Urban waterlogging optimization dynamic scheduling method and system based on active-passive cooperation and multiple scheduling rules
By constructing a three-objective coupled optimization model and a real-time feedback rolling optimization method for urban flood control, the passive lag and single-point control problems of traditional flood control scheduling are solved, realizing the coordinated linkage of the entire basin and the balance of energy consumption and storage capacity, and improving the accuracy and safety of scheduling.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJIN UNIV
- Filing Date
- 2026-04-07
- Publication Date
- 2026-06-09
AI Technical Summary
Traditional urban flood control relies on the experience-based control of single facilities, lacking coordinated action across the entire watershed. This results in passive responses and delayed scheduling, making it difficult to cope with extreme rainstorms and floods.
A dynamic scheduling method for urban flood control based on active-passive coordination and multiple scheduling rules is adopted. By constructing a three-objective coupled optimization model and combining real-time feedback rolling optimization, accurate dynamic scheduling is achieved in the pre-rain drainage and in-rain scheduling stages. A distributed physical simulation model and a dynamic multi-objective genetic algorithm are used to coordinate and link facilities.
It has achieved precise dynamic scheduling throughout the entire cycle, improved the coordination and robustness of scheduling, solved the problems of single-point control, uneven power distribution and the superposition of upstream and downstream pressure, ensured flood drainage safety and optimized energy consumption and reservoir capacity utilization.
Smart Images
Figure CN122175291A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of urban flood control and waterlogging management and water conservancy information technology, specifically to a method for optimizing dynamic scheduling of urban floodwater. Background Technology
[0002] Traditional rainfall monitoring and flood control primarily rely on experience-based control of individual facilities, lacking comprehensive basin-wide coordination between drainage facilities (pump stations, sluice gates) and storage spaces (ponds, reservoirs), resulting in weak systemic control. Currently, urban stormwater pump station scheduling is mostly in a "passive response" phase, struggling to adjust storage capacity in advance based on weather forecasts, and complex nonlinear hydraulic simulations often limit the real-time response time of scheduling. Relying solely on single facilities or localized scheduling is insufficient to support the defense needs against extreme rainstorms and floods. Therefore, there is an urgent need for a dynamic urban flood control optimization method and module based on active-passive coordination and multiple scheduling rules to achieve a shift from "single-point control" to "basin coordination," and from "passive response" to "proactive prediction," providing accurate and reliable decision support for urban flood prevention and control. Summary of the Invention
[0003] To address the need for a standardized BIM platform for the storage of municipal pipeline design data, this invention integrates various tools and technologies to assist designers in efficiently processing municipal pipeline data and ensuring its compliance with standardized specifications.
[0004] This invention aims to solve the technical problems of passive and lagging urban flood control, single-point regulation, uneven power distribution, superposition of upstream and downstream pressures, and imbalance between energy consumption and reservoir capacity utilization in traditional urban flood control. It proposes an optimized dynamic scheduling method and system for urban flood control based on active and passive coordination and multiple scheduling rules. The scheduling is divided into pre-rain drainage and in-rain scheduling stages. A three-objective coupled optimization model is constructed, and three scheduling rules drive the coordination of active and passive facilities. Combined with real-time feedback and rolling optimization, it achieves accurate dynamic scheduling throughout the entire cycle.
[0005] In order to achieve the above-mentioned objectives, the present invention proposes the following technical solution:
[0006] In a first aspect, this invention proposes a dynamic scheduling method for urban flood control optimization based on active-passive coordination and multiple scheduling rules, comprising the following steps: S1. Collect real-time rainfall forecast data, drainage facility status data, disaster-bearing body vulnerability weight data and underlying surface environmental attribute data of the target area. Perform spatial correction and standardization on the multi-source heterogeneous data. Divide the entire scheduling process into the pre-rainage stage and the in-rain scheduling stage according to the forecast rainfall start time. Construct a full-cycle discrete time step sequence. S2. Construct a dynamic scheduling optimization model that couples a distributed physical simulation model with a dynamic multi-objective genetic algorithm. Set three optimization objectives: minimize the weighted water accumulation risk value throughout the entire cycle, minimize the total power consumption of the pumping station throughout the entire cycle, and maximize the proportion of the actual water storage volume of each storage facility during the rainy period to its total usable volume after pre-drainage. S3. Establish physical mechanism constraints and operational constraints consisting of water balance of water catchment units, dynamic calculation of pipeline network and river channel, volume limit of storage facilities and pump gate regulation capacity, and limit the optimization search space of the dynamic scheduling optimization model. S4. In the pre-rain drainage stage, the pre-drainage rule based on meteorological forecast is used to calculate the amount of water that needs to be discharged in advance so as to free up reservoir capacity in advance. In the rain scheduling stage, dynamic control rules based on the vulnerability classification of disaster-bearing bodies are applied simultaneously to accurately allocate drainage power on demand and to realize the staggered runoff of upstream and downstream facilities through the cascade regulation and peak-shaving rule based on spatiotemporal coordination. The optimal combination of scheduling decision variables is generated through full-cycle hydraulic simulation and dynamic search. S5. By sensing the water accumulation status of nodes in the target area and the pipeline load in real time and feeding them back to the dynamic scheduling optimization model, the system recommends and executes a linkage scheme of gates, pumping stations and storage space that matches the current rainfall and drainage conditions from the generated optimal scheduling scheme set.
[0007] In some implementations, the objective functions corresponding to the three optimization objectives constructed in S2 are specifically as follows: S21, Objective function 1 is to minimize the weighted water accumulation risk value, introducing a vulnerability weight coefficient for the disaster-bearing body. Prioritize reducing waterlogging in high-risk areas; the calculation formula is as follows:
[0008]
[0009] In the formula: The total weighted waterlogging risk index, This represents the total number of time steps throughout the entire cycle. ,in, This represents the time step size for the pre-rainfall planning phase. This represents the time step size during the rain-related scheduling phase. Number the time steps. The total number of nodes. n Number the nodes. For nodes In the Weighted waterlogging risk value over a time step For the first The time interval of a time step For any node exist Real-time water level at any given moment. The highest water level that the node is allowed to remain unsubmerged. For any node Vulnerability weighting coefficient; S22, Objective function 2 is to minimize the total power consumption of the pumping station, and its calculation formula is as follows:
[0010] In the formula: This represents the total power consumption of the pumping station. This represents the total number of water pumps. Number the water pump. For the safety factor of the water pump, The density of water, It is the acceleration due to gravity. For the first The water pump is at the first The head difference between the inlet and outlet pumps at a time step For the first The water pump is at the first Drainage flow rate over a time step This refers to the efficiency of the water pump.
[0011] S23, Objective function 3 is to maximize the utilization rate of storage space, and its calculation formula is as follows:
[0012] In the formula: To maximize the overall utilization efficiency of storage space, The total number of storage spaces. Number the storage space. For real-time water storage, For the maximum effective volume, This represents the initial water storage volume before pre-discharge. This represents the actual pre-discharge volume during the pre-rainfall phase. This objective is transformed into a minimization objective by taking its negative value for unified optimization.
[0013] In some implementations, the physical mechanism constraints and operational constraints in S3 specifically include: S31, water balance constraints of the catchment unit, calculated using the following formula:
[0014] In the formula: For surface water depth, Net rainfall intensity, The overflow width of the water catchment unit. For the slope of the catchment unit, The area of the catchment unit. The Manning coefficient for the Earth's surface. To store water in the depression.
[0015] S32. Constraints for dynamic calculation of pipeline network and river channel, the calculation formula is as follows:
[0016]
[0017] In the formula: For the current moment, For the time step of the dynamic calculation, , For the next moment Current moment Flow rate in pipes or open channels For the water head, , The node head of the pipeline or open channel at the next moment or the current moment. , The current node head of the upstream and downstream pipelines or open channels. The cross-sectional area of the water passage. , This refers to the current cross-sectional area of the upstream and downstream pipelines or open channels. This represents the average cross-sectional area of the pipe or open channel at the current moment. The average cross-sectional area of the water flow over the time step The change within, Let be the free surface area of the node. The length of the pipe or open channel. The average hydraulic radius at the current moment. The average flow velocity at the current moment. It is the acceleration due to gravity. This is the overall drag coefficient, combined with the Manning roughness coefficient. n Relevant constants; S33. Constraints on storage space and regulation facilities, the calculation formula is as follows:
[0018]
[0019] In the formula: , For time period The initial and final values of the real-time volume of the storage tank, where I is the inflow rate. , For time period The initial and final values of the outflow rate within the container. For real-time water depth, , These are the minimum operating water depth and the maximum design water depth permitted by the facility, respectively.
[0020] S34. Pumping station and gate control capabilities and river safety constraints: limiting pumping station flow. Gate flow At the same time, the water level in the river channel should be controlled to not exceed the design elevation of the embankment, and the discharge flow should not exceed the safe carrying capacity of the downstream river channel.
[0021] In some implementations, the calculation formula for the pre-arrangement rules based on weather forecasts in S4 is as follows:
[0022] In the formula, For the amount of water that needs to be pre-discharged, The runoff coefficient, , To adjust the maximum capacity and current real-time capacity of the storage space, This represents the system's maximum active drainage flow rate.
[0023] In some implementations, the calculation formula for the dynamic control rule based on the vulnerability classification of the disaster-bearing body in S4 is as follows:
[0024] In the formula: For the first The facility in Decision values for each time step, including pump station power or gate opening. In order to be with the first Nodes associated with drainage facilities In the Weighted waterlogging risk value over a time step For the first The maximum drainage capacity of each facility ( ) is the correlation function between the drainage facility scheduling decision value, the weighted waterlogging risk value, and the facility's maximum drainage capacity.
[0025] In some implementations, the calculation formula for the spatiotemporal coordinated cascade storage and peak-shaving rule in S4 is as follows:
[0026] Among them, the forced downstream flow Defined as:
[0027] In the formula: For the upstream storage space in the first Controlled outflow over a time step This represents the maximum flow capacity of the outlet gate of the upstream storage space. , For upstream and downstream facilities in the first Real-time water storage at a time step , This represents the upper limit of the effective storage capacity of upstream and downstream water storage facilities. For upstream facilities in the first Real-time inflow traffic at a time step This represents the real-time discharge flow rate of the downstream pumping station.
[0028] Secondly, this invention proposes an urban flood control optimization dynamic scheduling system based on active-passive collaboration and multiple scheduling rules, used to implement any one of the urban flood control optimization dynamic scheduling methods described above, the system comprising: The data acquisition and preprocessing module is used to acquire real-time rainfall forecast data, drainage facility operation status data, disaster-bearing body vulnerability weight data, and underlying surface environmental attribute data of the target area. It performs spatial correction and standardization processing on multi-source heterogeneous data, and divides the scheduling stage according to the forecast rainfall start time and constructs a full-cycle discrete time step sequence. The model coupling optimization module is used to construct a dynamic scheduling optimization model that couples a distributed physical simulation model with a dynamic multi-objective genetic algorithm. It sets a three-objective optimization system with the minimum weighted water accumulation risk value throughout the entire cycle, the minimum total power consumption of the pumping station throughout the entire cycle, and the maximum proportion of the actual water storage capacity of the storage facilities during the rainy period to the total available volume after pre-drainage. It also loads physical mechanism and operational constraints to limit the optimization space. The scheduling rule-driven module has built-in pre-discharge rules based on weather forecasts, dynamic control rules based on the vulnerability classification of disaster-bearing bodies, and tiered storage and peak-shaving rules based on spatiotemporal coordination. It is used to drive the optimization logic of the model at different stages of pre-discharge before rain and scheduling during rain, and guide the coordinated linkage of gates, pumping stations, and storage spaces. The real-time perception feedback module is used to monitor the water level and pipeline load data of the target area nodes in real time, feed the monitoring data back to the model coupling optimization module, and realize the rolling optimization and status update of the model based on the deviation between the actual monitoring data and the predicted data. The scheme set generation module is used to generate the optimal scheduling scheme set for different combinations of rainfall and tide based on the optimization results of the model coupling optimization module, and extract representative scheduling schemes to build a multi-scenario control plan library. The output execution module is used to match the scheduling scheme corresponding to the current real-time rainfall and drainage conditions from the multi-scenario control plan library, output the optimal scheduling instruction, and accurately control the opening and closing of gates, the adjustment of pump station power, and the storage and discharge of the storage space.
[0029] In some implementations, the data acquisition and preprocessing module collects drainage facility operation status data including gate opening and closing status, pump station operating parameters, and real-time water level data of the storage tank; the real-time sensing feedback module uses sensors for real-time monitoring, and the sensors are water level sensors and flow sensors deployed at drainage nodes, pipe networks, storage facilities, and rivers in the target area.
[0030] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention breaks through the limitations of traditional passive and lagging scheduling. Through full-cycle phased scheduling, multi-objective coupled optimization, and multi-rule collaborative driving, it achieves precise linkage between active and passive facilities, solving problems such as single-point control, uneven power distribution, and superimposed pressure on upstream and downstream areas. Under the premise of ensuring drainage safety, it achieves a multi-objective balance between energy consumption and reservoir capacity utilization. Combined with real-time feedback and rolling optimization, it significantly improves the accuracy, coordination, and robustness of scheduling, providing an efficient and intelligent solution for urban flood control and drainage. Attached Figure Description
[0031] Figure 1 This is a flowchart of the overall process of the urban flood control dynamic scheduling method based on active-passive collaboration and multiple scheduling rules of the present invention.
[0032] Figure 2 This is a block diagram of the urban flood control dynamic scheduling system based on active-passive collaboration and multiple scheduling rules of the present invention.
[0033] Figure 3 This is a technical roadmap for the present invention. Detailed Implementation
[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments, providing a complete and clear description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are merely a part of the embodiments of the present invention. Unless otherwise specifically stated, the relative arrangement and numerical values of the components described in these embodiments do not limit the scope of the present invention. The present invention provides a dynamic scheduling method for urban flood control based on active-passive coordination and multiple scheduling rules, which can link urban pumping stations and storage tanks to achieve coordinated flood control combining active and passive methods.
[0035] Example 1: As Figure 1 The diagram illustrates the overall process of a dynamic scheduling method for urban flood control based on active-passive coordination and multiple scheduling rules, provided by this invention. The specific steps of this method are as follows: Step 1 involves collecting and preprocessing basic data for the target area, including real-time rainfall forecast data, real-time data on drainage facilities (such as gate status, pumping station operating parameters, and reservoir water levels), vulnerability weight data of disaster-bearing bodies, and environmental attribute data of the underlying surface. Spatial correction and standardization are performed on the acquired multi-source heterogeneous data. Based on the start time of the forecasted rainfall, the entire scheduling cycle is divided into a pre-rainfall drainage stage and a mid-rainfall scheduling stage, constructing a full-cycle discrete time step sequence to provide a unified time reference for subsequent active and passive coordinated scheduling.
[0036] Step 2: Construct a dynamic scheduling and optimization model that couples a distributed physical simulation model with a dynamic multi-objective genetic algorithm. This model focuses on the system's safety, energy efficiency, and space utilization throughout its entire lifecycle, setting three optimization objectives: Objective function 1 minimizes the weighted water accumulation risk value, introducing a vulnerability weight for the supporting structure. The calculation formula is as follows: The three optimization objectives constructed in S2 correspond to the following objective functions: S21, Objective Function 1 is to minimize the weighted water accumulation risk value, introducing a vulnerability weight coefficient for the disaster-bearing body. Prioritize reducing waterlogging in high-risk areas; the calculation formula is as follows:
[0037]
[0038] In the formula: The total weighted waterlogging risk index, This represents the total number of time steps throughout the entire cycle. ,in, This represents the time step size for the pre-rainfall planning phase. This represents the time step size during the rain-related scheduling phase. Number the time steps. The total number of nodes. n Number the nodes. For nodes In the Weighted waterlogging risk value over a time step For the first The time interval of a time step For any node exist Real-time water level at any given moment. The highest water level that the node is allowed to remain unsubmerged. For any node Vulnerability weighting coefficient; S22, Objective function 2 is to minimize the total power consumption of the pumping station, and its calculation formula is as follows:
[0039] In the formula: This represents the total power consumption of the pumping station. This represents the total number of water pumps. Number the water pump. The safety factor for the water pump; The density of water, The acceleration due to gravity is m / s². 2 , For the first The water pump is at the first The head difference between the inlet and outlet pumps at a time step, in meters. For the first The water pump is at the first Drainage flow rate at each time step, m 3 / s, This refers to the efficiency of the water pump.
[0040] S23, Objective function 3 is to maximize the utilization rate of storage space, and its calculation formula is as follows:
[0041] In the formula: To maximize the overall utilization efficiency of storage space, The total number of storage spaces. Number the storage space. For real-time water storage, m 3 , For the maximum effective volume, m 3 , The initial water storage volume before pre-discharge, m 3 , This represents the actual pre-discharge volume during the pre-rainfall stage, in m. 3 The objective is transformed into a minimization objective by taking its negative value, and then uniformly optimized.
[0042] Step 3: Establish multiple physical mechanism constraints and operational constraints to limit the search space of the optimization model and ensure the physical rationality of the scheduling scheme and the safety of facility operation. The constraints include physical mechanism constraints and operational constraints, as follows: The physical mechanism constraints and operational constraints in S3 specifically include: S31, water balance constraints of the catchment unit, calculated as follows:
[0043] In the formula: The depth of the surface water is in meters. Net rainfall intensity, mm / h The overflow width of the catchment unit, in meters. For the slope of the catchment unit, The area of the catchment unit is m.2 , The Manning coefficient for the Earth's surface. The depth of the depression for water storage is m.
[0044] S32. Constraints for dynamic calculation of pipeline network and river channel, the calculation formula is as follows:
[0045]
[0046] In the formula: For the current moment, For the time step of the dynamic calculation, , For the next moment Current moment Flow rate in pipes or open channels For the water head, , The node head of the pipeline or open channel at the next moment or the current moment. , The current node head of the upstream and downstream pipelines or open channels. The cross-sectional area of the water passage. , This refers to the current cross-sectional area of the upstream and downstream pipelines or open channels. This represents the average cross-sectional area of the pipe or open channel at the current moment. The average cross-sectional area of the water flow over the time step The change within, Let be the free surface area of the node. The length of the pipe or open channel. The average hydraulic radius at the current moment. The average flow velocity at the current moment. It is the acceleration due to gravity. This is the overall drag coefficient, combined with the Manning roughness coefficient. n Related constants.
[0047] The term "pipeline network" refers to a broad definition of drainage network, including underground pipes / segments (pressure flow / gravity flow pipes) and surface open channels / rivers (open channel drainage channels).
[0048] S33. Constraints on storage space and regulation facilities (volume limitation), the calculation formula is as follows:
[0049]
[0050] In the formula: , For a period of time ( The initial and final values of the real-time volume of the storage tank within the reservoir, m. 3 I represents the inflow rate, m 3 / s, , For a period of time ( The initial and final values of the outflow within ) m 3 / s, The water depth is in real time, in meters (m). , These are the minimum and maximum design water depths allowed for the facility, respectively, in meters (m).
[0051] S34. Pumping station and gate control capabilities and river safety constraints: limiting pumping station flow. Gate flow At the same time, the water level in the river channel should be controlled to not exceed the design elevation of the embankment, and the discharge flow should not exceed the safe carrying capacity of the downstream river channel.
[0052] Step 4: Dynamic search and optimization driven by core scheduling rules. This invention incorporates three core rules to constrain and guide the gate, pump, and pool linkage logic during the optimization process: Rule 1: This is a pre-discharge rule based on weather forecasts. The amount of water that needs to be pre-discharged is calculated based on the forecast rainfall before rainfall occurs. This is used to determine the optimal pre-drainage level for each facility through pre-rainfall optimization, preventing energy waste or excessive pre-drainage caused by blindly draining water, and setting this as the initial state of the model. The calculation formula is as follows:
[0053] In the formula, m is the amount of water that needs to be pre-discharged. 3 , The runoff coefficient, , To adjust the maximum volume and current real-time volume of the storage space, m 3 , The maximum active pumping flow rate of the system, m 3 / s.
[0054] Rule 2: A dynamic control rule based on the vulnerability classification of disaster-bearing bodies. During rainfall, it adjusts the control based on the risk of water accumulation at nodes. Actively adjust facility decision values If priority is given to protecting core disaster-bearing structures such as hospitals and subways, the calculation formula is as follows:
[0055] In the formula: For the first The facility in Decision values for each time step, including pump station power or gate opening. In order to be with the first Nodes associated with drainage facilities In the Weighted waterlogging risk value over a time step For the first The maximum drainage capacity of each facility, m 3 / s or m 2 / s, ( ) is the correlation function between the drainage facility scheduling decision value, the weighted waterlogging risk value, and the facility's maximum drainage capacity.
[0056] Rule 3: A tiered storage and peak-shaving rule based on spatiotemporal coordination. When upstream and downstream reservoirs are nearing saturation, the upstream and downstream storage ponds are linked to achieve coordinated peak-shaving across the entire basin. This rule uses logical judgment to find a balance between upstream safety and downstream storage capacity, achieving peak-shaving scheduling that trades time for space.
[0057]
[0058] Among them, the forced downstream flow Defined as:
[0059] In the formula: For the upstream storage space in the first A controlled outflow rate with a time step, m 3 / s; m is the maximum flow capacity of the outlet gate of the upstream storage space. 3 / s, , For upstream and downstream facilities in the first Real-time water storage at a time step, m 3 , , The effective storage capacity limit of upstream and downstream storage facilities, m 3 , For upstream facilities in the first Real-time inflow traffic at a time step, m 3 / s, The real-time discharge flow rate of the downstream pumping station, m 3 / s.
[0060] For example, consider the rainfall dispatching in a flood-prone area. The meteorological department forecasts heavy rainfall, and the system divides the entire dispatching cycle into a pre-rain stage ( ) and a rain stage ( ) based on the predicted rainfall start time. In the pre-rain stage ( ), the system activates rule one, calculating the pre-discharge volume Vpre based on the predicted rainfall. At this time, the system does not blindly empty the reservoir capacity, but rather achieves a balance between ensuring rainwater storage capacity and reducing pre-discharge power consumption through model optimization. It precisely instructs pumping stations to lower the water level of the storage tanks in the area to a specific elevation in advance, freeing up necessary storage space for rainfall and effectively preventing energy waste caused by excessive pre-discharge. Entering the rain stage ( ), as the rainfall intensity increases, the real-time sensing feedback module detects rising water levels at highly vulnerable nodes such as hospitals. The model coupling optimization module, according to rule two, dynamically increases the power of associated pumping stations using vulnerability weights, and uses the weighted water volume F1 of the area as the optimization core, achieving targeted tilting of drainage power towards high-risk areas. When the downstream river level is detected to be close to the levee elevation or the upstream reservoir capacity is approaching saturation, rule three is triggered for spatiotemporal coordinated regulation. The system first calculates the minimum discharge under the mandatory constraint of ensuring that the upstream facilities do not overflow; if the downstream storage capacity is insufficient or the drainage pressure is too high at this time, the upstream gates are linked to limit the outflow, so as to retain the flood in the upstream space as much as possible to achieve peak avoidance scheduling; after the downstream pumping station discharges water to lower the water level and restore the storage capacity, the system then gradually releases the upstream water storage according to the real-time drainage capacity of the downstream, thus achieving dynamic balance of the entire basin under the premise of ensuring the overall safety of the system.
[0061] Step 5: Real-time sensing feedback and scheduling execution. Relying on sensors to monitor water level and load data in real time and feed it back to the model, dynamic rolling optimization is performed by comparing the deviation between actual and predicted values, thereby correcting the optimization strategy and outputting gate, pump, and pool linkage scheduling instructions that are accurately matched with real-time operating conditions.
[0062] Example 2: Figure 2 As shown, this invention illustrates a dynamic scheduling system for urban flood control based on active-passive coordination and multiple scheduling rules, used to implement the aforementioned dynamic scheduling method for urban flood control. The system includes, in sequence, a data acquisition and preprocessing module 100, a model coupling optimization module 200, a scheduling rule driving module 300, a real-time perception feedback module 400, a scheme set generation module 500, and an output execution module 600. Wherein: The data acquisition and preprocessing module 100 is used to acquire real-time rainfall forecast data, drainage facility operation status data, disaster-bearing body vulnerability weight data and underlying surface environmental attribute data of the target area, perform spatial correction and standardization processing on multi-source heterogeneous data, and divide the scheduling stage according to the forecast rainfall start time and construct a full-cycle discrete time step sequence. The model coupling optimization module 200 is used to construct a dynamic scheduling optimization model that couples a distributed physical simulation model with a dynamic multi-objective genetic algorithm. It sets a three-objective optimization system with the minimum weighted water accumulation risk value throughout the entire cycle, the minimum total power consumption of the pumping station throughout the entire cycle, and the maximum proportion of the actual water storage capacity of the storage facilities during the rain stage to the total available volume after pre-drainage. It also loads physical mechanisms and operational constraints to limit the optimization space. The scheduling rule driving module 300 has built-in pre-discharge rules based on weather forecasts, dynamic control rules based on the vulnerability classification of disaster-bearing bodies, and tiered storage and peak-shaving rules based on spatiotemporal coordination. It is used to drive the optimization logic of the model at different stages of pre-discharge before rain and scheduling during rain, and guide the coordinated linkage of gates, pumping stations, and storage spaces. The real-time perception feedback module 400 is used to monitor the water level of the nodes in the target area and the load data of the pipe network in real time, feed the monitoring data back to the model coupling optimization module, and realize the rolling optimization and status update of the model based on the deviation between the actual monitoring data and the predicted data. The scheme set generation module 500 is used to generate the optimal scheduling scheme set for different combinations of rainfall and tide based on the optimization results of the model coupling optimization module, and extract representative scheduling schemes to build a multi-scenario control plan library. The output execution module 600 is used to match the scheduling scheme corresponding to the current real-time rainfall and drainage conditions from the multi-scenario control plan library, output the optimal scheduling instruction, and accurately control the opening and closing of gates, the adjustment of pump station power, and the storage and discharge of the storage space.
[0063] The data acquisition and preprocessing module 100 collects drainage facility operation status data including gate opening and closing status, pump station operating parameters, and real-time water level data of the storage tank; the real-time sensing feedback module uses sensors for real-time monitoring, and the sensors are water level sensors and flow sensors deployed at drainage nodes, pipe networks, storage facilities, and rivers in the target area.
[0064] like Figure 3 As shown, the technical route of this invention is as follows: First, multi-source data perception and full-cycle scenario construction are carried out to complete the data preprocessing and scheduling stage division; second, a full-cycle multi-objective optimization model is constructed to establish a three-objective optimization system of water accumulation risk, energy consumption, and reservoir capacity utilization; then, physical mechanism constraints and operational safety boundaries are set to limit the optimization search space; subsequently, decision variables are dynamically optimized through multiple scheduling rules to generate a linkage scheme between gate pumps and storage facilities; finally, the scheduling instructions are dynamically corrected through real-time perception feedback and optimal scheme execution.
[0065] It should be understood that the above description of the preferred embodiments is quite detailed and should not be construed as limiting the scope of the present invention. Those skilled in the art can make substitutions or modifications under the guidance of the present invention without departing from the scope of protection of the present invention, and all such substitutions or modifications fall within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the appended claims.
[0066] The specific embodiments described in this invention are merely illustrative and not intended to limit the scope of protection. Within the scope defined by the spirit and claims of this invention, various modifications and implementations are possible: the actions or steps recorded in the claims may be performed in a different order than those described in the embodiments; the processes shown in the accompanying drawings do not necessarily require a specific order or continuity, and may be used when multitasking or parallel processing is feasible; any specific modifications or implementations made by those skilled in the art based on the teachings of this invention after learning of this invention, without departing from the above-mentioned scope of protection, are all within the scope of protection of this invention.
Claims
1. A dynamic scheduling method for urban flood control based on active-passive coordination and multiple scheduling rules, characterized in that, Includes the following steps: S1. Collect real-time rainfall forecast data, drainage facility status data, disaster-bearing body vulnerability weight data and underlying surface environmental attribute data of the target area. Perform spatial correction and standardization on the multi-source heterogeneous data. Divide the entire scheduling process into the pre-rainage stage and the in-rain scheduling stage according to the forecast rainfall start time. Construct a full-cycle discrete time step sequence. S2. Construct a dynamic scheduling optimization model that couples a distributed physical simulation model with a dynamic multi-objective genetic algorithm. Set three optimization objectives: minimize the weighted water accumulation risk value throughout the entire cycle, minimize the total power consumption of the pumping station throughout the entire cycle, and maximize the proportion of the actual water storage volume of each storage facility during the rainy period to its total usable volume after pre-drainage. S3. Establish physical mechanism constraints and operational constraints consisting of water balance of water catchment units, dynamic calculation of pipeline network and river channel, volume limit of storage facilities and pump gate regulation capacity, and limit the optimization search space of the dynamic scheduling optimization model. S4. In the pre-rain drainage stage, the pre-drainage rule based on meteorological forecast is used to calculate the amount of water that needs to be discharged in advance so as to free up reservoir capacity in advance. In the rain scheduling stage, dynamic control rules based on the vulnerability classification of disaster-bearing bodies are applied simultaneously to accurately allocate drainage power on demand and to realize the staggered runoff of upstream and downstream facilities through the cascade regulation and peak-shaving rule based on spatiotemporal coordination. The optimal combination of scheduling decision variables is generated through full-cycle hydraulic simulation and dynamic search. S5. By sensing the water accumulation status of nodes in the target area and the pipeline load in real time and feeding them back to the dynamic scheduling optimization model, the system recommends and executes a linkage scheme of gates, pumping stations and storage space that matches the current rainfall and drainage conditions from the generated optimal scheduling scheme set.
2. The urban flood control dynamic scheduling method based on active-passive coordination and multiple scheduling rules as described in claim 1, characterized in that, The three optimization objectives constructed in S2 correspond to the following objective functions: S21, Objective Function 1 is to minimize the weighted water accumulation risk value, introducing a vulnerability weight coefficient for the disaster-bearing body. Prioritize reducing waterlogging in high-risk areas; the calculation formula is as follows: In the formula: The total weighted waterlogging risk index, This represents the total number of time steps throughout the entire cycle. ,in, This represents the time step size for the pre-rainfall planning phase. This represents the time step size during the rain-related scheduling phase. Number the time steps. The total number of nodes. n Number the nodes. For nodes In the Weighted waterlogging risk value over a time step For the first The time interval of a time step For any node exist Real-time water level at any given moment. The highest water level that the node is allowed to remain unsubmerged. For any node Vulnerability weighting coefficient; S22, Objective function 2 is to minimize the total power consumption of the pumping station, and its calculation formula is as follows: In the formula: This represents the total power consumption of the pumping station. This represents the total number of water pumps. Number the water pump. For the safety factor of the water pump, The density of water, It is the acceleration due to gravity. For the first The water pump is at the first The head difference between the inlet and outlet pumps at a time step For the first The water pump is at the first Drainage flow rate over a time step For the efficiency of the water pump; S23, Objective function 3 is to maximize the utilization rate of storage space, and its calculation formula is as follows: In the formula: To maximize the overall utilization efficiency of storage space, The total number of storage spaces. Number the storage space. For real-time water storage, For the maximum effective volume, This represents the initial water storage volume before pre-discharge. The target is the actual pre-discharge volume during the pre-rain stage. This target is transformed into a minimization target by taking its negative value for unified optimization.
3. The urban flood control dynamic scheduling method based on active-passive coordination and multiple scheduling rules as described in claim 1, characterized in that, The physical mechanism constraints and operational constraints in S3 specifically include: S31, water balance constraints of the catchment unit, calculated using the following formula: In the formula: For surface water depth, Net rainfall intensity, The overflow width of the water catchment unit. For the slope of the catchment unit, The area of the catchment unit. The Manning coefficient for the Earth's surface. To deepen the water storage in the depression; S32. Constraints for dynamic calculation of pipeline network and river channel, the calculation formula is as follows: In the formula: For the current moment, For the time step of the dynamic calculation, , For the next moment Current moment Flow rate in pipes or open channels For the water head, , The node head of the pipeline or open channel at the next moment or the current moment. , The current node head of the upstream and downstream pipelines or open channels. The cross-sectional area of the water passage. , This refers to the current cross-sectional area of the upstream and downstream pipelines or open channels. This represents the average cross-sectional area of the pipe or open channel at the current moment. The average cross-sectional area of the water flow over the time step The change within, Let be the free surface area of the node. The length of the pipe or open channel. The average hydraulic radius at the current moment. The average flow velocity at the current moment. It is the acceleration due to gravity. This is the overall drag coefficient, combined with the Manning roughness coefficient. n Relevant constants; S33. Constraints on storage space and regulation facilities, the calculation formula is as follows: In the formula: , For time period The initial and final values of the real-time volume of the storage tank, where I is the inflow rate. , For time period The initial and final values of the outflow rate within the container. For real-time water depth, , These are the minimum operating water depth and the maximum design water depth permitted by the facility, respectively. S34. Pumping station and gate control capabilities and river safety constraints: limiting pumping station flow. Gate flow At the same time, the water level in the river channel should be controlled to not exceed the design elevation of the embankment, and the discharge flow should not exceed the safe carrying capacity of the downstream river channel.
4. The urban flood control dynamic scheduling method based on active-passive coordination and multiple scheduling rules as described in claim 1, characterized in that, The calculation formula for the pre-arrangement rules based on weather forecasts in S4 is as follows: In the formula, For the amount of water that needs to be pre-discharged, The runoff coefficient, , To adjust the maximum capacity and current real-time capacity of the storage space, This represents the system's maximum active drainage flow rate.
5. The urban flood control dynamic scheduling method based on active-passive coordination and multiple scheduling rules as described in claim 1, characterized in that, The calculation formula for the dynamic control rule based on the vulnerability classification of the disaster-bearing body in S4 is as follows: In the formula: For the first The facility in Decision values for each time step, including pump station power or gate opening. In order to be with the first Nodes associated with drainage facilities In the Weighted waterlogging risk value over a time step For the first The maximum drainage capacity of each facility ( ) is the correlation function between the drainage facility scheduling decision value, the weighted waterlogging risk value, and the facility's maximum drainage capacity.
6. The urban flood control dynamic scheduling method based on active-passive coordination and multiple scheduling rules as described in claim 1, characterized in that, The calculation formula for the cascade water storage and peak-shaving rule based on spatiotemporal coordination in S4 is as follows: Among them, the forced downstream flow Defined as: In the formula: For the upstream storage space in the first Controlled outflow over a time step This represents the maximum flow capacity of the outlet gate of the upstream storage space. , For upstream and downstream facilities in the first Real-time water storage at a time step , This represents the upper limit of the effective storage capacity of upstream and downstream water storage facilities. For upstream facilities in the first Real-time inflow traffic at a time step This represents the real-time discharge flow rate of the downstream pumping station.
7. A dynamic scheduling system for urban flood control based on active-passive coordination and multiple scheduling rules, characterized in that, The system is used to implement the urban flood control optimization dynamic scheduling method according to any one of claims 1-6, the system comprising: The data acquisition and preprocessing module is used to acquire real-time rainfall forecast data, drainage facility operation status data, disaster-bearing body vulnerability weight data, and underlying surface environmental attribute data of the target area. It performs spatial correction and standardization processing on multi-source heterogeneous data, and divides the scheduling stage according to the forecast rainfall start time and constructs a full-cycle discrete time step sequence. The model coupling optimization module is used to construct a dynamic scheduling optimization model that couples a distributed physical simulation model with a dynamic multi-objective genetic algorithm. It sets a three-objective optimization system with the minimum weighted water accumulation risk value throughout the entire cycle, the minimum total power consumption of the pumping station throughout the entire cycle, and the maximum proportion of the actual water storage capacity of the storage facilities during the rainy period to the total available volume after pre-drainage. It also loads physical mechanism and operational constraints to limit the optimization space. The scheduling rule-driven module has built-in pre-discharge rules based on weather forecasts, dynamic control rules based on the vulnerability classification of disaster-bearing bodies, and tiered storage and peak-shaving rules based on spatiotemporal coordination. It is used to drive the optimization logic of the model at different stages of pre-discharge before rain and scheduling during rain, and guide the coordinated linkage of gates, pumping stations, and storage spaces. The real-time perception feedback module is used to monitor the water level and pipeline load data of the target area nodes in real time, feed the monitoring data back to the model coupling optimization module, and realize the rolling optimization and status update of the model based on the deviation between the actual monitoring data and the predicted data. The scheme set generation module is used to generate the optimal scheduling scheme set for different combinations of rainfall and tide based on the optimization results of the model coupling optimization module, and extract representative scheduling schemes to build a multi-scenario control plan library. The output execution module is used to match the scheduling scheme corresponding to the current real-time rainfall and drainage conditions from the multi-scenario control plan library, output the optimal scheduling instruction, and accurately control the opening and closing of gates, the adjustment of pump station power, and the storage and discharge of the storage space.
8. The urban flood control dynamic scheduling system based on active-passive coordination and multiple scheduling rules as described in claim 7, characterized in that, The data acquisition and preprocessing module collects drainage facility operation status data including gate opening and closing status, pump station operating parameters, and real-time water level data of the storage tank; the real-time sensing feedback module uses sensors for real-time monitoring, and the sensors are water level sensors and flow sensors deployed at drainage nodes, pipe networks, storage facilities, and rivers in the target area.