Intelligent control method and system for overflow and reuse of rainwater of viaduct

Abstract

This invention provides an intelligent control method and system for stormwater overflow and reuse in elevated bridges, relating to the field of adaptive control. The method includes: optimizing the setup of multiple stormwater storage components, the connectivity between the multiple perforated drainage pipes and the multiple stormwater storage components, the setup of multiple greening irrigation components, and the connectivity between the multiple stormwater storage components and the multiple greening irrigation components based on historical stormwater harvesting data from multiple perforated drainage pipes and historical irrigation data from the green belt of the elevated bridge; controlling the operation of the stormwater storage components and the opening degree of the overflow valves based on the connectivity between the multiple perforated drainage pipes and the multiple stormwater storage components and real-time stormwater harvesting data from the multiple perforated drainage pipes; and controlling the operation of multiple greening irrigation components based on real-time soil condition data from the green belt and the connectivity between the multiple stormwater storage components and the multiple greening irrigation components. This method has the advantages of realizing water resource recycling and reducing pressure on municipal pipe networks.

Description

Technical Field

[0001] This invention relates to the field of adaptive control, and in particular to an intelligent control method and system for stormwater overflow and reuse in elevated bridges. Background Technology

[0002] With the rapid increase in urban population density, urban traffic congestion has become increasingly serious. In order to improve urban traffic capacity while saving land and reducing construction costs, more and more cities are starting to build elevated highways in their urban areas.

[0003] In existing technologies, most elevated bridge rainwater systems adopt diversion systems, which divert rainwater through pipes to the drainage system laid under the ground auxiliary roads. On the one hand, the green plants in the green belts under the elevated bridges lack rainwater irrigation due to the obstruction of the elevated bridges, and often require additional municipal water for irrigation, which increases labor and water costs and does not make full use of rainwater on rainy days. On the other hand, rainwater from the bridge surface flows directly into the road drainage system, which does not make full use of the rainwater on the bridge, resulting in a waste of natural resources and at the same time increasing the pressure on the municipal pipe network.

[0004] Therefore, there is a need to provide intelligent control methods and systems for the overflow and reuse of rainwater from elevated bridges, so as to realize the recycling of water resources and reduce the pressure on municipal pipe networks. Summary of the Invention

[0005] This invention provides an intelligent control system for rainwater overflow and reuse on elevated bridges, applied to rainwater harvesting and reuse systems installed on elevated bridges. The rainwater harvesting and reuse system includes an elevated bridge rainwater harvesting component, multiple rainwater storage components, and multiple green space irrigation components. The elevated bridge rainwater harvesting component includes multiple perforated drainage pipes. An overflow valve is installed between the rainwater storage component and the municipal rainwater pipe. An irrigation water pipe is installed between the rainwater storage component and the green space irrigation components. The system includes: a data acquisition module for collecting historical rainwater harvesting data from the multiple perforated drainage pipes and historical soil condition data from the green belts of the elevated bridge; and a structure optimization module for optimizing the system based on the historical rainwater harvesting data from the multiple perforated drainage pipes and the historical soil condition data of the elevated bridge's green belts. The system utilizes historical irrigation data of the bridge's green belt to optimize the setup of multiple rainwater storage components, the connection relationships between multiple perforated drainage pipes and multiple rainwater storage components, the setup of multiple greening irrigation components, and the connection relationships between multiple rainwater storage components and multiple greening irrigation components; an overflow control module includes multiple overflow control units, one for each rainwater storage component. The overflow control units are used to control the operation of the rainwater storage components and the opening degree of the overflow valves based on the connection relationships between the multiple perforated drainage pipes and multiple rainwater storage components and the real-time rainwater recovery data of the multiple perforated drainage pipes; and an irrigation control module is used to control the operation of multiple greening irrigation components based on the real-time soil condition data of the green belt.

[0006] Furthermore, the historical rainwater harvesting data of the porous drainage pipes includes the water level and flow velocity of multiple porous drainage pipes at multiple historical rainfall points during multiple historical rainfall events; the historical soil condition data of the green belt of the viaduct includes soil temperature and humidity at multiple soil monitoring points at multiple historical time points; the structural optimization module, based on the historical rainwater harvesting data of the porous drainage pipes and the historical irrigation data of the green belt of the viaduct, optimizes the setting of multiple rainwater storage components, the connection relationship between multiple porous drainage pipes and multiple rainwater storage components, the setting of multiple greening irrigation components, and the connection relationship between multiple rainwater storage components and multiple greening irrigation components, including: based on multiple Historical rainwater harvesting data from perforated drainage pipes were used to determine the relevant characteristics of rainwater harvesting for multiple perforated drainage pipes. Based on soil temperature and humidity at multiple historical time points from multiple soil monitoring points, the green belt of the viaduct was divided into multiple irrigation areas, and multiple greening irrigation components were installed. Based on historical irrigation data from multiple greening irrigation components, the relevant characteristics of irrigation demand in multiple irrigation areas were determined. Based on the rainwater harvesting characteristics of multiple perforated drainage pipes and the relevant characteristics of irrigation demand in multiple irrigation areas, the installation of multiple rainwater storage components, the connectivity between multiple perforated drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components were optimized.

[0007] Furthermore, the rainwater harvesting correlation characteristics of the multiple porous drainage pipes include the rainwater harvesting correlation coefficient of any two porous drainage pipes; the irrigation demand correlation characteristics of the multiple irrigation areas include the irrigation demand correlation coefficient of any two irrigation areas.

[0008] Furthermore, the structure optimization module optimizes the setup of multiple rainwater storage components, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas. This includes: using an improved particle swarm optimization algorithm to optimize the setup of multiple rainwater storage components, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas.

[0009] Furthermore, by employing an improved particle swarm optimization algorithm based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas, the setup of multiple rainwater storage components, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components are optimized. This includes: determining multiple rainfall simulation scenarios based on historical rainwater harvesting data of porous drainage pipes; determining multiple irrigation demand scenarios based on historical irrigation data of multiple greening irrigation components; and optimizing the setup of multiple rainwater storage components, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components based on the rainwater harvesting characteristics of multiple porous drainage pipes, the irrigation demand characteristics of multiple irrigation areas, the rainfall simulation scenarios, and the multiple irrigation demand scenarios.

[0010] Furthermore, the elevated bridge rainwater harvesting component also includes a fully permeable pavement on both sides of the elevated bridge and a pebble layer beneath the fully permeable pavement. A permeable geotextile is placed between the fully permeable pavement and the pebble layer, and a porous drainage pipe is laid on the pebble layer. The rainwater storage component includes a distribution pool, a storage tank, and a vacuum device connected to the connecting pipe. An overflow plate is installed inside the storage tank to divide the internal space of the storage tank into a first storage area and a second storage area. The first storage area is located above the distribution pool. A filler area is installed inside the distribution pool. The first storage area is connected to the filler area. The vacuum device is installed on the storage tank. A first water level sensor is installed in the first storage area, and a second water level sensor is installed in the second storage area.

[0011] Furthermore, the overflow control unit controls the operation of the rainwater storage components based on the connection relationship between the multiple porous drain pipes and the multiple rainwater storage components and the real-time rainwater recovery data of the multiple porous drain pipes. This includes: determining whether to activate the vacuum device based on the water level of the first storage area collected by the first water level sensor at multiple consecutive time points; predicting the water level of the second storage area at multiple future time points based on the real-time rainwater recovery data of the porous drain pipes connected to the rainwater storage components and the water level of the second storage area collected by the second water level sensor at multiple consecutive time points; and determining the opening time of the overflow valve based on the predicted water level of the second storage area at multiple future time points.

[0012] Furthermore, the overflow control unit controls the opening degree of the overflow valve based on the connection relationship between multiple perforated drain pipes and multiple rainwater storage components and the real-time rainwater recovery data of the multiple perforated drain pipes. This includes: determining the initial opening degree of the overflow valve based on the predicted water level of the second water storage area at multiple future time points; and adjusting the opening degree of the overflow valve after the overflow valve is opened based on the real-time rainwater recovery data of the perforated drain pipes connected to the rainwater storage components and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points.

[0013] Furthermore, the side of the distribution tank away from the packing area is also connected to a municipal sewage pipe, and a drain valve is installed between the distribution tank and the municipal sewage pipe; the overflow control unit is also used to: determine whether to open the drain valve based on the water level of the first water storage area collected by the first water level sensor at multiple consecutive time points.

[0014] This invention provides an intelligent control method for rainwater overflow and reuse in elevated bridges, applied to the aforementioned intelligent control system for rainwater overflow and reuse in elevated bridges. The method includes: collecting historical rainwater harvesting data from multiple porous drainage pipes and historical soil condition data from the green belt of the elevated bridge; optimizing the setup of multiple rainwater storage components, the connectivity between the multiple porous drainage pipes and the multiple rainwater storage components, the setup of multiple green belt irrigation components, and the connectivity between the multiple rainwater storage components and the multiple green belt irrigation components based on the historical rainwater harvesting data from the multiple porous drainage pipes and the multiple rainwater storage components; controlling the operation of the rainwater storage components and the opening degree of the overflow valves based on the connectivity between the multiple porous drainage pipes and the multiple rainwater storage components and the real-time rainwater harvesting data from the multiple porous drainage pipes; and controlling the operation of multiple green belt irrigation components based on the real-time soil condition data of the green belt and the connectivity between the multiple rainwater storage components and the multiple green belt irrigation components.

[0015] Compared with existing technologies, the intelligent control method and system for rainwater overflow and reuse in elevated bridges provided by this invention have at least the following beneficial effects:

[0016] By monitoring the rainwater harvesting flow rate of the multi-hole drainage pipes and the water level of the rainwater storage components in real time, the opening of the overflow valves is dynamically adjusted to ensure that rainwater is preferentially collected by the storage components and overflows into the municipal pipe network only when the storage capacity is saturated. Based on historical rainwater harvesting data, the layout of the storage components and the connection relationship with the drainage pipes are optimized to improve the overall rainwater harvesting efficiency of the system, reduce ineffective overflows, and alleviate the load on the urban drainage system. Each rainwater storage component is equipped with an independent overflow control unit, which can adjust the valve opening differently according to local rainfall intensity, storage water level, and drainage pipe flow differences, avoiding resource waste or overflow risks caused by a "one-size-fits-all" control. Through a closed-loop design of data perception, structural optimization, and intelligent control, the efficient collection, storage, and reuse of rainwater resources from elevated bridges are achieved, while also considering the needs of green space irrigation and urban drainage safety, providing a replicable and scalable intelligent solution for urban stormwater management and green infrastructure construction. Attached Figure Description

[0017] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0018] Figure 1 This is a structural schematic diagram of an elevated bridge rainwater storage device according to some embodiments of this specification;

[0019] Figure 2 This is a structural schematic diagram of an elevated bridge rainwater harvesting assembly according to some embodiments of this specification;

[0020] Figure 3 This is a structural schematic diagram of a rainwater storage component according to some embodiments of this specification;

[0021] Figure 4 This is a schematic diagram of the module of the intelligent control system for rainwater overflow and reuse of elevated bridges, according to some embodiments of this specification.

[0022] Figure 5 This is a flowchart illustrating an intelligent control method for rainwater overflow and reuse in elevated bridges, based on some embodiments of this specification.

[0023] In the diagram, 1. Elevated bridge; 2. Elevated bridge rainwater harvesting system; 21. Fully permeable pavement; 22. Gravel layer; 23. Permeable geotextile; 24. Porous drainage pipe; 3. Rainwater storage system; 31. Distribution pool; 311. Filler area; 32. Water storage tank; 321. First water storage area; 322. Second water storage area; 323. Overflow plate; 33. Vacuum equipment; 4. Connecting pipe; 5. Municipal rainwater pipe; 6. Municipal sewage pipe; 7. Irrigation water pipe. Detailed Implementation

[0024] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0025] Figure 1 This is a structural schematic diagram of an elevated bridge rainwater storage device according to some embodiments of this specification, such as... Figure 1 As shown, the elevated bridge rainwater storage equipment includes an elevated bridge rainwater harvesting component 2 and multiple rainwater storage components 3.

[0026] Figure 2 This is a structural schematic diagram of the elevated bridge rainwater harvesting component 2 according to some embodiments shown in this specification, such as... Figure 2 As shown, the elevated bridge rainwater harvesting component 2 includes multiple porous drainage pipes 24. The elevated bridge rainwater harvesting component 2 also includes a fully permeable pavement 21 set on both sides of the elevated bridge 1 and a pebble layer 22 set under the fully permeable pavement 21. A permeable geotextile 23 is set between the fully permeable pavement 21 and the pebble layer 22. The porous drainage pipes 24 are laid on the pebble layer 22. Multiple water inlets are opened on the side of the porous drainage pipes 24 facing the fully permeable pavement 21.

[0027] Figure 3 This is a structural schematic diagram of the rainwater storage component 3 according to some embodiments of this specification, such as... Figure 3 As shown, a discharge valve is installed between the rainwater storage component 3 and the municipal rainwater pipe 5. The rainwater storage component 3 includes a distribution pool 31, a water storage tank 32, and a vacuum device 33, all connected to the connecting pipe 4. An overflow plate 323 is installed inside the water storage tank 32, dividing the internal space of the water storage tank 32 into a first water storage area 321 and a second water storage area 322. The first water storage area 321 is located above the distribution pool 31, which contains a packing area 311. The first water storage area 321 is connected to the packing area 311. The vacuum device 33 is installed on the water storage tank 32. A first water level sensor is installed in the first water storage area 321, and a second water level sensor is installed in the second water storage area 322. A municipal sewage pipe 6 is also connected to the side of the distribution pool 31 away from the packing area 311, and a drain valve is installed between the distribution pool 31 and the municipal sewage pipe 6. An irrigation water pipe 7 is installed between the water storage tank 32 and the greening irrigation component.

[0028] The working principle of the elevated bridge rainwater storage equipment is as follows: Rainwater from the elevated bridge 1 is discharged through the road cross slope to the fully permeable pavement on the side of the road. Under the action of the fully permeable pavement, the rainwater enters the porous drainage pipe 24 arranged under the fully permeable pavement. The rainwater on the elevated bridge 1 enters the distribution pool 31 through the drainage pipe. The denser impurities can be deposited at the bottom of the distribution pool 31, while the less dense impurities can be intercepted by the filling area 311. There is a permeable geotextile 23 between the filling area 311 and the water storage tank 32. After passing through the distribution pool 31 and the filling area 311, the rainwater enters the first water storage area 321. When the water level in the first water storage area 321 rises to a certain level, the vacuum device 33 system at the top of the water storage tank 32 starts to operate. Because the atmospheric pressure at the top decreases, the rainwater enters the first water storage area 321 faster under the action of atmospheric pressure. When the water level in the first water storage area 321 reaches the height of the overflow plate 323, the upper clean rainwater crosses the overflow plate 323 and enters the second water storage area 322. A flexible hose is connected to the bottom of the second water storage area 322, allowing clean rainwater from this area to be used for irrigation during dry, sunny days. Additionally, a rainwater overflow pipe is installed at the bottom of the second water storage area 322; when the water depth exceeds a certain level, excess rainwater flows through this overflow pipe into the municipal stormwater system for discharge. When water is needed on a sunny day, the drain valve connected to the distribution tank 31 is opened, breaking the vacuum in the storage tank 32. The rainwater stored in the first water storage area 321 quickly flushes the filler area 311 and the distribution tank 31, removing deposited impurities. The rainwater stored in the second water storage area 322 remains unaffected and can be used for irrigation.

[0029] Figure 4 This is a schematic diagram of the module of the intelligent control system for rainwater overflow and reuse of elevated bridges, as shown in some embodiments of this specification. Figure 4 As shown, the intelligent control system for rainwater overflow and reuse of elevated bridges may include a data acquisition module, a structural optimization module, an overflow control module, and an irrigation control module.

[0030] The data acquisition module is used to collect historical rainwater recycling data from multiple porous drainage pipes and historical soil condition data from the green belt of the overpass.

[0031] Specifically, the historical rainwater harvesting data for the perforated drainage pipes includes the water level and flow velocity of multiple perforated drainage pipes at multiple historical rainfall points during multiple historical rainfall events. Water level sensors and flow velocity sensors are installed inside the perforated drainage pipes to monitor changes in water level and flow velocity. Water level sensors can be of different types, such as float-type, pressure-type, or ultrasonic, and can accurately measure the water level height inside the perforated drainage pipe; flow velocity sensors can measure the flow velocity of rainwater using principles such as electromagnetic induction and the ultrasonic Doppler effect.

[0032] The historical soil condition data for the green belt of the viaduct includes soil temperature and humidity data from multiple soil monitoring points over multiple historical time periods. For each historical time period, soil temperature and humidity data from multiple historical time points within that period can be obtained. The time difference between the start time of that historical time period and the completion time of the most recent irrigation must be greater than a time difference threshold, for example, 1 hour.

[0033] The structure optimization module is used to optimize the settings of multiple rainwater storage components, the connectivity between multiple perforated drainage pipes and multiple rainwater storage components, the settings of multiple greening irrigation components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components, based on historical rainwater harvesting data of multiple perforated drainage pipes and historical irrigation data of the green belt of the viaduct.

[0034] Specifically, it includes:

[0035] Based on historical rainwater harvesting data of porous drainage pipes, rainwater harvesting characteristics of multiple porous drainage pipes were determined.

[0036] Based on soil temperature and humidity at multiple historical time points from multiple soil monitoring points, the green belt of the viaduct is divided into multiple irrigation areas, and multiple green irrigation components are installed.

[0037] Based on historical irrigation data from multiple greening irrigation components, the irrigation demand characteristics of multiple irrigation areas were determined.

[0038] Based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas, the settings of multiple rainwater storage components, the connection relationships between multiple porous drainage pipes and multiple rainwater storage components, and the connection relationships between multiple rainwater storage components and multiple greening irrigation components are optimized.

[0039] Specifically, the rainwater harvesting characteristics of multiple porous drainage pipes include the rainwater harvesting correlation coefficient between any two porous drainage pipes.

[0040] For any two porous drainage pipes, the water levels at multiple historical rainfall points during the historical rainfall process can be substituted into the correlation coefficient calculation formula (e.g., Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) to calculate the water level correlation coefficient for the two porous drainage pipes corresponding to the historical rainfall. Similarly, the flow velocities at multiple historical rainfall points during the historical rainfall process can be substituted into the correlation coefficient calculation formula to calculate the flow velocity correlation coefficient for the two porous drainage pipes corresponding to the historical rainfall. The average of the water level correlation coefficients and the average flow velocity correlation coefficients for the two porous drainage pipes corresponding to each historical rainfall is then calculated to obtain the mean water level correlation coefficient. Finally, the weighted sum of the mean water level correlation coefficient and the mean flow velocity correlation coefficient for the two porous drainage pipes is calculated to obtain the rainwater harvesting correlation coefficient.

[0041] Based on soil temperature and humidity data from multiple soil monitoring points at multiple historical time points, the Euclidean distance between any two soil monitoring points is calculated. Specifically, the Euclidean distances for soil temperature and soil humidity between any two soil monitoring points can be calculated based on these data. These Euclidean distances are then weighted and summed to obtain the final soil temperature-humidity Euclidean distance between the two monitoring points. Using clustering algorithms (e.g., K-Means, hierarchical clustering), the multiple soil monitoring points are clustered according to their Euclidean distance, resulting in multiple soil monitoring point clusters. It is understood that the smaller the Euclidean distance between two soil monitoring points, the greater the probability that they will be grouped into the same cluster. For each soil monitoring point cluster, the irrigation area corresponding to the cluster is determined based on the monitoring area covered by the included monitoring points (e.g., a circular area with a radius of 1m). Within the multiple irrigation areas, one irrigation area corresponds to one soil monitoring point cluster. A greening irrigation component can be installed at the center of the irrigation area corresponding to the soil monitoring point cluster. The parameters of the greening irrigation component can be adjusted according to the size of the irrigation area corresponding to the soil monitoring point cluster, such as adjusting the nozzle range, so that the greening irrigation component can irrigate most (e.g., 90%) of the irrigation area corresponding to the soil monitoring point cluster.

[0042] The correlation characteristics of irrigation demand in multiple irrigation areas include the correlation coefficient of irrigation demand between any two irrigation areas.

[0043] Historical irrigation data from multiple greening irrigation components can include the water consumption of each component over multiple historical time periods. This water consumption can be obtained through instantaneous flow integration (e.g., real-time data collection via a flow meter). The irrigation by these components is automatic, meaning that the component automatically performs watering when the temperature and humidity of the soil in the irrigation area (e.g., soil 10cm below the surface) meet irrigation conditions (e.g., temperature > 5℃ and soil moisture < 25%).

[0044] For any two irrigation areas, the water consumption of the two greening irrigation components during multiple historical time periods can be substituted into the correlation coefficient (e.g., Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) calculation formula to calculate the irrigation demand correlation coefficient between the two irrigation areas.

[0045] In some embodiments, the structure optimization module optimizes the setup of multiple rainwater storage components, the connectivity between the multiple porous drainage pipes and the multiple rainwater storage components, and the connectivity between the multiple rainwater storage components and the multiple greening irrigation components, based on the rainwater harvesting characteristics of the multiple porous drainage pipes and the irrigation demand characteristics of the multiple irrigation areas. This includes:

[0046] Based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas, the improved particle swarm optimization algorithm optimizes the setting of multiple rainwater storage components, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components.

[0047] Specifically, this may include the following processes:

[0048] S11. Based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas, a particle swarm is generated. Each particle represents a structural optimization scheme, which includes the number of rainwater storage components, the capacity of each rainwater storage component, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components.

[0049] S12, Initialize the particle velocity;

[0050] S13. Establish a multi-objective fitness function;

[0051] S14. Calculate the multi-objective fitness value of each particle using the multi-objective fitness function;

[0052] S14. For each particle, calculate the particle's position guidance value based on the rainwater harvesting characteristics of multiple porous drainage pipes, the irrigation demand characteristics of multiple irrigation areas, and the particle's multi-objective fitness value.

[0053] S15. Determine the global optimal position and the historical optimal position of each particle based on the position guidance value of each particle;

[0054] S16. Determine whether the termination condition is met. If yes, optimize the settings of multiple rainwater storage components, the connection between multiple perforated drainage pipes and multiple rainwater storage components, and the connection between multiple rainwater storage components and multiple greening irrigation components based on the particle with the highest multi-target fitness value in the current particle swarm. If no, execute S17.

[0055] S17. Update the position and velocity of each particle based on the global optimal position and the historical optimal position of each particle, and execute S14.

[0056] Specifically, each particle represents a structural optimization scheme and needs to encode the following information:

[0057] Number N of rainwater storage components s The sampled values ​​can be within a preset quantity range (e.g., 5-10, etc.). The preset quantity range can be determined based on the historical rainwater recovery data of the porous drainage pipe. If the historical rainwater recovery volume of the porous drainage pipe is large, the upper and lower limits of the preset quantity can be increased.

[0058] The connectivity between the porous drainage pipe and the storage components (represented by matrix A, A j,i =1 indicates that the porous drainage pipe j is connected to the rainwater storage component i.

[0059] The connectivity between the storage component and the irrigation component (represented by matrix B, B...) i,k =1 indicates that rainwater storage component i is connected to greening irrigation component k.

[0060] The generation of particles requires the following constraints to be met:

[0061] 1. Each perforated drain pipe must be connected to a rainwater storage unit;

[0062] 2. Each greening irrigation component must be connected to a rainwater storage component;

[0063] 3. Each rainwater storage unit must be connected to at least one perforated drain pipe and at least one greening irrigation unit;

[0064] 4. The correlation coefficient of rainwater recovery between any two porous drainage pipes connected by the same rainwater storage component is less than the preset rainwater recovery correlation coefficient threshold (e.g., 0.5). The preset rainwater recovery correlation coefficient threshold can be determined by experimental data.

[0065] 5. The correlation coefficient of irrigation demand between any two greening irrigation components connected to the same rainwater storage component is less than the preset correlation coefficient threshold for irrigation demand (e.g., 0.5). The preset correlation coefficient threshold for irrigation demand can be determined through experimental data.

[0066] Understandably, constraint 1 ensures that rainwater from all drainage pipes is recycled, avoiding resource waste. Constraint 2 guarantees water demand for all irrigated areas, avoiding irrigation blind spots. Constraint 3 prevents storage components from being "idle" (connected only to drainage pipes or only to irrigation components), ensuring efficient use of system resources. Constraint 4 distributes rainwater recycling from highly correlated drainage pipes to prevent excessive overflow from rainwater storage components due to a surge in synchronous rainwater recycling. That is, if the recycling volumes of two drainage pipes are highly correlated, their rainwater recycling volumes will reach their peak simultaneously. If they are connected to the same rainwater storage component, it may cause excessive overflow of that rainwater storage component, reducing rainwater recycling efficiency. By limiting the correlation coefficient of rainwater recycling and allocating low-correlation porous drainage pipes to the same component, the temporal distribution of recycling volume can be balanced, reducing the probability of overflow. Constraint 5 distributes water demand from highly correlated irrigation components to prevent rainwater storage components from drying out due to synchronous water shortage. That is, if the demand of two irrigation areas is highly correlated, their water consumption will surge synchronously. If they are connected to the same rainwater storage component, it may cause that rainwater storage component to dry out rapidly, affecting irrigation effectiveness. By limiting the correlation coefficient of irrigation demand, low-correlation irrigation areas can be assigned to the same correlation coefficient, which can smooth water demand and extend the water supply time of rainwater storage components.

[0067] The particle's velocity has the same dimension as its position vector, including:

[0068] Number of rainwater storage components Ns The adjustment amount;

[0069] The element changes of the connectivity matrix A between the porous drainage pipe and the storage component;

[0070] The elements of the connectivity matrix B between the storage component and the irrigation component change.

[0071] Randomly generate velocity values ​​and the number of rainwater storage components within a reasonable range (e.g., 1-2). Ns For discrete variables (such as connectivity relationships A and B), the connection states are randomly flipped according to probability.

[0072] A multi-objective fitness function needs to balance multiple objectives, including:

[0073] 1. Cost minimization: The system construction and maintenance costs are related to the number of rainwater storage components;

[0074] 2. Maximize efficiency: Rainwater utilization rate (e.g., the ratio of effective irrigation water to rainfall);

[0075] 3. Maximize reliability: Ensure water supply for greening irrigation (e.g., the matching degree between the total capacity of storage components and the demand).

[0076] 4. Storage uniformity: The uniformity of rainwater storage in each rainwater storage component can be characterized by the standard deviation of the amount of rainwater stored in the rainwater storage component.

[0077] In some embodiments, the improved particle swarm optimization algorithm calculates the multi-objective fitness value of a particle, including:

[0078] Based on historical rainwater harvesting data from porous drainage pipes, multiple rainfall simulation scenarios were identified.

[0079] Based on historical irrigation data from multiple greening irrigation components, multiple irrigation demand scenarios are identified.

[0080] Based on multiple rainfall simulation scenarios and multiple irrigation demand scenarios, the multi-target fitness value of particles is calculated.

[0081] Specifically, key features (such as total rainfall, peak intensity, and rainfall distribution type) are extracted for each rainfall event; K-means or hierarchical clustering is used to classify the rainfall events into several categories (such as "short-duration heavy rainfall," "long-duration light rain," and "uniform rainfall"); one rainfall event is selected from each category as a simulation scenario, or a synthetic scenario is generated through statistical methods (such as Monte Carlo simulation). Multiple sets of discrete rainfall simulation scenarios are obtained, for example:

[0082] Rainfall simulation scenario 1: Short-duration heavy rainfall (total rainfall 50mm, peak intensity 20mm / h, duration 1 hour);

[0083] Rainfall simulation scenario 2: prolonged light rain (total rainfall 30mm, evenly distributed, lasting 6 hours);

[0084] Rainfall simulation scenario 3: Extreme rainfall (total rainfall 100mm, peak intensity 40mm / h, duration 2 hours).

[0085] K-means or hierarchical clustering is used to cluster multiple historical time periods based on the water consumption of each greening irrigation component over multiple historical time periods, dividing the time periods into several categories (e.g., high water demand, medium water demand, low water demand, etc.). One historical time period is selected from each category as a simulation scenario, or a synthetic scenario is generated through statistical methods (such as Monte Carlo simulation). Multiple irrigation demand scenarios are obtained, where each irrigation demand scenario can include the water consumption required for irrigation of each greening irrigation component.

[0086] A simulation model of the rainwater harvesting and reuse system is built in simulation software (e.g., SWMM (Storm Water Management Model)). For each rainfall simulation scenario, the simulation model can be used to determine the total rainwater overflow and the water level of each rainwater storage component at the end of the rainfall in each rainfall simulation scenario.

[0087] For each rainwater storage component, the average water level of the rainwater storage component at the end of each rainfall simulation scenario is used as the initial water level of the rainwater storage component.

[0088] For each irrigation demand scenario, the simulation model is updated based on the initial water level of the rainwater storage components. For each irrigation demand scenario, the updated simulation model determines the total irrigation water shortage for each particle under each scenario and the water level of each rainwater storage component at the end of the irrigation demand scenario.

[0089] The multi-objective fitness value of the particles is calculated based on the total rainwater overflow in each rainfall simulation scenario and the water level of each rainwater storage component at the end of the rainfall, the total irrigation water shortage in each irrigation demand scenario and the water level of each rainwater storage component at the end of the irrigation demand scenario.

[0090] Specifically, the cost of a single rainwater storage component can be multiplied by the number of rainwater storage components corresponding to the particle to obtain the cost value of the particle.

[0091] For each rainfall simulation scenario, the ratio of the total rainfall in the simulation scenario minus the total rainwater overflow of the particles in that scenario to the total rainfall in that scenario can be calculated as the rainwater recovery efficiency for that scenario. The average rainwater recovery efficiency for each simulation scenario is then calculated to obtain the average rainwater recovery efficiency for the particles.

[0092] For each rainfall simulation scenario, the standard deviation of the water level at the end of the rainfall is calculated for each rainwater storage component to obtain the standard deviation of the water level corresponding to the rainfall simulation scenario. The mean of the standard deviations of the water level corresponding to each rainfall simulation scenario is then calculated to obtain the mean of the first standard deviation of the water level.

[0093] For each irrigation demand scenario, the average of the total irrigation water shortage for each particle under each irrigation demand scenario is calculated to obtain the average irrigation water shortage.

[0094] For each irrigation demand scenario, the standard deviation of the water level of each rainwater storage component at the end of the irrigation demand scenario is calculated to obtain the water level standard deviation corresponding to the irrigation demand scenario. The mean of the water level standard deviations corresponding to each irrigation demand scenario is calculated to obtain the second water level standard deviation mean.

[0095] Normalize the cost value, average rainwater harvesting efficiency, average standard deviation of the first water level, average irrigation water shortage, and average standard deviation of the second water level corresponding to the particle to generate normalized cost value, average rainwater harvesting efficiency, average standard deviation of the first water level, average irrigation water shortage, and average standard deviation of the second water level. Calculate the multi-objective fitness value of the particle based on the normalized cost value, average rainwater harvesting efficiency, average standard deviation of the first water level, average irrigation water shortage, and average standard deviation of the second water level.

[0096] For example, a multi-objective fitness function can be:

[0097]

[0098] in, For multi-objective fitness values, and To preset weights, and Greater than 0, ,For example, =0.1, =0.3、 =0.3, =0.3, The normalized cost value. This represents the normalized mean rainwater harvesting efficiency. This represents the mean of the standard deviations of the first water level after normalization. This represents the normalized mean standard deviation of the second water level. This represents the normalized mean of irrigation water deficit.

[0099] Understandable. This is the normalized cost value, directly related to the number of rainwater storage components (more components, higher cost). By taking the reciprocal, the cost minimization problem is transformed into maximizing this component value. The normalized mean rainwater harvesting efficiency directly reflects the system's ability to utilize rainwater. The smaller the normalized mean irrigation water shortage, the stronger the water supply security. By taking the reciprocal, the water shortage minimization problem is transformed into maximizing this component value. This is achieved through summation. By comprehensively weighing the imbalance under the two demands, and then taking the reciprocal, the problem of minimizing the standard deviation is transformed into maximizing the component value. The smaller the mean of the standard deviation of the first water level and the mean of the standard deviation of the second water level after normalization, the better the balance and the larger the component value.

[0100] For each rainwater storage component, the mean of the rainwater harvesting correlation coefficients between any two porous drainage pipes connected to the rainwater storage component (referred to as the mean rainwater harvesting correlation coefficient for the rainwater storage component) and the mean of the irrigation demand correlation coefficients between any two irrigation areas connected to the rainwater storage component (referred to as the mean irrigation demand correlation coefficient for the rainwater storage component) can be calculated. The mean of the rainwater harvesting correlation coefficients for each rainwater storage component (referred to as the mean rainwater harvesting correlation coefficient for the particle) and the mean of the irrigation demand correlation coefficients for each rainwater storage component (referred to as the mean irrigation demand correlation coefficient for the particle) can also be calculated. Based on the mean rainwater harvesting correlation coefficients for the particle, the mean irrigation demand correlation coefficients for the particle, and the multi-objective fitness value of the particle, the position guidance value of the particle is calculated. The smaller the mean rainwater harvesting correlation coefficient, the smaller the mean irrigation demand correlation coefficient, and the larger the multi-objective fitness value of the particle, the larger the position guidance value of the particle. For example, the first value is obtained by adding 1 to the mean of the rainwater harvesting correlation coefficients corresponding to the particles and then taking the reciprocal. The second value is obtained by adding 1 to the mean of the irrigation demand correlation coefficients corresponding to the particles and then taking the reciprocal. The first value, the second value, and the multi-objective fitness value of the particles are then weighted and summed to obtain the position guidance value of the particles.

[0101] The global optimal position can be the position of the particle with the largest position guidance value in the current iteration. The historical optimal position of a particle can be the position with the largest position guidance value during the search process from initialization to the present.

[0102] Understandably, by combining the correlation coefficients of rainwater harvesting and irrigation demand to calculate location guidance values, particles are guided to allocate low-correlation porous drainage pipes to the same rainwater storage component, reducing the risk of overflow. Simultaneously, low-correlation irrigation areas are connected to the same component, smoothing water demand and preventing system efficiency degradation due to supply-demand imbalances (synchronous overflow or depletion) in the storage component. The guidance value calculation based on correlation coefficients allows particle velocity updates to focus more on reducing system conflicts (such as the concentrated connection of low-correlation drainage pipes / irrigation areas), accelerating convergence to a Pareto optimal solution that balances efficiency and stability, and reducing invalid search paths.

[0103] The termination conditions may include reaching the maximum number of iterations or a stable fitness value (e.g., when the multi-objective fitness value of the particle swarm (such as the coverage of the Pareto front or the hypervolume index) is continuously within a certain range). N The change in the next iteration is less than a preset threshold. (like =0.001), indicating that the solution space has been fully explored and the algorithm has converged; particle positions stagnate (e.g., if the position update magnitude of all particles (such as Euclidean distance) is within a continuous range). M In each iteration, it is always less than δ (like δ =0.01×search space range), indicating that the particle swarm is trapped in a local optimum, and the iteration can be terminated, etc.

[0104] The existing particle swarm optimization algorithm can be used to update the position and velocity of particles. The position and velocity of each particle can be updated based on the global optimal position and the historical optimal position of each particle.

[0105] The overflow control module includes multiple overflow control units, with one overflow control unit corresponding to each rainwater storage component. The overflow control unit is used to control the operation of the rainwater storage component and the opening degree of the overflow valve based on the connection relationship between multiple perforated drain pipes and multiple rainwater storage components and the real-time rainwater recovery data of multiple perforated drain pipes.

[0106] In some embodiments, the overflow control unit controls the operation of the rainwater storage components based on the connectivity between the multiple porous drain pipes and the multiple rainwater storage components and the real-time rainwater recovery data of the multiple porous drain pipes, including:

[0107] Based on the water level of the first water storage area collected by the first water level sensor at multiple consecutive time points, determine whether to activate the vacuum equipment.

[0108] Based on real-time rainwater harvesting data from the porous drainage pipe connected to the rainwater storage component and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points, the water level of the second water storage area at multiple future time points is predicted.

[0109] The opening time of the overflow valve is determined based on the predicted water level of the second water storage area at multiple future time points.

[0110] Specifically, the average water level of the first water storage area collected by the first water level sensor at multiple consecutive time points is calculated to obtain the average water level. If the average water level is greater than the first preset water level, the vacuum device is activated. The first preset water level can be determined based on manual or experimental data.

[0111] Using a first water level prediction model (e.g., LSTM (Long Short-Term Memory Network) neural network or ARIMA (Autoregressive Integrated Moving Average Model) model), based on real-time rainwater harvesting data (e.g., water level, flow rate, etc.) from the porous drainage pipe connected to the rainwater storage component and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points, the water level of the second water storage area is predicted at multiple future time points. If the predicted water level at a certain future time point is greater than a second preset water level, then that future time is used as the opening time of the overflow valve. The second preset water level can be determined based on manual or experimental data.

[0112] In some embodiments, the overflow control unit controls the opening degree of the overflow valve based on the connectivity between the multiple perforated drain pipes and multiple rainwater storage components and the real-time rainwater recovery data of the multiple perforated drain pipes, including:

[0113] The initial opening degree of the overflow valve is determined based on the predicted water level of the second water storage area at multiple future time points;

[0114] After the overflow valve is opened, the opening degree of the overflow valve is adjusted based on the real-time rainwater recovery data from the porous drainage pipe connected to the rainwater storage component and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points.

[0115] Specifically, based on the predicted water level at the opening time of the overflow valve and the water level at the last future time point, the total overflow volume is calculated. Based on the total overflow volume and the duration of multiple future times, the average flow rate that the overflow valve needs to provide is calculated. The initial opening degree of the overflow valve is determined based on the average flow rate that the overflow valve needs to provide. For example, a correlation function between the flow rate and the opening degree of the overflow valve can be established in advance. By substituting the average flow rate that the overflow valve needs to provide into this correlation function, the initial opening degree of the overflow valve can be calculated.

[0116] A second water level prediction model (e.g., an LSTM (Long Short-Term Memory Network) neural network model) is used to predict the water level of the second water storage area at multiple future time points after the overflow valve is opened, based on real-time rainwater harvesting data from the porous drainage pipe connected to the rainwater storage component and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points. If the water level at at least one future time point is higher than the second preset water level, the water level at the last future time point and the second preset water level are calculated, and the supplementary overflow water volume is calculated. Based on the supplementary overflow water volume and the time length of multiple future time points, the supplementary flow rate that the overflow valve needs to provide is calculated. The supplementary flow rate that the overflow valve needs to provide and the average flow rate that the overflow valve needs to provide corresponding to the initial opening are summed and then input into the correlation function to obtain the adjusted opening of the overflow valve, thereby adjusting the opening of the overflow valve.

[0117] In some embodiments, the overflow control unit is further configured to:

[0118] Based on the water level of the first water storage area collected by the first water level sensor at multiple consecutive time points, it is determined whether to open the drain valve.

[0119] Specifically, if the standard deviation of the water level in the first water storage area collected by the first water level sensor at multiple consecutive time points is less than the standard deviation threshold (e.g., 0.2), it is determined that the drain valve will be opened, and the rainwater stored in the first water storage area can quickly flush the filling area and the distribution pool, carrying away the deposited impurities in the filling area and the distribution pool.

[0120] The irrigation control module is used to control the operation of multiple greening irrigation components based on real-time soil condition data of the green belt.

[0121] Specifically, if the temperature and humidity of the soil in the irrigation area where the greening irrigation component is located (e.g., the soil 10cm below the surface) meet the irrigation conditions (e.g., temperature > 5℃ and soil moisture < 25%), then the greening irrigation component will automatically perform one irrigation. The time interval between two adjacent irrigations must be greater than a preset time interval threshold (e.g., 10 minutes).

[0122] Figure 5 This is a flowchart illustrating an intelligent control method for stormwater overflow and reuse in elevated bridges, based on some embodiments of this specification. Figure 5 As shown, the intelligent control method for rainwater overflow and reuse of elevated bridges may include the following steps.

[0123] Historical rainwater harvesting data from multiple porous drainage pipes and historical soil condition data from the green belts of the elevated bridge were collected.

[0124] Based on historical rainwater harvesting data from multiple perforated drainage pipes and historical irrigation data from the green belts of the viaduct, the settings of multiple rainwater storage components, the connectivity between multiple perforated drainage pipes and multiple rainwater storage components, the settings of multiple greening irrigation components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components are optimized.

[0125] Based on the connection between multiple perforated drainage pipes and multiple rainwater storage components, and the real-time rainwater recovery data of the multiple perforated drainage pipes, the operation of the rainwater storage components and the opening of the overflow valve are controlled.

[0126] Based on the real-time soil condition data of the green belt and the connection relationship between multiple rainwater storage components and multiple green irrigation components, the operation of multiple green irrigation components is controlled.

[0127] The intelligent control method for stormwater overflow and reuse of elevated bridges can be applied to the aforementioned intelligent control system for stormwater overflow and reuse of elevated bridges, and will not be elaborated further here.

[0128] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. An intelligent control system for rainwater overflow and reuse in elevated bridges, characterized in that, A rainwater harvesting and reuse system is applied to elevated bridges. The system includes an elevated bridge rainwater harvesting component, multiple rainwater storage components, and multiple green space irrigation components. The elevated bridge rainwater harvesting component includes multiple perforated drainage pipes. An overflow valve is installed between the rainwater storage component and the municipal rainwater pipe. An irrigation pipe is installed between the rainwater storage component and the green space irrigation components. The data acquisition module is used to collect historical rainwater recycling data from multiple porous drainage pipes and historical soil condition data from the green belt of the overpass. The structure optimization module is used to optimize the settings of multiple rainwater storage components, the connection relationship between multiple perforated drainage pipes and multiple rainwater storage components, the settings of multiple greening irrigation components, and the connection relationship between multiple rainwater storage components and multiple greening irrigation components based on historical rainwater harvesting data of multiple perforated drainage pipes and historical irrigation data of green belts of elevated bridges. The overflow control module includes multiple overflow control units, with one overflow control unit corresponding to each rainwater storage component. The overflow control unit is used to control the operation of the rainwater storage component and the opening degree of the overflow valve based on the connection relationship between multiple perforated drain pipes and multiple rainwater storage components and the real-time rainwater recovery data of multiple perforated drain pipes. The irrigation control module is used to control the operation of multiple greening irrigation components based on real-time soil condition data of the green belt; The elevated bridge rainwater harvesting component also includes a fully permeable pavement set on both sides of the elevated bridge and a pebble layer set under the fully permeable pavement. A permeable geotextile is set between the fully permeable pavement and the pebble layer, and a porous drainage pipe is laid on the pebble layer. The rainwater storage component includes a distribution pool, a water storage tank, and a vacuum device connected to a connecting pipe. The water storage tank is equipped with an overflow plate, which divides the internal space of the water storage tank into a first water storage area and a second water storage area. The first water storage area is located above the distribution pool. The distribution pool is equipped with a filling area, and the first water storage area is connected to the filling area. The vacuum device is installed on the water storage tank. The first water storage area is equipped with a first water level sensor, and the second water storage area is equipped with a second water level sensor; The distribution tank is also connected to a municipal sewage pipe on the side away from the filling area, and a drain valve is installed between the distribution tank and the municipal sewage pipe.

2. The intelligent control system for rainwater overflow and reuse in elevated bridges according to claim 1, characterized in that, Historical rainwater harvesting data for perforated drainage pipes includes water levels and flow velocities at multiple historical rainfall points during multiple historical rainfall events. Historical soil condition data for the green belt of the viaduct includes soil temperature and humidity at multiple historical time points from multiple soil monitoring points; The structural optimization module includes: Based on historical rainwater harvesting data of porous drainage pipes, rainwater harvesting characteristics of multiple porous drainage pipes were determined. Based on soil temperature and humidity at multiple historical time points from multiple soil monitoring points, the green belt of the viaduct is divided into multiple irrigation areas, and multiple green irrigation components are installed. Based on historical irrigation data from multiple greening irrigation components, the irrigation demand characteristics of multiple irrigation areas were determined. Based on the rainwater harvesting characteristics of multiple porous drainage pipes and the irrigation demand characteristics of multiple irrigation areas, the settings of multiple rainwater storage components, the connection relationships between multiple porous drainage pipes and multiple rainwater storage components, and the connection relationships between multiple rainwater storage components and multiple greening irrigation components are optimized.

3. The intelligent control system for rainwater overflow and reuse in elevated bridges according to claim 2, characterized in that, The rainwater harvesting characteristics of the multiple porous drainage pipes include the rainwater harvesting correlation coefficient between any two porous drainage pipes. The irrigation demand correlation characteristics of the multiple irrigation areas include the irrigation demand correlation coefficient between any two irrigation areas.

4. The intelligent control system for rainwater overflow and reuse in elevated bridges according to claim 1, characterized in that, Based on historical rainwater harvesting data from porous drainage pipes, multiple rainfall simulation scenarios were identified. Based on historical irrigation data from multiple greening irrigation components, multiple irrigation demand scenarios are identified. Based on multiple rainfall simulation scenarios and multiple irrigation demand scenarios; By using an improved particle swarm optimization algorithm based on the rainwater harvesting characteristics of multiple porous drainage pipes, the irrigation demand characteristics of multiple irrigation areas, multiple rainfall simulation scenarios, and multiple irrigation demand scenarios, the algorithm optimizes the settings of multiple rainwater storage components, the connectivity between multiple porous drainage pipes and multiple rainwater storage components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components.

5. The intelligent control system for rainwater overflow and reuse in elevated bridges according to claim 1, characterized in that, The overflow control unit controls the operation of the rainwater storage components based on the connectivity between the multiple porous drainage pipes and the multiple rainwater storage components, and the real-time rainwater recovery data from the multiple porous drainage pipes, including: Based on the water level of the first water storage area collected by the first water level sensor at multiple consecutive time points, determine whether to activate the vacuum equipment. Based on real-time rainwater harvesting data from the porous drainage pipe connected to the rainwater storage component and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points, the water level of the second water storage area at multiple future time points is predicted. The opening time of the overflow valve is determined based on the predicted water level of the second water storage area at multiple future time points.

6. The intelligent control system for rainwater overflow and reuse in elevated bridges according to claim 5, characterized in that, The overflow control unit controls the opening degree of the overflow valve based on the connection relationship between multiple perforated drain pipes and multiple rainwater storage components and the real-time rainwater recovery data of the multiple perforated drain pipes, including: The initial opening degree of the overflow valve is determined based on the predicted water level of the second water storage area at multiple future time points; After the overflow valve is opened, the opening degree of the overflow valve is adjusted based on the real-time rainwater recovery data from the porous drainage pipe connected to the rainwater storage component and the water level of the second water storage area collected by the second water level sensor at multiple consecutive time points.

7. The intelligent control system for rainwater overflow and reuse in elevated bridges according to claim 1, characterized in that, The overflow control unit is also used for: Based on the water level of the first water storage area collected by the first water level sensor at multiple consecutive time points, it is determined whether to open the drain valve.

8. A smart control method for rainwater overflow and reuse in elevated bridges, characterized in that, The intelligent control system for stormwater overflow and reuse of elevated bridges as described in claim 1 includes: Historical rainwater harvesting data from multiple porous drainage pipes and historical soil condition data from the green belts of the elevated bridge were collected. Based on historical rainwater harvesting data from multiple perforated drainage pipes and historical irrigation data from the green belts of the viaduct, the settings of multiple rainwater storage components, the connectivity between multiple perforated drainage pipes and multiple rainwater storage components, the settings of multiple greening irrigation components, and the connectivity between multiple rainwater storage components and multiple greening irrigation components are optimized. Based on the connection between multiple perforated drainage pipes and multiple rainwater storage components, and the real-time rainwater recovery data of the multiple perforated drainage pipes, the operation of the rainwater storage components and the opening of the overflow valve are controlled. Based on the real-time soil condition data of the green belt and the connection relationship between multiple rainwater storage components and multiple green irrigation components, the operation of multiple green irrigation components is controlled.

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