depth-first search algorithm, based on preliminary inference of leaking nodes, continuously update and screen the scope of pipelines that may have leaks; after iterative optimization, output the local pipeline network area where leakage is most likely to occur.

[0154] Specifically, step S3 includes:

[0155] S31: Initialize variables; leakage flow list The calculation results of the S2 method, the leakage start and end nodes list of the leakage pipe section Initialized to an empty list, the marked visited node list is initialized to .

[0156] S32: Traverse the additional traffic list The flow value of each node in the , then add the flow value of the node to the leakage flow list , indicating that the node may be leaked;

[0157] S33: Traverse the leakage flow list For each node in the network, a depth-first search (DFS) is performed on the unvisited leakage nodes to determine the possible leakage nodes connected to them;

[0158] S34: Returns a list of both end nodes of all leaking pipe segments.

[0159] S4: Establish an observer and locate the specific leakage point; use the extended Kalman filter to build an observer model, fuse multi-source observation information, and dynamically estimate the leakage parameters in the suspected leaking pipe section; comprehensively analyze the filtering results and output the specific leaking pipe section and the leakage location coordinates.

[0160] Specifically, step S4 includes:

[0161] S41: Determine the state space expression for a single pipe segment with a unique leakage;

[0162] First, determine the hydraulic boundary conditions of the target pipe section, and use the pressure of the nodes at both ends of the pipe section as the input signal. and ; The flow at both ends is used as the system observation output signal, which is recorded as and . Set the system state variables to:

[0163]

[0164] in, Indicates the inlet flow rate of the pipe section (unit: m³ / s); Indicates the pressure at the midpoint (unit: m); Indicates the outlet flow rate of the pipe section (unit: m³ / s); Indicates the relative position of the leakage within the pipe section (value range 0 to 1, no unit); Indicates the leakage flow rate per unit time (unit: m³ / s).

[0165] Define the system input variables as:

[0166]

[0167] Define the system observation output as:

[0168]

[0169] The continuous-time state equation of the system is as follows:

[0170]

[0171] in, Function represents the current state and input Next, the derivative of the state variable.

[0172] S42: Discretized system variables;

[0173] The continuous-time state equation is discretized using Heun's method (an improved trapezoidal method). Assume the time step is , No. The state update for each time step is:

[0174]

[0175] This formula can approximately describe the state changes of nonlinear systems in a short period of time. The discrete state transfer function of the system is defined as follows:

[0176]

[0177] S43: Initialize state variable estimates and covariance matrix;

[0178] Set the initial state estimate to:

[0179]

[0180] in Denotes expectation. The error covariance matrix is ​​initialized as:

[0181]

[0182] S43: Initialize state variable estimates and covariance matrix;

[0183] Set the initial state estimate to:

[0184]

[0185] in Denotes expectation. The error covariance matrix is ​​initialized as:

[0186]

[0187] S44: state prediction;

[0188] For every moment , use the prediction model to estimate the current state:

[0189]

[0190] Simultaneous forecast error covariance matrix:

[0191]

[0192] in, The Jacobian matrix representing the system state function; represents the process noise covariance matrix.

[0193] S45: measurement update, residual calculation;

[0194] Compute the observation residuals:

[0195]

[0196] in is the observation matrix, which is:

[0197]

[0198] S46: state estimation correction;

[0199] Correct the state estimate based on the residuals:

[0200]

[0201] The Kalman gain matrix is:

[0202]

[0203] And update the error covariance:

[0204]

[0205] in is the observation noise covariance matrix, is the identity matrix.

[0206] S47: Estimation result output;

[0207] Through the continuous iteration of the above state prediction and correction, the state variables are finally obtained. and The convergence estimate of , that is, the location and leakage intensity of the only leakage point in the pipe section.

[0208] Example 2

[0209] like Figure 4 This embodiment provides a device for detecting and locating leakage in multiple pipes of a water supply network, for implementing the method of Example 1. The device is configured in a data processing and analysis device. The device includes: a pipeline monitoring data acquisition module, a pipeline leakage analysis result determination module, and an early warning module.

[0210] Pipeline monitoring data acquisition module: used to obtain pipeline-related data sent by the monitoring equipment, wherein the monitoring data includes water level data, water flow data and water pressure data of each node unit basin in the current monitoring area;

[0211] Pipeline leakage analysis result determination module: used to perform forecast evolution calculation and pipeline leakage analysis on monitoring point data to obtain leakage analysis results of the current monitoring area;

[0212] The early warning module generates early warning information and relevant data about the leaking pipeline, including leak point pipeline data, water flow data, water pressure data, and location data. The early warning information is then sent to the early warning device, which then notifies maintenance personnel based on the information.

[0213] Example 3

[0214] like Figure 5 This embodiment provides a system for detecting and locating leakage in multiple pipes of a water supply network, for implementing the method of Example 1, comprising: a pipe network monitoring device, a data acquisition device, a data processing and analysis device, a terminal device, and an early warning device, wherein the data acquisition device, the early warning device, and the terminal device are connected to the data processing and analysis device via a communication device;

[0215] Pipeline network monitoring equipment: used to monitor whether each node of the pipeline network is operating normally;

[0216] Data acquisition equipment: used to collect water pressure data, water level data, and water flow data of each node unit basin in the area;

[0217] Data processing and analysis equipment: used to obtain data sent by the pipe network data acquisition equipment, estimate water demand based on the scenario approximation method, use the depth-first algorithm to narrow the pipe network leakage area, and use the extended Kalman observer to perform specific leakage location analysis to obtain leakage detection results in the corresponding area;

[0218] Early warning device: if the leakage detection analysis result obtained by the data processing and analysis device is confirmed to be a leakage state, it generates early warning information, including pipeline data, water flow data, water pressure data and location data of the leakage point, and sends the early warning information to the early warning device, so that the early warning device notifies relevant maintenance personnel according to the early warning information;

[0219] Terminal device: used for receiving the analysis data and pipeline leakage analysis results sent by the data processing and analysis device, and visualizing the analysis data and the pipeline leakage analysis results;

[0220] It should be noted that in the embodiment of the above-mentioned pipeline leakage detection and positioning device, the modules included are only divided according to functional logic and are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0221] Finally, the above are merely embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments described herein, as various obvious variations, readjustments, and alternatives are possible without departing from the scope of the present invention. Therefore, while the above embodiments describe the present invention in detail, the present invention is not limited to the above embodiments. Further equivalent embodiments may be included without departing from the scope of the present invention. The scope of the present invention is defined by the claims.

Claims

1. A method for detecting and locating leakage in multiple pipes of a water supply network, comprising the following specific steps: S1: Build a pipe network model and sensor deployment. The system receives hydraulic data input by the user, analyzes it using mass and energy conservation equations, and combines node and pipeline information to create a hydraulic pipe network model. It then calculates the correlation matrix between nodes, combines a greedy algorithm with information redundancy, and selects optimal monitoring points to generate a pressure and flow sensor layout plan. The system then outputs a pipe network topology diagram and sensor deployment results. S2: Perform scenario approximation; determine whether the pipeline network is leaking. If there is a large difference between the sensor measurement value and the pipeline model simulation value, the pipeline network is considered to have leakage. Determine the flow and pressure sensors that are sensitive to changes in measurement data based on the relative fluctuation values ​​of the arranged sensors. Then, combine the correlation matrix and information redundancy to determine the optimization variables, construct an optimization model so that the hydraulic data under the original normal working conditions fit the hydraulic data under the leakage scenario, and finally introduce hydraulic data correction; output the possible leakage nodes and corresponding hydraulic parameters; S3: Narrow the scope of the leaking pipe network; using a node expansion strategy and a depth-first search algorithm, based on preliminary inferences of leaking nodes, continuously update and screen the scope of pipes that may have leaks; after iterative optimization, output the local pipe network area most likely to leak; S4: Establish an observer and locate the specific leakage point; use the extended Kalman filter to build an observer model, fuse multi-source observation information, and dynamically estimate the leakage parameters in the suspected leaking pipe section; comprehensively analyze the filtering results and output the specific leaking pipe section and the leakage location coordinates.

2. A method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 1, characterized in that: In step S1, building a pipe network model and sensor deployment specifically includes: S11: Based on the hydraulic data input by the system and the principles of conservation of mass and energy, the node continuity equations and pipe segment energy equations in the pipe network are constructed to accurately describe the hydraulic behavior of the system; S12: Establish a finite-dimensional mathematical model for each pipe segment in the pipe network and discretize the continuous hydraulic process to support subsequent sensitivity analysis and monitoring point optimization calculation; S13: Based on the perturbation analysis method, the pressure sensitivity matrix between nodes is calculated, and after normalization and threshold processing, a binary correlation matrix is ​​generated. , used to measure the degree of pressure correlation between nodes; S14: Initialize the sensor selection process, set the selected node set to empty, and the node set to be covered to the correlation matrix All columns in ; S15: Under the condition that the set of columns to be covered is not empty, perform the following operations: traverse all rows of the correlation matrix and find the row that can cover the most unselected columns; add the node corresponding to the row to the selected set, and update the set of columns to be covered accordingly, until all columns are covered or there are no rows to be selected; S16: Return the final selected row index. The corresponding node is the recommended location for installing the pressure sensor. Flow sensors are installed on the pipe sections connected to these nodes to achieve accurate perception of leakage or abnormal working conditions. The data measured by the sensors are combined into a joint observation vector. ,in Indicates flow meter reading, Indicates the pressure gauge reading, and are the number of flow and pressure sensors respectively, and the total observation dimension is .

3. A method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 1, characterized in that: In step S2, determining the leakage area and performing scenario approximation specifically include: S21: After determining the sensor layout using the sensor deployment algorithm, the residual between the hydraulic characteristics of the pipe network simulated by the model and the actual measured values ​​of the sensors is calculated. If there is a large difference, it is considered that the pipe network has leakage and the scenario approximation is required; S22: Calculate the relative fluctuation value based on the residual in S21, select the two pressure sensors and flow sensors with larger relative fluctuation values, calculate the information redundancy of the sensor-related nodes, change the selected sensor, and finally calculate the information redundancy of the sensor-related nodes and the correlation matrix. , determine the optimization variables; S23: Initialize the extra flow vector; initialize the extra flow vector of each node represented by the optimization variable to a 0 vector; S24: Calculate the characteristics of the pipe network and estimate the observation vector under the current state ,in represents the time step of the model operation, Indicates that the observation vector is The model estimate after time steps, Function represents the hydraulic calculation function based on Todini gradient algorithm, Represents the node additional water demand list; the observation vector is estimated With node index Matching, the updated pressure estimate is ; S25: define the optimization objective function; S26: Use the CSA Chameleon Swarm Optimization algorithm to solve the optimization objective function and obtain the optimal node additional water demand list ; S27: Use the best Re-simulate and update for several steps to obtain an estimate of the observation vector that approximates the leakage state And the optimal node additional water demand list for each relevant node ; S28: Hydraulic characteristic adjustment: After identifying the leaking pipe section, the upstream and downstream flows are weighted to fully approximate the leakage scenario; S29: Output the final observation vector estimate of the approximate leakage state And the optimal node additional water demand list for each relevant node .

4. A method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 1, characterized in that: In step S3, narrowing the scope of the leaking pipe network specifically includes: S31: Initialize variables; node additional water demand list The calculation results of the S2 method, the leakage start and end nodes list of the leakage pipe section Initialized to an empty list, the marked visited node list is initialized to ; S32: Traverse the node additional water demand list The flow value of each node in the , then add the flow value of the node to the node additional water demand list , indicating that the node may be leaked; S33: Traverse the node additional water demand list For each node in the network, a depth-first search (DFS) is performed on the unvisited leakage nodes to determine the possible leakage nodes connected to them; S34: Returns a list of both end nodes of all leaking pipe segments.

5. A method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 1, characterized in that: In step S4, establishing an observer and locating a specific leakage point specifically includes: S41: Determine the state space expression for a single pipe segment with a unique leakage; S42: Discretized system variables; The continuous-time state equation is discretized using Heun's method; let the time step be , No. The state update for each time step is: This formula approximately describes the state changes of a nonlinear system in a short period of time, thereby defining the discrete state transfer function of the system: S43: Initialize state variable estimates and covariance matrix; Set the initial state estimate to: in represents the expectation; the error covariance matrix is ​​initialized as: S44: state prediction; For every moment , use the prediction model to estimate the current state: Simultaneous forecast error covariance matrix: in, The Jacobian matrix representing the system state function; represents the process noise covariance matrix; S45: measurement update, residual calculation; Compute the observation residuals: in is the observation matrix, which is: S46: state estimation correction; S47: Estimation result output; Through the continuous iteration of the above state prediction and correction, the state variables are finally obtained. and The convergence estimate of , that is, the location and leakage intensity of the only leakage point in the pipe section.

6. A method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 5, characterized in that: Step S41 specifically includes: First, determine the hydraulic boundary conditions of the target pipe section, and use the pressure of the nodes at both ends of the pipe section as the input signal. and ; The flow at both ends is used as the system observation output signal, which is recorded as and ; Set the system state variables to: in, Indicates the inlet flow rate of the pipe section, unit: m³ / s; Indicates the pressure at the intermediate node, unit: m (meter water column); Indicates the outlet flow rate of the pipe section, unit: m³ / s; Indicates the relative position of the leakage in the pipe section, with a value range of 0 to 1 and no unit; Indicates the leakage flow rate per unit time, unit: m³ / s; Define the system input variables as: Define the system observation output as: The continuous-time state equation of the system is as follows: in, Function represents the current state and input Next, the derivative of the state variable.

7. A method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 5, characterized in that: Step S46 specifically includes: Correct the state estimate based on the residuals: The Kalman gain matrix is: And update the error covariance: in is the observation noise covariance matrix, is the identity matrix.

8. A water supply pipe leakage detection and locating device for implementing the method for detecting and locating leakage in multiple pipes of a water supply network according to claim 1, configured in a data processing and analysis device, characterized in that: include: Pipeline monitoring data acquisition module: used to obtain pipeline-related data sent by the monitoring equipment, wherein the monitoring data includes water level data, water flow data and water pressure data of each node unit basin in the current monitoring area; Pipeline leakage analysis result determination module: used to perform forecast evolution calculation and pipeline leakage analysis on monitoring point data to obtain leakage analysis results of the current monitoring area; Early warning module: used to generate early warning information and relevant data of the leaking pipeline, where the data includes pipeline data of the leaking point, water flow data, water pressure data and location data; and send the early warning information to the early warning device so that the early warning device notifies relevant maintenance personnel based on the early warning information.

9. A water supply pipe leakage detection and locating system implementing the method for detecting and locating leakage in multiple pipes of a water supply network as claimed in claim 1, characterized in that: It includes pipe network monitoring equipment, data acquisition equipment, data processing and analysis equipment, terminal equipment and early warning equipment, and the data acquisition equipment, early warning equipment and terminal equipment are connected to the data processing and analysis equipment through communication equipment; Pipeline network monitoring equipment: used to monitor the flow and pressure status of the pipeline network; Data acquisition equipment: used to collect water pressure data, water level data, and water flow data of each node unit basin in the area; Data processing and analysis equipment: used to obtain data sent by the pipe network data acquisition equipment, estimate water demand based on the scenario approximation method, use the depth-first algorithm to narrow the pipe network leakage area, and use the extended Kalman observer to perform specific leakage location analysis to obtain leakage detection results in the corresponding area; Early warning device: if the leakage detection analysis result obtained by the data processing and analysis device is confirmed to be a leakage state, it generates early warning information, including pipeline data, water flow data, water pressure data and location data of the leakage point, and sends the early warning information to the early warning device, so that the early warning device notifies relevant maintenance personnel according to the early warning information; Terminal device: used to receive the analysis data and pipeline leakage analysis results sent by the data processing and analysis device, and visualize the analysis data and the pipeline leakage analysis results.

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