A system and method for optimizing urban and rural water supply scheduling

Abstract

This invention discloses an urban and rural water supply scheduling optimization system and method, relating to the field of water conservancy engineering technology. It includes: a hydraulic and water quality dynamic coupling model module, a sudden pollution event simulation and source tracing module, a zoned pressure barrier dynamic construction module, a multi-objective collaborative optimization scheduling module, and a pipeline structure stress hydraulic state feedback module. By constructing a hydraulic and water quality dynamic coupling model and integrating sudden pollution event simulation and source tracing, zoned pressure barrier dynamic construction, multi-objective collaborative optimization scheduling, and pipeline structure stress feedback mechanisms, this invention achieves integrated and precise scheduling of urban and rural water supply systems under both normal and emergency conditions. It effectively overcomes the shortcomings of existing technologies, such as disconnected hydraulic and water quality models, low pollution source tracing efficiency, limited emergency control methods, and insufficient multi-objective coordination. This significantly improves the water quality safety assurance capability, pollution diffusion suppression efficiency, and pipeline network lifespan of the water supply system.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, specifically to an urban and rural water supply scheduling optimization system and method. Background Technology

[0002] Urban and rural water supply systems are vital infrastructure for ensuring residents' lives and socio-economic development. Optimizing their scheduling is crucial for ensuring water supply security, improving water quality compliance rates, and responding to sudden pollution incidents. With the continuous expansion of urban and rural water supply networks and their increasing complexity, water supply systems are susceptible to various factors during operation, such as water pollution, pipe ruptures, and water hammer effects. Therefore, advanced scheduling technologies are needed to achieve efficient and reliable hydraulic and water quality management. Traditional water supply scheduling methods often rely on experience-based judgment or simple hydraulic models, which are difficult to adapt to dynamically changing environmental demands. There is an urgent need to introduce intelligent, multi-objective, collaborative optimization methods.

[0003] Existing urban and rural water supply dispatching technologies primarily focus on the simulation and control of hydraulic conditions. For example, they adjust the operation of pumps and valves by monitoring parameters such as pipeline pressure and flow rate to achieve energy optimization or pressure balancing. Some systems also incorporate water quality monitoring functions, enabling real-time collection and alarm of water quality indicators at key nodes. Furthermore, some studies attempt to combine hydraulic models with water quality models to predict the migration and diffusion of pollutants. In terms of emergency response, current technologies typically control the scope of pollution by isolating valves or adjusting pump station operations, but they lack rapid and accurate pollution source tracing mechanisms and dynamic barrier construction capabilities.

[0004] However, existing technologies still have significant shortcomings: First, hydraulic and water quality models are often independent and fail to achieve dynamic coupling, resulting in insufficient accuracy in water quality prediction and an inability to accurately reflect the transport process of pollutants in the pipeline network; second, in the event of a sudden pollution incident, there is a lack of efficient source tracing methods, making it difficult to locate the pollution source and assess its release history in a timely manner; in addition, existing dispatching systems cannot quickly construct effective hydraulic barriers to suppress pollution spread in emergency modes, and their optimization objectives are singular, failing to consider multiple factors such as operating costs, pressure balance, water quality compliance, and pipeline lifespan; finally, the impact of pipeline structural stress and fatigue damage is often ignored, which may accelerate pipeline aging and increase operation and maintenance costs. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide an urban and rural water supply scheduling optimization system and method to solve the technical problems in the prior art, such as insufficient prediction accuracy due to the disconnect between hydraulic and water quality models, low source tracing efficiency and lack of rapid and effective pollution diffusion suppression methods under sudden pollution events, and difficulty in coordinating and optimizing multiple objectives such as operating costs, pipeline pressure, water quality safety and pipeline lifespan in conventional scheduling.

[0006] The first aspect of this application provides an urban and rural water supply scheduling optimization system, comprising: The hydraulic-water quality dynamic coupling model module is used to construct and update in real time a mathematical model that couples the hydraulic state of the pipeline network with the water quality transport and diffusion process. The model is described by the following coupling equations: , in, The concentration of a specific pollutant in the water body. For time, It is concentration Relative to time The partial derivatives, This is the velocity vector field calculated from the pipe network hydraulic model. The diffusion coefficient of pollutants, Source and sink terms for pollutants are used to describe the location of pollutants. and time The injection or removal of pollutants, This is the reaction rate constant of pollutants in water; The sudden pollution event simulation and source tracing module is connected to the hydraulic and water quality dynamic coupling model module. It is used to solve the coupling equation in reverse based on real-time and historical hydraulic and water quality data when water quality anomalies are detected, and to identify the location and release history of the pollution source. A zoned pressure barrier dynamic construction module, connected to the hydraulic and water quality dynamic coupling model module and the sudden pollution event simulation and source tracing module, is used to dynamically form one or more hydraulic barriers with specific pressure gradients between polluted and non-polluted areas after identifying pollution sources or high-risk areas, by optimizing the scheduling of water pumps and valves, to inhibit the diffusion of polluted water to clean areas; wherein, the construction of the hydraulic barriers is based on minimizing barrier construction energy consumption and maximizing barrier effectiveness as the objective function, and the barrier effectiveness... Through the pressure difference on both sides of the barrier With water concentration gradient The product is evaluated by integrating it over the barrier area: , in, The area of ​​a micro-element representing a hydraulic barrier; A multi-objective collaborative optimization scheduling module is connected to the hydraulic and water quality dynamic coupling model module, the sudden pollution event simulation and source tracing module, and the regional pressure barrier dynamic construction module, respectively. It is used to collaboratively optimize the total system operating cost, network-wide pressure balance, water quality compliance rate at key nodes, and pollution diffusion suppression efficiency under emergency conditions, in both normal and emergency scheduling modes. Specifically, this module dynamically adjusts the weights of each sub-objective in the multi-objective optimization model through an adaptive weight allocation mechanism based on scenario recognition. This adaptive weight allocation mechanism is based on a real-time risk assessment index. The risk assessment index is weighted and assigned accordingly. The calculation formula is: , in, The measured or predicted value of pollutant concentration at the i-th critical node; This refers to the water quality standard concentration. This is a pipeline rupture risk indicator function; This is a function representing the risk level of a water source area. , , These are the weighting coefficients for each risk item.

[0007] The pipeline structure stress hydraulic state feedback module is used to monitor or estimate the cyclic stress amplitude of the pipeline caused by water hammer effect and pressure fluctuation in real time, and predict the degree of fatigue damage accumulation of the pipeline by combining pipeline material and age data. This module is connected to the multi-objective collaborative optimization scheduling module and is used to introduce the pipeline fatigue damage rate as a constraint and penalty in the optimization scheduling to avoid operation schemes that accelerate pipeline aging.

[0008] A second aspect of this application provides a method for optimizing urban and rural water supply scheduling, applying the aforementioned urban and rural water supply scheduling optimization system. The method includes the following steps: S1. Construct and update the dynamic coupling model of hydraulics and water quality in real time; S2. Based on real-time data, perform normal multi-objective optimization scheduling to generate and execute the first scheduling instruction; S3. Real-time calculation of risk assessment index; S4. When the risk assessment index exceeds a preset threshold, the emergency dispatch mode is triggered, and the following steps are executed: S41. Initiate simulation and source tracing of sudden pollution events to identify pollution sources; S42. Based on the source tracing results, initiate the dynamic construction of the partitioned pressure barrier and generate barrier construction instructions; S43. Under the emergency dispatch objective, perform multi-objective collaborative optimization again, generate and execute a second dispatch instruction, the second dispatch instruction being integrated with the barrier construction instruction; S5. During the scheduling process, pipeline fatigue damage constraints are introduced to optimize scheduling operations and extend pipeline life.

[0009] S6. After the emergency dispatch mode is implemented, continuously monitor the actual diffusion of pollutants and the pressure inside and outside the barrier; S7. Based on monitoring data, dynamically fine-tune the operation of the water pump and valves through a feedback control algorithm to maintain the effectiveness of the hydraulic barrier until the water quality returns to normal and the risk assessment index is met. It has fallen back below the safe threshold.

[0010] In summary, the present invention has the following main beneficial effects: This invention achieves integrated and precise scheduling of urban and rural water supply systems under normal and emergency conditions by constructing a dynamic coupling model of hydraulics and water quality and integrating simulation and source tracing of sudden pollution events, dynamic construction of zoned pressure barriers, multi-objective collaborative optimization scheduling, and pipeline structure stress feedback mechanism. It effectively overcomes the shortcomings of existing technologies such as disconnection of hydraulic and water quality models, low efficiency of pollution source tracing, single emergency control methods, and insufficient coordination of multiple objectives, and significantly improves the water quality safety assurance capability, pollution diffusion suppression efficiency, and pipeline network service life of the water supply system. Attached Figure Description

[0011] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0013] The embodiments of the present invention will now be described.

[0014] Example 1 The present invention provides an urban and rural water supply scheduling optimization system, the system architecture of which is as follows: Figure 1 As shown, the system mainly includes a hydraulic and water quality dynamic coupling model module, a sudden pollution event simulation and source tracing module, a zoned pressure barrier dynamic construction module, a multi-objective collaborative optimization scheduling module, and a pipeline structure stress hydraulic state feedback module. These modules interact and transmit commands via a data bus or communication interface, collectively forming a closed-loop intelligent scheduling system.

[0015] I. Dynamic Coupling Model Module for Hydraulics and Water Quality This module is the core of the system's fundamental computation, used to construct and update in real time a mathematical model that dynamically couples the hydraulic state of the pipe network with the water transport and diffusion process. This model is described by the following coupling equations: ; The definitions, acquisition methods, and functions of each physical quantity are as follows: This refers to the concentration of a specific pollutant in the water body, specifically the concentration of a particular pollutant to be tracked, such as residual chlorine, turbidity, or a certain chemical pollutant. The unit is usually mg / L or CFU / mL (for microorganisms). Its initial values ​​and boundary conditions are obtained through water quality sensors deployed at key nodes in the pipeline network (such as water source outlets, pipeline ends, and sensitive user inlets).

[0016] Time is the continuous or discrete time variable for model operation, measured in seconds (s).

[0017] It is concentration Relative to time The partial derivative of is the instantaneous rate at which the pollutant concentration changes with time at a fixed, tiny point in the pipeline network.

[0018] The velocity vector field is calculated using a pipe network hydraulic model. This model is based on the mass and energy conservation equations (e.g., using the EPANET open-source toolkit or a commercial software such as the WaterGEMS solver engine). Input data includes: pipe network topology (pipe connections, lengths, diameters), pipe roughness coefficients, node water consumption patterns, pump characteristic curves (HQ curves), valve settings (opening degree or local resistance coefficients), and water levels in the tank / tower. By solving this model, the magnitude and direction of the velocity in each pipe, as well as the pressure at each node, are obtained.

[0019] The diffusion coefficient of pollutants comprehensively reflects the dispersion effect of pollutants caused by water turbulence and Brownian motion of molecules, and is measured in meters (m). 2 / s. This coefficient can be estimated using empirical formulas (related to Reynolds number and pipe flow velocity) or obtained through inversion correction of historical water quality data.

[0020] This refers to the concentration change caused by water flow (convection). For example, when a sewage mass flows past a monitoring node, the pollutant concentration at that node will increase accordingly.

[0021] This is due to concentration changes caused by molecular diffusion and turbulence (dispersion). Even when the water is still, pollutants will disperse from areas of high concentration to areas of low concentration.

[0022] The source and sink terms of pollutants are used to describe the coordinates of their location in the pipeline network. Location, Time This refers to the injection (source, positive value) or removal (sink, negative value) of pollutants. Examples of sources include continuous leakage at pollution incident sites and chlorine dosing at intermediate chlorination stations; examples of sinks include the adsorption of pollutants by biofilms on pipe walls and the consumption of residual chlorine by reactions with organic matter.

[0023] The rate constant for pollutants in water bodies characterizes the rate of first-order reaction kinetics processes such as self-degradation and inactivation by oxidants (e.g., chlorine) in water bodies, measured in seconds (s). -1 Its value can be estimated by laboratory measurement or by fitting the concentration decay data from multiple monitoring points in the pipeline network.

[0024] During system initialization, complete pipeline physical parameters are imported from the SCADA system or database. During operation, data from pressure sensors, flow meters, and water quality sensors are received in real time. Using this data, the state of the hydraulic model (node ​​pressure, pipeline flow) and the concentration field of the water quality model are corrected in real time using data assimilation techniques (e.g., ensemble Kalman filter EnKF), and key parameters (such as...) are optionally updated. and This ensures the accuracy of the model's predictions. The output of this module is a spatiotemporal distribution map of pollutant concentrations at all nodes in the pipeline network and inside the pipelines during the future prediction period.

[0025] II. Simulation and Source Tracing Module for Sudden Pollution Incidents This module is directly connected to the hydraulic and water quality dynamic coupling model module, and is used to detect water quality anomalies (such as concentration at a certain monitoring point). Exceeding the safety threshold When this is done, the location and release history of the pollution source can be quickly reversed to identify it.

[0026] This embodiment employs the adjoint state method to achieve reverse identification of pollution sources. The core of this method is to construct and solve an "adjoint equation" corresponding to the forward hydraulic-water quality coupling model, and to locate the pollution source by calculating the adjoint variables (i.e., the sensitivity field).

[0027] The specific implementation steps are as follows: 1. Define the adjoint equation: For the water quality transport coupling equation (forward model) in the hydraulic-water quality dynamic coupling model module: , Its corresponding adjoint equation is defined as: , The physical quantities in the formula are defined as follows: It is the accompanying variable (also known as the sensitivity function or influence function), which is a scalar field.

[0028] It is the first The spatial location of each water quality monitoring point.

[0029] At the monitoring point Location, Time The actual measured concentration of pollutants.

[0030] It was Dirac The function indicates that this item is only used at monitoring points. The value is not zero.

[0031] Other symbols , , The meaning is the same as that of the forward model, and it is provided by the hydraulic and water quality dynamic coupling model module.

[0032] Physical meaning: accompanying variables The physical meaning is the total difference between the measured and simulated concentration values ​​at all monitoring points throughout the entire observation period, for the location... ,time Pollutant release intensity Sensitivity. The higher the value, the more likely that place is to be a source of pollution.

[0033] 2. Set the terminal and boundary conditions for the adjoint equations: Terminal conditions: At the end of the observation period, set... =0.

[0034] Boundary conditions: On the computational boundaries of the pipeline network (such as water source inlets and closed ends), zero gradient boundary conditions are usually set, i.e. ,in The normal vector of the boundary.

[0035] 3. Solving the adjoint equation by inverse integration: The aforementioned adjoint equation is solved by inverse integration from the endpoint to the starting point in time. This process requires accessing spatiotemporal data such as the velocity field stored in the forward time calculation provided by the hydraulic-water quality dynamic coupling model module.

[0036] 4. Calculating the sensitivity field and identifying pollution sources: After solving the adjoint equation, the adjoint variable field in the entire time and space domain is obtained. The pollution source identification problem is transformed into finding the solution that minimizes the objective function (usually the sum of squares of the differences between measured and simulated values). Within the framework of the adjoint state method, this objective function addresses the pollution source. The gradient is proportional to .therefore: Through analysis By analyzing the spatiotemporal distribution of pollutants, the region with the largest absolute value is identified as the most likely location of the pollution source. .

[0037] At this location superior, Over time The change curve reflects the history of pollution source release. Possible temporal patterns. Typically, The longer time periods correspond to the periods when pollution sources are most active in releasing pollutants.

[0038] For example, suppose the system detects abnormal pollutant concentrations at multiple monitoring points between 8:00 AM and 10:00 AM. The source tracing module is activated, setting the observation period to 10:00 AM. The system obtains the total network flow velocity from the hydraulic-water quality dynamic coupling model module between 8:00 AM and 10:00 AM. diffusion coefficient Data such as... Construct the adjoint equation and use the actual monitored concentration sequence from 8:00 to 10:00. Substitute into the equation. Starting from t=10:00, perform the reverse integration of the accompanying equation until... =8:00, solve for the adjoint variable field. Analysis revealed that, At 9:00 AM, located at a certain point in the pipeline network node. The value shows a global maximum peak. Therefore, the system determines that the vicinity of this node is the most likely source of pollution and speculates that its main release time is around 9:00.

[0039] III. Dynamic Construction Module for Zonal Pressure Barriers This module connects to the two modules mentioned above and is activated after a pollution source is identified or a high-risk area is designated. Its core function is to dynamically construct a "hydraulic barrier" with a specific pressure gradient between the polluted area and the clean area by optimizing the scheduling of water pumps and valves, thereby physically inhibiting the spread of polluted water to the clean area.

[0040] Quantification of barrier effectiveness: Barrier effectiveness Defined as: ; in: It is the pressure difference (Pa) across the barrier interface.

[0041] It is the concentration gradient of pollutants at the barrier interface ((mg / L) / m).

[0042] It is the area of ​​the barrier element (m) 2 This is achieved by performing area integral calculations on the pipes or virtual interfaces where the barrier is located.

[0043] The physical meaning of this formula is: when the pollutant concentration changes most drastically (i.e., In areas with large air volumes, applying a greater pressure differential can more effectively counteract the migration of pollutants due to convection and diffusion.

[0044] Optimization Model and Solution: This module constructs barriers by solving an optimization problem. The optimal optimization problem is preferably constructed as a quadratic programming problem.

[0045] Decision variables: the speed or number of all adjustable water pumps in the system, and the opening degree of adjustable valves.

[0046] Objective function: Minimize [total energy consumption + ... ] or maximize [ - *Total energy consumption], of which and This is a weighting factor used to weigh energy consumption against effectiveness.

[0047] Key constraints: Barrier inner pressure constraint: . It is a critical pressure set to inhibit the outward diffusion of pollutants. Its value can be determined by simulation or experiment. It is usually set to a value slightly higher than the average pressure inside the polluted area to ensure that water does not flow from the clean area to the polluted area.

[0048] External pressure constraint of the barrier: . This is the minimum pressure difference required to maintain barrier stability, for example, 0.05 MPa, to ensure that the pressure gradient direction is always from the outside to the inside.

[0049] Other system constraints include minimum service pressure of nodes, maximum / minimum speed of pumps, valve opening range, and water level limits in the pool.

[0050] For example, the system determines that area A is a contaminated area. The module immediately starts, identifying the set of connecting pipes between A and clean area B. By solving the aforementioned quadratic programming problem, it is determined that the valve needs to be closed to 80% opening, the speed of pump_2 reduced to 70% of its rated speed, and pump_3 kept fully open. After implementing this plan, the average pressure on boundary A... Reduced to 0.18 MPa (< =0.2MPa), while the boundary pressure at point B is 0.2MPa. Maintain at 0.25 MPa (> =0.23MPa), successfully establishing an effective pressure barrier.

[0051] IV. Multi-objective collaborative optimization scheduling module This module is the system's decision-making center, connected to all the aforementioned modules, and is responsible for comprehensive scheduling and optimization under both normal and emergency modes.

[0052] Optimization Objectives: Collaboratively optimize the following four objectives: 1. Total system operating cost (F1): mainly the power consumption of the pump station, which can be calculated based on the power of the water pump, the operating time, and the electricity price.

[0053] 2. Overall network pressure balance (F2): This can be measured by the variance or standard deviation of the pressure of all nodes. The smaller the value, the more balanced the network.

[0054] 3. Water quality compliance rate at critical nodes (F3): defined as the concentration of pollutants at critical nodes. Below standard concentration The proportion of the number of nodes to the total number of critical nodes.

[0055] 4. Pollution diffusion suppression efficiency under emergency conditions (F4): This target is related to the effectiveness of regional pressure barriers. Strong correlation, can be partially used in emergency mode To characterize.

[0056] Adaptive weight allocation mechanism: The core of this mechanism is based on the real-time risk assessment index. Dynamically adjust the multi-objective optimization function Weighting coefficients in , , , .

[0057] Risk assessment index The calculation formula is: , The measured or predicted value of pollutant concentration at the i-th critical node; This refers to the water quality standard concentration. The sum of the absolute values ​​of pollutant concentration deviations from the standard value at all key nodes reflects the current water quality risk.

[0058] : Summation of the rupture risk indication functions for all pipelines. This is a pipeline rupture risk indicator function. It can be calculated based on the pipe's age, material, historical pipe burst records, and current real-time pressure fluctuations (especially water hammer pressure peaks). For example, ,in , , For the coefficient; where, Indicates the service life (in years) of the pipeline. The embrittlement coefficient is the material of the pipe (dimensionless, ranging from 0 to 1, determined according to the type of pipe and the degree of aging). This represents the dynamic pressure fluctuation amplitude (MPa) experienced by the pipeline at the current moment, which can be obtained in real time through water hammer simulation or pressure monitoring.

[0059] This is a risk level function for water sources, which can be comprehensively scored based on the water quality monitoring history of the water source, the distribution of surrounding pollution sources, hydrogeological conditions, etc., and divided into low, medium and high levels and quantified into numerical values.

[0060] , , These are the weighting coefficients for each risk item, used to balance the importance of the three types of risks.

[0061] Weighting strategy: when When the weights fall below the safety threshold, the system enters normal scheduling mode. At this time, the weights... (cost) and (Pressure equalization) is set high. (Water quality) and (Pollution suppression) is set low. The system generates and executes the first scheduling instruction, prioritizing economy and stability.

[0062] when If the threshold is exceeded, the system immediately switches to emergency dispatch mode. At this time, the system automatically reduces... and The weighting was greatly increased. and The system, based on the new weights, integrates barrier construction instructions, recalculates and optimizes the calculations to generate a second scheduling instruction focused on ensuring water quality safety and controlling pollution.

[0063] V. Pipeline Structure Stress Hydraulic State Feedback Module This module is used to evaluate the impact of scheduling operations on the lifespan of the pipeline network structure, and it is connected to the multi-objective collaborative optimization scheduling module, using pipeline fatigue damage as an optimization constraint.

[0064] Function: Real-time monitoring or estimation of the cyclic stress amplitude in pipelines caused by water hammer and daily pressure fluctuations through hydraulic transient simulation.

[0065] Fatigue damage prediction: The cumulative fatigue damage of the pipeline is estimated using Miner's linear cumulative damage rule. : , The actual number of cycles a pipeline undergoes under a specific cyclic stress amplitude. This can be obtained by analyzing pressure time series data using the rainflow counting method.

[0066] Fatigue life of the pipeline material under this cyclic stress amplitude (number of cycles until failure). The stress-life curve (SN curve) of the pipe material is used to determine the life of the pipe. This curve can be obtained through material experiments or literature.

[0067] Integration and Constraints: This module will integrate the current pipeline network... Values ​​and predictions of the incremental damage that may result from planned operations in the next scheduling cycle. Provided to the optimization module. During the multi-objective collaborative optimization scheduling module's solution process, As a hard constraint, or to This is added as a penalty term to the objective function. In this way, the optimization algorithm will automatically avoid scheduling schemes that could lead to drastic pressure fluctuations, thereby accelerating pipeline fatigue and aging.

[0068] For example, an emergency dispatch plan requires the rapid closure of a valve on a main pipeline to create a barrier. The pipeline structure stress-hydraulic state feedback module, through transient simulation, predicts that this operation will generate a pressure pulse as high as 0.8 MPa on a downstream section of aging cast iron pipe, corresponding to a stress amplitude exceeding the material fatigue limit. The calculation results in... Will exceed the preset threshold The optimization module therefore rejected the rapid valve-closing solution and instead selected an alternative with a slower valve-closing time and a more gradual pressure change.

[0069] Example 2 Based on the above system, see Figure 2 This invention also provides a method for optimizing urban and rural water supply scheduling, the specific steps of which are as follows: S1: Build and update the dynamic coupling model of hydraulics and water quality in real time.

[0070] Using basic pipeline data and real-time monitoring data, a dynamic coupling model of hydraulics and water quality is established, continuously operated, and calibrated in the dynamic coupling model module of hydraulics and water quality.

[0071] S2: Based on real-time data, perform normal multi-objective optimization scheduling, generate and execute the first scheduling instruction.

[0072] In risk assessment index (See S3 for calculation method) When the value is below the safety threshold, multi-objective optimization is performed with lower weights of w3 and w4 and higher weights of w1 and w2 to generate scheduling instructions that focus on economic operation and pressure balance and issue them to the execution equipment.

[0073] S3: Real-time calculation of risk assessment index .

[0074] According to the formula given in the hydraulic-water quality dynamic coupling model module, the current value is calculated periodically (e.g., every 5 minutes). value.

[0075] S4: Assessment and Emergency Response. Continuous Assessment Does it exceed the preset emergency threshold?

[0076] If the time limit is not exceeded, return to S2 and continue normal scheduling.

[0077] If the limit is exceeded, the emergency dispatch mode will be triggered immediately, and the following sub-steps will be executed sequentially: S41: Initiate simulation and source tracing of sudden pollution events to identify pollution sources.

[0078] The system invokes the sudden pollution event simulation and source tracing module, and uses methods such as the accompanying state method to reversely identify the location and release history of the pollution source.

[0079] S42: Initiate dynamic construction of partition pressure barriers, generating barrier construction instructions, specifically including: S421: Determine the isolation zone. Based on the location of the pollution source identified in S41 and the pollutant diffusion range simulated by the hydraulic model, determine the contaminated area that needs to be hydraulically isolated.

[0080] S422: Establish a pressure optimization model. With the objective of creating a stable and controllable pressure gradient at the boundary between the contaminated and uncontaminated zones, establish a quadratic programming model. The decision variables are pump and valve operations, and the constraints include... .and .

[0081] S423: Solve the model generation instruction. Solve the quadratic programming model to obtain specific pump and valve operation schemes, i.e., barrier construction instructions.

[0082] S43: Under the emergency dispatch objective, perform multi-objective collaborative optimization again, generate and execute the second dispatch instruction. At this time, the multi-objective collaborative optimization dispatch module has already adjusted according to the high... The weights were adaptively adjusted (w3 and w4 were significantly improved). Under this new weight, the optimization algorithm was rerun, and the barrier construction instructions generated in step S42 were incorporated into the optimization process as hard constraints or strong guidance. Finally, a second scheduling instruction with pollution control as the primary objective was generated and issued to the execution device.

[0083] S5: During the scheduling execution process, pipeline fatigue damage constraints are introduced.

[0084] This step is integral to the optimization processes of S2 and S4. During any optimization calculation in the multi-objective collaborative optimization scheduling module, the pipeline structure stress-hydraulic state feedback module must be invoked, and the pipeline fatigue damage increment constraint must be applied. The constraints incorporated into the optimization model ensure that all scheduling schemes are friendly to the pipeline network structure.

[0085] S6: Emergency monitoring and feedback.

[0086] After the emergency dispatch mode is executed, the system continuously monitors the actual diffusion of pollutants (via water quality sensors) and the pressure inside and outside the barrier (via pressure sensors).

[0087] S7: Dynamically fine-tuning to maintain the barrier.

[0088] Based on monitoring data from S6, the operation of water pumps and valves is dynamically fine-tuned using feedback control algorithms (such as proportional-integral-derivative control or model predictive control) to maintain the effectiveness of the hydraulic barrier. This process continues until monitoring data shows that water quality has returned to normal and The index fell below the safe threshold. At this point, the system automatically exited emergency mode and returned to normal S2 scheduling.

[0089] In summary, through the above-described system architecture and method steps, this invention achieves full-process, multi-objective, closed-loop optimized scheduling of urban and rural water supply systems from normal operation to emergency response, effectively improving water supply security and system resilience.

[0090] Although embodiments of the present invention have been shown and described, these specific embodiments are merely explanations of the invention and are not intended to limit it. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions, and variations to the embodiments as needed without departing from the principles and spirit of the invention, but such modifications, substitutions, and variations are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A system for optimizing urban and rural water supply scheduling, characterized in that: include: The hydraulic-water quality dynamic coupling model module is used to construct and update in real time a mathematical model that couples the hydraulic state of the pipe network with the water quality transport and diffusion process, described by the following coupling equations: ； in, It is the concentration of a specific pollutant in the water body. Relative to time The partial derivatives, This is the velocity vector field calculated from the pipe network hydraulic model. The diffusion coefficient of pollutants, Source and sink terms for pollutants are used to describe the location of pollutants. and time The injection or removal of pollutants, This is the reaction rate constant of pollutants in water; The sudden pollution event simulation and source tracing module is connected to the hydraulic and water quality dynamic coupling model module. It is used to solve the coupling equation in reverse based on real-time and historical hydraulic and water quality data when water quality anomalies are detected, and to identify the location and release history of the pollution source. A zoned pressure barrier dynamic construction module, connected to the hydraulic and water quality dynamic coupling model module and the sudden pollution event simulation and source tracing module, is used to dynamically form one or more hydraulic barriers with specific pressure gradients between polluted and non-polluted areas after identifying pollution sources or high-risk areas, by optimizing the scheduling of water pumps and valves, to inhibit the diffusion of polluted water to clean areas; wherein, the construction of the hydraulic barriers is based on minimizing barrier construction energy consumption and maximizing barrier effectiveness as the objective function, and the barrier effectiveness... Through the pressure difference on both sides of the barrier With water concentration gradient The product is evaluated by integrating it over the barrier area: ； in, The area of ​​a micro-element representing a hydraulic barrier; A multi-objective collaborative optimization scheduling module is connected to the hydraulic and water quality dynamic coupling model module, the sudden pollution event simulation and source tracing module, and the regional pressure barrier dynamic construction module, respectively. It is used to collaboratively optimize the total system operating cost, network-wide pressure balance, water quality compliance rate at key nodes, and pollution diffusion suppression efficiency under emergency conditions, in both normal and emergency scheduling modes. Specifically, the multi-objective collaborative optimization scheduling module dynamically adjusts the weights of each sub-objective in the multi-objective optimization model through an adaptive weight allocation mechanism based on scenario recognition. This adaptive weight allocation mechanism is based on a real-time risk assessment index. Weighting and risk assessment index The calculation formula is: ； in, The measured or predicted value of pollutant concentration at the i-th critical node; This refers to the water quality standard concentration. This is a pipeline rupture risk indicator function; This is a function representing the risk level of a water source area. , , These are the weighting coefficients for each risk item.

2. The urban and rural water supply scheduling optimization system according to claim 1, characterized in that, In the dynamic construction module of the partitioned pressure barrier, the hydraulic barrier is constructed by solving a quadratic programming problem with pressure as the decision variable. The constraints of this quadratic programming problem include: the pressure inside the barrier. satisfy Pressure on the outside of the barrier satisfy ,in The critical pressure for inhibiting the diffusion of pollutants, Minimum pressure differential required to maintain the barrier's effectiveness.

3. The urban and rural water supply scheduling optimization system according to claim 1, characterized in that, The sudden pollution event simulation and source tracing module uses the adjoint state method for reverse identification of pollution sources. By constructing an adjoint equation corresponding to the coupling equation of the hydraulic and water quality dynamic coupling model module and performing reverse integration, the sensitivity field of the concentration at the observation point to the location and release history of potential pollution sources is calculated, thereby locating the pollution source.

4. The urban and rural water supply scheduling optimization system according to claim 1, characterized in that, The system also includes: The pipeline structure stress hydraulic state feedback module is used to monitor or estimate the cyclic stress of the pipeline caused by water hammer effect and pressure fluctuation in real time, and predict the degree of fatigue damage accumulation of the pipeline by combining pipeline material and age data. The pipeline structure stress hydraulic state feedback module is connected to the multi-objective collaborative optimization scheduling module, and is used to introduce the pipeline fatigue damage rate as a constraint and penalty in the optimization scheduling to avoid operation schemes that accelerate pipeline aging.

5. The urban and rural water supply scheduling optimization system according to claim 4, characterized in that, The pipeline structure stress-hydraulic state feedback module uses Miner's linear cumulative damage rule to estimate the fatigue damage degree of the pipeline. : ； in, Let be the actual number of cycles the pipeline experiences under the stress amplitude of the i-th cycle. The fatigue life of the pipeline material under this cyclic stress amplitude; during the optimized scheduling process, the estimated damage increment within future scheduling cycles is controlled. Below the preset threshold .

6. A method for optimizing urban and rural water supply scheduling, characterized in that, The method, applied to the urban and rural water supply scheduling optimization system as described in any one of claims 1 to 5, comprises the following steps: S1. Construct and update the dynamic coupling model of hydraulics and water quality in real time; S2. Based on real-time data, perform normal multi-objective optimization scheduling to generate and execute the first scheduling instruction; S3, Real-time calculation of risk assessment index ; S4, when the aforementioned risk assessment index When the preset threshold is exceeded, the emergency dispatch mode is triggered, and the following steps are executed: S41. Initiate simulation and source tracing of sudden pollution events to identify pollution sources; S42. Based on the source tracing results, initiate the dynamic construction of the partitioned pressure barrier and generate barrier construction instructions; S43. Under the emergency dispatch objective, perform multi-objective collaborative optimization again, generate and execute a second dispatch instruction, the second dispatch instruction being integrated with the barrier construction instruction; S5. During the scheduling process, pipeline fatigue damage constraints are introduced to optimize scheduling operations and extend pipeline life.

7. The urban and rural water supply scheduling optimization method according to claim 6, characterized in that, Step S42, which involves initiating the dynamic construction of the partition pressure barrier, specifically includes: S421. Based on the location of the pollution source and the hydraulic model, determine the area that needs to be isolated; S422. With the goal of forming a stable and controllable pressure gradient at the boundary between the polluted and unpolluted areas, establish a pressure optimization model. S423. Solve the pressure optimization model to obtain the pump and valve operation scheme for establishing the hydraulic barrier.

8. The urban and rural water supply scheduling optimization method according to claim 6, characterized in that, The method further includes the following after step S5: S6. After the emergency dispatch mode is implemented, continuously monitor the actual diffusion of pollutants and the pressure inside and outside the barrier; S7. Based on monitoring data, dynamically fine-tune the operation of water pumps and valves through a feedback control algorithm to maintain the effectiveness of the hydraulic barrier until the water quality returns to normal and the risk assessment index is met. It has fallen back below the safe threshold.

9. The urban and rural water supply scheduling optimization method according to claim 7, characterized in that, In step S422, the pressure optimization model is a quadratic programming model, and its constraints include the pressure inside the barrier. Not exceeding the critical pressure Pressure on the outside of the barrier At least higher than the minimum pressure difference of the inner side .

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