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CN120368231B - A method, device and system for detecting and locating leakage in multiple pipes of a water supply network - Google Patents

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

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Publication number
CN120368231B
CN120368231B CN202510833601.9A CN202510833601A CN120368231B CN 120368231 B CN120368231 B CN 120368231B CN 202510833601 A CN202510833601 A CN 202510833601A CN 120368231 B CN120368231 B CN 120368231B
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leakage
data
node
pipeline
pipe
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CN120368231A (en
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温如
付明磊
张齐
金宇强
张文安
郑乐进
张涛
郑剑锋
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Hangzhou Laison Technology Co ltd
Zhejiang University of Technology ZJUT
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Hangzhou Laison Technology Co ltd
Zhejiang University of Technology ZJUT
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Abstract

A method for detecting and positioning multi-pipeline leakage of a water supply network includes the steps of constructing a network model and sensor deployment to create a complex hydraulic network model, screening optimal monitoring points to generate a pressure and flow sensor arrangement scheme, outputting a network topological graph and a sensor deployment result, performing scene approximation, constructing an optimization model to enable hydraulic data under original normal working conditions to fit hydraulic data under a leakage scene, finally introducing hydraulic data correction to output possible leakage nodes and corresponding hydraulic parameters, reducing the range of the leakage network, continuously updating and screening the pipeline range in which leakage is possible based on preliminary deduction of the leakage nodes, outputting a local pipeline area in which leakage is most likely to occur after iterative optimization, building an observer and positioning specific leakage points, dynamically estimating leakage parameters in suspected leakage pipeline sections, comprehensively analyzing filtering results, and outputting specific leakage pipeline sections and leakage position coordinates.

Description

Detection and positioning method, device and system for coping with multi-pipeline leakage of water supply network
Technical Field
The invention belongs to the field of multi-position leakage detection of urban water supply networks, and particularly relates to a detection and positioning method, device and system for coping with multi-pipeline leakage of a water supply network.
Background
At present, a water supply network still strives to solve the problems of operation length and water leakage at a plurality of positions. The multi-pipeline leakage of the complex pipe network is difficult to detect and position, affects the daily water consumption of urban residents, and causes the waste of natural resources and the loss of water enterprises. The positioning accuracy of multiple leakage losses of the complex water supply network is improved, and the method becomes a social problem to be solved urgently.
In the field of water supply network leakage detection, the equipment method is an advanced technology. The technology is mainly used for detecting leakage of the pipeline network by using various detection instruments. However, the accuracy of the detection result thereof depends largely on the experience of the operator. Moreover, this approach often requires significant time and effort to handle large pipe network leak detection. With the development of artificial intelligence and sensors, a data driving method has been developed, and the chinese patent publication CN115994487a "a water supply pipe network leakage positioning method based on correlation coefficients" proposes a water supply pipe network leakage positioning method based on an improved gray wolf optimization algorithm, which requires a large number of flowmeter arrangements under the conditions of annular and complex pipe networks, and has a large economic cost. The Chinese patent publication CN119123342A provides a method and a system for monitoring leakage of a town water supply network, which predict water consumption through a neural network, but the model requires a large amount of historical hydraulic data for training, has weak applicability to complex network in reality and costs a large amount of calculation cost.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method, a device and a system for detecting and positioning multi-pipeline leakage of a water supply network.
The first aspect of the invention relates to a detection and positioning method for coping with multi-pipeline leakage of a water supply network, which comprises the following specific steps:
s1, constructing a pipe network model and deploying sensors;
The system receives hydraulic data input by a user, analyzes the hydraulic data through a mass and energy conservation equation, combines node and pipeline information to create a complex hydraulic pipe network model, screens optimal monitoring points through calculating a correlation matrix among the nodes and combining a greedy algorithm and information redundancy degree to generate a pressure and flow sensor arrangement scheme, and outputs a pipe network topological graph and a sensor deployment result.
S2, performing scene approximation;
The method comprises the steps of judging whether a pipe network is leaked or not basically, if a sensor measured value and a pipe network model simulation value have large differences, considering that the pipe network is leaked, determining flow and pressure sensors sensitive to the change of measured data according to the relative fluctuation values of arranged sensors, determining optimization variables by combining a correlation matrix and information redundancy, constructing an optimization model to enable hydraulic data under original normal working conditions to fit the hydraulic data under a leakage scene, finally introducing hydraulic data correction, and outputting possible leakage nodes and corresponding hydraulic parameters.
S3, reducing the range of a leakage pipe network;
Adopting a node expansion strategy and a depth-first search algorithm, and continuously updating and screening a pipeline range with possible leakage based on preliminary inference of the leakage node; after iterative optimization, the local pipe network area where leakage is most likely to occur is output.
S4, establishing an observer and positioning a specific leakage point;
And constructing an observer model by using an extended Kalman filter, fusing multi-source observation information, dynamically estimating leakage parameters in the suspected leakage pipe section, comprehensively analyzing the filtering result, and outputting specific leakage pipe section and leakage position coordinates.
Further, in step S1, constructing the pipe network model and the sensor deployment specifically includes:
S11, constructing a node continuous equation and a pipe section energy equation in a pipe network according to the hydraulic data input by the system and based on the mass conservation and energy conservation principles so as to accurately describe the hydraulic behavior of the system;
S12, establishing a finite dimension mathematical model for each pipe section in the pipe network, discretizing a continuous hydraulic process to support subsequent sensitivity analysis and monitoring point optimization calculation;
s13, calculating a pressure sensitivity matrix among nodes based on a perturbation analysis method, and generating a binary correlation matrix through normalization and threshold processing The pressure correlation degree measuring device is used for measuring the pressure correlation degree between the nodes;
s14, initializing a sensor selection process, setting a selected node set as null, and setting a node set to be covered as a correlation matrix All columns in (a);
s15, under the condition that the column set to be covered is not empty, traversing all rows of the correlation matrix, searching for rows capable of covering the most unselected columns, adding the corresponding nodes of the rows into the selected set, and updating the column set to be covered according to the corresponding nodes until all columns are covered or no feasible selection exists;
And S16, returning the finally selected row index, wherein the corresponding node is the recommended position for installing the pressure sensor, and arranging a flow sensor on the pipe section connected with the nodes so as to realize accurate sensing of leakage or abnormal working conditions. The data measured by the sensor is combined into a joint observation vector WhereinIndicating a flow meter reading,Indicating the reading of the pressure gauge,And (3) withThe number of flow and pressure sensors respectively, and the total observation dimension is。
Further, in step S2, determining the leakage area and performing the scene approximation specifically includes:
S21, after the sensor arrangement is determined by utilizing a sensor deployment algorithm, residual errors between the hydraulic characteristics of the simulated pipe network and the actual measured values of the sensors can be calculated, if the large difference exists, the pipe network is considered to have leakage, and the following scene approximation method is carried out;
s22, calculating a relative fluctuation value according to the residual error in S21, selecting two pressure sensors and flow sensors with larger relative fluctuation value, calculating information redundancy of related nodes of the sensors, changing the selected sensors, and finally, according to the related nodes and the related matrix of the sensors Determining an optimization variable;
and S23, initializing an additional flow vector. Initializing an extra traffic vector of each node represented by the optimization variable to be a 0 vector;
S24, calculating pipe network characteristics, and calculating observation vector estimation in the current state WhereinThe time step in which the model is run is represented,Representing the observation vector atThe model estimate after a time step,The function represents a hydraulic calculation function based on Todini gradient algorithm,Representing the node's best list of additional traffic. Estimating an observation vectorNode indexMatching to obtain updated pressure estimate as;
S25, defining an optimization objective function;
S26, solving an optimization objective function by using a CSA color-changing Long Qun optimization algorithm to obtain an optimal extra-flow list ;
S27, using the optimumThe simulation is updated again for a plurality of steps to obtain the observation vector estimation approaching the leakage stateOptimal additional water demand list for each relevant node;
S28, hydraulic characteristic adjustment, namely after the leaking pipe section is identified, weight distribution is carried out on upstream and downstream flow so as to fully approximate the leaking scene;
s29, outputting final observation vector estimation approaching the leakage state And the optimal additional water demand for each relevant node;
Further, in step S3, the narrowing the range of the leakage pipe network specifically includes:
S31 initializing variable, leakage flow list For the calculation result in the S2 method, the head-tail node list of leakage of the leakage pipe sectionInitializing to empty list, initializing to tag access node list。
S32, traversing the extra flow listA traffic value of each node in the plurality if the traffic value of the node is greater than a threshold valueAdding the traffic value of the node into a leakage traffic listIndicating that there is a potential for leakage at the node;
s33, traversing the leakage flow list Performing a depth-first search (DFS) of the non-accessed leaky nodes to determine possible leaky nodes connected thereto;
And S34, returning to a list of nodes at two ends of all the leakage pipe sections.
Further, in step S4, establishing the observer and locating the specific leakage point specifically includes:
s41, determining a state space expression with unique leakage under a single pipe section;
firstly, determining the hydraulic boundary condition of a target pipe section, taking the pressure of nodes at two ends of the pipe section as input signals, and respectively recording And (3) withThe flow rates at the two ends are taken as system observation output signals and respectively recorded asAnd (3) with. The system state variables are set as follows:
Wherein, the Representing the inlet flow of the pipe section, wherein the unit is m3/s; Representing the pressure of the intermediate node in m (meter water column); Represents the outlet flow of the pipe section, and the unit is m3/s; the relative position of leakage in the pipe section is represented, the value range is 0 to 1, and no unit exists; the leakage flow rate per unit time is expressed in m3/s.
Defining system input variables as:
defining the system observation output as:
the continuous time state equation form of the system is as follows:
Wherein, the The function is expressed in the current stateAnd inputThe derivative of the state variable.
S42, discretizing system variables;
The continuous time state equation was discretized using Heun's method (a modified trapezoidal method). Set the time step as First, theThe status updates for the individual time steps are:
the formula may approximately describe the state change of a nonlinear system over a short period of time. Thereby defining a discrete state transfer function of the system:
s43, initializing state variable estimated values and covariance matrixes;
Setting an initial state estimate as:
Wherein the method comprises the steps of Indicating the desire. The error covariance matrix is initialized to:
s44, state prediction;
For each time instant Estimating a current state using a predictive model:
simultaneous prediction error covariance matrix:
Wherein, the A jacobian matrix representing a system state function; Representing a process noise covariance matrix.
S45, measuring and updating, and calculating residual errors;
Calculating an observation residual error:
Wherein the method comprises the steps of Is an observation matrix, here:
S46, state estimation correction;
Correcting state estimation based on residual errors:
Wherein the Kalman gain matrix is:
and updating the error covariance:
Wherein the method comprises the steps of Is the observed noise covariance matrix and,Is an identity matrix.
S47, outputting an estimation result;
through the continuous iteration of the state prediction and correction, the state variable is finally obtained And (3) withI.e. the location and leakage intensity of the only leakage point in the pipe section.
A second aspect of the present invention relates to a leakage detecting and positioning device for a water supply pipe, configured to a data processing and analyzing apparatus, comprising:
the pipeline monitoring data acquisition module is used for acquiring pipeline related data sent by monitoring equipment, wherein the monitoring data comprises water level data, water flow data and water pressure data of each node unit basin in the current monitoring area;
The pipeline leakage analysis result determining module is used for carrying out forecast evolution calculation and pipeline leakage analysis on the monitoring point data to obtain a leakage analysis result of the current monitoring area;
And the early warning module is used for generating early warning information and related data of the leakage pipeline, wherein the data comprise leakage point pipeline data, water flow data, water pressure data and position data. And sending the early warning information to early warning equipment so that the early warning equipment informs relevant maintenance personnel according to the early warning information.
The third aspect of the invention relates to a leakage detection and positioning system of a water supply pipe, which is characterized by comprising pipe network monitoring equipment, data acquisition equipment, data processing and analysis equipment, terminal equipment and early warning equipment, wherein the data acquisition equipment, the early warning equipment and the terminal equipment are connected with the data processing and analysis equipment through communication equipment;
The pipe network monitoring equipment is used for monitoring the flow and pressure states of the pipe network;
The data acquisition equipment is used for acquiring water pressure data, water level data and water flow data of each node unit basin in the area;
The data processing analysis equipment is used for acquiring data sent by the pipe network data acquisition equipment, estimating water demand based on a scene approximation method, reducing a pipe network leakage area by using a depth-first algorithm, and then carrying out specific leakage positioning analysis by using an extended Kalman observer so as to obtain a leakage detection result in a corresponding area;
The early warning equipment is used for generating early warning information including leakage point pipeline data, water flow data, water pressure data and position data if the leakage detection analysis result obtained by the data processing analysis equipment is confirmed to be in a leakage state, and sending the early warning information to the early warning equipment so that the early warning equipment informs related maintenance personnel according to the early warning information;
And the terminal equipment is used for receiving the analysis data and the pipeline leakage analysis result sent by the data processing and analysis equipment and visualizing the analysis data and the pipeline leakage analysis result.
The innovation points of the invention are as follows:
The invention is based on the observer technology (extended Kalman filter), and is carried out in three steps, firstly, the sensor arrangement is carried out on a pipe network by utilizing a pressure sensitivity matrix, then, a leakage area and a leakage pipe section are respectively determined by using a threshold method and a scene approximation method, the size of a possible leakage pipe network is reduced by a depth-first search algorithm, finally, an observer is designed on the leakage pipe section to determine leakage parameters, the leakage range is gradually reduced to determine a specific leakage pipe section, and the scale of the observer problem is obviously reduced. The detection method improves the leak detection precision and the leak detection efficiency, reduces the labor cost and helps staff find and overhaul in time.
Drawings
Fig. 1 is a schematic general flow diagram of a method for detecting and locating multiple leaks in a water supply network according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of specific implementation steps of a method for detecting and locating multiple leaks in a water supply network according to a first embodiment of the present invention.
FIG. 3 is a schematic diagram of a simulation of pipe leak detection and sensor arrangement in accordance with a first embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a leakage detecting and positioning device for a water supply pipe according to a second embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a water supply pipe leakage detecting and positioning system according to a third embodiment of the present invention.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
The invention will be described in detail below with reference to the drawings in connection with exemplary embodiments.
Example 1
Referring to fig. 1, 2 and 3, the method for detecting and positioning the multi-pipeline leakage of the water supply network comprises the following steps:
The method comprises the steps of S1, constructing a pipe network model and sensor deployment, receiving hydraulic data input by a user, analyzing through a mass and energy conservation equation, combining node and pipeline information to create a complex hydraulic pipe network model, calculating a correlation matrix among the nodes, combining a greedy algorithm and information redundancy degree, screening optimal monitoring points to generate a pressure and flow sensor deployment scheme, and outputting a pipe network topological diagram and a sensor deployment result.
Specifically, step S1 includes:
S11, constructing a node continuous equation and a pipe section energy equation in a pipe network according to the hydraulic data input by the system and based on the mass conservation and energy conservation principles so as to accurately describe the hydraulic behavior of the system;
The present example uses the Hazen-Williams (H-W) equation to calculate head loss. The effect of the pipe parameters in this formula is summarized at the H-W drag coefficient for each pipe In which the coefficient is oneIs defined as the pipeline parameter function of. Then, calculate the pipeline through H-W formulaThe head loss is as follows:
Wherein the method comprises the steps of Is a constant index of the values of the three,Is a pipelineIs a flow rate of (a).
Pumps are another important element in water supply networks and they are characterized by pressure flow curves that are used to correlate pump flow to pump head gain according to the specifications of each pump. Described by the following equation:
Wherein the method comprises the steps of ,AndIs the coefficient of the pump pressure flow curve, and the negative sign indicates that the head of the pump is gain rather than loss.
Thus, the energy conservation equation in the water supply network can be written as follows:
Wherein the method comprises the steps of Is the water head of an unknown node,,Matrix representing water flow direction relationship between nodes, ifIllustrating that the water flow is a slave nodeFlow direction nodeOtherwise ifThen representAndThe water flow direction relationship between the nodes is fromFlow direction. If it isThen describe the nodeAndNo direct connection is available.Is in each equationThe sum of the known heads that occur. In the vector representation, the vector of the known head is represented byGiven.
The mass conservation equation in the water supply network can be written as follows:
S12, establishing a finite dimension mathematical model for each pipe section in the pipe network, discretizing a continuous hydraulic process to support subsequent sensitivity analysis and monitoring point optimization calculation;
Will be Pipeline node at momentFlow rate in (a)Is marked as. Similarly, it willPipeline node at momentPressure in (a)Is marked as. By usingAndRepresentative ofAnd. The difference between two adjacent pipeline nodes is recorded as。Is the local acceleration caused by gravity.Is the cross-sectional area of the conduit.Is the inner diameter of the pipeline. For any number of pipe segments, a finite dimensional model may be obtained:
Wherein the method comprises the steps of 、、AndRespectively corresponding to boundary conditions、、And. Coefficient of frictionWhich itself depends on the flow rate, which will make sense when considering the subsequent leakage situation.The model of (2) is:
Wherein the method comprises the steps of Indicating the roughness of the pipe and,For Reynolds number, the calculation formula is as follows: Wherein Is the kinematic viscosity of water and is used for the preparation of a liquid,Is the firstFlow of the segment pipe section.
S13, calculating a pressure sensitivity matrix among nodes based on a perturbation analysis method, and generating a binary correlation matrix through normalization and threshold processingThe pressure correlation degree measuring device is used for measuring the pressure correlation degree between the nodes;
based on pipe network microscopic analysis method, calculation Node pairThe sensitivity of the nodes is as follows:
Wherein the method comprises the steps of ,Node for requiring water under reference working conditionAndIs used for controlling the water pressure of the water,,Is thatAfter the node flow is changed,The water pressure of the node.
For the firstThe element values of the columns are subjected to minimum and maximum normalization, so that the node pairs of all the nodes can be obtainedDimensionless sensitivity matrix for node in need of water change,Is the first of (2)Line 1The column elements are:
Wherein the method comprises the steps of AndRespectively represent sensitivity matrixIs the first of (2)Minimum and maximum values of columns. In order to express the strength of the correlation, a correlation threshold is set
S14, initializing a sensor selection process, setting a selected node set as null, and setting a node set to be covered as a correlation matrixAll columns in (a);
s15, under the condition that the column set to be covered is not empty, traversing all rows of the correlation matrix, searching for rows capable of covering the most unselected columns, adding the corresponding nodes of the rows into the selected set, and updating the column set to be covered according to the corresponding nodes until all columns are covered or no feasible selection exists;
And S16, returning the finally selected row index, wherein the corresponding node is the recommended position for installing the pressure sensor, and arranging a flow sensor on the pipe section connected with the nodes so as to realize accurate sensing of leakage or abnormal working conditions. The data measured by the sensor is combined into a joint observation vector WhereinIndicating a flow meter reading,Indicating the reading of the pressure gauge,And (3) withThe number of flow and pressure sensors respectively, and the total observation dimension is。
S2, performing scene approximation, namely performing basic judgment on whether the pipe network is leaked or not, and if the measured value of the sensor arranged in the S1 and the simulation value of the pipe network model have larger differences, considering that the pipe network is leaked, introducing a scene approximation method, and determining two flow and pressure sensors according to the relative fluctuation value of the arranged sensors. And determining an optimization variable by combining the correlation matrix and the information redundancy, constructing an optimization model to enable the hydraulic data under the original normal working condition to fit the hydraulic data under the leakage scene, and finally introducing hydraulic data correction, and outputting possible leakage nodes and corresponding hydraulic parameters.
Specifically, step S2 includes:
S21, after the sensor arrangement is determined by utilizing a sensor deployment algorithm, residual errors between the hydraulic characteristics of the simulated pipe network and the actual measured values of the sensors can be calculated, if the large difference exists, the pipe network is considered to have leakage, and the following scene approximation method is carried out;
s22, calculating a relative fluctuation value according to the residual error in S21, selecting two pressure sensors and flow sensors with larger relative fluctuation value, calculating information redundancy of related nodes of the sensors, changing the selected sensors, and finally, according to the related nodes and the related matrix of the sensors Determining an optimization variable;
and S23, initializing an additional flow vector. Initializing an extra traffic vector of each node represented by the optimization variable to be a 0 vector;
S24, calculating pipe network characteristics, and calculating observation vector estimation in the current state WhereinThe time step in which the model is run is represented,Representing the observation vector atThe model estimate after a time step,The function represents a hydraulic calculation function based on Todini gradient algorithm,Representing the node's best list of additional traffic. Estimating an observation vectorNode indexMatching to obtain updated pressure estimate as;
S25, defining an optimization objective function, wherein the scene of the water supply network under the normal working condition is defined asThe scene when the pipe network is leaked is defined as a leakage sceneThe purpose of the scene approximation is to enable the node pressure and the pipe section flow characteristics in the pipe network to be equal to those of the leakage scene by changing the water demand of the related water-demand nodes under normal conditionsApproximately equal.
The sensor is noted asThese are provided withThe node with strong sensor correlation is,. Is provided withThe additional water demand at the node isAnd define. The scene approximation problem can be described by the following optimization model:
Wherein the method comprises the steps of Is thatThe measured values of the flow and pressure sensors in the pipe network at the moment,Is thatApplying at the corresponding pipe network node at any timeThen, solving the obtained pipe network flow pressure characteristics by using a pipe network characteristic calculation methodPipe section flow and node pressure with sensors installed therein are noted as。Is a weight matrix of sensors, and if the measurements between each sensor are independent, then the matrixIs a diagonal matrix and a larger value of a diagonal element indicates a higher accuracy of the corresponding sensor.Is the total water leakage of the water supply network area becauseFluctuation value with uncertainty so that leak amount is set。
S26, solving an optimization objective function by using CSA (color change Long Qun optimization algorithm) to obtain an optimal additional flow list;
S27, using the optimumThe simulation is updated again for a plurality of steps to obtain the observation vector estimation approaching the leakage stateAnd the optimal additional water demand for each relevant node;
S28, hydraulic characteristic adjustment;
The flow pressure characteristics of the leakage scene are approximated only by changing the water demand of the nodes, but the upstream and downstream flows cannot be approximated in the leakage pipe section, so that the upstream and downstream flows are required to be weighted after the leakage pipe section is identified, so that the leakage scene is approximated fully.
Is provided withIs thatThe pressure difference between the nodes at the two ends of the pipe section,Is thatThe diameter of the tube section,The multiple leaks relate to the same node but do not include the number of pipe sections with flow meters installed. According to Darcy-Wei Siba hz equation (Darcy-Weisbach equation), the flow in the pipe is related to the pressure difference at the nodes at the ends of the pipe section, and the pipe diameter is set with the following distribution weights:
related pipe section after weight distribution The flow calculation form of (a) is as follows:
s29, outputting final observation vector estimation approaching the leakage state And the optimal additional water demand for each relevant node;
S3, reducing the range of the leakage pipe network, adopting a node expansion strategy and a depth-first search algorithm, continuously updating and screening the range of the pipeline with the possible leakage based on the preliminary inference of the leakage nodes, and outputting the local pipe network area with the most possible leakage after iterative optimization.
Specifically, step S3 includes:
S31 initializing variable, leakage flow list For the calculation result in the S2 method, the head-tail node list of leakage of the leakage pipe sectionInitializing to empty list, initializing to tag access node list。
S32, traversing the extra flow listA traffic value of each node in the plurality if the traffic value of the node is greater than a threshold valueAdding the traffic value of the node into a leakage traffic listIndicating that there is a potential for leakage at the node;
s33, traversing the leakage flow list Performing depth-first search DFS on the unaccessed leaky nodes to determine possible leaky nodes connected to the DFS;
And S34, returning to a list of nodes at two ends of all the leakage pipe sections.
S4, establishing an observer and positioning specific leakage points, utilizing an extended Kalman filter to establish an observer model, fusing multi-source observation information, dynamically estimating leakage parameters in suspected leakage pipe sections, comprehensively analyzing filtering results, and outputting specific leakage pipe sections and leakage position coordinates.
Specifically, step S4 includes:
s41, determining a state space expression with unique leakage under a single pipe section;
firstly, determining the hydraulic boundary condition of a target pipe section, taking the pressure of nodes at two ends of the pipe section as input signals, and respectively recording And (3) withThe flow rates at the two ends are taken as system observation output signals and respectively recorded asAnd (3) with. The system state variables are set as follows:
Wherein, the Representing the inlet flow (unit: m 3/s) of the pipe section; representing the pressure of the intermediate node (unit: m); Represents the pipe section outlet flow (unit: m 3/s); indicating the relative position of the leak in the pipe section (value range 0 to 1, no unit); the leakage flow rate per unit time (unit: m 3/s) is shown.
Defining system input variables as:
defining the system observation output as:
the continuous time state equation form of the system is as follows:
Wherein, the The function is expressed in the current stateAnd inputThe derivative of the state variable.
S42, discretizing system variables;
The continuous time state equation was discretized using Heun's method (a modified trapezoidal method). Set the time step as First, theThe status updates for the individual time steps are:
the formula may approximately describe the state change of a nonlinear system over a short period of time. Thereby defining a discrete state transfer function of the system:
s43, initializing state variable estimated values and covariance matrixes;
Setting an initial state estimate as:
Wherein the method comprises the steps of Indicating the desire. The error covariance matrix is initialized to:
s43, initializing state variable estimated values and covariance matrixes;
Setting an initial state estimate as:
Wherein the method comprises the steps of Indicating the desire. The error covariance matrix is initialized to:
s44, state prediction;
For each time instant Estimating a current state using a predictive model:
simultaneous prediction error covariance matrix:
Wherein, the A jacobian matrix representing a system state function; Representing a process noise covariance matrix.
S45, measuring and updating, and calculating residual errors;
Calculating an observation residual error:
Wherein the method comprises the steps of Is an observation matrix, here:
S46, state estimation correction;
Correcting state estimation based on residual errors:
Wherein the Kalman gain matrix is:
and updating the error covariance:
Wherein the method comprises the steps of Is the observed noise covariance matrix and,Is an identity matrix.
S47, outputting an estimation result;
through the continuous iteration of the state prediction and correction, the state variable is finally obtained And (3) withI.e. the location and leakage intensity of the only leakage point in the pipe section.
Example 2
As shown in fig. 4, the present embodiment provides a device for detecting and positioning leakage of multiple pipelines of a water supply network, for implementing the method of embodiment 1, where the device is configured in a data processing and analyzing apparatus. The device comprises a pipeline monitoring data acquisition module, a pipeline leakage analysis result determination module and an early warning module.
The pipeline monitoring data acquisition module is used for acquiring pipeline related data sent by monitoring equipment, wherein the monitoring data comprises water level data, water flow data and water pressure data of each node unit basin in the current monitoring area;
The pipeline leakage analysis result determining module is used for carrying out forecast evolution calculation and pipeline leakage analysis on the monitoring point data to obtain a leakage analysis result of the current monitoring area;
And the early warning module is used for generating early warning information and related data of the leakage pipeline, wherein the data comprise leakage point pipeline data, water flow data, water pressure data and position data. And sending the early warning information to early warning equipment so that the early warning equipment informs relevant maintenance personnel according to the early warning information.
Example 3
As shown in fig. 5, the present embodiment provides a detection and positioning system for coping with multi-pipeline leakage of a water supply network, for implementing the method of embodiment 1, including a network monitoring device, a data acquisition device, a data processing analysis device, a terminal device and an early warning device, where the data acquisition device, the early warning device and the terminal device are connected with the data processing analysis device through a communication device;
The pipe network monitoring equipment is used for monitoring whether each node of the pipe network normally operates;
The data acquisition equipment is used for acquiring water pressure data, water level data and water flow data of each node unit basin in the area;
The data processing analysis equipment is used for acquiring data sent by the pipe network data acquisition equipment, estimating water demand based on a scene approximation method, reducing a pipe network leakage area by using a depth-first algorithm, and then carrying out specific leakage positioning analysis by using an extended Kalman observer so as to obtain a leakage detection result in a corresponding area;
The early warning equipment is used for generating early warning information including leakage point pipeline data, water flow data, water pressure data and position data if the leakage detection analysis result obtained by the data processing analysis equipment is confirmed to be in a leakage state, and sending the early warning information to the early warning equipment so that the early warning equipment informs related maintenance personnel according to the early warning information;
the terminal equipment is used for receiving the analysis data and the pipeline leakage analysis result sent by the data processing and analysis equipment and visualizing the analysis data and the pipeline leakage analysis result;
It should be noted that in the above embodiment of the pipeline leakage detection domain positioning 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 implemented, and the specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Finally, the above is merely an example of the invention and the technical principles applied. Those skilled in the art will appreciate that the invention is not limited to the specific embodiments described herein, as various obvious changes, rearrangements, and substitutions are possible without departing from the scope of the invention. Therefore, although the above embodiments have described the present invention in detail, the present invention is not limited to the above embodiments. Many other equivalent embodiments may be made without departing from the spirit and scope of the present invention. The scope of the invention is defined by the claims.

Claims (9)

1.一种应对供水管网多管道漏损的检测和定位方法，包括以下具体步骤：1. A method for detecting and locating leakage in multiple pipes of a water supply network, comprising the following specific steps: S1：构建管网模型与传感器部署；系统接收用户输入的水力数据，通过质量与能量守恒方程分析，结合节点与管道信息，创建水力管网模型；通过计算节点间的相关性矩阵，结合贪心算法以及信息冗余程度，筛选最优监测点，生成压力和流量传感器布置方案；输出管网拓扑图及传感器部署结果；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：进行情景近似；对管网是否漏损进行判断，如果传感器测量值和管网模型模拟值存在较大差异，则认为该管网出现漏损；根据布置的传感器的相对波动值大小确定对测量数据变化敏感的流量和压力传感器；再结合相关性矩阵和信息冗余度确定优化变量，构建优化模型使得原有正常工况下的水力数据拟合漏损场景下的水力数据，最后引入水力数据修正；输出可能漏损节点及对应水力参数；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：缩小漏损管网范围；采用节点扩展策略与深度优先搜索算法，基于泄漏节点初步推断，不断更新与筛选可能存在漏损的管道范围；迭代优化后，输出最可能发生泄漏的局部管网区域；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：建立观测器并定位具体漏损点；利用扩展卡尔曼滤波器构建观测器模型，融合多源观测信息，动态估计疑似泄漏管段中的泄漏参数；综合分析滤波结果，输出具体漏损管段及泄漏位置坐标。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.如权利要求1所述的一种应对供水管网多管道漏损的检测和定位方法，其特征在于，在步骤S1中，构建管网模型与传感器部署具体包括：2. The method for detecting and locating leakage in multiple pipes of a water supply network according to claim 1, wherein, in step S1, constructing a pipe network model and deploying sensors specifically comprises: S11：根据系统输入的水力数据，并且基于质量守恒与能量守恒原理，构建管网中的节点连续方程与管段能量方程，以准确描述系统的水力行为；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：对管网中的各个管段建立有限维数学模型，离散化连续水力过程，以支持后续敏感性分析和监测点优化计算；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：基于微扰分析方法，计算节点间的压力敏感性矩阵，并经归一化及阈值处理，生成二值相关性矩阵 ，用于衡量节点间的压力关联程度；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：初始化传感器选取过程，设置已选节点集合为空，待覆盖节点集合为相关性矩阵 中的所有列；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：在待覆盖列集合非空的条件下，执行如下操作：遍历相关性矩阵的所有行，查找能够覆盖最多未选列的行；将该行对应节点加入已选集合，并据此更新待覆盖列集合，直至所有列被覆盖或无行可选为止；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：返回最终选中的行索引，所对应的节点即为推荐安装压力传感器的位置；在这些节点所连接的管段上设置流量传感器，以实现对漏损或异常工况的精准感知；将传感器测得的数据组成联合观测向量，其中表示流量计读数，表示压力计读数，与分别为流量与压力传感器的数量，总观测维度为。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.如权利要求1所述的一种应对供水管网多管道漏损的检测和定位方法，其特征在于，在步骤S2中，确定漏损区域与进行情景近似具体包括：3. The method for detecting and locating leakage in multiple pipes of a water supply network according to claim 1, wherein in step S2, determining the leakage area and performing scenario approximation specifically comprises: S21：利用传感器部署算法确定传感器布置后，计算出模型模拟的管网水力特性与传感器的实际测量值之间的残差，若存在较大差异，则认为该管网出现漏损，需要进行场景近似；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：根据S21所述残差计算相对波动值，选取相对波动值较大的两个压力传感器和流量传感器，计算传感器有关节点的信息冗余度，改变选择的传感器，最后根据传感器关联的节点和相关性矩阵 ，确定优化变量；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：初始化额外流量向量；将优化变量代表的每个节点额外流量向量初始化为0向量；S23: Initialize the extra flow vector; initialize the extra flow vector of each node represented by the optimization variable to a 0 vector; S24：计算管网特征，计算当前状态下的观测向量估计，其中表示模型运行的时间步长，表示观测向量在个时间步长后的模型估计值，函数表示基于Todini梯度算法的水力计算函数，表示节点额外需水量列表；将观测向量估计与节点索引匹配，得到更新后的压力估计为；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：定义优化目标函数；S25: define the optimization objective function; S26：使用CSA变色龙群优化算法求解优化目标函数，获得最佳的节点额外需水量列表；S26: Use the CSA Chameleon Swarm Optimization algorithm to solve the optimization objective function and obtain the optimal node additional water demand list ; S27：用最优的重新进行模拟更新若干步，得到逼近泄漏状态的观测向量估计及每个相关节点的最优的节点额外需水量列表；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：水力特征调整；在识别到漏损管段后，对上下游流量进行权重分配，以充分近似漏损场景；S28: Hydraulic characteristic adjustment: After identifying the leaking pipe section, the upstream and downstream flows are weighted to fully approximate the leakage scenario; S29：输出最终的逼近泄漏状态的观测向量估计及每个相关节点的最优的节点额外需水量列表。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.如权利要求1所述的一种应对供水管网多管道漏损的检测和定位方法，其特征在于，在步骤S3中，缩小漏损管网范围具体包括：4. The method for detecting and locating leakage in multiple pipes of a water supply network according to claim 1, wherein, in step S3, narrowing the scope of the leaking pipe network specifically comprises: S31：初始化变量；节点额外需水量列表为S2方法中计算结果，漏损管段泄露首尾节点列表初始化为空列表，标记访问节点列表初始化为；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：遍历节点额外需水量列表中的每一个节点的流量值，如果该节点的流量值大于阈值，则将该节点的流量值加入节点额外需水量列表，表示该节点有可能存在泄露；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：遍历节点额外需水量列表中的每个节点，对未访问的漏损节点进行深度优先搜索DFS，以确定与其相连的可能泄露节点；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：返回所有泄露管段的两端节点列表。S34: Returns a list of both end nodes of all leaking pipe segments. 5.如权利要求1所述的一种应对供水管网多管道漏损的检测和定位方法，其特征在于，在步骤S4中，建立观测器并定位具体漏损点具体包括：5. The method for detecting and locating leakage in multiple pipes of a water supply network according to claim 1, wherein in step S4, establishing an observer and locating a specific leakage point specifically comprises: S41：确定单一管段下出现唯一漏损的状态空间表达式；S41: Determine the state space expression for a single pipe segment with a unique leakage; S42：离散化系统变量；S42: Discretized system variables; 采用Heun’s方法将连续时间状态方程离散化；设时间步长为，第个时间步的状态更新为：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: 该公式近似描述非线性系统在短时间内的状态变化，由此定义系统的离散状态转移函数：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：初始化状态变量估计值和协方差矩阵；S43: Initialize state variable estimates and covariance matrix; 设定初始状态估计为：Set the initial state estimate to: 其中表示期望；误差协方差矩阵初始化为：in represents the expectation; the error covariance matrix is initialized as: S44：状态预测；S44: state prediction; 对于每一时刻，使用预测模型估算当前状态：For every moment , use the prediction model to estimate the current state: 同时预测误差协方差矩阵：Simultaneous forecast error covariance matrix: 其中，表示系统状态函数的雅可比矩阵；表示过程噪声协方差矩阵；in, The Jacobian matrix representing the system state function; represents the process noise covariance matrix; S45：测量更新，计算残差；S45: measurement update, residual calculation; 计算观测残差：Compute the observation residuals: 其中是观测矩阵，此处为：in is the observation matrix, which is: S46：状态估计校正；S46: state estimation correction; S47：估计结果输出；S47: Estimation result output; 通过上述状态预测与修正的不断迭代，最终得到状态变量中与的收敛估计值，即该管段中唯一漏损点的位置与漏损强度。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.如权利要求5所述的一种应对供水管网多管道漏损的检测和定位方法，其特征在于，步骤S41具体包括：6. The method for detecting and locating leakage in multiple pipes of a water supply network according to claim 5, wherein step S41 specifically comprises: 首先，确定目标管段的水力边界条件，以该管段两端节点的压力作为输入信号，分别记与；以两端的流量作为系统观测输出信号，分别记作与；设定系统状态变量为：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: 其中，表示管段进口流量，单位：m³/s；表示中间节点的压力，单位：m（米水柱）；表示管段出口流量，单位：m³/s；表示漏损在管段内的相对位置，取值范围0至1，无单位；表示单位时间内的漏损流量，单位：m³/s；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; 定义系统输入变量为：Define the system input variables as: 定义系统观测输出为：Define the system observation output as: 系统的连续时间状态方程形式如下：The continuous-time state equation of the system is as follows: 其中，函数表示在当前状态和输入下，状态变量的导数。in, Function represents the current state and input Next, the derivative of the state variable. 7.如权利要求5所述的一种应对供水管网多管道漏损的检测和定位方法，其特征在于，步骤S46具体包括：7. The method for detecting and locating leakage in multiple pipes of a water supply network according to claim 5, wherein step S46 specifically comprises: 基于残差修正状态估计：Correct the state estimate based on the residuals: 其中卡尔曼增益矩阵为：The Kalman gain matrix is: 并更新误差协方差：And update the error covariance: 其中是观测噪声协方差矩阵，是单位矩阵。in is the observation noise covariance matrix, is the identity matrix. 8.实施权利要求1所述的一种应对供水管网多管道漏损的检测和定位方法的一种供水管的漏损检测和定位装置，配置于数据处理分析设备，其特征在于，包括：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 it comprises: 管道监测数据获取模块：用于获取监测设备发送的管道相关数据，其中，所述监测数据包括当前监测区域内各节点单元流域的水位数据、水流量数据和水压数据；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; 管道漏损分析结果确定模块：用于对监测点数据进行预报演化计算和管道漏损分析，得到当前监测区域的漏损分析结果；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; 预警模块：用于生成预警信息和漏损管道的相关数据，其中数据包括漏损点管道数据、水流数据、水压数据以及及位置数据；并将所述预警信息发送至预警设备，以使所述预警设备根据所述预警信息通知相关维修人员。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.实施权利要求1所述的一种应对供水管网多管道漏损的检测和定位方法的一种供水管的漏损检测和定位系统，其特征在于，包括管网监测设备、数据采集设备、数据处理分析设备、终端设备以及预警设备，所述数据采集设备、预警设备以及终端设备通过通信设备与所述数据处理分析设备连接；9. A water supply pipe leakage detection and locating system for implementing the method for detecting and locating multiple pipe leakage in a water supply network as described in claim 1, characterized in that it comprises 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; 管网监测设备：用于监测管网的流量和压力状态；Pipeline network monitoring equipment: used to monitor the flow and pressure status of the pipeline network; 数据采集设备：用于采集区域内各节点单元流域的水压数据、水位数据、水流量数据；Data acquisition equipment: used to collect water pressure data, water level data, and water flow data of each node unit basin in the area; 数据处理分析设备：用于获取管网数据采集设备发送的数据，并基于场景近似方法对用水需求进行估计，使用深度优先算法缩小管网漏损区域后，使用扩展卡尔曼观测器进行具体漏损定位分析，从而得到对应区域内的漏损检测结果；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; 预警设备：对所述数据处理分析设备得到的漏损检测分析结果，若确认为漏损状态，则生成预警信息，包括漏损点管道数据、水流数据、水压数据及位置数据，并将所述预警信息发送至预警设备，以使所述预警设备根据所述预警信息通知相关维修人员；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; 终端设备：用于接收所述数据处理分析设备发送的解析数据和管道漏损分析结果，并可视化所述解析数据和所述管道漏损分析结果。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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