Low-carbon emission real-time control method, device and equipment for urban rainfall flood regulation and storage system

Abstract

The invention relates to the technical field of environmental engineering, in particular to a low-carbon emission real-time control method and device for an urban rainfall flood regulation and storage system, and the method comprises the steps: obtaining the real-time monitoring data of a target region and the operation state of the urban rainfall flood regulation and storage system; the running state of the urban rainfall flood regulation and storage system is updated according to the real-time monitoring data, it is determined that the current time period is in a target control time period according to the updated running state, the predicted rainfall capacity and predicted inflow capacity of a target area are obtained, and the target control time period comprises pre-rain control time and an in-rain control time period; if the current time period is the in-rain control time period, the opening degree state of a valve is controlled according to the predicted inflow, the available volume of the storage pond and carbon emission generated in the control process, and if the current time period is the pre-rain control time, the operation state of the pump station is controlled according to the predicted rainfall, the real-time water level of the storage pond and the carbon emission generated in the control process. Therefore, the problem that the flood control effect and low-carbon operation cannot be guaranteed at the same time in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of environmental engineering technology, and in particular to a method, device, equipment and medium for real-time control of low-carbon emissions in urban stormwater storage systems. Background Technology

[0002] With accelerating urbanization and intensifying climate change, urban flooding has become a major obstacle to sustainable development. The frequency and intensity of extreme rainfall have increased significantly, putting pressure on traditional drainage systems. Currently, the common approach is to expand gray infrastructure to improve flood control capacity, but this has problems such as diminishing marginal benefits and high carbon emissions.

[0003] Therefore, IoT-based real-time stormwater control technology has emerged. Through intelligent sensing and dynamic regulation, real-time control technology can improve the flood control capabilities of existing equipment without expanding infrastructure.

[0004] However, while real-time control can improve flood control efficiency, it also brings additional energy consumption and carbon emissions. Currently, most related technologies only focus on optimizing hydrological parameters such as flow rate and water level, and have not yet incorporated energy consumption and carbon emissions during operation into the control objectives. Therefore, they cannot achieve real-time control that both ensures flood control effectiveness and low-carbon operation. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for real-time control of low-carbon emissions in urban stormwater storage systems, in order to solve the problem that related technologies cannot simultaneously guarantee flood control effectiveness and low-carbon operation.

[0006] The first aspect of this application provides a method for real-time control of low-carbon emissions from an urban stormwater storage system, comprising the following steps: acquiring real-time monitoring data of a target area and the operating status of the urban stormwater storage system; updating the operating status of the urban stormwater storage system based on the real-time monitoring data; determining that the current time period is within a target control period based on the updated operating status; acquiring the predicted rainfall and predicted inflow of the target area, wherein the target control period includes pre-rain control time and rain control time; if the current time period is a rain control time, controlling the opening status of valves based on the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process; if the current time period is a pre-rain control time, controlling the operating status of pumping stations based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process.

[0007] Optionally, in one embodiment of this application, the method of controlling the opening state of valves based on the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process includes: determining a target flow threshold based on the predicted inflow and the available volume of the storage tank; inputting at least one candidate control strategy and the target flow threshold into a prediction process model, wherein the prediction process model outputs the flood control benefits and carbon emission costs of at least one candidate control strategy; constructing a multi-objective optimization objective function based on the flood control benefits and carbon emission costs of at least one candidate control strategy, adjusting the objective weight coefficients of the flood control benefits and carbon emission costs, solving the multi-objective optimization objective function using a genetic algorithm, and determining the target control strategy based on the solution results, wherein the target control strategy includes the opening state of each valve.

[0008] Optionally, in one embodiment of this application, the running prediction process model includes a flood control performance assessment calculation formula and a carbon emission control performance assessment calculation formula.

[0009] The formula for calculating flood control performance assessment is as follows:

[0010]

[0011] Where, r f r represents the rate of reduction in total flood volume through the outflow process. p r represents the peak flow reduction rate. o Represents peak reduction in volume utilization. To implement control of the outflow process at the discharge outlet, To implement control of the outflow process at the discharge outlet, For the discharge outflow process after control is implemented, t is the current time, N is the prediction time domain length or the time length until the end of the simulation, Δt is the time step, and V a,t Let t be the available water storage capacity of the storage tank at time t. Its maximum value is the maximum capacity of the storage tank, and its minimum value is 0.

[0012] The formula for calculating the performance evaluation of carbon emission control is as follows:

[0013] C = C o +C p

[0014] Where C represents the total carbon emissions of the real-time control process, C o C represents the carbon emissions from the operation of the intake gate. p This refers to the carbon emissions from the operation of the outflow pumping station.

[0015] Optionally, in one embodiment of this application, the formula for calculating the target traffic threshold is:

[0016]

[0017] Among them, V a,t V represents the current available volume of the storage tank. peak The peak flood volume, To predict the inflow process, Q target t represents the expected target flow threshold, t represents the current calculation step size, and N represents the prediction time domain duration.

[0018] Optionally, in one embodiment of this application, the objective function for multi-objective optimization is calculated as follows:

[0019]

[0020] Among them, W F W is the weighting coefficient for flood control benefits. C V is the weighting factor for carbon emission costs. max This represents the maximum available water storage capacity of the reservoir in a single control decision. To predict the flood event during a given period, Q target V represents the expected target flow threshold, Δt is the simulation time step, and V a,t f represents the current available volume of the storage tank. electric As a carbon emission factor of electrical energy, This represents the absolute value of the opening change during a single control period. C is the electrical energy consumed in a single complete start-stop cycle. max To predict the maximum carbon emissions that control decisions may generate within a given time period, the scenario is where control is fully on and off at each control step.

[0021] Optionally, in one embodiment of this application, the operating status of the pumping station is controlled based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process, including: predicting the predicted inflow of the storage tank based on the predicted rainfall; determining the available volume of the storage tank based on the real-time water level of the storage tank; and determining the operating status of the pumping station based on the predicted inflow and the available volume.

[0022] Optionally, in one embodiment of this application, determining the operating status of the pumping station based on the predicted inflow and available volume includes: if the predicted inflow is less than or equal to the available volume, the operating status of the pumping station is closed; if the predicted inflow is greater than the available volume, the operating status of the pumping station is open.

[0023] A second aspect of this application provides a low-carbon emission real-time control device for an urban stormwater storage system, comprising: an acquisition module for acquiring real-time monitoring data of a target area and the operating status of the urban stormwater storage system; a judgment module for updating the operating status of the urban stormwater storage system based on the real-time monitoring data, determining that the current time period is within a target control period based on the updated operating status, and acquiring the predicted rainfall and predicted inflow of the target area, wherein the target control period includes pre-rain control time and rain control time; and a prediction module for controlling the opening status of valves based on the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process if the current time period is a rain control period, and controlling the operating status of pump stations based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process if the current time period is a pre-rain control period.

[0024] Optionally, in one embodiment of this application, the prediction module is further configured to determine a target flow threshold based on the predicted inflow and the available volume of the storage tank; input at least one candidate control strategy and the target flow threshold into the prediction process model, the prediction process model outputs the flood control benefits and carbon emission costs of at least one candidate control strategy; construct a multi-objective optimization objective function based on the flood control benefits and carbon emission costs of at least one candidate control strategy, adjust the objective weight coefficients of flood control benefits and carbon emission costs, solve the multi-objective optimization objective function using a genetic algorithm, and determine the target control strategy based on the solution results, wherein the target control strategy includes the opening state of each valve.

[0025] Optionally, in one embodiment of this application, the running prediction process model includes a flood control performance assessment calculation formula and a carbon emission control performance assessment calculation formula.

[0026] The formula for calculating flood control performance assessment is as follows:

[0027]

[0028] Where, r f r represents the rate of reduction in total flood volume through the outflow process. p r represents the peak flow reduction rate. o Represents peak reduction in volume utilization. To implement control of the outflow process at the discharge outlet, To implement control of the outflow process at the discharge outlet, For the discharge outflow process after control is implemented, t is the current time, N is the prediction time domain length or the time length until the end of the simulation, Δt is the time step, and V a,t Let t be the available water storage capacity of the storage tank at time t. Its maximum value is the maximum capacity of the storage tank, and its minimum value is 0.

[0029] The formula for calculating the performance evaluation of carbon emission control is as follows:

[0030] C = C o +C p

[0031] Where C represents the total carbon emissions of the real-time control process, C o C represents the carbon emissions from the operation of the intake gate. p This refers to the carbon emissions from the operation of the outflow pumping station.

[0032] Optionally, in one embodiment of this application, the formula for calculating the target traffic threshold is:

[0033]

[0034] Among them, V a,t V represents the current available volume of the storage tank. peak The peak flood volume, To predict the inflow process, Q target t represents the expected target flow threshold, t represents the current calculation step size, and N represents the prediction time domain duration.

[0035] Optionally, in one embodiment of this application, the objective function for multi-objective optimization is calculated as follows:

[0036]

[0037] Among them, W F W is the weighting coefficient for flood control benefits. C V is the weighting factor for carbon emission costs. max This represents the maximum available water storage capacity of the reservoir in a single control decision. To predict the flood process during a given period, Q target V represents the expected target flow threshold, Δt is the simulation time step, and V a,t f represents the current available volume of the storage tank. electric As a carbon emission factor of electrical energy, This represents the absolute value of the opening change during a single control period. C is the electrical energy consumed in a single complete start-stop cycle. max To predict the maximum carbon emissions that control decisions may generate within a given time period, the scenario is where control is fully on and off at each control step.

[0038] Optionally, in one embodiment of this application, the prediction module is further configured to predict the predicted inflow of the storage tank based on the predicted rainfall; determine the available volume of the storage tank based on the real-time water level of the storage tank; and determine the operating status of the pumping station based on the predicted inflow and the available volume.

[0039] Optionally, in one embodiment of this application, the prediction module is further configured to: if the predicted inflow is less than or equal to the available volume, the pump station is in a closed operating state; if the predicted inflow is greater than the available volume, the pump station is in an open operating state.

[0040] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a real-time low-carbon emission control method for an urban stormwater storage system as described in the above embodiments.

[0041] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a real-time low-carbon emission control method for an urban stormwater storage system as described in the above embodiments.

[0042] Therefore, this application has the following beneficial effects:

[0043] Real-time monitoring data of the target area, including rainfall, flow rate, and water level, is acquired, and the current operational status of the urban stormwater storage system is simultaneously obtained. Based on this real-time monitoring data, the operational status of the urban stormwater storage system is updated, and the updated system status is used to determine whether the current time period belongs to the preset target control period. If it belongs to the target control period, the predicted rainfall and predicted inflow for the target area are further acquired. The target control period includes two phases: pre-rain control time and during-rain control time. If the current time period is during the during-rain control period, the valve opening is dynamically adjusted based on the predicted inflow, the current available volume of the storage tank, and the carbon emissions generated during the control process to achieve optimized control of the system operation. If the current time period is pre-rain control time, the pump station's operational status is rationally adjusted based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control. This solves the problem that related technologies cannot simultaneously guarantee flood control effectiveness and low-carbon operation.

[0044] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0045] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 This is a flowchart of a real-time low-carbon emission control method for an urban stormwater storage system according to an embodiment of this application;

[0047] Figure 2A conceptual diagram of a stormwater storage system for a real-time low-carbon emission control method for an urban stormwater storage system according to an embodiment of this application;

[0048] Figure 3 This is a flowchart of the rainy day control decision optimization calculation for the real-time low-carbon emission control method of the urban stormwater storage system according to an embodiment of this application;

[0049] Figure 4 A flowchart illustrating the real-time low-carbon emission control method for an urban stormwater storage system according to an embodiment of this application.

[0050] Figure 5 This is an example diagram of a low-carbon emission real-time control device for an urban stormwater storage system according to an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0052] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0053] The following describes, with reference to the accompanying drawings, a method, apparatus, electronic device, and storage medium for real-time low-carbon emission control of an urban stormwater storage system according to embodiments of this application. Addressing the problems mentioned in the background art, this application provides a method for real-time low-carbon emission control of an urban stormwater storage system. In this method, firstly, real-time monitoring data of the target area is acquired, including information such as rainfall, flow rate, and water level, and simultaneously, the current operating status of the urban stormwater storage system is acquired. Based on the aforementioned real-time monitoring data, the operating status of the urban stormwater storage system is updated, and the updated system status is used to determine whether the current time period belongs to a preset target control period. If it belongs to the target control period, the predicted rainfall and predicted inflow of the target area are further acquired. The target control period includes two stages: pre-rain control time and rain control time. If the current time period is the rain control time, the valve opening is dynamically adjusted based on the predicted inflow, the current available volume of the storage tank, and the carbon emissions generated during the control process to achieve optimized control of the system operation. If the current time period is the pre-rain control time, the pump station's operating status is reasonably adjusted by combining the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during control. This solves the problem that related technologies cannot simultaneously guarantee flood control effectiveness and low-carbon operation.

[0054] Specifically, Figure 1This is a schematic flowchart illustrating a real-time low-carbon emission control method for an urban stormwater storage system provided in an embodiment of this application.

[0055] like Figure 1 As shown, the real-time low-carbon emission control method for the city's stormwater storage system includes the following steps:

[0056] In step S101, real-time monitoring data of the target area and the operating status of the urban stormwater storage system are acquired.

[0057] The monitoring data is collected in real time by deployed sensors, rain gauges, water level gauges, flow meters, and other equipment to track rainfall, water level changes, flow velocity and flow rate, and pipeline hydraulic status in the target area. This data is transmitted to the control center wirelessly or via wired means, serving as the fundamental information source for scheduling and control. Urban stormwater storage systems are a combination of facilities within urban drainage systems used to temporarily store and regulate stormwater volume, alleviate drainage pressure, and prevent urban flooding.

[0058] Understandably, real-time data on rainfall, water level, and flow rate can reflect the evolving trend of the current rainstorm process, helping to identify potential risk areas in a timely manner. Meanwhile, system operation information such as reservoir water levels, pump station operating status, and pipeline hydraulic conditions provides necessary boundary conditions and initial parameters for optimizing control strategies.

[0059] This application utilizes widely used urban stormwater storage models, such as the SWMM (Storm Water Management Model), to simulate the runoff generation, confluence processes, and hydraulic states of key control nodes in the control area, thereby constructing a simulation and predictive control process for the urban stormwater system. The SWMM is a hydrological and hydraulic model used to simulate urban rainfall-runoff processes. This application constructs a baseline model based on the SWMM model of the control area for the optimization calculation process of real-time control. A baseline model of the target control area is constructed based on the widely used SWMM. The baseline model is built according to basic information such as the underlying surface type, pipeline structure, and land use type of the area. For areas with monitoring data, the model parameters can be calibrated and verified through the monitoring rainfall process of typical secondary floods and the outflow process at discharge outlets. For areas without monitoring data, the parameter values ​​recommended in the SWMM model user manual can be used. The constructed SWMM simulation is used as the baseline model of the control area for subsequent calculation processes such as inflow prediction.

[0060] In step S102, the operating status of the urban stormwater storage system is updated based on real-time monitoring data. Based on the updated operating status, it is determined that the current time period is within the target control period. The predicted rainfall and predicted inflow of the target area are obtained. The target control period includes the pre-rain control time and the rain control period.

[0061] The target control period is the key time for system regulation, typically divided into pre-rain control and during-rain control periods. Pre-rain control is the preparatory control period implemented before rainfall to enhance the system's storage capacity; the during-rain control period is the critical time for dynamic scheduling of rainwater storage and discharge during rainfall. Predicted rainfall is the result of forecasts based on meteorological forecasting models or meteorological data service platforms, predicting the intensity, duration, and spatial distribution of rainfall likely in the future. Predicted inflow is the surface runoff entering the drainage system under predicted rainfall conditions, obtained from SWMM simulations.

[0062] Understandably, by updating the operational status of urban stormwater storage systems through real-time monitoring data, a comprehensive understanding of the actual operating conditions and hydraulic parameters of various facilities can be obtained, providing a reliable basis for subsequent storage and discharge decisions. Furthermore, by combining the updated operational status with assessments of whether the target control period has begun, and by obtaining predicted rainfall and inflow rates, it is possible to identify impending heavy rainfall events and their potential runoff pressure in advance, enabling proactive intervention and scientific regulation.

[0063] This application's embodiments introduce a stormwater storage module and control strategy into the SWMM baseline model, and simulate the modification structure or design scheme of an actual stormwater storage tank, such as... Figure 2 As shown, this embodiment of the application sets up a diversion-type storage tank downstream of the drainage system. The storage tank is connected to the main drainage system pipeline through an inflow pipe, and the inflow process of the storage tank is controlled by the opening and closing of gates. At the same time, a pumping station is set up to control the outflow process of the storage tank, which leads to the downstream outlet for discharging temporary stored water. Sensors and wireless transmission technology are deployed to monitor and update the system status in real time, including rain gauges deployed on the ground, flow meters deployed at inflow nodes, outflow nodes, and rainwater outlets, and water level gauges deployed in the storage tank, to monitor key system statuses such as real-time rainfall, inflow flow of the storage tank, outflow flow of the storage tank, outlet flow, and hydrology of the storage tank. In addition, water level gauges can be deployed at flood-prone points according to actual control needs to monitor the overflow and flooding situation at the nodes. Corresponding to the real-time monitoring data update of the system status in actual situations, during the simulation process, the hydraulic status of each node and pipeline in the SWMM model is gradually updated by saving and reading the hot start file.

[0064] This application embodiment, based on the construction of a real-time control system model, further constructs a process prediction model with the same structure. This prediction model receives rainfall forecast data for future periods, as well as candidate control strategies, and simulates the inflow process and operating status of the prediction system in future stages, thereby providing support for identifying the target control period. Based on the prediction results and the current water storage status of the storage tank, it determines whether the current period is within the target control period. The target control period includes the pre-rainfall control period (i.e., the period before rainfall begins) and the mid-rainfall control period (during rainfall). Once the system determines that it has entered the target control period, it acquires the predicted rainfall and predicted inflow for the target area, which are then used for the formulation and optimization of subsequent storage control strategies.

[0065] The control process of this application embodiment mainly includes four steps. The first step is to update the status of the control system in real time, including the current rainfall, forecast rainfall, inflow and outflow flow of the storage tank, and the current water level of the storage tank. The second step is to judge the weather conditions. If the current rainfall or the inflow flow of the storage tank is greater than 0, it is judged to be a control period during rain; otherwise, it is judged to be a control period before rain. The third step is to calculate the control decision. In rainy weather, the multi-objective model prediction control module is executed to calculate the opening and closing control decision of the storage tank inlet gate. In dry weather, the prediction rule control module is executed to calculate the operation control decision of the storage tank outlet pumping station. The fourth step is to implement the control decision. The gate and pumping station setpoints are adjusted and the control decision is implemented.

[0066] In step S103, if the current time period is a rain control period, the opening status of the valve is controlled according to the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process. If the current time period is a pre-rain control period, the operating status of the pump station is controlled according to the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process.

[0067] The available volume of the stormwater storage tank refers to the space currently available for storing rainwater, typically determined by the real-time water level. Carbon emissions are energy-related emissions generated during the control process due to the operation of equipment such as pump stations and gates. The valve opening status indicates the current degree of openness of the control device regulating water flow. The pump station operating status represents whether the pump station is currently operating and its operating intensity.

[0068] Understandably, during the rain control period, dynamically adjusting the valve opening based on the predicted inflow, available storage tank volume, and carbon emissions can effectively guide rainwater into the tank, preventing the downstream drainage system from overloading and reducing energy consumption and carbon emissions while ensuring drainage capacity. During the pre-rain control period, by predicting rainfall, analyzing the real-time water level of the storage tank and carbon emission factors, and optimizing the pump station operation, some water storage space can be released in advance to make room for the upcoming rainfall, thereby enhancing the system's storage capacity and emergency preparedness.

[0069] This application embodiment, during the rain control period, aims to reduce flooding and lower carbon emissions as dual control objectives, employing a multi-objective model predictive control method to dynamically adjust the opening of the inlet gate of the storage reservoir. First, the target control flow rate of the outlet is determined by combining the predicted inflow process and the current available storage capacity. Simultaneously, the carbon emissions generated during the regulation process are assessed based on the emission factor method. The emission factor method is a commonly used method for estimating greenhouse gas emissions. It calculates the total carbon emissions by multiplying the consumption of a certain energy source by the corresponding unit carbon emission factor.

[0070] In this embodiment of the application, the prediction rule control module is activated before the rain, and the start-up and shutdown control rules of the pumping station are formulated based on short-term rainfall forecast information and the current available volume of the storage tank, so as to realize proactive drainage before the rain, free up storage space and improve the overall operating efficiency of the drainage system.

[0071] In one embodiment of this application, the method of controlling the opening state of valves based on the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process includes: determining a target flow threshold based on the predicted inflow and the available volume of the storage tank; inputting at least one candidate control strategy and the target flow threshold into a prediction process model, wherein the prediction process model outputs the flood control benefits and carbon emission costs of at least one candidate control strategy; constructing a multi-objective optimization objective function based on the flood control benefits and carbon emission costs of at least one candidate control strategy, adjusting the objective weight coefficients of flood control benefits and carbon emission costs, solving the multi-objective optimization objective function using a genetic algorithm, and determining the target control strategy based on the solution results, wherein the target control strategy includes the opening state of each valve.

[0072] The target flow threshold is a control objective value determined based on predicted inflow and storage capacity, used to constrain the system's drainage or storage behavior. Candidate control strategies are optional control schemes designed according to different control objectives and implementation methods, used for subsequent screening and optimization. The prediction process model is consistent with the actual control system structure and is used to simulate the operational effects of candidate strategies over a future period. The multi-objective optimization objective function is an optimization model constructed by comprehensively considering both flood control benefits and carbon emission costs, often used to make trade-offs between the two. Objective weight coefficients are used to adjust the importance ratio between the two optimization objectives to meet the needs of different scenarios. The genetic algorithm is an intelligent optimization algorithm based on natural selection, often used in solving complex multi-objective problems. The target control strategy is the optimal control scheme determined after optimization.

[0073] Understandably, the embodiments of this application first calculate the target flow threshold based on the predicted inflow and the available volume of the storage tank to ensure that the control strategy has clear constraints. Subsequently, multiple candidate control strategies are simulated and evaluated through a prediction process model to quantify their flood control benefits and carbon emission costs, thereby providing data support for scientific selection. On this basis, a multi-objective optimization method is introduced to construct the objective function, and by adjusting the weight coefficients of flood control and carbon emission, the system can flexibly balance safety and environmental friendliness according to actual needs. Finally, a genetic algorithm is used to solve the problem, ensuring that the global optimal or near-optimal solution is obtained quickly under complex constraints.

[0074] In one embodiment of this application, the running prediction process model includes a flood control performance assessment calculation formula and a carbon emission control performance assessment calculation formula.

[0075] The formula for calculating flood control performance assessment is as follows:

[0076]

[0077]

[0078] Where, r f r represents the rate of reduction in total flood volume through the outflow process. p r represents the peak flow reduction rate. o Represents peak reduction in volume utilization. To implement control of the outflow process at the discharge outlet, For the discharge outflow process after control is implemented, t is the current time, N is the prediction time domain length or the time length until the end of the simulation, Δt is the time step, and V a,t Let t be the available water storage capacity of the regulating reservoir. Its maximum value is the maximum capacity of the regulating reservoir, and its minimum value is 0.

[0079] The formula for calculating the performance evaluation of carbon emission control is as follows:

[0080] C = C o +C p

[0081] Where C represents the total carbon emissions of the real-time control process, C o C represents the carbon emissions from the operation of the intake gate. p This refers to the carbon emissions from the operation of the outflow pumping station.

[0082] It is understood that this application's embodiments introduce a flood control performance evaluation index: flood volume reduction rate r. f Peak flow reduction rate r p and peak reduction in volume utilization rate r oThis approach enables the quantification and evaluation of the effectiveness of different control strategies in reducing flood discharge and peak flow, thereby providing scientific guidance for flood control scheduling decisions and improving the system's flood control efficiency. Combined with carbon emission control performance evaluation, the operating carbon emissions of the intake gates and outflow pumping stations are calculated separately, and the total carbon emissions C of the real-time control process are obtained, introducing an environmental impact assessment for the control strategies. Finally, a multi-dimensional quantitative evaluation of flood control benefits and environmental costs is achieved, incorporating both into a multi-objective optimization framework to minimize carbon emissions while ensuring flood control safety.

[0083] In this application embodiment, the flood control performance of candidate control strategies is evaluated during the decision-making process to quantify the control effect. Wherein, r f Used to measure the effectiveness of the strategy in overall water control; r p Reflects the strategy's ability to reduce flood peaks; o The lower the index, the higher the utilization efficiency of the unit water storage capacity in reducing flood peaks. Through comprehensive evaluation of the above indicators, a scientific comparison and optimization of the flood control and water storage effects of different control strategies can be achieved.

[0084] During real-time control of a stormwater storage tank, the operating states of gates and pumping stations change dynamically as the control strategy is continuously adjusted. Compared to static control, real-time control generates additional power consumption due to the frequent adjustments to the operating states of gate hoists and pumping station motors. Based on the emission factor method, the total electrical energy consumed in the control process can be converted into an equivalent carbon emission by multiplying it by the carbon emission factor of electrical energy.

[0085] Specifically, the opening change of the intake gate is driven by the gate hoist, and the power consumption is calculated by multiplying the gate's operating time by the motor power. The operating time is calculated using the cumulative value of the gate opening change and the opening / closing speed. The operating energy consumption of the outflow pumping station is calculated based on the work done by gravity to overcome the flow of water being pumped out. The calculation formula is as follows:

[0086] C = C o +C p

[0087] C o =f electric ×E o

[0088]

[0089] C p =f electric ×E p

[0090]

[0091] Cr =C mpc -C mpc-carbon

[0092]

[0093] Among them, E o E is the electrical energy consumed by the gate hoist. p The electrical energy consumed by the pumping station, f electric Carbon emission factor of electricity consumption This represents the change in gate opening. P is the electrical energy consumed by a gate in a single complete opening and closing. h where h is the gate hoist motor power, h is the gate height, and v is the gate height. open / close ρ is the gate opening and closing speed, g is the density of water, η is the acceleration due to gravity, ΔH is the operating efficiency, and Q is the head of the outflow pumping station. out,t C represents the outflow of water pumped out by the pumping station during time period t. mpc For traditional real-time control methods, that is, low-carbon real-time control methods that only consider environmental benefits and carbon emissions, C mpc-carbon C represents the carbon emissions generated by the low-carbon real-time control method proposed in this application embodiment. r For carbon emission reduction, r c This represents the carbon emission reduction rate.

[0094] In one embodiment of this application, the formula for calculating the target traffic threshold is:

[0095]

[0096] Among them, V a,t V represents the current available volume of the storage tank. peak The peak flood volume, To predict the inflow process, Q target t represents the expected target flow threshold, t represents the current calculation step size, and N represents the prediction time domain duration.

[0097] Understandably, unlike related technologies that rely on user-preset target flow rates, this application dynamically calculates the target flow rate for each rainfall event based on predicted rainfall and real-time available water storage capacity. This target flow rate is obtained by constructing and solving a system of equations with predicted inflow volume and available water storage capacity as variables, serving as a benchmark value for subsequent outflow optimization and control. By fully utilizing existing water storage resources, flood volumes exceeding the downstream drainage system's capacity are impounded, thereby ensuring system operational safety while simultaneously reducing the total flood volume and peak flow, improving control efficiency and flood control performance.

[0098] In this embodiment, rainfall forecast data for future periods is input into the SWMM benchmark model calibrated and verified based on measured rainfall-runoff data to simulate and calculate the possible inflow processes at each inflow node, thereby obtaining the predicted inflow rate. Based on this, and combined with the real-time water level of the current storage tank, the available storage capacity of the storage tank is calculated, and the target flow threshold corresponding to the current rainfall event is further determined.

[0099] In one embodiment of this application, the objective function for multi-objective optimization is calculated as follows:

[0100]

[0101] Among them, W F W is the weighting coefficient for flood control benefits. C V is the weighting factor for carbon emission costs. max This represents the maximum available water storage capacity of the reservoir in a single control decision. To predict the flood process during a given period, Q target V represents the expected target flow threshold, Δt is the simulation time step, and V a,t f represents the current available volume of the storage tank. electric As a carbon emission factor of electrical energy, This represents the absolute value of the opening change during a single control period. C is the electrical energy consumed in a single complete start-stop cycle. max To predict the maximum carbon emissions that control decisions may generate within a given time period, the scenario is where control is fully on and off at each control step.

[0102] It is understood that the embodiments of this application incorporate flood control objectives and carbon emission objectives into the optimization framework of model predictive control to construct a multi-objective optimization objective function. Flood control benefits are defined as the water storage capacity utilization rate index, and carbon emission costs are defined as the equivalent carbon emissions obtained by converting the electrical energy consumed in the control process. Normalization processing and weight coefficients are then performed to balance flood control benefits and carbon emission costs, flexibly respond to the constraints of different rainfall intensities and carbon emission level requirements, and achieve real-time control of rainwater and flood storage with low carbon emissions.

[0103] This application constructs flood control performance evaluation methods and carbon emission control performance evaluation methods to comprehensively evaluate the control strategy to be optimized, and calculates the corresponding flood control benefits F for each. benefit And carbon emission costs C cost To meet the optimization needs of different application scenarios, a weighting coefficient is introduced during the objective function construction process to balance the two, thereby taking into account both environmental benefits and carbon emission control, and achieving multi-objective collaborative optimization.

[0104] In the specific modeling process, the flood control benefit F benefit With carbon emission cost Ccost Normalization was performed separately. Normalization is a data preprocessing method that aims to transform variables with different dimensions or value ranges to the same scale. Flood control benefit F benefit The water storage capacity utilization rate of the regulating reservoir is quantified as the peak reduction volume utilization rate r. o Assuming that under ideal conditions, the water storage capacity can be fully utilized to store and retain peak outflow, achieving a combined reduction in peak flow and water volume; the carbon emission cost is quantified as the product of the total electrical energy consumed by the control process gate opening changes and the carbon emission factor, converted into an equivalent carbon emission, and normalized to a value range consistent with flood control benefits. The specific formula is as follows:

[0105]

[0106]

[0107] Among them, W F W is the weighting coefficient corresponding to flood control benefits. C V represents the weighting coefficient corresponding to carbon emission costs. max V represents the maximum available water storage capacity of the reservoir in a single control decision. min Let V be the minimum usable water storage capacity of the reservoir in a single control decision. max =V a,t V min =0, C max C represents the potential maximum value of the cumulative change in aperture over a control time domain. min C is the potential minimum value of the cumulative change in opening degree within a control time domain. max C corresponds to the carbon emissions generated by the complete on / off control process during each control period. min Take 0.

[0108] To achieve comprehensive optimization control of flood control benefits and carbon emission costs, this application's embodiments introduce adjustable target weight coefficients when constructing the objective function, corresponding to flood control benefits F. benefit Weighting coefficient W F With carbon emission cost C cost Weighting coefficient W C This weighting coefficient can be dynamically adjusted according to actual application needs, thereby flexibly responding to different rainfall intensities or carbon emission control requirements. Weighting ratio W F :W C It can be set in the range of 50:1 to 1:50 to achieve a relative emphasis on controlling flood control and low-carbon goals.

[0109] In this embodiment, a genetic algorithm is used as the optimization tool to solve the constructed multi-objective optimization problem. A genetic algorithm is an intelligent optimization algorithm that simulates natural selection and biological evolution mechanisms, used to find optimal or near-optimal solutions to complex problems. Specifically, as... Figure 3 As shown in the embodiment of this application, the candidate control strategy u for the intake gate is... o,t The data is discretized and encoded using a 5-bit binary string to represent different openness combinations, forming the initial population of individuals in the genetic algorithm. During the iteration process, selection, crossover, and mutation operations are performed on the population to progressively evolve and optimize the control strategy until the set maximum number of iterations or fitness threshold is reached, thereby obtaining the optimal control strategy that satisfies the requirements of multi-objective trade-offs.

[0110] In one embodiment of this application, the operating status of the pumping station is controlled based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process. This includes: predicting the predicted inflow of the storage tank based on the predicted rainfall; determining the available volume of the storage tank based on the real-time water level of the storage tank; and determining the operating status of the pumping station based on the predicted inflow and the available volume.

[0111] It is understood that the embodiments of this application, by combining predicted rainfall, real-time water level in the storage tank, and carbon emissions generated during the control process, dynamically predict the inflow and available volume of the storage tank, thereby scientifically determining the operating status of the pumping station. This improves the accuracy of predicting future inflow conditions and enhances the foresight of control, enabling dynamic adjustment of pumping station start-up and shutdown, maximizing the utilization of storage tank capacity, and improving the system's response to rainfall events. Simultaneously, by incorporating carbon emission considerations, it effectively reduces pumping station energy consumption, contributing to green and low-carbon urban stormwater management.

[0112] Specifically, in this embodiment, the operating status of the control equipment is adjusted in real time according to the control strategy determined by optimization calculation or control rules. During dry weather, to assist in control decision-making, this embodiment simulates the predicted inflow V of the storage tank for future periods based on a completed baseline model and inputting rainfall forecast data for future periods. I Simultaneously, by combining the current water level information of the regulating reservoir obtained from real-time monitoring, its available water storage capacity V during the predicted period is calculated. a,t This provides a basis for subsequent pump station operation control and drainage strategies.

[0113] In one embodiment of this application, determining the operating status of a pumping station based on the predicted inflow rate and available volume includes: if the predicted inflow rate is less than or equal to the available volume, the operating status of the pumping station is closed; if the predicted inflow rate is greater than the available volume, the operating status of the pumping station is open.

[0114] Understandably, by comparing the predicted inflow with the available volume of the storage tank, the start-up and shutdown status of the pumping station is scientifically determined, achieving dynamic adjustment of the pumping station's operation. This control method, based on real-time prediction and capacity status, improves the system's response efficiency and storage capacity while effectively reducing energy consumption.

[0115] Specifically, this application determines the pump station operation decision based on control rules. The start-up and shutdown control of the pump station is achieved by comparing the predicted total inflow with the available water storage volume. When the predicted total inflow is less than or equal to the available water storage volume of the regulating reservoir, the pump station is shut down without prior drainage; when the predicted total inflow is greater than the available water storage volume of the regulating reservoir, the pump station is turned on to empty the water storage volume to cope with the predicted inflow. The specific formula is as follows:

[0116]

[0117] In this context, 0 indicates that the pump station is in a closed state, and 1 indicates that the pump station is in a closed state.

[0118] After the control strategy is implemented, the embodiments of this application will update the status information such as water level and flow rate of the storage tank in real time, and advance the control process to the next time period to achieve closed-loop feedback control.

[0119] This application's embodiments describe the real-time control process and optimization mechanism of an urban stormwater storage system based on multi-objective model predictive control, such as... Figure 4 As shown, it is mainly divided into three core modules: pre-rain control (pump station), real-time control system status update, and rain control (gate), as well as a dynamic illustration of the control process over time.

[0120] The pre-rain control (pump station) section on the left monitors the actual water level in the reservoir in real time, along with predicted rainfall and inflow, to calculate the reservoir's available capacity. It then compares the predicted inflow with the available capacity. If the predicted inflow is less than or equal to the available capacity, the pump station is off, indicating that the reservoir has sufficient capacity to accommodate future water inflows and no pre-emptive drainage is needed. If the predicted inflow exceeds the available capacity, the pump station is activated to pre-emptively drain water and create space to cope with the pressure from future rainfall.

[0121] The real-time control system's status update section collects rainfall, water level, and flow data in real time through sensors to update the model status of the urban stormwater system. The system determines whether it is currently in a period requiring control. If it is not in a control period, no operation is performed; otherwise, based on the control strategy and rainfall forecast data, it enters one of two paths: pre-rain control or rain control.

[0122] The rain control (gate) section on the right, based on predicted inflow and current available storage capacity, combines multiple candidate control strategies and evaluates the flood control performance and carbon emission costs of each strategy by running a predictive process model. A multi-objective optimization method (genetic algorithm) is used to handle the trade-off between flood control benefits and carbon emission costs, generating the optimal control decision. This decision is mainly reflected in the gate opening state, aiming to minimize peak flow while reducing carbon emissions, achieving a balance between flood control and environmental protection.

[0123] The lower part of the diagram illustrates the dynamic progression of the control process over time. The control flow gradually transitions from the pre-rain control phase to the rain control phase, with operations executed in segments over time steps (Δt). The inflow forecast curve displays the future rainfall intensity. The pre-rain control phase primarily focuses on early drainage to create storage space, while the rain control phase dynamically adjusts the gate opening to control the storage capacity and outflow of the stormwater reservoir, ensuring peak flow reduction and drainage safety. The entire control process is continuously updated and progressively advanced based on real-time monitoring data and forecast information, forming a closed-loop intelligent control system.

[0124] Figure 4 The exhibition comprehensively showcases the complete intelligent control closed loop of urban stormwater storage systems, from data acquisition, model updates, decision calculations to execution feedback. It particularly highlights the application of multi-objective optimization in balancing flood control effectiveness and carbon emission control, as well as the system's differentiated response strategies for different rainfall periods.

[0125] The low-carbon emission real-time control method for urban stormwater storage systems proposed in this application first acquires real-time monitoring data of the target area, including rainfall, flow rate, and water level, and simultaneously acquires the current operating status of the urban stormwater storage system. Based on the aforementioned real-time monitoring data, the operating status of the urban stormwater storage system is updated, and the updated system status is used to determine whether the current time period belongs to a preset target control period. If it belongs to the target control period, the predicted rainfall and predicted inflow of the target area are further acquired. The target control period includes two stages: pre-rain control time and rain control time. If the current time period is the rain control time, the valve opening is dynamically adjusted based on the predicted inflow, the current available volume of the storage tank, and the carbon emissions generated during the control process to achieve optimized control of the system operation. If the current time period is the pre-rain control time, the pump station's operating status is reasonably adjusted based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control. This solves the problem that related technologies cannot simultaneously guarantee flood control effectiveness and low-carbon operation.

[0126] Next, referring to the accompanying drawings, a low-carbon emission real-time control device for an urban stormwater storage system according to an embodiment of this application is described.

[0127] Figure 5This is a block diagram of a low-carbon emission real-time control device for an urban stormwater storage system according to an embodiment of this application.

[0128] like Figure 5 As shown, the low-carbon emission real-time control device 10 of the urban stormwater storage system includes: an acquisition module 101, a judgment module 102, and a prediction module 103.

[0129] The system includes: an acquisition module 101, which acquires real-time monitoring data of the target area and the operating status of the urban stormwater storage system; a judgment module 102, which updates the operating status of the urban stormwater storage system based on the real-time monitoring data, determines whether the current time period is within the target control period based on the updated operating status, and acquires the predicted rainfall and predicted inflow of the target area, wherein the target control period includes the pre-rain control time and the rain control period; and a prediction module 103, which, if the current time period is the rain control period, controls the opening status of the valves based on the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process; and if the current time period is the pre-rain control time, controls the operating status of the pumping station based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process.

[0130] In one embodiment of this application, the prediction module 103 is further configured to determine a target flow threshold based on the predicted inflow and the available volume of the storage tank; input at least one candidate control strategy and the target flow threshold into the prediction process model, the prediction process model outputs the flood control benefits and carbon emission costs of at least one candidate control strategy; construct a multi-objective optimization objective function based on the flood control benefits and carbon emission costs of at least one candidate control strategy, adjust the objective weight coefficients of flood control benefits and carbon emission costs, solve the multi-objective optimization objective function using a genetic algorithm, and determine the target control strategy based on the solution results, wherein the target control strategy includes the opening state of each valve.

[0131] In one embodiment of this application, the running prediction process model includes a flood control performance assessment calculation formula and a carbon emission control performance assessment calculation formula.

[0132] The formula for calculating flood control performance assessment is as follows:

[0133]

[0134] in, To implement control of the outflow process at the discharge outlet, For the discharge outflow process after control is implemented, t is the current time, N is the prediction time domain length or the time length until the end of the simulation, Δt is the time step, and V a,t Let t be the available water storage capacity of the storage tank at time t. Its maximum value is the maximum capacity of the storage tank, and its minimum value is 0.

[0135] The formula for calculating the performance evaluation of carbon emission control is as follows:

[0136] C = C o +C p

[0137] Where C represents the total carbon emissions of the real-time control process, C o C represents the carbon emissions from the operation of the intake gate. p This refers to the carbon emissions from the operation of the outflow pumping station.

[0138] In one embodiment of this application, the formula for calculating the target traffic threshold is:

[0139]

[0140] Among them, V a,t V represents the current available volume of the storage tank. peak The peak flood volume, To predict the inflow process, Q target t represents the expected target flow threshold, t represents the current calculation step size, and N represents the prediction time domain duration.

[0141] In one embodiment of this application, the objective function for multi-objective optimization is calculated as follows:

[0142]

[0143] Among them, W F W is the weighting coefficient for flood control benefits. C V is the weighting factor for carbon emission costs. max This represents the maximum available water storage capacity of the reservoir in a single control decision. To predict the flood event during a given period, Q target V represents the expected target flow threshold, Δt is the simulation time step, and V a,t f represents the current available volume of the storage tank. electric As a carbon emission factor of electrical energy, This represents the absolute value of the opening change during a single control period. C is the electrical energy consumed in a single complete start-stop cycle. max To predict the maximum carbon emissions that control decisions may generate within a given time period, the scenario is where control is fully on and off at each control step.

[0144] In one embodiment of this application, the prediction module 103 is further configured to predict the predicted inflow of the storage tank based on the predicted rainfall; determine the available volume of the storage tank based on the real-time water level of the storage tank; and determine the operating status of the pumping station based on the predicted inflow and the available volume.

[0145] In one embodiment of this application, the prediction module 103 is further configured to operate the pumping station in a closed state if the predicted inflow is less than or equal to the available volume, and in an open state if the predicted inflow is greater than the available volume.

[0146] It should be noted that the explanation of the above-mentioned embodiment of the real-time control method for low-carbon emissions of urban stormwater storage system also applies to the real-time control device for low-carbon emissions of urban stormwater storage system in this embodiment, and will not be repeated here.

[0147] The low-carbon emission real-time control device for urban stormwater storage systems proposed in this application first acquires real-time monitoring data of the target area, including rainfall, flow rate, and water level, and simultaneously acquires the current operating status of the urban stormwater storage system. Based on the aforementioned real-time monitoring data, the operating status of the urban stormwater storage system is updated, and the updated system status is used to determine whether the current time period belongs to a preset target control period. If it belongs to the target control period, the predicted rainfall and predicted inflow of the target area are further acquired. The target control period includes two stages: pre-rain control time and rain control time. If the current time period is the rain control time, the valve opening is dynamically adjusted based on the predicted inflow, the current available volume of the storage tank, and the carbon emissions generated during the control process to achieve optimized control of the system operation. If the current time period is the pre-rain control time, the pump station's operating status is reasonably adjusted based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control. This solves the problem that related technologies cannot simultaneously guarantee flood control effectiveness and low-carbon operation.

[0148] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0149] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0150] When the processor 602 executes the program, it implements the real-time low-carbon emission control method for the urban stormwater storage system provided in the above embodiments.

[0151] Furthermore, electronic devices also include:

[0152] Communication interface 603 is used for communication between memory 601 and processor 602.

[0153] The memory 601 is used to store computer programs that can run on the processor 602.

[0154] The memory 601 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0155] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0156] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0157] The processor 602 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0158] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for real-time control of low-carbon emissions in an urban stormwater storage system.

[0159] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0160] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0161] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0162] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0163] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0164] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for real-time control of low-carbon emissions in an urban stormwater storage system, characterized in that, The urban stormwater storage system includes pumping stations and valves for the storage tank, wherein the method includes the following steps: Acquire real-time monitoring data of the target area and the operational status of the urban stormwater storage system; The operation status of the urban stormwater storage system is updated based on the real-time monitoring data. Based on the updated operation status, it is determined that the current time period is within the target control period. The predicted rainfall and predicted inflow of the target area are obtained. The target control period includes the pre-rain control time and the rain control period. If the current time period is the rain control period, the valve opening status is controlled according to the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process. If the current time period is the pre-rain control period, the pump station operation status is controlled according to the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process.

2. The method for real-time control of low-carbon emissions in urban stormwater storage systems according to claim 1, characterized in that, The step of controlling the valve opening state based on the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process includes: The target flow threshold is determined based on the predicted inflow and the available volume of the storage tank; The prediction process model is run by inputting at least one candidate control strategy and the target flow threshold, and the prediction process model outputs the flood control benefits and carbon emission costs of the at least one candidate control strategy. Based on the flood control benefits and carbon emission costs of the at least one candidate control strategy, a multi-objective optimization objective function is constructed. The objective weight coefficients of the flood control benefits and the carbon emission costs are adjusted. The objective function of the multi-objective optimization is solved using a genetic algorithm. The target control strategy is determined based on the solution results. The target control strategy includes the opening state of each valve.

3. The method for real-time control of low-carbon emissions in urban stormwater storage systems according to claim 2, characterized in that, The operational prediction process model includes calculation formulas for flood control performance assessment and carbon emission control performance assessment. The formula for calculating the flood control performance evaluation is as follows: Where, r f r represents the rate of reduction in total flood volume through the outflow process. p r represents the peak flow reduction rate. o Represents peak reduction in volume utilization. To implement control of the outflow process at the discharge outlet, To implement control of the outflow process at the discharge outlet, For the discharge outflow process after control is implemented, t is the current time, N is the prediction time domain length or the time length until the end of the simulation, Δt is the time step, and V a,t Let t be the available water storage capacity of the regulating reservoir; The formula for calculating the carbon emission control performance assessment is as follows: C=C o +C p Where C represents the total carbon emissions of the real-time control process, C o C represents the carbon emissions from the operation of the intake gate. p This refers to the carbon emissions from the operation of the outflow pumping station.

4. The method for real-time control of low-carbon emissions in urban stormwater storage systems according to claim 2, characterized in that, The formula for calculating the target traffic threshold is: Among them, V a,t V represents the current available volume of the storage tank. peak The peak flood volume, To predict the inflow process, Q target t represents the expected target flow threshold, t represents the current calculation step size, and N represents the prediction time domain duration.

5. The method for real-time control of low-carbon emissions in urban stormwater storage systems according to claim 2, characterized in that, The formula for calculating the objective function of the multi-objective optimization is as follows: Among them, W F W is the weighting coefficient for flood control benefits. C V is the weighting factor for carbon emission costs. max This represents the maximum available water storage capacity of the reservoir in a single control decision. To predict the flood process during a given period, Q target V represents the expected target flow threshold, Δt is the simulation time step, and V a,t f represents the current available volume of the storage tank. electric As a carbon emission factor of electrical energy, This represents the absolute value of the opening change during a single control period. C is the electrical energy consumed in a single complete start-stop cycle. max This is to predict the maximum carbon emissions that control decisions may generate during the predicted period.

6. The method for real-time control of low-carbon emissions in urban stormwater storage systems according to claim 1, characterized in that, The method of controlling the operation of the pumping station based on the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process includes: The predicted inflow rate of the storage tank is predicted based on the predicted rainfall. The usable volume of the storage tank is determined based on the real-time water level of the storage tank. The operating status of the pumping station is determined based on the predicted inflow rate and the available volume.

7. The method for real-time control of low-carbon emissions in urban stormwater storage systems according to claim 6, characterized in that, Determining the operating status of the pumping station based on the predicted inflow and the available volume includes: If the predicted inflow is less than or equal to the available volume, the pumping station is in a closed operating state. If the predicted inflow is greater than the available volume, the pumping station is in the "on" operating state.

8. A low-carbon emission real-time control device for an urban stormwater storage system, characterized in that, The urban stormwater storage system includes pumping stations and valves for the storage tank, wherein the device includes: The acquisition module is used to acquire real-time monitoring data of the target area and the operating status of the urban stormwater storage system; The judgment module is used to update the operating status of the urban stormwater storage system based on the real-time monitoring data, determine that the current time period is within the target control period based on the updated operating status, and obtain the predicted rainfall and predicted inflow of the target area. The target control period includes the pre-rain control time and the rain control period. The prediction module, if the current time period is the rain control period, controls the opening state of the valve according to the predicted inflow, the available volume of the storage tank, and the carbon emissions generated during the control process; if the current time period is the pre-rain control period, controls the operation state of the pumping station according to the predicted rainfall, the real-time water level of the storage tank, and the carbon emissions generated during the control process.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the low-carbon emission real-time control method for an urban stormwater storage system according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the real-time low-carbon emission control method for the urban stormwater storage system according to any one of claims 1-7.

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