Service area low-impact measure optimization method and equipment based on SWMM model

Abstract

The invention relates to the technical field of urban area configuration, and particularly discloses a service area low-impact measure optimization method and equipment based on an SWMM model. According to the method, the rainfall flood management model of the target service area is constructed through the storm flood management model (SWMM), runoff response under the rainfall condition can be accurately simulated in combination with sub catchment area division and low-impact measure optimization, and compared with a traditional static design method, the low-impact measure design efficiency is remarkably improved, and the method has the advantages of being high in practicability and the like. Therefore, the waterlogging problem caused by the rainstorm in the urban service area is effectively relieved; according to the method, the efficiency coefficient is introduced as a quantitative index, iterative optimization is carried out through comparison with the preset threshold value, and it is ensured that the layout parameters of the low-influence measures can dynamically adapt to terrain, soil and rainfall characteristics of different service areas.

Description

Technical Field

[0001] The present invention relates to the technical field of urban area configuration, and particularly relates to an optimization method and device for low impact measures in service areas based on the SWMM model. Background Art

[0002] With the acceleration of the urbanization process, functional service areas have emerged frequently, and the increase in impervious area therein has led to increasingly serious rainstorm and flood problems in the service areas. When facing extreme weather events, traditional drainage systems often struggle to effectively manage rainstorm and flood, and are prone to waterlogging and water pollution. Moreover, the rainstorm and flood management in general service areas mostly relies on traditional drainage systems or single low impact development measures, and it is difficult to effectively cope with the sharp increase in runoff and the risk of waterlogging caused by heavy rainfall.

[0003] The design of existing low impact development (LID) measures often relies on empirical judgment or simple estimation, lacking systematic quantitative evaluation means, resulting in unreasonable measure layout or low efficiency. Moreover, traditional LID design methods usually do not fully consider the topographic features, soil permeability and rainfall intensity changes in service areas, leading to significant differences in the effects of measures in different regions or under extreme weather. Summary of the Invention

[0004] In order to overcome the above problems existing in the design of existing low impact measures in service areas, the present invention provides an optimization method and device for low impact measures in service areas based on the SWMM model.

[0005] In order to achieve the above invention objectives, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides an optimization method for low impact measures in service areas based on the SWMM model, and the method includes:

[0007] Construct a rainstorm and flood management model according to the pipe network information, topographic data and land use data of the target service area, and set the rainfall intensity according to the climate characteristics and design requirements of the target service area;

[0008] Divide the target service area into sub-catchment areas, and set the characteristic parameters of each sub-catchment area;

[0009] Set the low impact measures for each sub-catchment area and the layout parameters of the low impact measures;

[0010] Calculate the efficiency coefficient of the target service area according to the rainfall intensity, characteristic parameters and layout parameters of the low impact measures, and when the efficiency coefficient is lower than a preset threshold, optimize the low impact measures.

[0011] According to a specific implementation manner, in the above optimization method, the low impact measures include grass swales, bioretention grids, porous pavements and infiltration galleries.

[0012] According to a specific embodiment, in the above optimization method, the layout parameters include size, depth, and coverage ratio; optimizing the low impact measures specifically includes at least one of the following:

[0013] Adjust the size and depth of the grass swale and bioretention grid;

[0014] Increase or decrease the coverage ratio of the low impact measures;

[0015] Change the type or combination method of the low impact measures.

[0016] According to a specific embodiment, the above optimization method further includes:

[0017] Substitute the optimized low impact measures into the calculation of the efficiency coefficient, and repeat the optimization until the efficiency coefficient reaches the preset threshold.

[0018] According to a specific embodiment, in the above optimization method, the bioretention grid includes a surface layer, a soil layer, and a water storage layer.

[0019] According to a specific embodiment, in the above optimization method, the sub-catchment area includes a pervious area, an impervious area in a depression, and an impervious area without a depression.

[0020] According to a specific embodiment, the above optimization method further includes:

[0021] When calculating the efficiency coefficient of the target service area, select the infiltration model of the sub-catchment area, including at least one of the Horton model, the Green-ampt model, and the SCS infiltration model.

[0022] According to a specific embodiment, in the above optimization method, the characteristic parameters include an impervious rate, a characteristic width, a slope, a roughness coefficient, depression storage, infiltration parameters, and LID parameters.

[0023] According to a specific embodiment, the calculation formula of the efficiency coefficient is:

[0024]

[0025] Wherein, q i实测 is the measured value of the i-th drainage outlet, q i模拟 is the simulated value of the i-th drainage outlet, is the average value of the measured flow, and N is the total number of measured flows.

[0026] In a second aspect, the present invention provides an electronic device, the device includes a memory and a processor;

[0027] Wherein, the memory is used to store a computer program; the processor is used to call and execute the computer program so that the device executes an optimization method for low impact measures in a service area based on the SWMM model as described in any one of the above.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] The present invention constructs a rainwater and flood management model for the target service area through the Storm Water Management Model (SWMM), and combines sub-catchment area division and low impact measure optimization, which can accurately simulate the runoff response under rainfall conditions. Compared with the traditional static design method, it significantly improves the design efficiency of low impact measures, thereby effectively alleviating the waterlogging problem caused by heavy rain in urban service areas; the present invention introduces an efficiency coefficient as a quantitative index and conducts iterative optimization through comparison with a preset threshold to ensure that the layout parameters of low impact measures can dynamically adapt to the terrain, soil, and rainfall characteristics of different service areas; this adaptive optimization mechanism overcomes the defects of strong empirical dependence and lack of systematic evaluation in traditional Low Impact Development (LID) design, and improves the scientificity and applicability of the design; by optimizing low impact measures, the present invention can improve the rainwater and flood management efficiency of the service area and reduce the risk of waterlogging. At the same time, the present invention combines the SWMM model and low impact measures to provide a method for optimizing rainwater and flood management in the service area, which can not only improve the rainwater and flood management efficiency, reduce water pollution, but also enhance the city's adaptability to extreme weather, and bring environmental and economic benefits at the same time. Description of the Drawings

[0030] Figure 1 An optimization method and device for low impact measures in a service area based on the SWMM model provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of the water flow path provided by an embodiment of the present invention;

[0032] Figure 3 A schematic diagram of the SWMM model provided by an embodiment of the present invention. Detailed Embodiments

[0033] The present invention will be further described in detail below in combination with test examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments. All technologies implemented based on the content of the present invention belong to the scope of the present invention.

[0034] In existing rainwater and flood management solutions, the configuration of low-impact measures often does not fully consider resource utilization efficiency and environmental impact, resulting in high construction costs or limited ecological benefits. At the same time, the adjustment and optimization of low-impact measures are often static and one-time, making it difficult to dynamically improve according to actual effects. Moreover, with the intensification of climate change in recent years, the frequency and intensity of extreme rainfall events have increased, and traditional rainwater and flood management measures are difficult to meet the flood control requirements of urban service areas under extreme weather conditions.

[0035] Therefore, the present invention simulates different rainfall scenarios through the SWMM model and optimizes the layout parameters of low-impact measures, establishing a systematic optimization process to solve the problems of discontinuous and incomplete optimization processes in traditional methods.

[0036] Specifically, please refer to Figure 1 , which shows a schematic flow chart of a method for optimizing low-impact measures in a service area based on the SWMM model provided by an embodiment of the present invention. The method includes:

[0037] Step 1: Construct a rainwater and flood management model based on the pipe network information, terrain data, and land use data of the target service area, and set the rainfall intensity according to the climate characteristics and design requirements of the target service area.

[0038] Among them, the rainfall intensity may include rainfall duration, rainfall peak, and rainfall distribution characteristics, such as once in 1 year, once in 2 years, once in 5 years, etc.

[0039] Step 2: Divide the target service area into sub-catchment areas and set the characteristic parameters of each sub-catchment area.

[0040] Specifically, the sub-catchment areas include permeable area, impermeable area in depressions, and impermeable area without depressions.

[0041] Step 3: Set the low-impact measures for each sub-catchment area and the layout parameters of the low-impact measures.

[0042] In this embodiment, the low-impact measures include overgrown grass swales, bioretention grids, porous pavements, and infiltration channels. It can be understood that overgrown grass swales may include depressed green spaces, rain gardens, grassed swales, and green space vegetation systems; bioretention grids may include grass pavers, permeable pavements, and improved pavements, mainly for bioretention; porous pavements include permeable pavements and improved pavements, which have a large permeability rate and are mainly used for infiltration; infiltration channels include various pipeline measures.

[0043] Step 4: Calculate the efficiency coefficient of the target service area according to the rainfall intensity, characteristic parameters, and layout parameters of the low-impact measures, and optimize the low-impact measures when the efficiency coefficient is lower than a preset threshold.

[0044] In a possible implementation, the layout parameters include size, depth, and coverage ratio; optimizing the low impact measures specifically includes at least one of the following:

[0045] Adjust the size and depth of the over-water grass ditch and the bioretention grid;

[0046] Increase or decrease the coverage ratio of the low impact measures;

[0047] Change the type or combination method of the low impact measures.

[0048] Furthermore, the method further includes:

[0049] Substitute the optimized low impact measures into the calculation of the efficiency coefficient and repeat the optimization until the efficiency coefficient reaches the preset threshold.

[0050] Specifically, the SWMM stormwater management model is a dynamic rainfall runoff simulation calculation model, which has the hydrological process between urban stormwater and pipe networks, covering functions such as precipitation, evapotranspiration, depression storage, interception, hardened pavement, depression infiltration, stormwater interception, saturated and unsaturated infiltration, recharge of infiltration water volume, and surface runoff simulation. The SWMM model is mostly used to simulate the dynamic precipitation runoff process of urban rainstorms and has good effects in the simulation process of urban rainwater pipes, channels design, water pollution, and runoff volume. The SWMM model can realize the runoff simulation and pipe network transmission simulation of the service area based on the pipe network information, topographic data, and land use data of the service area. At the same time, in the SWMM model, the LID function is added. By building LID facilities in the model, the runoff control effect of the service area before and after the layout of LID facilities can be compared.

[0051] In this embodiment, the research area is generalized into sub-catchment areas in the SWMM model for optimizing low impact measures. This is the basic unit that constitutes the SWMM model. Each sub-catchment area calculates the flow process according to its own characteristics, then superimposes each area according to the flow routing method, and finally adds it to the pipe network system. The urban surface runoff generation process includes initial loss and subsequent loss. Generally, SWMM only calculates infiltration and depression storage. Each sub-catchment area includes depression impervious areas, non-depression impervious areas, and pervious areas.

[0052] During rainfall, the surface runoff of different sub-catchment areas in the entire service area will flow into the stormwater inlets of the drainage pipe network along the slope. The whole process is the confluence module. Confluence includes infiltration, evaporation, and surface runoff. Provide the maximum surface water storage through ponding, surface wetting, and interception.

[0053] The pipe network transmission system includes basic units such as pipe segments, nodes, water storage facilities, and outlets. Each basic unit needs to ensure sufficient drainage capacity. In SWMM, the simulation of the pipe network transmission system is achieved through relevant settings of hydraulic parameters. There are three methods for simulating the pipe network in the SWMM model, namely the steady flow method, the dynamic wave method, and the kinematic wave method. In this embodiment, the kinematic wave method is used for calculation.

[0054] Low Impact Development (LID) measures include vegetated swales, bioretention cells, porous pavements, and infiltration galleries. LID measures on sub-catchment areas provide some functions of rainfall, runoff storage, and evaporation of stored water (except for rain barrels). Rainwater infiltration mostly occurs in vegetated swales, and may also occur in bioretention cells, porous pavements, and infiltration galleries, reducing the flow rate into the pipe network, thereby reducing the pressure on the drainage pipe network. The water flow path is as Figure 2 shown.

[0055] (1) The surface layer corresponds to the ground (or pavement), directly receiving rainfall, runoff from the upstream ground, or excessive runoff in the storage depression, and generating surface outflows into the drainage system or water flows into the ground.

[0056] (2) The pavement layer is the porous concrete or asphalt layer for a continuous porous pavement system, or the paving bricks and filling materials in a modular system.

[0057] (3) The soil layer is the soil in the bioretention cell, used to support the growth of vegetation.

[0058] (4) The water storage layer is the gravel layer, providing a water storage environment for bioretention cells, porous pavements, and infiltration gallery systems.

[0059] (5) The subsurface drain system conveys the outflow of the gravel water storage layer of bioretention cells, porous pavement systems, and infiltration galleries (usually porous pipes) into the drainage pipe network.

[0060] Method for dividing sub-catchment areas:

[0061] For the service area, the sub-catchment areas are divided. Depending on the research object, the following methods are adopted for dividing sub-catchment areas:

[0062] (1) Manual division method. According to the layout of the rainwater pipe network, the distribution of buildings, and the roads, the sub-catchment areas are manually divided. This method is applicable to research objects with simple pipe networks and small areas. In the case of complete data in the research area, the manual division method has high accuracy and good results.

[0063] (2) Thiessen polygon method. Through the ArcGIS software, the sub-catchment areas are divided according to the pipe network nodes, generating Thiessen polygons for the division of the drainage pipe network. This method is applicable to research areas with large areas.

[0064] (3) Combined division method. Through areas such as terrain, river channels, and roads, first conduct a large-scale area division, then divide small sub-areas through the Thiessen polygon method, and finally make manual adjustments. Generally, the accuracy is higher than the second method.

[0065] 2) Division of sub-catchment areas in the service area:

[0066] Due to the small area of the service area and detailed service area data, the manual division method is used to divide the sub-catchment areas in the service area. The areas in the sub-catchment areas are obtained through ArcGIS.

[0067] There are three infiltration models in SWMM, namely the Horton model, the Green-ampt model, and the SCS infiltration model. In this embodiment, the Horton model suitable for the service area is selected as the infiltration model for the underlying surface area.

[0068] According to a specific implementation manner, combined with the low-impact development construction plan of the Baoping Expressway service area, a low-impact development service area SWMM stormwater management model is constructed, as shown in Figure 3 According to the service area design document, and comprehensively considering the topological structure relationship of the drainage pipe network, and referring to the internal pipe network distribution in the service area, the drainage pipe network in the service area is generalized, and the main drainage pipes are retained. The generalized map of the service area is as shown in Figure 3 shown.

[0069] The design rainfall refers to the rainstorm formula in Baoji City:

[0070]

[0071] In the formula: i is the design rainstorm intensity, mm / min; T is the recurrence interval, a; t is the rainfall duration, min.

[0072] Combining the land construction type, terrain factors, and meteorological factors of the expressway service area, the design effects of the design rainfall recurrence intervals T of 1, 2, 3, 5, 10, and 15 a are studied. The rainfall duration t is 120 min, and the rain peak coefficient takes the empirical value r = 0.4, that is, the maximum rainstorm intensity is reached at 48 min of rainfall. Calculate the peak and cumulative rainfall amounts of the rain type for subsequent SWMM model simulation.

[0073] Parameters of the sub-catchment area:

[0074] 1. Impervious rate of the sub-catchment area.

[0075] The impervious rate of the sub-catchment area is determined through land use information and referring to the empirical values in each region (Chang Xiaodong et al., 2016). Among them, the impervious coefficients of roads and building land are taken as 0.90, while the impervious coefficients of grasslands, rain gardens, grassed swales, and sunken green spaces are taken as 0.10.

[0076] 2. Characteristic width of sub-catchment area.

[0077] The characteristic width (Width) of the sub-catchment area is the overland flow width generated by runoff within the sub-catchment area after rainfall-induced runoff. The width has a significant impact on the simulation results of the SWMM model. Currently, there are the following four methods for calculating the characteristic width:

[0078] ① Width = 1.7 * MAX(Height, Width);

[0079] ② Width = K * Sqrt(area) (0.2 < K < 0.5);

[0080] ③ Width = K * Perimeter (0 < K < 1);

[0081] ④ Width = Area / Flow Length.

[0082] Method ①: The characteristic width is 1.7 times the maximum value of the length (or width); Method ②: The characteristic width is the coefficient K multiplied by the square root of the area; Method ③: The characteristic width is the coefficient K multiplied by the perimeter of the sub-catchment area; Method ④: The characteristic width is the area divided by the longest flow path in the sub-catchment area. The calculation methods of the four methods are all simple and easy to understand, but there are also disadvantages. Among them, the key coefficient K value is also a sensitive parameter. The large value range leads to a large difference in the results of the characteristic width. At the same time, the irregular shape of the sub-catchment area makes it difficult to use Method ①. In this paper, Method ④ is selected as the method to determine the width of the sub-catchment area. The area of the sub-catchment area is obtained by actual measurement of Google satellite images, and the longest flow path in the sub-catchment area is obtained by measuring the distance from the drainage outlet to the farthest point in the area. The characteristic width of each sub-catchment area.

[0083] 3. Slope of sub-catchment area.

[0084] When calculating the slope of the sub-catchment area, a refined contour map is made based on the high-precision DEM of the study area. The slope of the sub-catchment area is calculated by the following formula:

[0085]

[0086] In the formula: S is the slope of the sub-catchment area; H2 is the highest elevation of the sub-catchment area, m; H1 is the lowest elevation of the sub-catchment area, m; L is the straight-line distance between H2 and H1, m.

[0087] 4. Roughness coefficient and depression storage.

[0088] The infiltration and non-infiltration roughness coefficients, and the infiltration and non-infiltration depression storage parameters are obtained by referring to empirical values and historical data. The roughness coefficient represents the roughness of the sub-catchment area, and the storage coefficient is the storage height of the infiltration area and the impervious area of the sub-catchment.

[0089] Infiltration parameters:

[0090] The attributes of the Horton infiltration model include the initial infiltration rate, the steady infiltration rate, and the decay constant. Other infiltration parameters include the drainage time, which represents the time required for the soil to dry completely. Referring to the empirical parameters of Guyuan City and combining with the SWMM user manual, the Horton infiltration model and the infiltration parameters are determined.

[0091] LID parameters:

[0092] Set the low impact measures and the layout parameters of the low impact measures for each sub-catchment area. According to the actual situation, set 4 LID measures including rain barrels, grassed swales, sunken green spaces, and rain gardens, which mainly include a surface layer, a soil layer, and a storage layer. The surface layer parameters include the storage depth, vegetation coverage, and roughness coefficient; the soil layer parameters include the soil thickness, porosity, water production capacity, wilting point, hydraulic conductivity, hydraulic conductivity gradient, and suction head; the storage layer parameters include the storage height, void ratio, and hydraulic conductivity. The storage depth of the surface layer is the distance from the storage water level to the bottom of the measure. The porosity of the soil layer represents the compactness of the soil material; the water production capacity is the soil water holding capacity, representing the water retention and holding capacity of the soil; the wilting point is the wilting coefficient; the hydraulic conductivity represents the amount of water passing through a unit area per unit time under a unit water potential gradient when the soil is saturated; the suction head is the soil water suction force, which is the adsorption and retention ability of the soil matrix to water, referring to the negative pressure of soil water. The storage height of the storage layer is the highest height of the water stored inside the soil; the porosity and hydraulic conductivity of the storage layer are similar to those in the soil.

[0093] The surface layer storage depth of the LID measures is obtained through actual measurement. Since the bioretention measures are all covered with vegetation, the vegetation coverage is taken as 0.9, and the roughness coefficient and surface slope are taken as empirical values; the soil thickness of the soil layer is obtained from the design drawing, the hydraulic conductivity is obtained from the relevant design documents, and the porosity, water production capacity, wilting point, hydraulic conductivity gradient, and suction head are obtained by referring to the empirical parameters; through actual measurement and research, there is no storage layer for the bioretention measures, and the storage height of the rain barrel is 1250 mm.

[0094] After setting and selecting various parameters, model verification is required to determine the rationality of the model parameters. During a rainfall, monitor the actual runoff process at the drainage outlet. Input the measured rainfall data into the model, and at the same time input the selected parameters. Compare the differences between the actual runoff process at the drainage outlet and the runoff process in the model, and adjust the parameters so that the model simulation results are within the allowable error range. According to the requirements of the "Hydrological and Hydro-Meteorological Forecasting Specification", the Nashi-Sutcliffe efficiency coefficient (E NS ) is used as the evaluation index for model calibration. Nashi-Sutcliffe represents the fitting degree between the observed value and the simulated value. In this embodiment, the preset threshold is set to 0.5.

[0095] E NS The calculation formula is:

[0096]

[0097] where, q i实测 is the measured value of the i-th drainage outlet, q i模拟 is the simulated value of the i-th drainage outlet, is the average value of the measured flow, and N is the total number of measured flows.

[0098] If the efficiency coefficient is lower than the preset threshold, optimize the low impact measures, and repeat the calculation of the efficiency coefficient until the efficiency coefficient reaches the preset threshold.

[0099] The present invention constructs a rainwater and flood management model for the target service area through the Storm Water Management Model (SWMM), and combines the sub-catchment area division and the optimization of low impact measures, which can accurately simulate the runoff response under rainfall conditions. Compared with the traditional static design method, it significantly improves the design efficiency of low impact measures, thereby effectively alleviating the waterlogging problem caused by heavy rain in urban service areas; the present invention introduces the efficiency coefficient as a quantitative index, and conducts iterative optimization through comparison with the preset threshold to ensure that the layout parameters of low impact measures can dynamically adapt to the terrain, soil and rainfall characteristics of different service areas; this adaptive optimization mechanism overcomes the defects of strong empirical dependence and lack of systematic evaluation in traditional Low Impact Development (LID) design, and improves the scientificity and applicability of the design; the present invention can improve the rainwater and flood management efficiency of the service area and reduce the waterlogging risk by optimizing low impact measures. At the same time, the present invention combines the SWMM model and low impact measures to provide a method for optimizing the rainwater and flood management of the service area, which can not only improve the rainwater and flood management efficiency, reduce water pollution, but also enhance the city's adaptability to extreme weather, and bring environmental and economic benefits at the same time.

[0100] In addition, through the precise setting of the characteristic parameters of sub-catchment areas and the reasonable configuration of low-impact measures, the present invention can maximize the retention and infiltration of rainwater under limited space and investment conditions, reducing the pressure on downstream drainage pipe networks. Compared with traditional single LID measures, this method supports the combined optimization of multiple low-impact measures (such as bioretention basins, infiltration trenches, green roofs, etc.), significantly improving the utilization efficiency of land resources and construction costs.

[0101] On the other hand, an embodiment of the present invention also provides an electronic device, which includes a processor, a network interface, and a memory. The processor, the network interface, and the memory are interconnected. Among them, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the above-mentioned optimization method for low-impact measures in a service area based on the SWMM model.

[0102] In an embodiment of the present invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0103] It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed by a hardware decoding processor or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. The processor reads the information in the storage medium and combines its hardware to complete the steps of the above method.

[0104] The storage medium can be a memory, for example, it can be a volatile memory or a non-volatile memory, or it can include both volatile and non-volatile memories.

[0105] Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory.

[0106] The volatile memory can be a Random Access Memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0107] The storage media described in the embodiments of the present invention are intended to include but not limited to these and any other suitable types of memories.

[0108] It should be understood that the system disclosed in the present invention can be implemented in other ways. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the communication connection between the modules can be through some interfaces, and the indirect coupling or communication connection of the server or unit can be electrical or other forms.

[0109] In addition, each functional module in the various embodiments of the present invention can be integrated in a processing unit, or each module can exist physically alone, or two or more modules can be integrated in a processing unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0110] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0111] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing low impact measures in a service area based on the SWMM model, characterized in that, The method includes: Construct a rainwater management model based on the pipe network information, terrain data, and land use data of the target service area, and set the rainfall intensity according to the climate characteristics and design requirements of the target service area; Divide the target service area into sub-catchment areas and set the characteristic parameters of each sub-catchment area; Set the low impact measures for each sub-catchment area and the layout parameters of the low impact measures; Calculate the efficiency coefficient of the target service area according to the rainfall intensity, characteristic parameters, and layout parameters of the low impact measures, and optimize the low impact measures when the efficiency coefficient is lower than the preset threshold.

2. The optimization method for low impact measures in a service area based on the SWMM model according to claim 1, characterized in that, The low impact measures include grass swales, bioretention grids, porous pavements, and infiltration galleries.

3. A method for optimizing low impact measures in a service area based on the SWMM model according to claim 2, characterized in that, The layout parameters include size, depth, and coverage ratio; optimizing the low impact measures specifically includes at least one of the following: Adjust the size and depth of the grass swales and bioretention grids; Increase or decrease the coverage ratio of the low impact measures; Change the type or combination method of the low impact measures.

4. The optimization method for low impact measures in service areas based on the SWMM model according to claim 3, characterized in that, The method further includes: Substitute the optimized low impact measures into the calculation of the efficiency coefficient and repeat the optimization until the efficiency coefficient reaches the preset threshold.

5. The optimization method for low impact measures in a service area based on the SWMM model according to claim 3, wherein The bioretention grid includes a surface layer, a soil layer, and a water storage layer.

6. The optimization method of low impact measures in the service area based on the SWMM model according to claim 1, characterized in that, The sub-catchment area includes a permeable area, a depression impermeable area, and a non-depression impermeable area.

7. A method for optimizing low impact measures in a service area based on the SWMM model according to claim 1, characterized in that The method further includes: When calculating the efficiency coefficient of the target service area, select the infiltration model of the sub-catchment area, including at least one of the Horton model, the Green-ampt model, and the SCS infiltration model.

8. The optimization method of low impact measures in the service area based on the SWMM model according to claim 1, characterized in that The characteristic parameters include impermeability rate, characteristic width, slope, roughness coefficient, depression storage, and infiltration parameters.

9. The optimization method of low impact measures in the service area based on the SWMM model according to claim 1, characterized in that The calculation formula of the efficiency coefficient is: where q i实测 is the measured value of the i-th drainage outlet, and q i模拟 is the simulated value of the i-th drainage outlet, is the average measured flow rate, and N is the total number of measured flow rates.

10. An electronic device, characterized in that, The device includes a memory and a processor; Wherein, the memory is used to store a computer program; the processor is used to call and execute the computer program so that the device executes a method for optimizing low impact measures in a service area based on the SWMM model according to any one of claims 1 to 9.

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