Method, device and system for detecting and positioning leakage of multiple pipelines of water supply network

Abstract

The invention discloses a detection and positioning method, device and system for coping with multi-pipeline leakage of a water supply pipe network. The method comprises the following steps: constructing a pipe network model, deploying sensors, creating a complex hydraulic pipe network model, screening an optimal monitoring point, and generating a pressure and flow sensor arrangement scheme; outputting a pipe network topological graph and a sensor deployment result; performing scene approximation, constructing an optimization model to enable the hydraulic data under the original normal working condition to fit the hydraulic data under the leakage scene, finally introducing hydraulic data correction, and outputting possible leakage nodes and corresponding hydraulic parameters; reducing a leakage pipe network range, and continuously updating and screening a pipeline range possibly having leakage based on preliminary inference of leakage nodes; after iterative optimization, outputting a local pipe network area where leakage is most likely to occur; and establishing an observer, positioning a specific leakage point, dynamically estimating leakage parameters in a suspected leakage pipe section, comprehensively analyzing a filtering result, and outputting a specific leakage pipe section and a leakage position coordinate.

Description

Technical Field

[0001] The present invention belongs to the field of multi-location leakage detection of 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] At present, water supply networks are still struggling to solve the problem of operating length and multiple leaks. Multiple pipe leaks in complex pipe networks are not only difficult to detect and locate, affecting the daily water consumption of urban residents, but also causing waste of natural resources and losses for water companies. Improving the accuracy of locating multiple leaks in complex water supply networks has become a social problem that needs to be solved urgently.

[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. In addition, when dealing with leakage detection of 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 method for locating leakage in a water supply network based on correlation coefficient" proposes a method for locating leakage in a water supply network based on an improved gray wolf optimization algorithm. Under the conditions of annular and complex pipelines, a large number of flow meters need to be arranged, and the economic cost is relatively high. Chinese patent publication CN119123342A "A method and system for monitoring leakage in urban water supply networks" proposes a method and system for monitoring leakage in urban water supply networks, which predicts water consumption through neural networks, but the model requires a large amount of historical hydraulic data for training, has weak applicability to complex pipelines in reality, and costs a lot of computing costs. Summary of the invention

[0004] In order to solve 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] The 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 the mass and energy conservation equations, combines node and pipeline information, and creates a complex hydraulic network model; by calculating the correlation matrix between nodes, combined with the greedy algorithm and the degree of information redundancy, it selects the optimal monitoring points and generates pressure and flow sensor layout plans; and outputs the network topology diagram and sensor deployment results.

[0008] S2: Perform scenario approximation;

[0009] Make a basic judgment on whether the pipe network is leaking. If there is a large difference between the sensor measurement value and the pipe network model simulation value, it is considered that the pipe network has a leak; determine the flow rate and pressure sensors sensitive to the change of measurement data according to the relative fluctuation value of the arranged sensors; then determine the optimization variables in combination with the correlation matrix and information redundancy, construct an optimization model to fit the hydraulic data under the leakage scenario with the hydraulic data under the original normal working condition, and finally introduce the correction of hydraulic data; output the possible leakage nodes and the corresponding hydraulic parameters.

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

[0011] Adopt the node expansion strategy and depth-first search algorithm. Based on the preliminary inference of the leakage node, continuously update and screen the possible leaking pipe ranges; after iterative optimization, output the local pipe network area where leakage is most likely to occur.

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

[0013] Use the extended Kalman filter to construct an observer model, fuse multi-source observation information, and dynamically estimate the leakage parameters in the suspected leakage pipe section; comprehensively analyze the filtering results and output the specific leakage pipe section and the leakage position coordinates.

[0014] Furthermore, in step S1, the construction of the pipe network model and sensor deployment specifically includes:

[0015] S11: According to the hydraulic data input by the system and based on the principles of mass conservation and energy conservation, construct the node continuity equation and pipe section energy equation in the pipe network to accurately describe the hydraulic behavior of the system;

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

[0017] S13: Based on the perturbation analysis method, calculate the pressure sensitivity matrix between nodes, and generate a binary correlation matrix after normalization and threshold processing , which is used to measure the pressure correlation degree between nodes;

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

[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 to find the row that can cover the most unselected columns; add the corresponding node of this 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 option to select;

[0020] S16: Return the index of the finally selected row, and the corresponding node is the position where the pressure sensor is recommended to be installed; install flow sensors on the pipe segments connected to these nodes to achieve precise perception of leakage or abnormal conditions. The data measured by the sensors will form a joint observation vector , where represents the flowmeter reading, represents the pressure gauge reading, and are the numbers of the 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 includes:

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

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

[0024] S23: Initialize the additional flow vector. Initialize the additional flow vector of each node represented by the optimization variable as a zero vector;

[0025] S24: Calculate the pipe network characteristics and calculate the estimated observation vector in the current state , where represents the time step of the model operation, represents the model estimate of the observation vector after time steps, function represents the hydraulic calculation function based on the Todini gradient algorithm, represents the list of the best additional flows of the nodes. Match the estimated observation vector with the node index to obtain the updated pressure estimate as ;

[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 list of the best additional flows ;

[0028] S27: Use the optimal to re - perform simulation and update for several steps to obtain an estimated observation vector approaching 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, perform weight allocation on the upstream and downstream flows to fully approximate the leakage scenario;

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

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

[0032] S31: Initialize variables; The leakage flow list is the calculation result in the S2 method, and the list of the start and end nodes of the leakage of the leaking pipe section is initialized as an empty list, and the list of marked visited nodes is initialized as .

[0033] S32: Traverse each node's flow value in the additional flow list If the flow value of this node is greater than the threshold , then add the flow value of this node to the leakage flow list , indicating that there may be a leak at this node;

[0034] S33: Traverse each node in the leakage flow list and perform a depth - first search (DFS) on the unvisited leakage nodes to determine the potentially leaking nodes connected to them;

[0035] S34: Return the list of the two - end nodes of all leaking pipe sections.

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

[0037] S41: Determine the state - space expression for the unique leakage in a single pipe section;

[0038] First, determine the hydraulic boundary conditions of the target pipe section. Use the pressures at the two ends of this pipe section as input signals, denoted as and respectively; Use the flows at both ends as the system observation output signals, denoted as and respectively. Set the system state variables as:

[0039]

[0040] Among them, represents the inlet flow rate of the pipe segment, unit: m³ / s; represents the pressure at the intermediate node, unit: m (meter water column); represents the outlet flow rate of the pipe segment, unit: m³ / s; represents the relative position of leakage within the pipe segment, value range 0 to 1, unitless; represents the leakage flow rate per unit time, unit: m³ / s.

[0041] Define the system input variables as:

[0042]

[0043] Define the system observed output as:

[0044]

[0045] The continuous-time state equation of the system is in the following form:

[0046]

[0047] Among them, The function represents the derivative of the state variable at the current state and input .

[0048] S42: Discretize the system variables;

[0049] Use Heun's method (a modified trapezoidal method) to discretize the continuous-time state equation. Let the time step be , and the state update at the -th time step is:

[0050]

[0051] This formula can approximately describe the state change of the nonlinear system in a short time. Thus, define the discrete state transition function of the system:

[0052]

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

[0054] Set the initial state estimate as:

[0055]

[0056] Among them represents the expectation. The error covariance matrix is initialized as:

[0057]

[0058] S44: State prediction;

[0059] For each moment , estimate the current state using the prediction model:

[0060]

[0061] Meanwhile, predict the error covariance matrix:

[0062]

[0063] where represents the Jacobian matrix of the system state function; represents the process noise covariance matrix.

[0064] S45: Measurement update, calculate the residual;

[0065] Calculate the observation residual:

[0066]

[0067] where is the observation matrix, which is here:

[0068]

[0069] S46: State estimation correction;

[0070] Correct the state estimation based on the residual:

[0071]

[0072] where the Kalman gain matrix is:

[0073]

[0074] And update the error covariance:

[0075]

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

[0077] S47: Output of the estimation result;

[0078] Through continuous iteration of the above state prediction and correction, finally obtain in the state variables and The convergence estimation value, i.e., the location and leakage intensity of the only leakage point in the pipe section.

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

[0080] A pipeline monitoring data acquisition module: used to acquire pipeline-related data sent by monitoring devices, where the monitoring data includes water level data, water flow data, and water pressure data of each node unit watershed in the current monitoring area;

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

[0082] An early warning module: used to generate early warning information and related data of the leaking pipeline, where the data includes pipeline data, water flow data, water pressure data, and location data of the leakage point. And send the early warning information to the early warning device so that the early warning device can notify relevant maintenance personnel according to the early warning information.

[0083] The third aspect of the present invention relates to a leakage detection and positioning system for a water supply pipe, characterized by including a pipe network monitoring device, a data acquisition device, a data processing and analysis device, a terminal device, and an early warning device. The data acquisition device, the early warning device, and the terminal device are connected to the data processing and analysis device through a communication device;

[0084] A pipe network monitoring device: used to monitor the flow and pressure status of the pipe network;

[0085] A data acquisition device: used to collect water pressure data, water level data, and water flow data of each node unit watershed in the area;

[0086] A data processing and analysis device: used to acquire the data sent by the pipe network data acquisition device, estimate the water use demand based on the scenario approximation method, use the depth-first algorithm to narrow the pipeline leakage area, and then use the extended Kalman observer for specific leakage location analysis to obtain the leakage detection result in the corresponding area;

[0087] An early warning device: for the leakage detection and analysis result obtained by the data processing and analysis device, if it is confirmed as a leakage state, generate early warning information, including pipeline data, water flow data, water pressure data, and location data of the leakage point, and send the early warning information to the early warning device so that the early warning device can notify relevant maintenance personnel according to the early warning information;

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

[0089] The innovation points of the present invention are as follows:

[0090] Based on the observer technology (extended Kalman filter), the present invention is carried out in three steps. First, the pressure sensitivity matrix is used to arrange sensors for the pipe network. Then, the threshold method and the scenario approximation method are used to determine the leakage area and the leakage pipe section respectively. The size of the possible leakage pipe network is reduced by the depth-first search algorithm. Finally, an observer is designed for the leakage pipe section to determine the leakage parameters, and the leakage range is gradually reduced to determine the specific leakage pipe section, significantly reducing the scale of the observer problem. This detection method improves the accuracy and efficiency of leakage detection, reduces labor costs, and helps the staff to discover and repair in time. Description of the Drawings

[0091] Figure 1 It is a schematic diagram of the overall process of a detection and positioning method for multiple leakages in a water supply pipe network in Embodiment 1 of the present invention.

[0092] Figure 2 It is a schematic diagram of the specific implementation steps of a detection and positioning method for multiple leakages in a water supply pipe network in Embodiment 1 of the present invention.

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

[0094] Figure 4 It is a schematic diagram of the structure of a water supply pipe leakage detection and positioning device provided in Embodiment 2 of the present invention.

[0095] Figure 5 It is a schematic diagram of the structure of a water supply pipe leakage detection and positioning system provided in Embodiment 3 of the present invention. Detailed Embodiments

[0096] The following will describe in detail the specific embodiments of the embodiments of the present invention with reference to the drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

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

[0098] Embodiment 1

[0099] Refer to Figure 1 、 Figure 2 、 Figure 3 A detection and positioning method for multiple pipe leakages in a water supply pipe network according to the present invention includes the following steps:

[0100] S1: Construct a pipe network model and sensor deployment; The system receives the hydraulic data input by the user, analyzes through the mass and energy conservation equations, combines node and pipe information, creates a complex hydraulic pipe network model; By calculating the correlation matrix between nodes, combining the greedy algorithm and the degree of information redundancy, screening the optimal monitoring points, generating the layout scheme of pressure and flow sensors; Output the pipe network topology map and sensor deployment results.

[0101] Specifically, step S1 includes:

[0102] S11: According to the hydraulic data input by the system and based on the principles of mass conservation and energy conservation, construct the node continuity equation and the pipe segment energy equation in the pipe network to accurately describe the hydraulic behavior of the system;

[0103] In this example, the Hazen-Williams (H-W) formula is used to calculate the head loss. The influence of the pipe parameters in this formula is summarized in the H-W resistance coefficient of each pipe which is a function of the pipe parameters and is defined as . Then, calculate the head loss on the pipe through the H-W formula, specifically as follows:

[0104]

[0105] where is a constant exponent, is the flow rate of the pipe .

[0106] Pumps are another important element in the water supply pipe network, and 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. Described by the following equation:

[0107]

[0108] where , and are the coefficients of the pump pressure-flow curve, and the negative sign represents that the pump head is a gain rather than a loss.

[0109] Therefore, the energy conservation equation in the water supply pipe network can be written in the following form:

[0110]

[0111] where is the head of the unknown node, , , a matrix representing the water flow direction relationship between nodes, if , indicating that the water flow is from node to node . Conversely, if it means that and the water flow direction relationship between the nodes is from to . If it indicates that there is no direct connection relationship between nodes and . is the sum of the known water heads that appear in each equation . In vector notation, the vector of known water heads is given by .

[0112] The mass conservation equation in the water supply pipe network can be written in the following form:

[0113]

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

[0115] Denote the flow rate at the pipe node at time as . Similarly, denote the pressure at the pipe node at time as . Use and to represent and . Denote the difference between two adjacent pipe nodes as . is the local acceleration caused by 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] where , , and correspond to the boundary conditions , , and . The friction coefficient which depends on the flow rate and will be meaningful when considering subsequent leakage situations. The model is:

[0119]

[0120] where represents the pipe roughness, is the Reynolds number, and the calculation formula is as follows: , where is the kinematic viscosity of water, is the flow rate of the

[0121] S13: Based on the perturbation analysis method, calculate the pressure sensitivity matrix between nodes, and through normalization and threshold processing, generate a binary correlation matrix to measure the degree of pressure correlation between nodes;

[0122] Based on the microscopic analysis method of the pipe network, calculate the sensitivity of node pair to node

[0123]

[0124] where , is the water pressure of water demand node and under the reference working condition, , is the water pressure of node after the flow rate of node changes.

[0125] Perform min-max normalization on the element values of the th column to obtain the dimensionless sensitivity matrix of all nodes to the change in water demand of node . The element in the th row and th column of th row and th column is:

[0126]

[0127] where and respectively represent the minimum and maximum values of the th column of the sensitivity matrix . To express the strength of the correlation, a correlation threshold

[0128]

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

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

[0131] S16: Return the index of the finally selected row, and the corresponding node is the location where the pressure sensor is recommended to be installed; set flow sensors on the pipe segments connected to these nodes to achieve precise perception of leakage or abnormal conditions. The data measured by the sensors will form a joint observation vector , where represents the flowmeter reading, represents the pressure gauge reading, and are the numbers of flow and pressure sensors respectively, and the total observation dimension is .

[0132] S2: Conduct scenario approximation; make a basic judgment on whether the pipe network leaks. If there is a large difference between the measured values of the sensors arranged as in S1 and the simulated values of the pipe network model, it is considered that the pipe network leaks. Therefore, introduce the scenario approximation method, determine two flow sensors and two pressure sensors respectively according to 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 to fit the hydraulic data under the leakage scenario with the hydraulic data under the original normal condition, and finally introduce hydraulic data correction; output the possible leakage nodes and corresponding hydraulic parameters.

[0133] Specifically, step S2 includes:

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

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

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

[0137] S24: Calculate the pipe network characteristics and calculate the estimated observation vector at the current state , where represents the time step of the model operation, represents the model estimated value of the observation vector after time steps, The function represents the hydraulic calculation function based on the Todini gradient algorithm, represents the list of the optimal additional flows of the nodes. Match the estimated observation vector with the node index to obtain the updated pressure estimate as ;

[0138] S25: Define the optimization objective function; The scenario under normal conditions of the water supply pipe network is defined as , and the scenario when there is a leakage in the pipe network is defined as the leakage scenario . The purpose of scenario approximation is to make the node pressures and pipe segment flow characteristics in the pipe network approximately equal to those of the leakage scenario by changing the water demands of relevant water demand nodes under normal conditions.

[0139] Denote the sensors as . Suppose the nodes strongly correlated with these sensors are , . Suppose the additional water demand at node is , and define

[0140]

[0141]

[0142]

[0143] where is the measured value of the flow and pressure sensors in the pipe network at time, is the flow and pressure characteristics of the pipe network obtained by solving using the pipe network characteristics calculation method after applying at the corresponding pipe network nodes at time, The flow of the pipe segments and the node pressures where sensors are installed in . is the weight matrix of the sensors. Assuming that the measurements between each sensor are independent, then the matrix is a diagonal matrix, and the larger the value of the diagonal element, the higher the accuracy of the corresponding sensor. is the total leakage volume in the water supply pipe network area. Because has uncertainty, the fluctuation value of the leakage volume is set .

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

[0145] S27: Re - perform simulation updates for several steps with the optimal to obtain the observed vector estimate approaching the leakage state and the optimal additional water demand for each relevant node ;

[0146] S28: Hydraulic characteristic adjustment;

[0147] Only by changing the node water demand can the flow - pressure characteristics of the leakage scenario be approximated, but the upstream and downstream flows cannot be approximated in the leakage pipe section. Therefore, it is necessary to allocate weights to the upstream and downstream flows after identifying the leakage pipe section to fully approximate the leakage scenario.

[0148] Let be the pressure difference between the two end nodes of the pipe section, be the diameter of the pipe section, be the number of multiple leakages involving the same node but not including the pipe sections installed with flow meters. According to the Darcy - Weisbach equation, the flow in the pipeline is related to the pressure difference between the two end nodes of the pipe section and the pipe diameter. The following weight allocation is set:

[0149]

[0150] After weight allocation, the flow calculation form of the relevant pipe section is as follows:

[0151]

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

[0153] S3: Narrow down the scope of the leaking pipe network; adopt the node expansion strategy and depth-first search algorithm, and based on the preliminary inference of the leakage node, continuously update and screen the possible leaking pipe ranges; after iterative optimization, output the local pipe network area where leakage is most likely to occur.

[0154] Specifically, step S3 includes:

[0155] S31: Initialize variables; the leakage flow rate list is the calculation result in method S2, and the list of the start and end nodes of the leaking pipe segments is initialized as an empty list, and the list of marked visited nodes is initialized as .

[0156] S32: Traverse each node's flow rate value in the additional flow rate list If the flow rate value of this node is greater than the threshold , then add the flow rate value of this node to the leakage flow rate list , indicating that there may be a leak at this node;

[0157] S33: Traverse each node in the leakage flow rate list Perform a depth-first search DFS on the unvisited leakage nodes to determine the possible leaking nodes connected to them;

[0158] S34: Return the list of the two end nodes of all leaking pipe segments.

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

[0160] Specifically, step S4 includes:

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

[0162] First, determine the hydraulic boundary conditions of the target pipe segment, and use the pressures at the two end nodes of this pipe segment as input signals, denoted as and respectively; use the flow rates at both ends as the system observation output signals, denoted as and respectively. Set the system state variables as:

[0163]

[0164] Among them, represents the inlet flow rate of the pipe segment (unit: m³ / s); represents the pressure at the intermediate node (unit: m); Denotes the flow rate at the outlet of the pipe segment (unit: m³ / s); Denotes the relative position of the leakage within the pipe segment (value range 0 to 1, dimensionless); Denotes the leakage flow rate per unit time (unit: m³ / s).

[0165] Define the system input variables as:

[0166]

[0167] Define the system observed output as:

[0168]

[0169] The continuous-time state equation of the system is in the following form:

[0170]

[0171] Wherein, The function represents the derivative of the state variable at the current state and the input under the condition.

[0172] S42: Discretize the system variables;

[0173] Use Heun's method (an improved trapezoidal method) to discretize the continuous-time state equation. Let the time step be , and the state update at the th time step is:

[0174]

[0175] This formula can approximately describe the state change of the nonlinear system in a short time. Thus, define the discrete state transition function of the system:

[0176]

[0177] S43: Initialize the estimated values of the state variables and the covariance matrix;

[0178] Set the initial state estimate as:

[0179]

[0180] Wherein denotes the expectation. The error covariance matrix is initialized as:

[0181]

[0182] S43: Initialize the estimated values of the state variables and the covariance matrix;

[0183] Set the initial state estimate to be:

[0184]

[0185] where represents the expectation. The error covariance matrix is initialized to:

[0186]

[0187] S44: State prediction;

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

[0189]

[0190] Meanwhile, predict the error covariance matrix:

[0191]

[0192] where, represents the Jacobian matrix of the system state function; represents the process noise covariance matrix.

[0193] S45: Measurement update, calculate the residual;

[0194] Calculate the observation residual:

[0195]

[0196] where is the observation matrix, which is here:

[0197]

[0198] S46: State estimate correction;

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

[0200]

[0201] where the Kalman gain matrix is:

[0202]

[0203] And update the error covariance:

[0204]

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

[0206] S47: Estimated result output;

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

[0208] Embodiment 2

[0209] As Figure 4 , this embodiment provides a detection and positioning device for coping with multi-pipe leakage in a water supply network to implement the method of Embodiment 1. This 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 a warning module.

[0210] Pipeline monitoring data acquisition module: It is used to acquire pipeline-related data sent by monitoring devices. Among them, 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: It is used to perform prediction evolution calculation and pipeline leakage analysis on the monitoring point data to obtain the leakage analysis result of the current monitoring area;

[0212] Warning module: It is used to generate warning information and related data of the leakage pipeline, where the data includes leakage point pipeline data, water flow data, water pressure data, and location data. And send the warning information to a warning device so that the warning device can notify relevant maintenance personnel according to the warning information.

[0213] Embodiment 3

[0214] As Figure 5 , this embodiment provides a detection and positioning system for coping with multi-pipe leakage in a water supply network to implement the method of Embodiment 1, including: a pipe network monitoring device, a data acquisition device, a data processing and analysis device, a terminal device, and a warning device. The data acquisition device, the warning device, and the terminal device are connected to the data processing and analysis device through a communication device;

[0215] Pipe network monitoring device: It is used to monitor whether each node of the pipe network is operating normally;

[0216] Data acquisition device: It is 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 device: It is used to obtain the data sent by the pipeline network data acquisition device, estimate the water demand based on the scenario approximation method, use the depth-first algorithm to narrow down the pipeline leakage area, and then use the extended Kalman observer for specific leakage location analysis, so as to obtain the leakage detection results in the corresponding area;

[0218] Early warning device: For the leakage detection and analysis results obtained by the data processing and analysis device, if it is confirmed as the 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 can notify relevant maintenance personnel according to the early warning information;

[0219] Terminal device: It is used to receive the parsed data and pipeline leakage analysis results sent by the data processing and analysis device, and visualize the parsed data and the pipeline leakage analysis results;

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

[0221] Finally, the above are only the embodiments of the present invention and the applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, because various obvious changes, re-adjustments and alternative solutions are possible without departing from the protection scope of the present invention. Therefore, although the above embodiments have described the present invention in detail, the present invention is not limited to the above embodiments. Without exceeding the scope of the concept of the present invention, more other equivalent embodiments can also be included. The scope of the present invention is defined by the claims.

Claims

1. A detection and location method for multi - pipeline leakage in a water supply network, including the following specific steps: S1: Construct a pipe network model and deploy sensors; The system receives the hydraulic data input by the user, analyzes through the mass and energy conservation equations, combines node and pipe information to create a complex hydraulic pipe network model; By calculating the correlation matrix between nodes, combining the greedy algorithm and the degree of information redundancy, the optimal monitoring points are screened, and a layout plan for pressure and flow sensors is generated; Output the pipe network topology map and the sensor deployment result; S2: Conduct scenario approximation; Make a basic judgment on whether the pipe network leaks. If there is a large difference between the sensor measurement value and the simulation value of the pipe network model, it is considered that the pipe network leaks; Determine the flow and pressure sensors sensitive to the change of measurement data according to the relative fluctuation value of the deployed sensors; Then, combine the correlation matrix and information redundancy to determine the optimization variables, construct an optimization model to fit the hydraulic data under the leakage scenario with the hydraulic data under the original normal working condition, and finally introduce hydraulic data correction; Output the possible leakage nodes and corresponding hydraulic parameters; S3: Narrow down the scope of the leaking pipe network; Adopt the node expansion strategy and depth - first search algorithm. Based on the preliminary inference of the leakage node, continuously update and screen the possible leaking pipe ranges; After iterative optimization, output the local pipe network area where leakage is most likely to occur; S4: Establish an observer and locate the specific leakage point; Use the extended Kalman filter to construct an observer model, fuse multi - source observation information, and dynamically estimate the leakage parameters in the suspected leakage pipe section; Comprehensively analyze the filtering results and output the specific leakage pipe section and the leakage position coordinates.

2. The detection and positioning method for multi - pipeline leakage in a water supply network according to claim 1, wherein, In step S1, constructing the pipe network model and deploying sensors specifically includes: S11: According to the hydraulic data input by the system and based on the principles of mass conservation and energy conservation, construct the node continuity equation and pipe segment energy equation in the pipe network to accurately describe the hydraulic behavior of the system; S12: Establish a finite - dimensional mathematical model for each pipe segment in the pipe network, discretize the continuous hydraulic process to support subsequent sensitivity analysis and monitoring point optimization calculation; S13: Based on the perturbation analysis method, calculate the pressure sensitivity matrix between nodes, and generate a binary correlation matrix through normalization and threshold processing , which is used to measure the degree of pressure correlation between nodes; S14: Initialize the sensor selection process, set the selected node set to be empty, and the node set to be covered to all columns in the correlation matrix ; 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 to find the row that can cover the most unselected columns; Add the node corresponding to this 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; S16: Return the index of the finally selected row. The corresponding node is the location where the pressure sensor is recommended to be installed. Install flow sensors on the pipe segments connected to these nodes to achieve precise perception of leakage or abnormal conditions. The data measured by the sensors will form a joint observation vector , where represents the flowmeter reading, represents the pressure gauge reading, and are the numbers of the flow and pressure sensors respectively, and the total observation dimension is .

3. A detection and positioning method for coping with multi-pipeline leakage in a water supply network according to claim 1, characterized in that, In step S2, determining the leakage area and conducting scenario approximation specifically includes: S21: After determining the sensor layout using the sensor deployment algorithm, calculate the residual between the simulated hydraulic characteristics of the pipe network model and the actual measurement values of the sensors. If there is a large difference, it is considered that the pipe network leaks and scenario approximation is required; S22: Calculate the relative fluctuation value according to the residual in S21, select two pressure sensors and flow sensors with relatively large relative fluctuation values, calculate the information redundancy of the nodes related to the sensors, change the selected sensors, and finally determine the optimization variables according to the nodes associated with the sensors and the correlation matrix , and determine the optimization variables; S23: Initialize the additional flow vector; Initialize the additional flow vector of each node represented by the optimization variable as a zero vector; S24: Calculate the characteristics of the pipe network and calculate the estimated observation vector in the current state , where represents the time step of the model operation, represents the model estimated value of the observation vector after time steps, The function represents the hydraulic calculation function based on the Todini gradient algorithm, represents the list of the optimal additional flow rates of the nodes; match the estimated observation vector with the node index to obtain the updated pressure estimate as ; S25: Define the optimization objective function; S26: Use the CSA chameleon swarm optimization algorithm to solve the optimization objective function and obtain the best additional traffic list ; S27: Use the optimal to perform simulation updates for several steps again to obtain an estimated observation vector approaching the leakage state and the optimal additional water demand list for each relevant node ; S28: Hydraulic characteristic adjustment; After identifying the leakage pipe section, allocate weights to the upstream and downstream flows to fully approximate the leakage scenario; S28: Output the observed vector estimate of the final approximate leakage state and the optimal additional water demand of each relevant node .

4. The detection and positioning method for multi-pipeline leakage in a water supply network according to claim 1, characterized in that, In step S3, narrowing down the scope of the leaking pipe network specifically includes: S31: Initialize variables; leakage flow rate list It is the calculation result in the S2 method, the list of the starting and ending nodes of the leakage in the leakage pipe segment Initialize it as an empty list, and initialize the marked access node list as ; S32: Traverse each node in the extra traffic list for the traffic value. If the traffic value of the node is greater than the threshold , add the traffic value of the node to the leakage traffic list , indicating that there may be a leak at this node; S33: Traverse each node in the list of leakage flow rates, and perform a depth-first search (DFS) on the unvisited leakage nodes to determine the possible leakage nodes connected to them; ​ S34: Return the list of the two - end nodes of all leaking pipe sections.

5. A detection and positioning method for coping with multi-pipe leakage in a water supply network according to claim 1, characterized in that, In step S4, establishing an observer and locating the specific leakage point specifically includes: S41: Determine the state - space expression of a unique leakage occurring in a single pipe segment; S42: Discretize the system variables; Discretize the continuous-time state equation using Heun's method; assume the time step is , and the state update at the -th time step is: This formula approximately describes the state change of a nonlinear system in a short time, thereby defining the discrete - state transition function of the system: S43: Initialize the state - variable estimate value and the covariance matrix; Set the initial state estimate as: wherein denotes an expectation; the error covariance matrix is initialized as: S44: State prediction; For each moment , use the prediction model to estimate the current state: Meanwhile, predict the error - covariance matrix: Among them, represents the Jacobian matrix of the system state function; represents the process noise covariance matrix; S45: Measurement update, calculate the residual; Calculate the observation residual: Among them is the observation matrix, which is: S46: State - estimate correction; S47: Output the estimation result; Through continuous iteration of the above state prediction and correction, the convergent estimated values of and in the state variables are finally obtained, which are the location and leakage intensity of the only leakage point in this pipe section.

6. The detection and positioning method for multi-pipeline leakage in a water supply network according to claim 5, characterized in that, Step S41 specifically includes: First, determine the hydraulic boundary conditions of the target pipe section. Take the pressures at the two end nodes of this pipe section as input signals, denoted respectively as and ; take the flows at both ends as system observation output signals, denoted respectively as and ; set the system state variables as: Among them, represents the inlet flow rate of the pipe segment, unit: m³ / s; represents the pressure at the intermediate node, unit: m (meter water column); represents the outlet flow rate of the pipe segment, unit: m³ / s; represents the relative position of leakage within the pipe segment, value range 0 to 1, dimensionless; represents the leakage flow rate per unit time, unit: m³ / s; Define the system input variable as: Define the system observation output as: The continuous - time state - equation form of the system is as follows: Among them, The function represents at the current state and the input, the derivative of the state variable.

7. A detection and positioning method for coping with multi-pipe leakage in a water supply network according to claim 5, characterized in that Step S46 specifically includes: Correct the state estimate based on the residual; Where the Kalman - gain matrix is: And update the error covariance: wherein is the observation noise covariance matrix, is the identity matrix.

8. A water supply pipe leakage detection and positioning device for implementing the detection and positioning method for multi-pipe leakage in a water supply network according to claim 1, configured in a data processing and analysis device, characterized in that, Including: Pipeline - monitoring data acquisition module: Used to acquire pipeline - related data sent by monitoring devices. Among them, 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 prediction - evolution calculation and pipeline - leakage analysis on the monitoring - point data to obtain the leakage - analysis result of the current monitoring area; Early - warning module: Used to generate early - warning information and relevant data of the leaking pipeline. The data includes pipeline data of the leakage 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 can notify relevant maintenance personnel according to the early - warning information.

9. A leakage detection and location system for a water supply pipe implementing the leakage detection and location method for multiple pipelines in a water supply network according to claim 1, characterized in that, Including pipeline - network monitoring devices, data - acquisition devices, data - processing and analysis devices, terminal devices, and early - warning devices. The data - acquisition devices, early - warning devices, and terminal devices are connected to the data - processing and analysis device through communication devices; Pipeline - network monitoring devices: Used to monitor the flow and pressure states of the pipeline network; Data - acquisition devices: Used to acquire water - pressure data, water - level data, and water - flow data of each node - unit basin in the area; Data - processing and analysis devices: Used to acquire the data sent by the pipeline - network data - acquisition device, estimate the water - use demand based on the scenario - approximation method, use the depth - first algorithm to narrow the pipeline - leakage area, and then use the extended - Kalman observer to perform specific leakage - location analysis, thereby obtaining the leakage - detection result in the corresponding area; Early - warning devices: For the leakage - detection and analysis result obtained by the data - processing and analysis device, if it is confirmed as a leakage state, generate early - warning information, including pipeline data of the leakage 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 can notify relevant maintenance personnel according to the early - warning information; Terminal devices: Used to receive the parsed data and pipeline - leakage analysis result sent by the data - processing and analysis device, and visualize the parsed data and the pipeline - leakage analysis result.

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