Urban community waterlogging toughness portrait construction method and system based on rainwater overload rate

Abstract

The invention discloses an urban community waterlogging toughness portrait construction method and system based on a rainwater overload rate, and relates to the technical field of urban safety toughness portrait construction. The method comprises the following steps: acquiring post-raining state scene data of a target area, and preprocessing the post-raining state scene data; calculating surface runoff generation features and surface confluence features of the target area in real time based on the preprocessed post-rain state scene data; and calculating a rainwater overload rate of the target area according to the surface runoff generation characteristics and the surface confluence characteristics, and constructing a waterlogging toughness portrait of the target area based on the calculated rainwater overload rate. By means of the method, the problems that existing waterlogging toughness evaluation is static and dynamic portraying is difficult can be effectively solved, the rainwater overload rate serves as a core toughness index in a breakthrough mode, the dynamic toughness portraying of an underlying surface system of an urban community is achieved, and therefore the precision of the constructed urban community waterlogging toughness portraying is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban safety resilience image construction, and in particular to a method and system for constructing an urban district waterlogging resilience image based on rainwater overload rate. BACKGROUND

[0002] With the continuous expansion of urban construction land, the natural surface that can be stored and infiltrated is replaced by a large area of impermeable hard pavement, resulting in a sharp increase in surface runoff and a sharp decrease in infiltration during rainfall.

[0003] Under the background of global warming, extreme rainfall weather causes more and more cities to suffer from waterlogging disasters of different degrees, seriously affecting urban ecology and socio-economic development.

[0004] At present, the concept of waterlogging resilience is used to represent the ability of the underlying surface system to keep from being flooded by rainwater infiltration under the influence of waterlogging, which is a dynamic process related to time; and the rainstorm waterlogging resilience in the prior art is mostly evaluated by constructing a static resilience evaluation index system, which is difficult to depict the dynamic and real resilience of the city with the evolution of rainstorm intensity and time, resulting in the problem of low precision of the constructed urban district waterlogging resilience image. SUMMARY

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide a method and system for constructing an urban district waterlogging resilience image based on rainwater overload rate, which can effectively solve the problems of static evaluation of existing waterlogging resilience and difficulty in dynamic image, and breakthroughly uses rainwater overload rate as a core resilience index to realize dynamic resilience image of the underlying surface system of the urban district, thereby improving the precision of the constructed urban district waterlogging resilience image.

[0006] The purpose of the present application is achieved by the following technical solutions: A method for constructing an urban district waterlogging resilience image based on rainwater overload rate, the method comprising the following steps: Obtain rain after state scene data of a target area, and pre-process the rain after state scene data; Real-time calculate the surface runoff characteristics and surface confluence characteristics of the target area based on the pre-processed rain after state scene data; According to the surface runoff characteristics and the surface confluence characteristics, calculate the rainwater overload rate of the target area, and construct the waterlogging resilience image of the target area based on the calculated rainwater overload rate.

[0007] In the technical scheme, the rain-after state scene data is preprocessed, so as to improve the practicability and reliability of the data in subsequent calculation, and improve the efficiency of data calculation. The surface runoff characteristics and the surface confluence characteristics of the target region calculated based on the preprocessed rain-after state scene data can dynamically and intuitively reflect the surface runoff and confluence of the target region, effectively solve the problem of staticity and difficulty in dynamic imaging of the existing waterlogging resilience evaluation, and further calculate the rainwater overload rate of the target region. Finally, the waterlogging resilience image of the target region is constructed based on the calculated rainwater overload rate, which breaks through the core resilience index of rainwater overload rate and realizes the dynamic resilience imaging of the underlying surface system of the urban community, thereby improving the accuracy of the constructed waterlogging resilience image of the urban community.

[0008] Further, the rain-after state scene data of the target region is obtained, and the rain-after state scene data is preprocessed. The rain-after state scene data of the target region includes rainfall data, terrain data, pipe network data, and underlying surface data. The rainfall data, terrain data, pipe network data, and underlying surface data are subjected to data cleaning and attribute input processing.

[0009] Further, the rain-after state scene data is preprocessed, and the rain-after state scene data is preprocessed. Based on the preprocessed rain-after state scene data, a preset surface runoff calculation model is used to calculate the permeable area, the impervious area with depression storage, and the impervious area without depression storage of the target region, and the surface runoff characteristics are calculated based on the calculated permeable area, impervious area with depression storage, and impervious area without depression storage. Based on the preprocessed rain-after state scene data and the surface runoff characteristics, a preset surface confluence calculation model is used to calculate the surface confluence characteristics of the target region.

[0010] Further, based on the preprocessed rain-after state scene data, a preset surface runoff calculation model is used to calculate the permeable area, the impervious area with depression storage, and the impervious area without depression storage of the target region. The permeable area is calculated The expression is:

[0011] Wherein, represents the permeable area The flow rate; represents the rainfall intensity; represents the infiltration intensity; represents the time interval; the impervious area with depression storage The yield of the impervious area without depression storage is calculated, and the expression is as follows:

[0012] wherein, the yield of the impervious area with depression storage is calculated; the rainfall is calculated; the depression storage is calculated; the impervious area without depression storage The yield of the impervious area without depression storage is calculated, and the expression is as follows:

[0013] wherein, the yield of the impervious area without depression storage is calculated, the rainfall is calculated, the evaporation is calculated; and the surface runoff calculation model is a deep learning model.

[0014] Further, the process of calculating the surface runoff characteristics based on the calculated permeable area, the impervious area with depression storage and the impervious area without depression storage comprises: The actual yield of the permeable area in a unit time is calculated, and the expression is as follows:

[0015] wherein, the yield of the permeable area is calculated, the rainfall intensity is calculated, the time interval is calculated; when the actual rainfall intensity is greater than the infiltration capacity, the yield of the permeable area is positive, indicating that there is surface runoff; when , then , indicating no runoff or zero runoff; the yield of the impervious area with depression storage is calculated according to the prior deduction of the depression storage ; and the yield of the impervious area without depression storage is calculated according to the deduction of the evaporation to obtain the surface runoff characteristics.

[0016] Further, in the process of calculating the surface runoff characteristics by using the preset surface runoff calculation model, the Horton model is used to set parameters of the preset surface runoff calculation model to calculate the real-time rainwater infiltration rate of the target area, and the expression is as follows:

[0017] wherein, represents the real-time rainfall infiltration rate at the moment, represents the stable infiltration rate, represents the initial infiltration rate; represents the infiltration constant, represents time.

[0018] Further, based on the pre-processed post-rain state scene data and the surface runoff characteristics, the process of calculating the surface runoff characteristics of the target region by using a preset surface runoff calculation model includes: Based on the pre-processed post-rain state scene data and the surface runoff characteristics, the flow basin data of the surface runoff of the target region is obtained, and the target region is divided into a plurality of sub-flow basins according to the flow basin data; Based on the plurality of sub-flow basins, a plurality of sub-catchment areas are obtained, and a preset surface runoff model is used to calculate the surface runoff characteristics of the target region according to the plurality of sub-catchment areas, and the expression is:

[0019]

[0020]

[0021] wherein, represents the surface water storage, represents the water depth, represents time, represents the surface area, represents the rainfall intensity, represents the runoff of different permeable areas, represents the outflow, represents the sub-flow basin overland flow width, represents the surface Manning coefficient, represents the maximum surface depression storage, represents the sub-catchment area slope, and respectively represent the initial value and the final value of the water depth in a certain period of time; the surface runoff calculation model is a deep learning model.

[0022] Further, according to the surface runoff characteristics and the surface runoff characteristics, the process of calculating the rainwater overload rate of the target region includes: According to the surface runoff characteristics, a preset rainwater overload rate calculation model is used to calculate the maximum permeation amount of the target region, and the expression is:

[0023] wherein, A represents the maximum permeation amount; According to the surface runoff characteristics, a preset rainwater overload rate calculation model is used to calculate an actual runoff C of the target area, and an expression is as follows:

[0024] wherein, represents a total water accumulation amount in the surface runoff characteristics, represents a total area of the underlying surface; Based on the maximum permeation amount A and the actual runoff C, a rainwater overload rate G of the target area is calculated, and an expression is as follows:

[0025]

[0026]

[0027] then

[0028] wherein, represents the rainwater overload rate of the target area, represents a comprehensive permeation coefficient, represents a design flow, represents a comprehensive runoff coefficient, and the rainwater overload rate calculation model is a deep learning model.

[0029] Further, a process of constructing a waterlogging resilience image of the target area based on the calculated rainwater overload rate includes: calculating a resilience quantitative representation index of the target area based on the rainwater overload rate G of the target area , and an expression is as follows:

[0030] wherein, represents a rainwater overload rate function value per unit time; using a preset resilience image model, a waterlogging resilience image of the target area is constructed based on the calculated resilience quantitative representation index of the target area , and the resilience image model is a neural network model.

[0031] A system for constructing a waterlogging resilience image of a city community based on a rainwater overload rate, the system includes: a data acquisition module configured to acquire post-rain state scene data of a target area; a data processing module configured to pre-process the post-rain state scene data; a feature calculation module configured to calculate, in real time, surface runoff characteristics and surface confluence characteristics of the target area based on the pre-processed post-rain state scene data; The feature calculation module is further configured to calculate a rainwater overload rate of the target region according to the surface runoff generation feature and the surface confluence feature. The image construction module is configured to construct a waterlogging resilience image of the target region based on the calculated rainwater overload rate.

[0032] Compared with the prior art, the present application has the following advantages: The present application provides a rainwater overload rate-based urban community waterlogging resilience image construction method and system, which can effectively improve the practicability and reliability of data in subsequent calculation process by preprocessing the obtained post-rain state scene data, thereby improving the efficiency of data calculation; the surface runoff generation feature and the surface confluence feature of the target region calculated based on the preprocessed post-rain state scene data can dynamically and intuitively reflect the surface runoff generation and surface confluence of the target region, effectively solving the problem of static evaluation and difficulty in dynamic imaging of existing waterlogging resilience, thereby further calculating the rainwater overload rate of the target region, and finally constructing a waterlogging resilience image of the target region based on the calculated rainwater overload rate, which breaks through the rainwater overload rate as a core resilience index and realizes dynamic resilience imaging of the underlying surface system of the urban community, thereby improving the accuracy of the constructed urban community waterlogging resilience image. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A rainwater overload rate-based urban community waterlogging resilience image construction method provided by the present application is shown in the flowchart; Figure 2 A rainwater overload rate-based urban community waterlogging resilience image construction method provided by the present application is shown in the principle diagram; Figure 3 A resilience image model constructs a waterlogging resilience image, as shown in the diagram; Figure 4 A case elevation data diagram in the experiment provided by the present application is shown in the diagram; Figure 5 A case region underlying surface diagram in the experiment provided by the present application is shown in the diagram; Figure 6 A rainfall analysis process diagram in the experiment provided by the present application is shown in the diagram; Figure 7 A rainfall analysis process diagram in the experiment provided by the present application is shown in the diagram; Figure 8 A drainage simulation zoning diagram in the experiment provided by the present application is shown in the diagram; Figure 9 An external discharge flow curve diagram in the experiment provided by the present application is shown in the diagram; Figure 10 A water accumulation point simulation diagram in the experiment provided by the present application is shown in the diagram; Figure 11 A schematic diagram of the calculation result of the water accumulation amount of the water accumulation point in the experiment provided for the embodiment of the present application is shown in the following figure: Figure 12 A schematic diagram of the calculation result of the water depth of the water accumulation point in the experiment provided for the embodiment of the present application is shown in the following figure: Figure 13 A schematic diagram of the constructed resilience curve image in the experiment provided for the embodiment of the present application is shown in the following figure: Figure 14 A schematic diagram of the structure of a city district waterlogging resilience image construction system based on rainwater overload rate provided for the embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The preferred embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing the specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0036] Embodiment one: The present embodiment provides a city district waterlogging resilience image construction method based on rainwater overload rate, referring to Figure 1 , the method comprises the following steps: Step S1: obtaining the post-rain state scene data of the target area, and pre-processing the post-rain state scene data; Step S2: real-time calculating the surface runoff generation characteristics and surface runoff concentration characteristics of the target area based on the pre-processed post-rain state scene data; Step S3: calculating the rainwater overload rate of the target area according to the surface runoff generation characteristics and the surface runoff concentration characteristics, and constructing the waterlogging resilience image of the target area based on the calculated rainwater overload rate.

[0037] As a preferred embodiment, in step S1, the process of obtaining the post-rain state scene data of the target area and pre-processing the post-rain state scene data comprises: The post-rain state scene data of the target area includes rainfall data, terrain data, pipe network data and underlying surface data; The rainfall data, terrain data, pipe network data and underlying surface data are subjected to data cleaning and attribute entry processing.

[0038] Specifically, first is data acquisition and cleaning. The post-rain state scene data of the target area is acquired through various means (such as consulting materials, field research, Python web crawler, etc.) and the acquired original data is cleaned and attribute entered to complete the preparation work of the initial data.

[0039] Scene simulation is performed, such as Figure 2 As shown in the figure, the underlying surface system of an old urban community is selected as the research object and is divided into "three zones and five elements". The three zones are high, low and middle zones according to elevation, and the five elements are the main research indicators of the underlying surface system, including roof, road, pavement, green land and water body. Based on SWMM, different return period rainfall data, current rainwater pipe data, site terrain and underlying surface material rain combined runoff coefficient data are used to construct the scene under the post-rain state of the city.

[0040] According to the characteristics of each constituent element of the underlying surface system of the old community, the waterlogging resilience capability is evaluated by means of SWMM. The evaluation results include the outflow of the underlying surface system after rainfall, the location of the waterlogging point, the waterlogging amount, the waterlogging time, and the waterlogging resilience index based on the rainwater overload rate. The resilience curve is drawn to express the current evaluation results, and the result analysis of the resilience evaluation is given.

[0041] As a preferred embodiment, in step S2, the process of real-time calculating the surface runoff characteristics and surface confluence characteristics of the target area based on the preprocessed post-rain state scene data includes: Step S21: Based on the preprocessed post-rain state scene data, a preset surface runoff calculation model is used to calculate the permeable area, impervious area with depression storage and impervious area without depression storage of the target area, and based on the calculated permeable area, impervious area with depression storage and impervious area without depression storage, the surface runoff characteristics are calculated. Step S22: Based on the preprocessed post-rain state scene data and the surface runoff characteristics, a preset surface confluence calculation model is used to calculate the surface confluence characteristics of the target area.

[0042] In step S21, based on the preprocessed post-rain state scene data, a preset surface runoff calculation model is used to calculate the permeable area, impervious area with depression storage and impervious area without depression storage of the target area, and based on the calculated permeable area, impervious area with depression storage and impervious area without depression storage, the surface runoff characteristics are calculated. Surface runoff refers to the process of rainfall becoming net rain after loss. SWMM divides the catchment area into three types of permeable, impervious and impervious with depression storage. Therefore, runoff can be composed of three parts: Permeable area The runoff of the impervious area with depression storage is equal to the rainfall minus the initial loss caused by depression storage and infiltration; The impervious area with depression storage The runoff of the impervious area with depression storage is equal to the rainfall minus the initial loss caused by depression storage and infiltration; The impervious area without depression storage The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss.

[0043] The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss. The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss.

[0044] wherein, denotes the impervious area The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss. denotes the rainfall intensity (mm / h); denotes the infiltration intensity (mm / h); denotes the time interval (h); The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss. The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss.

[0045] wherein, denotes the impervious area with depression storage The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss. denotes the rainfall (mm); denotes the depression storage (mm); The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss. The runoff of the impervious area without depression storage is equal to the rainfall minus the evaporation loss.

[0046] wherein, denotes the runoff of the impervious area without depression storage (mm), denotes the rainfall (mm), denotes the evaporation (mm); the surface runoff calculation model is a deep learning model. Further, the process of calculating the surface runoff characteristics based on the calculated impervious area, impervious area with depression storage and impervious area without depression storage comprises:

[0047] Further, the process of calculating the surface runoff characteristics based on the calculated impervious area, impervious area with depression storage and impervious area without depression storage comprises: There are three main infiltration models in SWMM: the Horton model, the Green-Ampt model, and the runoff curve number method. The Horton model is suitable for application in sub-basins after urban area division, and most experts and scholars have adopted this model in urban stormwater runoff calculation. Therefore, in setting the modeling parameters, this paper uses the Horton model to set the parameters of the preset surface runoff calculation model to calculate the real-time rainwater infiltration rate of the target area. The expression is:

[0048] in, express Real-time rainwater infiltration rate (mm / h) at any given moment. This represents the stable infiltration rate (mm / h). Indicates the initial infiltration rate (mm / h); Represents the infiltration constant. Indicates time (h).

[0049] To further reveal the relationship between infiltration rate and three different surface areas ( Permeable area Impermeable area with depression storage capacity The present invention, based on the surface runoff calculation model and the infiltration formula of the Horton model, quantitatively links the real-time infiltration rate to the surface runoff. and The permeable area is calculated in a coupled manner to dynamically obtain the value per unit time. The actual total amount of infiltration in the area Calculate the permeable area per unit time. The actual output flow rate is expressed as:

[0050] in, The flow rate (mm) represents the permeable area. Indicates rainfall intensity (mm / h). for Infiltration rate at any given time (mm / h) Indicates the time interval (h); When the actual rainfall intensity exceeds the infiltration capacity, the flow rate generated by the permeable area is... A positive value indicates the presence of surface runoff; when At that time, This indicates no flow generation or zero flow generation; the impermeable area with depression storage capacity. The flow rate is based on the water storage capacity of the depression. the pre-deduction of the depression storage capacity (mm); the impervious area of the depression storage capacity The yield of the flow is deducted according to the evaporation amount (mm) to obtain the surface yield flow characteristics.

[0051] As a preferred embodiment, in step S22, based on the pre-processed post-rain state scene data and the surface yield flow characteristics, the process of calculating the surface flow characteristics of the target region by using a preset surface flow calculation model includes: Specifically, when rainfall occurs, runoff is generated after loss through the interception of the surface, ground depression filling, etc. Except for a small number of rainfall directly entering natural water bodies in natural water body areas (lakes, etc.), most of the yield flow enters the underground pipe network system through the rainwater inlet of the urban drainage pipe network in the form of slope flow. The surface flow process refers to the process of collecting the net rain of each sub-basin to the pipe network inlet or directly discharging into the river. Its task is to convert the net rain process of each sub-basin into the flow of the basin.

[0052] Based on the pre-processed post-rain state scene data and the surface yield flow characteristics, the flow data of the surface flow of the target region is obtained, and the target region is divided into a plurality of sub-basins according to the flow data; Based on the plurality of sub-basins, a plurality of sub-catchment areas are obtained, and a preset surface flow model is used to calculate the surface flow characteristics of the target region according to the plurality of sub-catchment areas, that is, each sub-catchment area is generalized as a nonlinear reservoir model, that is, the Manning equation and the continuity equation are solved simultaneously, and the expression is:

[0053]

[0054]

[0055] wherein, represents the surface water storage, represents the water depth, represents the time, represents the surface area, represents the rainfall intensity, represents the yield flow of different impervious areas, represents the outflow, represents the sub-basin overland flow width, represents the surface Manning coefficient, represents the maximum depression storage depth of the surface, represents the slope of the sub-catchment area, and respectively represent the initial value and the final value of the water depth in a certain period; the surface flow calculation model is a deep learning model.

[0056] In a preferred embodiment, step S3, the process of calculating the rainwater overload rate of the target area based on the surface runoff characteristics and the surface runoff characteristics, includes: Specifically, surface waterlogging overload refers to the occurrence of surface runoff when, under known comprehensive runoff and comprehensive permeability coefficients, the design runoff and maximum saturated infiltration are obtained. Under different return period rainfall scenarios, when the cumulative infiltration of rainfall exceeds the saturated infiltration of the underlying surface system, surface runoff occurs. The overload rate is defined as the ratio of the overload amount (the difference between the actual rainfall runoff and the design runoff of the underlying surface system under different return periods) to the maximum permeability of the underlying surface.

[0057] In this invention, there is a close data calculation relationship among the "surface runoff calculation model", "surface runoff calculation model", and "rainwater overload rate calculation model", as detailed below: ① The surface runoff calculation model considers the effect of rainfall on different types of underlying surfaces (permeable area). , areas with depressions that retain impermeable water Non-depression impermeable area The net rainwater runoff generated at that time ( , , The model performs time-period calculations. Based on parameters such as rainfall intensity, infiltration rate, depression storage capacity, and evaporation, it calculates the initial runoff of each surface unit per unit time.

[0058] ② The surface runoff calculation model uses the net rainfall calculated by the runoff generation model as the input boundary condition. Combined with hydraulic parameters such as topographic slope, surface roughness, and depression depth, it simulates the flow path, velocity, and time delay of rainwater on the surface using the Manning formula and the continuity equation, and calculates the runoff outflow at different time points. By combining the water accumulation process (V, h), a dynamic total runoff curve can be obtained.

[0059] ③ The rainwater overload rate calculation model uses the key output results provided by the first two models as the input basis: First, the surface runoff calculation model provides the system's infiltration capacity (i.e., maximum infiltration A): Based on the surface runoff characteristics, the maximum infiltration rate of the target area is calculated using a preset rainwater overload rate calculation model, expressed as:

[0060] in, Here, A represents the steady-state infiltration rate (mm / h) in the Horton formula, and A represents the maximum infiltration rate. For time intervals (h), Permeable area (m²); Second, the surface runoff calculation model provides actual runoff (i.e., actual load C) in different rainfall periods: According to the surface runoff characteristics, the actual runoff C of the target area is calculated by using a preset rainwater overload rate calculation model, and the expression is:

[0061] wherein, represents the total water accumulation on the surface in the surface runoff characteristics, represents the total area of the underlying surface (m²); Third, combined with the known design flow (B) and the comprehensive penetration / runoff coefficient (a, b), the rainwater overload rate sequence (G) changing with time is obtained according to the overload rate calculation formula Finally, based on the G value sequence of each period, a 'rainwater overload rate-time' curve is drawn, which provides key dynamic data support for the urban waterlogging resilience portrait.

[0062] Based on the maximum penetration amount A and the actual runoff C, the rainwater overload rate G of the target area is calculated, and the expression is:

[0063]

[0064]

[0065] Substitute formula ② into ③, then

[0066] wherein, represents the rainwater overload rate of the target area, represents the comprehensive penetration coefficient, represents the design flow, represents the comprehensive runoff coefficient, wherein the rainwater overload rate calculation model is a deep learning model.

[0067] Further, the process of constructing the waterlogging resilience portrait of the target area based on the calculated rainwater overload rate includes: ​​Specifically, resilience refers to a system's ability to resist and absorb external disturbances, as well as its capacity to recover quickly after such disturbances. Surface waterlogging overload refers to the occurrence of surface runoff when, under different return periods of rainfall scenarios, the cumulative infiltration of rainfall exceeds the saturated infiltration capacity of the underlying surface system, given the comprehensive runoff coefficient and comprehensive permeability coefficient. The overload rate is defined as the ratio of the difference between the actual rainfall runoff and the design runoff of the underlying surface system under different return periods to the underlying surface infiltration. In profiling urban waterlogging resilience, the concept of an "overload resilience curve" is primarily used to characterize system resilience (as shown in the figure below). This curve uses time as the horizontal axis and the rainwater overload rate as the vertical axis to reflect the change in system rainwater overload over time, thus visually defining the system's resilience level.

[0068] To further quantify the ability of urban residential area underlying surface systems to withstand and recover from rainwater overload during rainfall disturbances, a quantitative characterization index of the resilience of the target area is calculated based on the rainwater overload rate G of the target area. The expression is:

[0069] in, The quantitative characterization index representing the resilience of the target region (i.e., the total resilience response value of the system) represents the time from the initial disturbance. Time until system stabilizes Internally, the overall response of the system to rainwater overload during rainfall; The function value of the rainwater overload rate per unit time represents the value at any given moment. t The relative overload level of the subsystem; This marks the starting point of the rainwater overload. This is the time point at which the system fully recovers and the overload rate returns to zero.

[0070] The pre-defined resilience profile model is used to calculate quantitative resilience indicators for the target region. Construct a flood resilience profile for the target area, such as Figure 3 As shown, the resilience profiling model is a neural network model.

[0071] This formula, by integrating the area under the "rainwater overload rate – time" curve, reflects the system's cumulative stress resistance throughout the entire rainfall event. A smaller value indicates a faster recovery and more stable response to rainfall disturbances, signifying higher resilience; conversely, a larger value indicates a greater and more sustained overload response under rainfall disturbances, indicating lower resilience. Therefore, It can serve as a key indicator for measuring the differences in urban community flood resilience under different rainfall scenarios.

[0072] In this embodiment, by preprocessing the acquired post-rain condition scene data, the practicality and reliability of the data in subsequent calculations can be effectively improved, thereby increasing the efficiency of data calculation. The surface runoff and surface runoff characteristics of the target area calculated based on the preprocessed post-rain condition scene data can dynamically and intuitively reflect the surface runoff and surface runoff conditions of the target area. This effectively addresses the problem of static and undynamic profiling in existing urban flooding resilience assessments, thereby further calculating the rainwater overload rate of the target area. Finally, based on the calculated rainwater overload rate, an urban flooding resilience profile of the target area is constructed. This breakthrough uses the rainwater overload rate as the core resilience indicator, realizing a dynamic resilience profile of the urban community underlying surface system, thereby improving the accuracy of the constructed urban community urban flooding resilience profile.

[0073] Example 2: This embodiment provides corresponding experimental data based on the method described in Embodiment 1, as follows: (1) Overview of the study area Since 2000, heavy rainfall during the flood season has led to frequent urban flooding disasters in major cities. One city, with the largest population and the second highest urbanization level in my country, possesses the underlying surface conditions conducive to urban flooding and a large affected area. The study area is a residential community within this city. Built in 1994, the community covers a total area of ​​5.6 hectares, has 11 buildings with a total building area of ​​200,000 square meters, 1,098 households, a greening rate of 30%, and relatively flat terrain.

[0074] (2) Data processing and site basic condition analysis 1) Topography and Pipeline Network like Figure 4 As shown, ArcGIS software can be used to combine urban elevation data and pipeline data to build urban DEM models and 3D pipeline models, making the stormwater system three-dimensional and concrete, and promoting the effective construction of SWMM models. ArcGIS is used to analyze the topography of a certain community to obtain the water flow direction and flow information, providing necessary geographic information data for the optimization of the stormwater system. Bigemap is used to extract the elevation of the community, and ArcGIS's 3D Analysis tool, through ArcScene's 3D visualization function, produces an elevation analysis map, as shown in the figure below. The area shows a terrain trend of high in the south and low in the north, with the central and northern areas clearly forming catchment areas and concentrated drainage outlets. Similarly, the area's pipe network is drawn in CAD, and ArcGIS is used to match the pipe network with the terrain, assigning elevation values ​​to the pipe network information, and performing nested analysis with the terrain-based water flow simulation map.

[0075] 2) Underlayment Surface Composition The comprehensive runoff coefficients for four different materials were obtained by consulting literature, as shown in Table 1 below:

[0076] Detailed data on the underlying surface of the residential area were collected through satellite imagery and on-site surveys. The underlying surface system was broken down, and its materials consist of asphalt roads, green spaces, building roofs, paving, concrete surfaces, permeable paving, and water bodies, as shown in Table 2. Hard paving constitutes a large proportion and significantly impacts the site's water retention capacity. A detailed plan view of the site's underlying surface system is provided. Figure 5 As shown.

[0077] Table 2:

[0078] ① Roof: The total area is 9272 m², and the material is impermeable. Rainwater is drained to the ground through rain pipes.

[0079] ② Paving: The total area is 6758 m². The sidewalks, some outdoor courtyards and squares use non-grass permeable paving, covering an area of ​​573 m². The outdoor parking lot (cars) uses grass permeable paving, covering an area of ​​480 m².

[0080] ③ Roads: The total area is 11,332 m², and the road surface is made of cement and asphalt, mostly of impermeable materials.

[0081] ④ Green space: covering a total area of ​​13,780 m², mostly planted with shrubs and herbaceous plants.

[0082] ⑤ Water body: Total area 49m², submersion depth 0.5m, average effective water depth 0.35m, and storage capacity 17.15m³.

[0083] (3) Construction of rainfall scenario simulation 1) Rainfall Flow Analysis The site will receive and distribute rainfall through various means, including rainwater collection via pipes, surface runoff, surface infiltration, and evaporation loss. Rainwater from the roof will be partially discharged into sunken green spaces via downpipe connections, and the remainder will be discharged directly into a rainwater storage tank via downpipes. Rainwater falling on paved surfaces and ordinary green spaces will be partially infiltrated through permeable paving, partially discharged into sunken green spaces, and the remainder will be discharged directly into the rainwater storage tank via downpipes. Rainwater falling on roads will flow directly into sunken green spaces where they are located, while rainwater from other road sections will be discharged into the rainwater storage tank via downpipes. Based on the above rainwater flow design, see [link to relevant documentation]. Figure 6 To determine the convergence relationship.

[0084] 2) Rainfall selection Because the study area is small, it can be considered that the rainfall is uniformly distributed. Because the project does not have annual rainfall data, the annual runoff empty rate is simplified into a single field rainfall of short duration. In this study, the calculation rainfall data of the design target annual runoff control rate of 75% is used to check the control ability under the design rainfall. In this paper, 1 year, 3 years, 5 years, 10 years and 50 years of different return period rainstorm are designed, and Chicago rain type is selected as the design rain type, the design rainfall duration is 3 hours, and the rainfall runoff data and water quality data are obtained by monitoring. The rainfall data under different return periods is obtained by the city rainstorm formula in SWMM and the Chicago rain type generator.

[0085] Combined with the design standard of the drainage pipe network in the study area, 5 kinds of return period rainfall, rainfall time and rainfall amount statistics are shown in Table 3:

[0086] Through data analysis, the rainfall amount change process under different return periods is obtained, which is expressed by the rainfall intensity curve diagram as shown in Figure 7 .

[0087] Under different return period rainfalls, the rainfall intensity process line is single-peak distribution, the rainfall intensity gradually increases at the beginning, reaches the maximum value, and then gradually decreases. The rainfall amount increases continuously with the increase of rainfall duration, and before the peak, the increase speed of rainfall amount is faster; after the peak, the increase speed of rainfall amount tends to be flat. And the larger the return period of rainfall, the greater the increase speed of rainfall amount, the greater the rainfall amount, and the greater the rainfall intensity. Under different return periods of rainfall, the larger the return period, the later the peak time, but the subsequent maintenance time is longer.

[0088] 3) SWMM Rainfall and Drainage Simulation ① Input data Model initial parameter setting: According to the underlying surface data, terrain conditions and other information of the study area, the node and pipe parameters, sub-catchment area and width, slope, impermeability, permeable and impermeable area Manning coefficient, permeable and impermeable area surface depression storage, infiltration model, etc. Related parameters are preliminarily set, and the drainage system of the study area is generalized.

[0089] ② Analysis of simulation results Through investigation of the site and according to the site terrain, design slope and other data, the current situation of the site catchment area is analyzed, and the rationality and bearing capacity of the catchment area are evaluated. The site catchment area is divided into high and low belts, middle belts and low belts, as shown in Figure 8 .

[0090] The pipe external discharge flow under the conditions of return period 1 year, 3 years, 5 years, 10 years and 50 years of rainfall is simulated and analyzed, as shown in Figures 6-11 .

[0091] Referring toFigure 9 The results show that there is outflow in the site under the five rainfall return period scenarios. The larger the rainfall return period, the greater the total amount of outflow, the longer the duration of peak runoff per unit of time, and the slower the recovery. In addition, except for the runoff peak under the rainfall return period of 10 years (considering the influence of errors), the runoff peak under different rainfall return periods is positively correlated with the rainfall.

[0092] ③ Ponding point analysis i. Ponding point distribution Through SWMM simulation, the ponding point distribution map is obtained, and the results show that there are two ponding points in the site, which is the same as the research results. Ponding point 01 has an area of 2480.5 m² and is distributed at the north entrance of the community, seriously affecting resident travel. Ponding point 02 has an area of 2218.7 m² and is located in the central area of the community. Comparing the site catchment area, it can be seen that the ponding points are related to the site topography and are located in low-lying areas, as shown in Figure 10 .

[0093] ii. Ponding point water accumulation analysis The results show that under the five rainfall return period scenarios, ponding point 01 and ponding point 02 in the site have different degrees of ponding, and the total amount of ponding in ponding point 02 is relatively large. The larger the rainfall return period, the greater the peak value of ponding, the longer the duration of ponding, and the later the peak time. The ponding amount of the two ponding points gradually increases at the beginning of the rainfall, and the rainfall increases rapidly before the peak. After the peak, the rainfall increases gradually. The speed slows down. The difference is that ponding point 01 will quickly decrease to a certain level after reaching the peak, and maintain this ponding amount, and ponding point 02 will slowly decrease to a certain level after reaching the peak, and then the ponding amount gradually increases, as shown in Figure 11 .

[0094] iii. Ponding point depth analysis Under the five rainfall return period scenarios, the ponding depth of ponding point 01 is greater than that of ponding point 02. In the most common rainfall return period of 1 year and 3 years, the ponding depth of the site is less than 10 cm. In the rainfall return period of 50 years, the ponding depth can reach 22 cm. The ponding depth of the two ponding points gradually increases at the beginning of the rainfall, and the increase speed is faster before the peak. The ponding depth will deepen with the increase of the rainfall return period, and the larger the rainfall return period, the later the peak time. The difference is that ponding point 01 maintains a certain ponding depth after the peak; ponding point 02 rapidly decreases to a certain level after reaching the peak, and then the ponding amount gradually increases, as shown in Figure 12 .

[0095] (4) Draw the resilience curve image According to the calculation method of rainwater overload rate based on SWMM, the overload rates of 1 year, 3 years, 5 years, 10 years and 50 years of rainfall return period are calculated respectively. First, the rainfall runoff process of different return periods of the site is simulated by using SWMM to obtain the relevant calculation data, and the rainwater overload rate of the site in different time periods is calculated.

[0096] According to the method of subgrade waterlogging resilience curve imaging, the overload resilience curve of the overload rate changing with time is drawn, as shown in Figure 13

[0097] ① When the rainfall return period is 1 year, the rainwater overload of the site subgrade system occurs at 13 minutes of cumulative rainfall, and the maximum overload rate of 518% is reached at 31 minutes, that is, the overload amount is 5.18 times the maximum permeability. Subsequently, the overload rate shows a downward trend, and the overload rate reaches 0% at 68 minutes, and the overload amount of the site subgrade system is 0.

[0098] ② When the rainfall return period is 3 years, the rainwater overload of the site subgrade system occurs at 45 minutes of cumulative rainfall, and the maximum overload rate of 1200% is reached at 67 minutes, that is, the overload amount is 12 times the maximum permeability. Subsequently, the overload rate shows a downward trend, and the overload rate reaches 0% after 132 minutes, and the overload amount of the site subgrade system is 0.

[0099] ③ When the rainfall return period is 5 years, the rainwater overload of the site subgrade system occurs at 62 minutes of cumulative rainfall, and the maximum overload rate of 1630% is reached at 86 minutes, that is, the overload amount is 16.3 times the maximum permeability. Subsequently, the overload rate shows a downward trend, and the overload rate reaches 0% after 160 minutes, and the overload amount of the site subgrade system is 0.

[0100] ④ When the rainfall return period is 10 years, the rainwater overload of the site subgrade system occurs at 81 minutes of cumulative rainfall, and the maximum overload rate of 2110% is reached at 116 minutes, that is, the overload amount is 21.1 times the maximum permeability. Subsequently, the overload rate shows a downward trend, and the overload rate reaches 0% after 199 minutes, and the overload amount of the site subgrade system is 0.

[0101] ⑤ When the rainfall return period is 50 years, the rainwater overload of the site subgrade system occurs at 119 minutes of cumulative rainfall, and the maximum overload rate of 3505% is reached at 178 minutes, that is, the overload amount is 35.05 times the maximum permeability. Subsequently, the overload rate shows a downward trend, and the overload rate reaches 0% after 306 minutes, and the overload amount of the site subgrade system is 0.

[0102] In this embodiment, the post-rain state scene data mainly involves flood-prone point distribution data, rainfall data, rainwater pipeline, DEM elevation data, land use data, building contour data and other related data. See Table 4 below for details: ​

[0103] (1) Flooding point distribution data: Flooding point distribution data is mainly obtained through literature and reports, with information such as geographical location, water depth, water time, drainage method, and land use.

[0104] (2) Rainfall data: Rainfall data is mainly obtained through relevant planning websites and literature, with information such as annual rainfall, daily rainfall, and rainfall frequency.

[0105] (3) Rainwater pipe network data: Rainwater pipe network data is mainly obtained from planning departments, with information such as rainwater pipe grade and location, and improved pipe diameter and length through ArcGIS software.

[0106] (4) DEM elevation data: DEM elevation data used in the study is processed through Bigemap platform and ArcGIS software, serving as spatial reference.

[0107] (5) Slope data: Slope data used in the study is processed through Bigemap platform and ArcGIS software, serving as spatial reference.

[0108] (6) Land use data: Land use data is obtained through Bigemap platform, including specific land area, land boundary, and land property information.

[0109] (7) Urban building contour vector data: Collected building contour data is obtained through Bigemap platform and current research, and improved through ARCGIS software to obtain building area, number of floors, height, and location information.

[0110] (8) Subsurface material data of residential area: First, collect recognizable material data through Bigemap platform, and then further determine and refine through field research to obtain information such as location, area, and proportion of green land, cement, paving, permeable paving, and soil.

[0111] Example Three: The embodiment provides a city inrush resilience portrait construction system based on rainwater overload rate, which refers to Figure 14 , and the system comprises: A data acquisition module for acquiring post-rain state scene data of a target area; A data processing module for preprocessing the post-rain state scene data; A feature calculation module for calculating the surface runoff generation features and surface runoff concentration features of the target area in real time based on the preprocessed post-rain state scene data; The feature calculation module is further configured to calculate a rainwater overload rate of the target region according to the surface runoff generation feature and the surface runoff concentration feature. An image construction module is configured to construct a waterlogging resilience image of the target region based on the calculated rainwater overload rate.

[0112] The above merely illustrates the embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation based on the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for constructing a flood resilience profile of urban residential areas based on rainwater overload rate, characterized in that, The method includes the following steps: Acquire post-rain scene data of the target area and preprocess the post-rain scene data; Real-time calculation of surface runoff generation and surface runoff characteristics of the target area based on preprocessed post-rain condition scene data; Based on the surface runoff characteristics and surface runoff characteristics, the rainwater overload rate of the target area is calculated, and a waterlogging resilience profile of the target area is constructed based on the calculated rainwater overload rate.

2. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 1, characterized in that, The process of acquiring post-rain scene data of the target area and preprocessing the post-rain scene data includes: The post-rain condition scene data of the target area includes rainfall data, terrain data, pipeline network data, and underlying surface data; The rainfall data, topographic data, pipeline data, and underlying surface data are cleaned and their attributes are entered.

3. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 1, characterized in that, The process of calculating the surface runoff generation and surface runoff characteristics of the target area in real time based on preprocessed post-rain condition scene data includes: Based on the preprocessed post-rain condition scene data, the permeable area, impermeable area with depression storage capacity, and impermeable area without depression storage capacity of the target area are calculated using a preset surface runoff calculation model. The surface runoff characteristics are then calculated based on the calculated permeable area, impermeable area with depression storage capacity, and impermeable area without depression storage capacity. Based on the preprocessed post-rain scene data and surface runoff characteristics, the surface runoff characteristics of the target area are calculated using a pre-set surface runoff calculation model.

4. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 3, characterized in that, Based on preprocessed post-rain condition scene data, the process of calculating the permeable area, impermeable area with depression storage capacity, and impermeable area without depression storage capacity of the target area using a preset surface runoff calculation model includes: Calculate permeable area The output flow rate is expressed as: in, Indicates permeable area Production flow; Indicates rainfall intensity; Indicates the infiltration intensity; Indicates a time interval; Calculate the impermeable area of ​​the depression storage capacity. The output flow rate is expressed as: in, Indicates the impermeable area with depression storage capacity. Production flow; Indicates rainfall amount; Indicates the storage capacity of the depression; Calculate the impermeable area for the storage capacity of depressions. The output flow rate is expressed as: in, The impermeable area representing the amount of water that can be stored in depressions. The output flow, Indicates rainfall amount, This represents evaporation; the surface runoff calculation model is a deep learning model.

5. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 4, characterized in that, The process of calculating surface runoff characteristics based on the calculated permeable area, impermeable area with depression storage capacity, and impermeable area without depression storage capacity includes: Calculate the permeable area per unit time The actual output flow rate is expressed as: in, This represents the flow rate generated by the permeable area. Indicates rainfall intensity. Indicates a time interval; When the actual rainfall intensity exceeds the infiltration capacity, the flow rate generated by the permeable area is... A positive value indicates the presence of surface runoff; when At that time, This indicates no flow generation or zero flow generation; the impermeable area with depression storage capacity. The flow rate is based on the water storage capacity of the depression. The initial deduction; the impermeable area of ​​the aforementioned non-depression storage capacity. The production flow rate is based on the evaporation rate. The deduction is used to obtain the surface runoff characteristics.

6. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 5, characterized in that, In the process of calculating surface runoff characteristics using a pre-set surface runoff calculation model, the Horton model is used to set the parameters of the pre-set surface runoff calculation model in order to calculate the real-time rainwater infiltration rate of the target area. The expression is as follows: in, express Real-time rainwater infiltration rate at any given moment. Indicates the stable infiltration rate. Indicates the initial infiltration rate; Represents the infiltration constant. Indicates time.

7. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 6, characterized in that, Based on the preprocessed post-rainfall scene data and surface runoff characteristics, the process of calculating the surface runoff characteristics of the target area using a pre-defined surface runoff calculation model includes: Based on the preprocessed post-rain scene data and surface runoff characteristics, watershed data of surface runoff in the target area are obtained, and the target area is divided into several sub-watersheds according to the watershed data. Several sub-catchments are derived from several sub-watersheds, and the surface runoff characteristics of the target area are calculated using a pre-defined surface runoff model based on these sub-catchments. The expression is as follows: in, Indicates the amount of surface water. Indicates water depth. Indicates time, Represents the land surface area. Indicates the intensity of rainfall. This indicates the flow rate for different permeable areas. Indicate the flow rate, Indicates the width of the sub-basin floodplain. Represents the Manning coefficient of the Earth's surface. Indicates the maximum depth of a depression on the Earth's surface. Indicates the slope of the sub-catchment area. and These represent the initial and final values ​​of water depth for a certain period of time, respectively; the surface runoff calculation model is a deep learning model.

8. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 7, characterized in that, The process of calculating the rainwater excess rate of the target area based on the surface runoff characteristics and the surface runoff characteristics includes: Based on the surface runoff characteristics, the maximum infiltration rate of the target area is calculated using a preset rainwater overload rate calculation model, expressed as: Where A represents the maximum permeation rate; Based on the surface runoff characteristics, the actual runoff C of the target area is calculated using a preset rainwater overload rate calculation model, and the expression is: in, This represents the total surface water volume in the surface runoff characteristics. Indicates the total area of ​​the underlying surface; Based on the maximum infiltration rate A and the actual runoff C, the rainwater overload rate G of the target area is calculated as follows: but in, Indicates the rainwater overload rate of the target area. Indicates the overall permeability coefficient. Indicates the design flow rate. The comprehensive runoff coefficient is represented by the rainwater overload rate calculation model, which is a deep learning model.

9. The method for constructing urban community flood resilience profiles based on rainwater overload rate according to claim 8, characterized in that, The process of constructing a flood resilience profile of a target area based on the calculated rainwater excess rate includes: Calculate the quantitative characterization index of the resilience of the target area based on the rainwater overload rate G of the target area. The expression is: in, The function value representing the rainwater overload rate per unit time; The pre-defined resilience profile model is used to calculate quantitative resilience indicators for the target region. A flood resilience profile of the target area is constructed, wherein the resilience profile model is a neural network model.

10. A system for constructing urban community flood resilience profiles based on rainwater overload rate, characterized in that, The system includes: The data acquisition module is used to acquire post-rain scene data of the target area; The data processing module is used to preprocess the post-rain scene data; The feature calculation module is used to calculate the surface runoff generation and surface runoff characteristics of the target area in real time based on the preprocessed post-rain condition scene data; The feature calculation module is also used to calculate the rainwater overload rate of the target area based on the surface runoff characteristics and the surface runoff characteristics; The profile building module is used to construct a waterlogging resilience profile of the target area based on the calculated rainwater overload rate.

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