Urban drainage system real-time control method, device, equipment and medium

Abstract

The invention relates to the technical field of flood control and drainage of urban pipe networks, in particular to a real-time control method, device, equipment and medium for an urban drainage system, and the method comprises the steps: carrying out the initialization of a DDDP-TL optimization algorithm according to a pre-constructed initial pipe network SWMM model of a target city; obtaining current actually-measured rainfall data of the target city and forecast rainfall data of multiple periods in the future; updating the initial pipe network SWMM model according to the current actually measured rainfall data to obtain an updated pipe network SWMM model; and based on the initialized DDDP-TL optimization algorithm, performing rolling optimization on the updated pipe network SWMM model by using the current actually measured rainfall data and the forecast rainfall data of multiple periods in the future so as to obtain a final control trajectory. Therefore, the problems that an existing urban inland inundation and combined system overflow control scheme depends on control rules, the optimization operation complexity is high, and the regulation and storage capacity of a pipe network is not fully utilized are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban pipe network flood control and drainage, and in particular to a real-time control method, device, equipment and medium for an urban drainage system based on a discrete differential dynamic programming method. Background Art

[0002] With the acceleration of global urbanization and the intensification of climate change, urban flooding risks are becoming increasingly severe, and water resource management issues are particularly prominent in large cities. Leveraging existing urban drainage infrastructure and developing a real-time control method for urban drainage systems based on discrete differential dynamic programming is crucial for addressing urban flooding during heavy rain, ensuring public health and safety, and reducing economic losses.

[0003] In urban waterlogging and combined sewer overflow (CSO) management, active control of existing drainage infrastructure can not only effectively improve the performance of the drainage system, but also require lower capital investment compared to building or expanding new drainage facilities. Existing active control methods mostly rely on control rules to optimize the operating efficiency of storage tanks, pumping stations and lakes with flood regulation capabilities, but research on the storage capacity in pipelines is relatively lacking. Although the average pipe network storage capacity per square kilometer of urban area in my country exceeds 5000m3, the current research on active control methods is still incomplete. 3 However, the network's storage capacity is underutilized. Furthermore, when calculating the optimal scheduling plan for urban drainage networks, the complexity of the optimization operation increases exponentially with the addition of control nodes (such as intercepting wells and pumping stations), which can easily lead to the "curse of dimensionality" problem, making the actual optimization process difficult to implement. Summary of the Invention

[0004] The present invention provides a real-time control method, device, equipment and medium for urban drainage systems to solve the problems of existing urban waterlogging and combined sewer overflow control schemes relying on multiple control rules, the complexity of optimization operations growing exponentially with the increase of control nodes, and the underutilization of the pipe network storage capacity.

[0005] A first embodiment of the present invention provides a real-time control method for an urban drainage system, comprising the following steps: initializing a DDDP-TL optimization algorithm based on a pre-constructed initial pipe network SWMM model of a target city to obtain an initialized DDDP-TL optimization algorithm;

[0006] Obtaining the current measured rainfall data of the target city and the forecast rainfall data for multiple time periods in the future;

[0007] Updating the initial pipe network SWMM model according to the current measured rainfall data to obtain an updated pipe network SWMM model;

[0008] Based on the initialized DDDP-TL optimization algorithm, the updated pipe network SWMM model is subjected to rolling optimization using the current measured rainfall data and the forecast rainfall data for multiple time periods in the future to obtain a final control trajectory.

[0009] Optionally, the initializing the DDDP-TL optimization algorithm according to the pre-constructed initial pipe network SWMM model of the target city to obtain an initialized DDDP-TL optimization algorithm includes:

[0010] Determining the number of discrete layers of the DDDP-TL optimization algorithm and the initial control trajectory of each control node of the pipeline network based on the initial pipeline network SWMM model;

[0011] The objective function, decision variables, state variables and constraints of the DDDP-TL optimization algorithm are set according to the number of discrete layers and the initial control trajectory of each control node of the pipeline network to obtain the initialized DDDP-TL optimization algorithm.

[0012] Optionally, the objective function is expressed as:

[0013]

[0014] Among them, F is the total overflow, N F is the number of waterlogging overflow nodes, N C is the number of combined sewer overflow nodes, FL i,t is the flood overflow at node i during period t, CSO i,t is the combined sewer overflow at node i during period t, α and β are the weight coefficients of the two overflow control methods, T is the total number of simulation periods, and Δt is the time step.

[0015] Optionally, the decision variable is the set of openings of all control nodes; the state variable is the sum of the pipe water storage and overflow within the control area of the control node; and the constraints include initial conditions, boundary conditions, hydraulic constraints, facility operation condition constraints, and time constraints.

[0016] Optionally, the DDDP-TL optimization algorithm after the initialization is used to perform rolling optimization on the updated pipe network SWMM model using the current measured rainfall data and the future multiple time period forecast rainfall data to obtain a final control trajectory, including:

[0017] The forecast rainfall data for time period t is input into the updated pipe network SWMM model, and the state of the updated pipe network SWMM model is updated using the initialized DDDP-TL optimization algorithm; the forecast rainfall data for each future time period is input into the updated pipe network SWMM model, and the state update process is iteratively executed until all control nodes are optimized and the number of iterations reaches a preset threshold, thereby obtaining the final control trajectory, where t is the current time period.

[0018] Optionally, inputting the forecast rainfall data for period t into the updated pipe network SWMM model, and updating the state of the updated pipe network SWMM model using the initialized DDDP-TL optimization algorithm includes:

[0019] Inputting the forecast rainfall data for period t into the updated pipe network SWMM model, and reading the hot start file for period t-1 of the updated pipe network SWMM model;

[0020] Based on the initialized DDDP-TL optimization algorithm, the state transfer calculation of the updated pipe network SWMM model is performed according to the forecast rainfall data of the t period and the hot start file of the t-1 period to obtain the current control trajectory of the t period, and the state of the updated pipe network SWMM model is updated according to the current control trajectory of the t period.

[0021] The second aspect of the present invention provides a real-time control device for an urban drainage system, comprising: an initialization module for initializing a DDDP-TL optimization algorithm based on a pre-constructed initial pipe network SWMM model of a target city to obtain an initialized DDDP-TL optimization algorithm; an acquisition module for acquiring the current measured rainfall data of the target city and the forecast rainfall data for multiple time periods in the future; an update module for updating the initial pipe network SWMM model based on the current measured rainfall data to obtain an updated pipe network SWMM model; and a rolling optimization module for performing rolling optimization on the updated pipe network SWMM model based on the initialized DDDP-TL optimization algorithm using the current measured rainfall data and the forecast rainfall data for multiple time periods in the future to obtain a final control trajectory.

[0022] The third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the real-time control method for the urban drainage system as described in the above embodiment.

[0023] A fourth aspect of the present invention provides a computer program product, which implements the above-mentioned real-time control method for the urban drainage system when the computer program / instructions are executed by a processor.

[0024] A fifth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned real-time control method for the urban drainage system.

[0025] The real-time control method, device, equipment and medium for urban drainage systems proposed in the embodiments of the present invention take into account the current situation that the increase in control nodes in urban drainage systems is prone to dimensional disasters and the current situation of insufficient utilization of existing urban pipe network storage space. The multidimensional problem of flood control and drainage in the pipe network system is decomposed into multiple one-dimensional sub-problems, thereby improving the calculation speed. The urban drainage system is controlled in real time by using rolling optimization calculations to meet the scheduling needs of the urban drainage system. The real-time control method can adapt to most urban conditions and has strong adaptability and scalability.

[0026] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flow chart of a real-time control method for an urban drainage system provided by an embodiment of the present invention;

[0029] Figure 2 A technical roadmap for a real-time control method for an urban drainage system taking into account the regulation and storage function of a drainage network, provided by an embodiment of the present invention;

[0030] Figure 3 A schematic diagram illustrating a specific implementation of a real-time control method for an urban drainage system provided by an embodiment of the present invention;

[0031] Figure 4 A specific study area location and drainage facility distribution map provided by an embodiment of the present invention;

[0032] Figure 5 A schematic diagram of the main interface of the SWMM model for a specific study area provided by an embodiment of the present invention;

[0033] Figure 6 A block diagram of a real-time control device for an urban drainage system provided by an embodiment of the present invention;

[0034] Figure 7 This is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0036] The following describes the real-time control method, device, equipment and medium for an urban drainage system according to embodiments of the present invention with reference to the accompanying drawings.

[0037] Figure 1 A flow chart of a real-time control method for an urban drainage system provided by an embodiment of the present invention.

[0038] like Figure 1 As shown, the real-time control method for urban drainage system includes the following steps:

[0039] In step S101, the discrete differential dynamic programming (DDDP-TL) optimization algorithm is initialized according to the pre-constructed initial pipe network SWMM model of the target city to obtain an initialized DDDP-TL optimization algorithm.

[0040] In some embodiments, the DDDP-TL optimization algorithm is initialized based on a pre-built initial pipe network SWMM model of the target city to obtain an initialized DDDP-TL optimization algorithm, including:

[0041] The number of discrete layers of the DDDP-TL optimization algorithm and the initial control trajectory of each control node in the pipe network are determined based on the initial pipe network SWMM model;

[0042] The objective function, decision variables, state variables and constraints of the DDDP-TL optimization algorithm are set according to the number of discrete layers and the initial control trajectory of each control node in the pipeline network to obtain the initialized DDDP-TL optimization algorithm.

[0043] In the actual implementation process, the discrete number of layers of the discrete differential dynamic programming DDDP-TL optimization algorithm and the initial control trajectory (i.e., initial control strategy) of each control node (such as the intercepting well) of the pipeline network are determined based on the pre-built EPA-SWMM5 model of the target city. The objective function, decision variables, state variables, constraints, and optimization corridors of the DDDP-TL optimization algorithm are set according to the initial control trajectory of each control node of the discrete number of layers of the pipeline network.

[0044] The objective function of the DDDP-TL optimization algorithm is to minimize the sum of the urban pipe network overflow and the combined system overflow, which can be expressed as follows:

[0045]

[0046] Among them, F is the total overflow, N F is the number of waterlogging overflow nodes, N C is the number of combined sewer overflow nodes, FL i,t is the flood overflow at node i during period t, CSO i,t is the combined sewer overflow at node i during period t, α and β are the weight coefficients of the two overflow control methods, T is the total number of simulation periods, and Δt is the time step.

[0047] When optimizing and solving an urban pipe network model, to address the curse of dimensionality that often arises under conditions with multiple control nodes, an embodiment of the present invention decomposes the multidimensional (M-dimensional) optimization control problem into M one-dimensional subproblems through discrete differentiation. Each subproblem is then solved through dynamic programming while keeping the control trajectory of other control nodes unchanged. In the DDDP-TL optimization algorithm, the decision variable is the set of openings of all control nodes, and the state variable is the sum of the pipe water storage and overflow within the control area of the control node.

[0048] Constraints include initial conditions, boundary conditions, hydraulic constraints, facility operation condition constraints and time constraints. The initial conditions are the model state at the start and end of the optimization and the initial control trajectory. The boundary conditions are the water level conditions of external rivers, lakes and oceans connected to the urban pipeline network, the elevation and coordinate restrictions of each water conservancy facility in the pipeline network model. The hydraulic constraint is that the sum of the inflow water volume during the entire simulation process is equal to the sum of the outflow water volume; the water body maintains a unidirectional flow from upstream to downstream (except for the outlet connected to the external water body, the water body backflow is not considered). The facility operation condition constraints are that the flow rate does not exceed the upper limit of the pipeline flow rate, the flow velocity does not exceed the upper limit of the pipeline flow velocity, the upper limit of the reservoir capacity and the water level-capacity curve are considered, and the flow and power restrictions of the pump station are considered. The time constraint is that the simulation optimization time must be less than the control time step.

[0049] In step S102, the current measured rainfall data of the target city and the forecast rainfall data for multiple time periods in the future are obtained.

[0050] In step S103, the initial pipe network SWMM model is updated according to the current measured rainfall data to obtain an updated pipe network SWMM model.

[0051] During the actual implementation process, the current measured rainfall data of the target city and the forecast rainfall data for multiple periods in the future can be obtained through the monitored rainfall amount, rainfall intensity and other information released by the meteorological department, and the initial pipe network SWMM model status can be updated according to the current measured rainfall data, that is, the control nodes of the pipe network can be updated according to the current measured rainfall data to obtain the updated pipe network SWMM model.

[0052] In step S104, based on the initialized DDDP-TL optimization algorithm, the updated pipe network SWMM model is subjected to rolling optimization using the current measured rainfall data and the forecast rainfall data for multiple time periods in the future to obtain the final control trajectory.

[0053] In some embodiments, based on the initialized DDDP-TL optimization algorithm, the updated pipe network SWMM model is subjected to rolling optimization using the current measured rainfall data and the forecast rainfall data for multiple future periods to obtain the final control trajectory, including:

[0054] The forecast rainfall data for period t is input into the updated pipe network SWMM model, and the state of the updated pipe network SWMM model is updated using the initialized DDDP-TL optimization algorithm. The forecast rainfall data for each future period is input into the updated pipe network SWMM model, and the state update process is iteratively executed until all control nodes are optimized and the number of iterations reaches the preset threshold, thereby obtaining the final control trajectory, where t is the current period.

[0055] In some embodiments, the rainfall data forecasted for period t is input into the updated pipe network SWMM model, and the updated pipe network SWMM model is updated using the initialized DDDP-TL optimization algorithm, including:

[0056] Input the forecast rainfall data for period t into the updated pipe network SWMM model, and read the hot start file for period t-1 of the updated pipe network SWMM model;

[0057] Based on the initialized DDDP-TL optimization algorithm, the state transfer calculation of the updated pipe network SWMM model is performed according to the forecast rainfall data of period t and the hot start file of period t to obtain the current control trajectory of period t, and the state of the updated pipe network SWMM model is updated according to the current control trajectory of period t.

[0058] In the actual implementation process, Figure 2 and 3 As shown, a rolling optimization operation is performed based on the updated pipe network SWMM model and the initialized DDDP-TL optimization algorithm, and the current iteration number j=1 and the current control node i=1 are set.

[0059] The overflow rate under the current control strategy is evaluated based on the objective function of the updated pipe network SWMM model. If i>N, where N is the total number of control nodes, then run j=j+1 and determine whether j and i satisfy j>M and i>N, where M is the total number of outer iterations. If so, the final control trajectory is obtained. Otherwise, the forecast rainfall data for period t is input into the updated pipe network SWMM model, and the hot start file for period t-1 of the updated pipe network SWMM model is read. Based on the initialized DDDP-TL optimization algorithm, the state transition calculation is performed on the updated pipe network SWMM model according to the forecast rainfall data for period t and the hot start file for period t-1 to search for the current control strategy for period t. The hot start file for period t is saved at the same time. After saving, it is determined whether the current period is greater than the preset threshold T. If not, the state transition calculation is iteratively performed. Otherwise, the above process is iteratively performed on the next pair of control nodes until all control nodes are updated, thereby obtaining the final control trajectory (i.e., the final control strategy) of the target urban drainage system.

[0060] The recursive equation for calculating the state transition of the updated pipe network SWMM model based on the forecast rainfall data for period t and the hot start file for period t-1 is:

[0061]

[0062] min CF i,t+1 (S i,t+1 )=F i,t (S i,t ,O i,t )+CF i,t (S i,t )

[0063] Where S i,t is the water storage capacity in the control area of the ith control node during period t, O i,t is the control strategy of the ith control node in time period t, F i,t (S i,t ,O i,t ) is the total overflow in period t, Flooding i,t is the flood overflow at node i during period t, CSO i,t is the combined sewer overflow at node i during period t, L i represents the time lag of the flow from the i-th control node to the system outlet, CF i,t (S i,t ) is the system cumulative overflow from the initial period to the current period (t period).

[0064] The real-time control method for the urban drainage system proposed in the embodiment of the present invention is further illustrated below through a specific example of a part of Yueyang area.

[0065] Step 1: Build a SWMM model of the urban pipe network in parts of Yueyang and determine model parameters such as pipe size, roughness, and coordinates. The number of DDDP-TL discrete layers (set to 5 in this example) and the initial control trajectory of each control node in the pipe network (in this example, the intercepting well) are determined. The optimal corridor is formed based on the number of discrete layers.

[0066] Step 2: Update the SWMM model state based on the latest measured rainfall data. This time period is recorded as t, and the opening of the interception wells at the initial time is set to 1, that is, the interception wells are 100% open to allow water to flow through completely.

[0067] Step three, such as Figure 3 As shown in Figure 1, current measured rainfall data and forecast rainfall data for multiple future time periods are input into the updated SWMM model of the pipe network. The DDDP-TL optimization algorithm is then used to calculate the optimal control trajectory for the time period t to t+n. This is the trajectory of the opening state of each intercepting well in the pipe network over time during the time period t to t+n, where n represents the number of control time steps. The DDDP-TL optimization algorithm's operational objective is to minimize the sum of the urban pipe network's flood overflow and the combined system overflow. The objective function is:

[0068]

[0069] Where F is the total overflow, N F and N C Represent the number of waterlogging overflow nodes and combined system overflow nodes, FL i,t and CSO i,t where α represents the flooding overflow and combined sewer overflow at node i during period t, and β represents the weight coefficients of the two overflow control methods. In this example, α is set to 20 and β is set to 1. T is the total number of simulation periods, which is 7 hours. Δt is the time step, which is 20 minutes.

[0070] In the optimization algorithm of this embodiment, the decision variable is the opening degree of all interception wells, and the state variable is the sum of the pipeline water storage capacity and the overflow capacity within the interception well control area.

[0071] The constraints in this embodiment include initial condition constraints, pipe network model boundary constraints, lake water level constraints, hydraulic constraints, facility operation condition constraints, and time constraints.

[0072] The specific process of using the DDDP-TL optimization algorithm to calculate the optimal control trajectory in the period t to t+n is as follows:

[0073] (1) Perform rolling optimization based on the updated pipe network SWMM model and the initial conditions of the initialized DDDP-TL optimization algorithm, setting j = 1, i = 1. In this embodiment, j is the current iteration number, and i is the current control node (i.e., the interception well);

[0074] (2) Evaluate the overflow under the current control strategy based on the objective function. If i>N, run j=j+1, where N is the total number of interception wells, which is set to 3 in the embodiment (i.e. Figure 4 C1 to C3);

[0075] (3) Determine whether j and i satisfy j>M and i>N. If not, proceed to (4). If so, proceed to (8), where M is the total number of outer layer iterations, which is set to 20 in the embodiment.

[0076] (4) Input the current measured rainfall data into the updated pipe network SWMM model, and use the initialized DDDP-TL optimization algorithm to optimize the control strategy of interception well i (if i>N, then set i=1 and enter a new round of outer iteration). The status of other interception wells remains unchanged. At this time, t=1, where t is the time period.

[0077] (5) Update system status: read the hot start file for period t-1, perform state transfer calculation based on the updated SWMM model, search for the optimal control strategy for period t, and save the hot start file for period t;

[0078] (6) Determine whether t and T satisfy t>T. If so, proceed to (7). If not, run t=t+1 and proceed to step 5).

[0079] (7) Update the control strategy of flow control node i, set i = i + 1, and enter (2);

[0080] (8) The optimal control trajectory is found, the DDDP-TL optimization is completed, and the optimal simulation results are output.

[0081] Step 4: Under the condition that the control time step is greater than the actual time required for each optimization step, the control strategy for the period t to t+1 is executed based on the calculation results of step 3;

[0082] Step 5: Enter the next period, use the optimal control trajectory calculated in step 3 as the initial control trajectory of DDDP-TL, and repeat steps 2 to 4 until the rolling optimization simulation is completed.

[0083] In summary, the real-time control method for an urban drainage system proposed in an embodiment of the present invention has the following beneficial effects:

[0084] (1) The concept of dynamic planning and optimization control of urban drainage systems was introduced, transforming the traditional "rule-based drainage" model into a dual management model of "dynamic water storage + optimized drainage control", providing a new technical means for urban flood control and drainage management in my country;

[0085] (2) Based on the discrete differential dynamic programming algorithm (DDDP-TL), the curse of dimensionality problem under multiple control nodes is effectively alleviated and the optimization solution efficiency is significantly improved;

[0086] (3) The rolling optimization calculation is used to achieve real-time control of the urban drainage system, which better meets the scheduling needs of the urban drainage system;

[0087] (4) Fully utilizing the storage space in the drainage network, optimizing the control capability of waterlogging overflow and combined sewer overflow, and improving the response efficiency of the drainage system;

[0088] (5) It is applicable to the dynamic control scenarios of most urban drainage systems. It can help improve the real-time dispatching capability and comprehensive flood control efficiency of urban drainage systems while maintaining the existing drainage network infrastructure, achieve more efficient drainage management and flood risk control, and has strong adaptability and promotion.

[0089] Next, the real-time control device for an urban drainage system according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0090] Figure 6 This is a block diagram of a real-time control device for an urban drainage system provided by an embodiment of the present invention.

[0091] like Figure 6 As shown, the urban drainage system real-time control device 60 includes: an initialization module 601, an acquisition module 602, an update module 603 and a rolling optimization module 604.

[0092] The initialization module 601 is used to initialize the DDDP-TL optimization algorithm based on the pre-built initial pipe network SWMM model of the target city to obtain the initialized DDDP-TL optimization algorithm. The acquisition module 602 is used to obtain the current measured rainfall data and the forecast rainfall data for multiple future time periods for the target city. The update module 603 is used to update the initial pipe network SWMM model based on the current measured rainfall data to obtain the updated pipe network SWMM model. The rolling optimization module 604 is used to perform rolling optimization on the updated pipe network SWMM model based on the initialized DDDP-TL optimization algorithm using the current measured rainfall data and the forecast rainfall data for multiple future time periods to obtain the final control trajectory.

[0093] In some embodiments, the initialization module 601 includes:

[0094] A determination unit is used to determine the number of discrete layers of the DDDP-TL optimization algorithm and the initial control trajectory of each control node of the pipeline network according to the initial pipeline network SWMM model;

[0095] The setting unit is used to set the objective function, decision variables, state variables and constraints of the DDDP-TL optimization algorithm according to the number of discrete layers and the initial control trajectory of each control node of the pipeline network to obtain the initialized DDDP-TL optimization algorithm.

[0096] In some embodiments, the objective function is expressed as:

[0097]

[0098] Among them, F is the total overflow, N F is the number of waterlogging overflow nodes, N C is the number of combined sewer overflow nodes, FL i,t is the flood overflow at node i during period t, CSO i,t is the combined sewer overflow at node i during period t, α and β are the weight coefficients of the two overflow control methods, T is the total number of simulation periods, and Δt is the time step.

[0099] In some embodiments, the decision variable is the set of openings of all control nodes;

[0100] The state variable is the sum of the pipe water storage and overflow within the control area of the control node;

[0101] Constraints include initial conditions, boundary conditions, hydraulic constraints, facility operation condition constraints and time constraints.

[0102] In some embodiments, the rolling optimization module 604 includes:

[0103] A state updating unit inputs the rainfall forecast data for period t into the updated pipe network SWMM model, and updates the state of the updated pipe network SWMM model using the initialized DDDP-TL optimization algorithm;

[0104] An iterative update unit is used to input the forecast rainfall data for each future time period into the updated pipe network SWMM model and iteratively execute the state update process until all control nodes are optimized and the number of iterations reaches a preset threshold, thereby obtaining the final control trajectory, where t is the current time period.

[0105] In some embodiments, the status update unit includes:

[0106] The input and acquisition subunit is used to input the forecast rainfall data for period t into the updated pipe network SWMM model and read the hot start file for period t of the updated pipe network SWMM model;

[0107] The state update subunit is used to perform state transfer calculation on the updated pipe network SWMM model based on the initialized DDDP-TL optimization algorithm, the forecast rainfall data of period t and the hot start file of period t-1, so as to obtain the current control trajectory of period t, and update the state of the updated pipe network SWMM model according to the current control trajectory of period t-.

[0108] It should be noted that the above explanation of the embodiment of the real-time control method for an urban drainage system is also applicable to the real-time control device for an urban drainage system of this embodiment, and will not be repeated here.

[0109] The real-time control device for an urban drainage system proposed in an embodiment of the present invention has the following beneficial effects:

[0110] (1) The concept of dynamic planning and optimization control of urban drainage systems was introduced, transforming the traditional "rule-based drainage" model into a dual management model of "dynamic water storage + optimized drainage control", providing a new technical means for urban flood control and drainage management in my country;

[0111] (2) Based on the discrete differential dynamic programming algorithm (DDDP-TL), the curse of dimensionality problem under multiple control nodes is effectively alleviated and the optimization solution efficiency is significantly improved;

[0112] (3) The rolling optimization calculation is used to achieve real-time control of the urban drainage system, which better meets the scheduling needs of the urban drainage system;

[0113] (4) Fully utilizing the storage space in the drainage network, optimizing the control capability of waterlogging overflow and combined sewer overflow, and improving the response efficiency of the drainage system;

[0114] (5) It is applicable to the dynamic control scenarios of most urban drainage systems. It can help improve the real-time dispatching capability and comprehensive flood control efficiency of urban drainage systems while maintaining the existing drainage network infrastructure, achieve more efficient drainage management and flood risk control, and has strong adaptability and promotion.

[0115] Figure 7 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0116] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0117] When the processor 702 executes the program, the real-time control method for the urban drainage system provided in the above embodiment is implemented.

[0118] Furthermore, the electronic device further includes:

[0119] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0120] The memory 701 is used to store computer programs that can be run on the processor 702 .

[0121] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0122] If the memory 701, processor 702, and communication interface 703 are implemented independently, the communication interface 703, memory 701, and processor 702 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0123] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0124] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0125] An embodiment of the present invention further provides a computer program product, which implements the above-mentioned real-time control method for the urban drainage system when the computer program / instructions are executed by a processor.

[0126] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned real-time control method for the urban drainage system when the program is executed by a processor.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0130] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0131] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0132] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0133] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0134] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A real-time control method for an urban drainage system, characterized in that: The following steps are involved: Initialize the DDDP-TL optimization algorithm according to the pre-built initial pipe network SWMM model of the target city to obtain the initialized DDDP-TL optimization algorithm; Obtaining the current measured rainfall data of the target city and the forecast rainfall data for multiple time periods in the future; Updating the initial pipe network SWMM model according to the current measured rainfall data to obtain an updated pipe network SWMM model; Based on the initialized DDDP-TL optimization algorithm, the updated pipe network SWMM model is subjected to rolling optimization using the current measured rainfall data and the forecast rainfall data for multiple time periods in the future to obtain a final control trajectory.

2. The real-time control method for urban drainage system according to claim 1, characterized in that: Initializing the DDDP-TL optimization algorithm based on the pre-built initial pipe network SWMM model of the target city to obtain the initialized DDDP-TL optimization algorithm includes: Determining the number of discrete layers of the DDDP-TL optimization algorithm and the initial control trajectory of each control node of the pipeline network based on the initial pipeline network SWMM model; The objective function, decision variables, state variables and constraints of the DDDP-TL optimization algorithm are set according to the number of discrete layers and the initial control trajectory of each control node of the pipeline network to obtain the initialized DDDP-TL optimization algorithm.

3. The real-time control method for urban drainage system according to claim 2, characterized in that: The expression of the objective function is: Among them, F is the total overflow, N F is the number of waterlogging overflow nodes, N C is the number of combined sewer overflow nodes, FL i,t is the flood overflow at node i during period t, CSO i,t is the combined sewer overflow at node i during period t, α and β are the weight coefficients of the two overflow control methods, T is the total number of simulation periods, and Δt is the time step.

4. The real-time control method for urban drainage system according to claim 2, characterized in that: The decision variable is the opening set of all control nodes; The state variable is the sum of the pipe water storage and overflow within the control area of the control node; The constraints include initial conditions, boundary conditions, hydraulic constraints, facility operation condition constraints and time constraints.

5. The real-time control method for urban drainage system according to claim 1, characterized in that: The DDDP-TL optimization algorithm after the initialization is used to perform rolling optimization on the updated pipe network SWMM model using the current measured rainfall data and the future multiple-period forecast rainfall data to obtain a final control trajectory, including: Inputting the rainfall forecast data for period t into the updated pipe network SWMM model, and updating the state of the updated pipe network SWMM model using the initialized DDDP-TL optimization algorithm; The forecast rainfall data for each future time period is input into the updated pipe network SWMM model, and the state update process is iteratively executed until all control nodes are optimized and the number of iterations reaches a preset threshold, thereby obtaining the final control trajectory, where t is the current time period.

6. The real-time control method for urban drainage system according to claim 5, characterized in that: The step of inputting the rainfall forecast data for the period t into the updated pipe network SWMM model and updating the state of the updated pipe network SWMM model using the initialized DDDP-TL optimization algorithm includes: Inputting the forecast rainfall data for period t into the updated pipe network SWMM model, and reading the hot start file for period t-1 of the updated pipe network SWMM model; Based on the initialized DDDP-TL optimization algorithm, the state transfer calculation of the updated pipe network SWMM model is performed according to the forecast rainfall data of the t period and the hot start file of the t-1 period to obtain the current control trajectory of the t period, and the state of the updated pipe network SWMM model is updated according to the current control trajectory of the t period.

7. A real-time control device for an urban drainage system, characterized in that: include: An initialization module is used to initialize the DDDP-TL optimization algorithm based on the pre-built initial pipe network SWMM model of the target city to obtain an initialized DDDP-TL optimization algorithm; An acquisition module is used to obtain the current measured rainfall data of the target city and the forecast rainfall data for multiple time periods in the future; An updating module, configured to update the initial pipe network SWMM model according to the current measured rainfall data to obtain an updated pipe network SWMM model; A rolling optimization module is used to perform rolling optimization on the updated pipe network SWMM model based on the initialized DDDP-TL optimization algorithm using the current measured rainfall data and the forecast rainfall data for multiple time periods in the future to obtain a final control trajectory.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the real-time control method for an urban drainage system according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the real-time control method for an urban drainage system according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the real-time control method for an urban drainage system as described in any one of claims 1 to 6.

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