A hydroelastic foundation digital acceptance method based on multi-source data

Abstract

The application discloses a kind of water elasticity infrastructure digital acceptance methods based on multi-source data, fusion unmanned aerial vehicle oblique photography image, real scene three-dimensional model, DEM, DOM, hydrology and water quality simulation data, vegetation index and social and economic benefit index multi-source data.Through the construction real scene three-dimensional model, topography digital comparison is carried out;Fusion GIS and PCSWMM model, realize water elasticity infrastructure layout acceptance, hydrology and water quality simulation and performance evaluation;Based on DEM and model output data, combined with Python simulation, realize waterlogging water safety risk acceptance;Using multi-source remote sensing and three-dimensional model, water elasticity infrastructure ecological function diagnosis and comprehensive benefit accounting are carried out.The application builds whole process digital acceptance model covering water safety, water environment and water ecology, replaces traditional mode relying on artificial experience and discrete measurement, realizes the objectification, precision and intelligentization of acceptance work, improves the scientific nature and reliability of water elasticity infrastructure construction project acceptance.

Description

Technical Field

[0001] This invention belongs to the field of urban management technology, specifically relating to a digital acceptance method for water-resilient infrastructure based on multi-source data. Background Technology

[0002] Water-resilient infrastructure is an organic whole comprised of green infrastructure, grey infrastructure, and blue spaces, aiming to achieve comprehensive resilience of urban water systems. Green infrastructure primarily refers to Low Impact Development (LID) facilities such as rain gardens, permeable paving, and green roofs, focusing on source runoff control and water purification. Grey infrastructure includes stormwater pipe networks, storage tanks, and pumping stations, undertaking runoff transport and storage functions. Blue spaces encompass wetlands, landscape water bodies, and river buffer zones, providing end-of-pipe storage and ecological purification services. Although these facilities differ in form and function, their acceptance requires evaluation from multiple dimensions, including spatial layout rationality, hydrological control efficiency, water quality improvement effects, facility operation status, and overall benefits. The acceptance logic and methods are highly similar.

[0003] Digital acceptance methods are increasingly being used in urban planning and construction. This approach integrates multiple technologies to achieve precise simulation and verification of construction projects, providing scientific data support for project management. However, existing digital acceptance methods remain relatively simplistic in their data application, typically relying solely on design drawings, as-built measurement data, or limited on-site monitoring data for comparison and verification. This single-source data-driven acceptance model often focuses on the static consistency comparison between the engineering entity and design drawings, as well as the instantaneous simulation verification of hydrological performance, and has not yet formed a systematic and comprehensive acceptance system based on multi-source data. Specifically, existing methods lack the ability to integrate and apply multiple data sources, such as real-world 3D models, spatial layout of water-resilient infrastructure, and measured hydrological and water quality data, failing to achieve multi-dimensional cross-verification from spatial layout, hydrological processes, water quality control to facility status. Therefore, existing acceptance conclusions are insufficient to comprehensively and objectively guarantee the comprehensive benefits and sustainability of water safety, water environment, and water ecology of water-resilient infrastructure during long-term operation. Summary of the Invention

[0004] The purpose of this invention is to provide a digital acceptance method for water-resilient infrastructure based on multi-source data. Taking Low Impact Development (LID) facilities as a typical example, this method integrates multi-source data, including UAV oblique photogrammetry, digital elevation models (DEMs), digital orthophoto maps (DOMs), realistic 3D models, hydrological and water quality simulation data, vegetation indices, and socio-economic benefit indicators. This overcomes the limitations of traditional acceptance methods, which are often singular, static, and overemphasize engineering entities while neglecting ecological functions and long-term benefits. The method achieves a quantifiable, end-to-end acceptance process, from comparing topographic and design consistency, verifying the spatial layout of water-resilient facilities, assessing the effectiveness of hydrological and water quality control, and simulating detailed urban flooding risks, to diagnosing facility functional status and conducting quantitative pre-assessment of comprehensive benefits.

[0005] To achieve the above objectives, this invention provides a digital acceptance method for water-resilient infrastructure based on multi-source data, comprising the following steps: S1. Acceptance of comparison between real-scene 3D modeling and terrain digitization; S2, Digital Acceptance of Spatial Layout of Water Resilient Infrastructure; S3, Hydrological process simulation and digital performance acceptance; S4. Digital acceptance of water quality simulation and pollution control; S5. Refined simulation and digital acceptance of urban flooding risk; S6. Digital diagnostics of the functional status of water-resilient infrastructure; S7. Digital accounting and pre-evaluation of comprehensive benefits.

[0006] As a further aspect of the present invention: the specific implementation steps for the S1 real-scene 3D modeling and terrain digitization comparison and acceptance include: S1.1. By setting up ground control points and planning flight routes, systematically acquire multi-angle oblique photogrammetric image data of the Low Impact Development Facility (LID) project area; S1.2 Perform quality checks and preprocessing on the acquired images; S1.3 Perform aerial triangulation encryption and multi-view image dense matching to generate high-precision point cloud data and complete the UAV indoor 3D reconstruction; S1.4 Apply point cloud filtering algorithm to identify and extract ground point cloud data in the project area, distinguish ground points from non-ground points, remove non-ground points to obtain clean ground point cloud data, and then generate digital elevation model (DEM) through spatial interpolation. S1.5. Divide the clean ground point cloud data into multiple different triangular units, and construct an irregular triangular mesh model through these triangular units to simulate the shape and elevation changes of the terrain surface, thereby realizing the creation of an irregular triangular mesh (TIN). S1.6. Based on the constructed TIN spatial location information, it is divided into blocks to form a textureless white model. The parameterized correspondence between the two-dimensional image and the three-dimensional geometric model is established. The spectral features of the multi-view image are projected onto the surface of the white model to generate a real-world three-dimensional model. S1.7 Based on the generated real-scene 3D model, digital differential correction technology is used to eliminate image distortion caused by terrain undulation and camera tilt, and generate a digital orthophoto map (DOM) with unified coordinates and scale. S1.8 Calculate the plane accuracy error of each check point. The plane accuracy error is calculated by comparing the actual measured coordinates of the check point with the plane coordinates predicted by the model. Formula for calculating the mean square error in the X direction: ; in, The mean square error of the X-axis coordinate is... Let n be the residual of the i-th checkpoint in the X direction, and n be the number of checkpoints involved in the calculation. Formula for calculating the mean square error in the Y direction: ; in, The mean square error of the Y-axis coordinate. Let be the residual in the Y direction at the i-th checkpoint; Formula for calculating plane mean square error σ: ; Substituting the actual measured coordinates of the checkpoints and the elevation coordinates measured by the model into the formula, the accuracy error of the elevation is calculated: ; in, This is the elevation error. Let be the residual of the elevation of the i-th checkpoint; S1.9 Set the plane accuracy error range and elevation accuracy error range to within ±5cm, use the DEM model to make contour lines, and compare them with the contour lines of the design drawing to complete the acceptance of the real scene 3D modeling and terrain digital comparison.

[0007] As a further aspect of the present invention: the specific implementation steps for the digital acceptance of the spatial layout of S2 water-resilient infrastructure include: S2.1 Import the original rainwater pipeline drawings of the project area into the Geographic Information System (GIS) platform, and separate and extract the core pipeline network elements such as pipe sections, manholes and discharge outlets from the drawing data; S2.2. The elements extracted in S2.1 are converted into standard GIS vector feature classes using data conversion tools, thereby establishing independent pipeline, manhole, and discharge outlet layers; S2.3 In the GIS platform, edit or build the attribute tables for each created feature class, and enter or associate the key attributes required for modeling for each feature, including: unique number, upstream and downstream connection relationship, pipe diameter, pipe length, elevation and facility type; S2.4. The GIS feature classes with the completed geometric and attribute information are matched and mapped according to the format and structure required by the modeling software model through model interface tools or data conversion scripts, and finally exported as the input file of the modeling software to complete the digital establishment of the drainage network basic model. S2.5. Based on the DEM and DOM models generated in S1, sub-catchment areas are delineated using a combination of hydrological analysis and manual verification. S2.6 Based on the defined sub-catchments, extract and calculate the key modeling parameters of each sub-catchment, including: area, average slope, proportion of impermeable area, and feature width; organize and structure the parameters of each sub-catchment to complete the construction of the sub-catchment in the modeling software model; S2.7. Based on the DOM model generated in step 1, identify the actual distribution of LIDs, compare the actual calculated location area of ​​low-impact development facilities on the GIS platform with the location area on the design drawings, calculate their location matching degree, and thus complete the digital acceptance of LID spatial layout: ; If the matching degree is ≥90%, the LID layout is accepted; if the matching degree is <90%, the LID layout is rejected. S2.8. Add the accepted LID information, sub-catchment parameters, and rainfall data to the modeling software model for refined simulation, and complete the modeling software model construction.

[0008] As a further aspect of the present invention, the specific implementation steps of S3 include: S3.1 The accuracy of the runoff-water quality simulation model in the computational modeling software is evaluated using two indicators: the Nash efficiency coefficient (NSE) and the relative error (RE). The formulas for calculating NSE and RE are as follows: ; ; in, Let be the observed value at time t, in m³ / s; The average value is the observed value, m³ / s; The simulated value at time t is m³ / s; T is the number of observation data. The closer the NSE value is to 1, the better the simulation effect and the higher the reliability of the model. The closer the RE value is to 0, the better the simulation results match the average value of the observed data; If the NSE value is much less than 0, it indicates that the model is unreliable; S3.2 Select multiple independent measured rainfall-runoff process data to calibrate and validate the modeling software model; when the NSE of multiple rainfall simulation results is greater than 0.7 and the absolute value of RE does not exceed 10%, the model is deemed to have passed the validation, its parameter settings are reasonable, and it has the ability to reliably simulate the actual hydrological process in the project area, and can be used for subsequent performance evaluation. S3.3 Using a validated model, input the design rainfall conditions, and based on the hydrodynamic principles of surface runoff generation, runoff confluence, and pipe network runoff, simulate the hydrological processes in the project area and calculate key hydrological performance indicators; S3.4 Compare the calculated index values ​​with the design target values. If they meet or exceed the design requirements, the hydrological process simulation and digital performance of the project area are deemed to have passed the acceptance test, indicating that the project has met the expected water security requirements in terms of total runoff control, pipeline drainage capacity, and urban flooding risk prevention. If the model verification fails or the performance assessment is not up to standard, the model input data, sub-catchment division, or LID parameters need to be checked and adjusted until the model is reliable and the assessment meets the standards.

[0009] As a further aspect of the present invention: the specific implementation steps of S4 water quality simulation and pollution control digital acceptance include: S4.1 In the modeling software model constructed in S2, enable its water quality simulation module, assign a pollutant accumulation and scouring model to the surface of the sub-catchment area, and assign corresponding pollutant removal parameters to various LIDs. S4.2 Select at least two measured rainfall event sequences as model inputs, and set typical initial pollutant background concentrations for the project area for the model; S4.3 Run the modeling software model to perform hydrological-water quality coupled simulation, and simultaneously simulate the accumulation, flushing, transport and LID purification process of pollutants; S4.4 Based on the pollutant flux data output by the simulation, calculate the key water quality performance indicators used for assessment, including: total pollution load reduction rate and event average concentration (EMC) reduction rate. S4.5. Compare the calculated water quality performance indicators with the design requirements or preset acceptance thresholds of the project area. If all key indicators meet or exceed the thresholds, the digital acceptance of water quality simulation and pollution control in the project area is qualified. Through the comprehensive effect of LID, the project can effectively control non-point source pollution and achieve the quantitative goal of improving water environment quality.

[0010] As a further aspect of the present invention: the specific implementation steps of the refined simulation and digital acceptance of S5 urban flooding risk include: S5.1 Input high-precision DEM data and sub-catchment vector data including sub-catchment node overflow attributes, and use the GIS spatial clipping tool to clip the global DEM into multiple independent sub-region terrain rasters according to the boundaries of each sub-catchment. S5.2 Automated processing of the input sub-catchment terrain raster: First, obtain the minimum elevation value of the sub-catchment terrain raster; then, based on this value, generate a raster mask to identify the lowest point; subsequently, convert the raster mask into point features, and finally obtain the coordinates of the lowest point from the point features, outputting them as the water accumulation seed points of the current sub-catchment. S5.3. Using the obtained seed point coordinates as the starting position, apply the region growing algorithm to perform spatial connectivity analysis on the sub-region terrain data to identify all potential flooded areas connected to the seed point terrain. S5.4 Within the defined connected domain, a numerical iterative algorithm is used to solve for the accurate water level by balancing the submerged water volume and the node overflow output by the modeling software. Then, a submerged depth distribution map of the sub-catchment area is generated through grid calculation. S5.5. For each sub-catchment, execute S5.2 to S5.4 sequentially to generate the corresponding inundation depth raster file for each sub-catchment. S5.6 Merge all the sub-catchment inundation depth raster files generated in S5.5 to generate an overall urban flooding risk distribution map of the project area; based on this risk distribution map, extract the inundation depth information of key areas and compare it with the design or safety-allowed maximum inundation depth standard; if the inundation depth of all key locations does not exceed the allowable standard, the project area's refined simulation and digital acceptance of urban flooding risk is deemed qualified, indicating that the project has a reliable urban flooding prevention and control capability under the design rainfall conditions and effectively protects urban water safety; otherwise, it is deemed unqualified.

[0011] As a further aspect of the present invention: the specific implementation steps of digital diagnostics of the functional status of S6 water-resilient infrastructure include: S6.1 Based on the DOM model generated by S1, quantitative indicators such as the normalized differential vegetation index are used to perform preliminary calculations and graphical representations of the green coverage and growth vitality of the LID area, assisting acceptance personnel in quickly identifying areas with bare, withered, or insufficient vegetation coverage. S6.2 Based on the DEM model generated in S1, the micro-topographic catchment area of ​​the LID unit is simulated using hydrological analysis tools. Combined with the DOM image within this area, the acceptance personnel are assisted in visually judging the color uniformity, texture consistency, and whether there are obvious siltation or damage marks on the permeable pavement surface. The permeability performance is also checked in conjunction with the as-built material list. S6.3 Based on the real-world 3D model generated by S1, the system automatically generates and measures the clearance dimensions within the specified radius and 3D buffer zone of key maintenance point manholes and overflow outlets, identifies whether there are fixed obstacles encroaching on the maintenance space, and outputs a quantitative analysis report. S6.4 Summarize the verification and analysis results from S6.1 to S6.3 to form a three-dimensional integrated diagnostic report that includes vegetation status diagrams, permeability observation records, and quantitative data on operation and maintenance space. This report objectively presents the functional status of LID in the early stage of completion, provides a comprehensive digital decision-making basis for whether the project passes functional acceptance, and also provides a basic guarantee for LID to continue to play its water ecological service functions such as rainwater infiltration, runoff purification, and groundwater recharge.

[0012] As a further aspect of this invention: the specific implementation steps of the S7 comprehensive benefit digital accounting and pre-evaluation include: S7.1 Utilize GIS spatial analysis tools to calculate the effective service coverage rate of the newly added or renovated green space to the surrounding residential areas after the project is completed, and generate a quantitative indicator report representing the social benefits of the project. S7.2 Based on the refined simulation results of waterlogging risk generated by S5, estimate the direct economic losses that may be avoided from waterlogging under the set return period of rainfall, form a digital estimate of economic benefits, and thus quantify the disaster reduction benefits brought by the project in terms of water security.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a comprehensive, accurate, and efficient acceptance method for water-resilient infrastructure based on multi-source data. By integrating multi-source data such as UAV oblique photogrammetry, real-world 3D models, DEM, DOM, hydrological and water quality simulation data, vegetation indices, and socio-economic benefit indicators, it overcomes the limitations of traditional acceptance methods that are singular, static, and focus on the engineering entity while neglecting ecological functions and long-term benefits. It achieves a full-chain, quantifiable acceptance process, from comparing topographic and design consistency, verifying the spatial layout of water-resilient infrastructure, assessing the effectiveness of hydrological and water quality control, and simulating waterlogging risks, to diagnosing the functional status of facilities and quantitatively pre-evaluating comprehensive benefits. This systematically ensures the comprehensive effectiveness of projects in terms of water safety, water environment, and water ecology.

[0014] This invention not only simplifies the data collection and processing process and improves the automation and intelligence of the acceptance process, but also enhances the scientific nature and accuracy of project management. It helps to optimize resource allocation and ensures that water-resilient infrastructure can truly achieve the expected flood control and disaster reduction capabilities, water purification effects and ecological restoration benefits, providing a solid guarantee for the sustainable development of cities.

[0015] This invention, by adding a facility functional status diagnosis and comprehensive benefit pre-assessment stage, deepens the acceptance dimension from "engineering entity compliance" to "effective ecological function," ensuring that water-resilient infrastructure possesses reliable operational status and continuous service capability from the initial stage of completion. This lays the foundation for the continued functioning of subsequent water ecological functions such as rainwater infiltration, runoff purification, and groundwater recharge. Simultaneously, this invention expands the acceptance perspective to the entire project lifecycle, conducting a preliminary assessment of facility maintainability and long-term stability during the acceptance phase. This provides data support for later operation and maintenance management, effectively controlling long-term operation and maintenance costs and potential risks. Furthermore, by quantitatively assessing the ecological and socio-economic benefits of projects in water environment improvement, water security assurance, and water ecological services, this invention provides a systematic and scientific decision-making basis for investment benefit analysis, long-term performance evaluation, and comprehensive value presentation of water-resilient infrastructure construction. Attached Figure Description

[0016] Figure 1 This is a flowchart of the digital acceptance method for water-resilient infrastructure construction projects according to the present invention; Figure 2 This is a flowchart of the real-scene 3D modeling and terrain digitization acceptance process based on UAV oblique photography of the present invention; Figure 3 A digital flowchart for constructing the PCSWMM basic model of this invention; Figure 4 This is a flowchart illustrating the digital acceptance process for water quality simulation and pollution control based on PCSWMM and digital terrain analysis, as presented in this invention. Figure 5This invention presents an automatic extraction process for hydrological feature points in sub-catchment areas based on Python. Detailed Implementation

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] A digital acceptance method for water-resilient infrastructure based on multi-source data is disclosed, applicable to the acceptance of various types of water-resilient infrastructure. To clearly illustrate the technical solution of this invention, a low-impact development facility (LPD) within water-resilient infrastructure is used as a typical example. A LPD construction project in City A is selected as an example. Figure 1 As shown, it includes the following steps: S1. Real-scene 3D modeling and terrain digitization comparison and acceptance.

[0019] The specific implementation steps are as follows: Figure 2 As shown, it includes: S1.1. By setting up ground control points and planning flight routes, systematically acquire multi-angle oblique photographic image data of the project area; Among them, the formula for setting the flight altitude is: ; In the formula, H is the aerial photography altitude, m; f is the focal length of the aerial survey camera lens, mm; GSD is the ground resolution, cm; a is the pixel size, μm; ∆H is the terrain relief correction amount, m; S1.2 Perform quality checks and preprocessing on the acquired images to ensure that the images are clear, have uniform color, and meet the requirements of subsequent processing; S1.3 Perform aerial triangulation encryption and multi-view image dense matching to generate high-precision point cloud data and complete the UAV indoor 3D reconstruction; S1.4 Apply point cloud filtering algorithms (such as Cloth Simulation Filter, CSF) to identify and extract ground point cloud data within the project area, distinguish ground points from non-ground points, remove non-ground points such as trees, buildings and other surface objects to obtain clean ground point cloud data, and then generate a digital elevation model (DEM) through spatial interpolation. S1.5. Divide the clean ground point cloud data into multiple different triangular units, and construct an irregular triangular mesh model through these triangular units to simulate the shape and elevation changes of the terrain surface, thereby realizing the creation of an irregular triangular network (TIN). S1.6. Based on the constructed TIN spatial location information, it is divided into blocks to form a textureless white model. The parameterized correspondence between the two-dimensional image and the three-dimensional geometric model is established. The spectral features of the multi-view image are projected onto the surface of the white model to generate a real-world three-dimensional model. S1.7 Based on the generated real-scene 3D model, digital differential correction technology is used to eliminate image distortion caused by terrain undulation and camera tilt, and generate a digital orthophoto map (DOM) with unified coordinates and scale. S1.8 Calculate the plane accuracy error of each check point. The plane accuracy error is calculated by comparing the actual measured coordinates of the check point with the plane coordinates predicted by the model. Formula for calculating the mean square error in the X direction: ; in, The mean square error of the X-axis coordinate is... Let n be the residual (measured coordinates - model measured coordinates) of the i-th checkpoint in the X direction, and n be the number of checkpoints involved in the calculation. Formula for calculating the mean square error in the Y direction: ; in, The mean square error of the Y-axis coordinate. The residual (measured coordinates - model measured coordinates) of the i-th checkpoint in the Y direction. Formula for calculating plane mean square error σ: ; Substituting the actual measured coordinates of the checkpoints and the elevation coordinates measured by the model into the formula, the accuracy error of the elevation is calculated: ; in, This is the elevation error. The residual of the elevation of the i-th checkpoint (measured coordinates - model measured coordinates); S1.9 Set the plane accuracy error range and elevation accuracy error range to within ±5cm, use the DEM model to make contour lines, and compare them with the contour lines of the design drawing to complete the acceptance of the real scene 3D modeling and terrain digital comparison.

[0020] S2. Digital Acceptance of Spatial Layout of Water-Resilient Infrastructure. This step uses low-impact development (LID) facilities such as rain gardens and permeable paving as examples to conduct digital acceptance of the spatial layout of water-resilient infrastructure. Specific implementation steps are as follows: Figure 3 As shown, it includes: S2.1 Import the original rainwater pipe CAD drawings of the project area (low impact development facility construction project area) into the geographic information system GIS platform (such as ArcGIS), and use GIS tools to separate and extract the core pipe network elements of pipe segments (line elements), manholes and discharge outlets (point elements) from the CAD data; S2.2. Convert the elements extracted in S2.1 into standard GIS vector feature classes (such as Shapefile format) using a data conversion tool to create independent pipeline, manhole, and discharge outlet layers. S2.3 In the GIS platform, edit or build the attribute tables for each created feature class, and enter or associate the key attributes required for modeling for each feature, including: unique number, upstream and downstream connection relationship, pipe diameter, pipe length, elevation and facility type, etc. S2.4. The GIS feature classes with the completed geometric and attribute information are matched and mapped according to the format and structure required by the PCSWMM model through model interface tools or data conversion scripts (such as StormDesk), and finally exported as a standard PCSWMM input file (.inp) to complete the digital establishment of the drainage network basic model. S2.5. Based on the DEM and DOM models generated in S1, sub-catchment areas are delineated using a combination of hydrological analysis and manual verification. Furthermore, the method of combining hydrological analysis with manual verification includes: using the hydrological analysis tools provided by the GIS platform, calculating depression filling, flow direction, runoff accumulation and watershed extraction of the DEM, automatically identifying natural hydrological units to complete the hydrological analysis, and then combining the manually set design discharge outlet location to correct the boundary and complete the digital division of the sub-catchment area. S2.6 Based on the defined sub-catchments, extract and calculate the key modeling parameters of each sub-catchment, including: area, average slope, proportion of impermeable area, and feature width; organize and structure the parameters of each sub-catchment to complete the sub-catchment construction in the PCSWMM model; S2.7. Based on the DOM model generated in step 1, identify the actual distribution of LIDs such as permeable paving and rain gardens. Compare the actual calculated location area of ​​low-impact development facilities on the GIS platform with the location area on the design drawings to calculate the location matching degree, thereby completing the digital acceptance of LID spatial layout. ; If the matching degree is ≥90%, the LID layout is accepted; if the matching degree is <90%, the LID layout is rejected. S2.8. Add the accepted LID information, sub-catchment parameters, and rainfall data to the PCSWMM model for refined simulation, and complete the PCSWMM model construction.

[0021] S3. Hydrological process simulation and digital performance acceptance.

[0022] The specific implementation steps include: S3.1 Calculate the accuracy of the runoff-water quality simulation of PCSWMM. The accuracy of the model is evaluated by two indicators: Nash efficiency coefficient (NSE) and relative error (RE). The formulas for calculating NSE and RE are as follows: ; ; in, Let be the observed value at time t, in m³ / s; The average value is the observed value, m³ / s; The simulated value at time t is m³ / s; T is the number of observation data. The closer the NSE value is to 1, the better the simulation effect and the higher the reliability of the model. The closer the RE value is to 0, the better the simulation results match the average value of the observed data; If the NSE value is much less than 0, it indicates that the model is unreliable; S3.2 Select multiple independent measured rainfall-runoff process data to calibrate and validate the PCSWMM model. When the NSE of multiple rainfall simulation results is greater than 0.7 and the absolute value of RE does not exceed 10%, the model is deemed to have passed validation, its parameter settings are reasonable, and it has the ability to reliably simulate the actual hydrological process in the project area, and can be used for subsequent performance evaluation. S3.3 Using a validated model, input the design rainfall conditions, and based on the hydrodynamic principles of surface runoff generation, runoff confluence, and pipe network runoff, simulate the hydrological processes in the project area and calculate key hydrological performance indicators; The core calculation principle is as follows: The formula for calculating surface runoff is: ; Where L represents runoff, p represents rainfall, e represents evaporation, b represents depression filling, and f represents infiltration, all in mm. Using the PCSWMM model, the project area was subdivided into several sub-catchments, and the surface runoff model was applied to calculate the runoff generation in each sub-catchment. Based on the surface permeability of each sub-catchment, three types were identified: impermeable areas with depression storage capacity (A1), impermeable areas without depression storage capacity (A2), and permeable areas (A3). Runoff calculations were performed for each of these three types of surfaces, and the results were then superimposed to obtain the total runoff of each sub-catchment. The flow rate R1 of the impermeable zone A1 with depression storage capacity: ; The flow rate R2 of the impermeable zone A2 without depression storage: ; Permeable zone A3 flow rate R3: ; Where i is the rainfall intensity (mm / s); f(t) is the infiltration intensity at time t (mm / s); To calculate surface runoff, the surface runoff can be obtained by simultaneously applying the continuity equation and the Manning formula. The calculation formula is as follows: Continuity equation: ; Where d is a differential operator in calculus, representing an infinitesimal change; V is the surface water volume (m³); D is the water depth (m); t is the time (s); A is the bottom area (m²); r is the net rainfall intensity (mm / s); and Q is the outflow rate (m³ / s). Manning's formula: ; Where m is the Manning roughness coefficient; W is the width of the sub-catchment area, in meters; S is the slope of the sub-catchment area; d p The maximum water depth in the depression is measured in mm. The flow in the pipe network is calculated using the dynamic wave method, which combines the continuity equation, the momentum conservation equation, and the nodal control equation to simulate and calculate the hydraulic behavior within the pipe. The calculation formula is as follows: Continuity equation: ; Momentum equation: ; in, This is a partial differential symbol, indicating that the derivative of a multivariable function is taken with respect to one variable, while the other variables are treated as constants. A is the cross-sectional area of ​​the pipe (m²); M is the flow rate (m³ / s); x is the distance (m); t is the time (s); g is the acceleration due to gravity; h is the hydrostatic head (m); S... f The coefficient of friction; Friction slope S f : ; Where m is the Manning roughness coefficient; R is the hydraulic radius, m; and v is the water flow velocity, m / s. Nodal governing equations: ; Among them, A sk Q represents the free surface area of ​​the nodes, in square meters. t The flow rate to and from the node is m³ / s; S3.4 Compare the calculated index values ​​with the design target values. If they meet or exceed the design requirements, the hydrological process simulation and digital performance of the project area are deemed to have passed the acceptance test, indicating that the project has met the expected water security requirements in terms of total runoff control, pipeline drainage capacity, and urban flooding risk prevention. If the model verification fails or the performance assessment is not up to standard, the model input data, sub-catchment division, or LID parameters need to be checked and adjusted until the model is reliable and the assessment meets the standards.

[0023] S4. Digital acceptance of water quality simulation and pollution control.

[0024] The specific implementation steps are as follows: Figure 4 As shown, it includes: S4.1 In the PCSWMM model constructed in S2, enable its water quality simulation module, assign a pollutant accumulation and scouring model to the surface of the sub-catchment area, and assign corresponding pollutant removal parameters to various LIDs (such as rain gardens, infiltration ponds, and vegetated swales). S4.2 Select at least two representative measured rainfall event sequences (one light rain and one moderate to heavy rain) as model inputs. At the same time, set typical initial background pollutant concentrations for the project area for the model. S4.3 Run the PCSWMM model to perform hydrological-water quality coupled simulation. While completing the hydrological process calculations described in S3, the model simulates the water quality process based on the following principles. Calculate the cumulative pollutants and the flushing process separately: A saturation function is chosen to simulate the pollutant accumulation process. The formula for calculating the cumulative pollutant using the saturation function is as follows: ; Where B is the cumulative amount of pollutants, kg; c1 is the maximum cumulative amount, kg / ha; c2 is the half-saturation cumulative constant; and t is time, s. The pollutant scour function uses an exponential function, and the formula for calculating exponential scour is as follows: ; Where W is the pollutant scouring load, kg / h; c3 is the scouring coefficient, mm. -1 ; q is the runoff rate per unit area of ​​the sub-catchment area, mm / h; c4 is the scour index; B p The cumulative amount of pollutants per unit area is expressed in kg / ha.

[0025] S4.4 Based on the pollutant flux data output by the simulation, calculate the key water quality performance indicators used for assessment, including: total pollution load reduction rate and event average concentration (EMC) reduction rate. Specifically, the total pollution load reduction rate = [1 - (total outlet pollutant load / total inlet pollutant load)] × 100%; Event average concentration = Total outlet pollutant load / Total outlet runoff; Concentration reduction rate = [1 - (average concentration of exiting event / average concentration of inlet event)] × 100%; LID Individual Contribution Rate Analysis: By comparing model scenarios (such as turning some LIDs on / off), the contribution ratio of different types of LIDs to the overall pollution load reduction is quantified.

[0026] S4.5. Compare the calculated water quality performance indicators with the design requirements or preset acceptance thresholds of the project area. If all key indicators meet or exceed the thresholds, the digital acceptance of water quality simulation and pollution control in the project area is qualified. Through the comprehensive effect of LID, the project can effectively control non-point source pollution and achieve the quantitative goal of improving water environment quality.

[0027] Preset acceptance thresholds can be determined based on project design requirements. For example, requirements could include: a total reduction rate of suspended solids (SS) pollution load ≥60%, and an event-average concentration reduction rate ≥50%; and a total phosphorus (TP) pollution load reduction rate ≥50%. When simulation results meet these thresholds, the project's water quality control performance can be considered to have met design expectations.

[0028] S5. Refined simulation and digital acceptance of urban flooding risk.

[0029] The specific implementation steps include: S5.1 Input high-precision DEM data and sub-catchment vector data including sub-catchment node overflow attributes, and use GIS spatial clipping tools (such as Clip Management) to clip the global DEM into multiple independent sub-region terrain rasters according to the boundaries of each sub-catchment. S5.2, such as Figure 5 As shown, the core algorithm developed based on Python and GIS integrated environment automatically processes the input sub-catchment terrain raster: First, the minimum elevation value of the sub-catchment terrain raster is obtained; then, based on this value, a raster mask identifying the lowest point is generated; subsequently, the raster mask is converted into point features, and finally the coordinates of the lowest point are obtained from the point features, and the output is the water accumulation seed point of the current sub-catchment. S5.3. Using the obtained seed point coordinates as the starting position, apply the region growing algorithm to perform spatial connectivity analysis on the sub-region terrain data to identify all potential flooded areas connected to the seed point terrain. S5.4 Within the defined connected domain, a numerical iterative algorithm is used to solve for the accurate water level by balancing the submerged water volume and the node overflow output by the PCSWMM model. Then, a submerged depth distribution map of the sub-catchment area is generated through raster calculation. S5.5. For each sub-catchment, execute S5.2 to S5.4 sequentially to generate the corresponding inundation depth raster file for each sub-catchment. S5.6 Merge all the sub-catchment inundation depth raster files generated in S5.5 to generate an overall urban flooding risk distribution map of the project area; based on this risk distribution map, extract the inundation depth information of key areas (such as road low points and building entrances) and compare it with the maximum inundation depth standard allowed by design or safety; if the inundation depth of all key locations does not exceed the allowable standard, the project area's refined simulation and digital acceptance of urban flooding risk is deemed qualified, indicating that the project has a reliable urban flooding prevention and control capability under the design rainfall conditions and effectively ensures urban water safety; otherwise, it is deemed unqualified.

[0030] S6. Digital Diagnosis of the Functional Status of Water Resilient Infrastructure. This step uses LID (Limited Information Disclosure) as an example to perform a digital diagnosis of the functionality of water resilient infrastructure. Specific implementation steps include: S6.1 Based on the DOM model generated by S1, quantitative indicators such as the normalized differential vegetation index are used to perform preliminary calculations and graphical representations of the green coverage and growth vitality of LID (such as rain gardens and vegetated swales) areas, assisting acceptance personnel in quickly identifying areas with bare, withered, or insufficient vegetation coverage. S6.2 Based on the DEM model generated in S1, the micro-topographic catchment area of ​​the LID unit is simulated using hydrological analysis tools. Combined with the DOM image within this area, the acceptance personnel are assisted in visually judging the color uniformity, texture consistency, and whether there are obvious siltation or damage marks on the permeable pavement surface. The permeability performance is also checked in conjunction with the as-built material list. S6.3 Based on the real-world 3D model generated by S1, the system automatically generates and measures the clearance dimensions within the specified radius and 3D buffer zone of key maintenance point manholes and overflow outlets, identifies whether there are fixed obstacles encroaching on the maintenance space, and outputs a quantitative analysis report. S6.4 Summarize the verification and analysis results from S6.1 to S6.3 to form a three-dimensional integrated diagnostic report that includes vegetation status diagrams, permeability observation records, and quantitative data on operation and maintenance space. This report objectively presents the functional status of LID in the early stage of completion, provides a comprehensive digital decision-making basis for whether the project passes functional acceptance, and also provides a basic guarantee for LID to continue to play its water ecological service functions such as rainwater infiltration, runoff purification, and groundwater recharge.

[0031] S7. Digital accounting and pre-evaluation of comprehensive benefits.

[0032] The specific implementation steps include: S7.1 Utilize GIS spatial analysis tools to calculate the effective service coverage rate of the newly added or renovated green space to the surrounding residential areas after the project is completed, and generate a quantitative indicator report representing the social benefits of the project. S7.2 Based on the refined simulation results of waterlogging risk generated by S5, estimate the direct economic losses that may be avoided from waterlogging under the set return period of rainfall, form a digital estimate of economic benefits, and thus quantify the disaster reduction benefits brought by the project in terms of water security.

[0033] This case study uses a low-impact development (LID) facility construction project in City A as the research object, deeply integrating multi-source data such as UAV oblique photogrammetry, 3D modeling, realistic 3D models, DEM, DOM, hydrological and water quality simulation data, vegetation index, and socio-economic benefit indicators. Through comparisons of 3D reconstruction with design models, GIS spatial analysis with design drawings, NSE and RE numerical indicators, water quality simulation pollutant reduction rate indicators, and simulated inundation depth and raster data, acceptance tests were conducted for realistic 3D modeling and digital terrain comparison, digital acceptance of water resilient infrastructure spatial layout, digital acceptance of hydrological process simulation and effectiveness, digital acceptance of water quality simulation and pollution control, and refined simulation and digital acceptance of urban flooding risk. Furthermore, the study expands upon digital diagnosis of the functional status of water resilient infrastructure and digital accounting and pre-assessment of comprehensive benefits, ultimately comprehensively judging whether the water resilient infrastructure project has passed acceptance from multiple dimensions such as water security, water environment, and water ecology.

[0034] This invention integrates multiple digital technologies to construct a full-chain, quantifiable acceptance system, transforming the acceptance process from subjective judgment to objective, data-driven assessment. It expands the acceptance dimensions from the single aspect of engineering entity compliance to a comprehensive multi-source dimension encompassing hydrological regulation capabilities, water purification efficiency, facility ecological functions, and long-term operational risks. Through automated and model-based analysis, it significantly improves the efficiency, accuracy, and scientific rigor of the acceptance process, providing solid technical support for ensuring the effectiveness and long-term management of water-resilient infrastructure construction.

Claims

1. A digital acceptance method for water-resilient infrastructure based on multi-source data, characterized in that, Includes the following steps: S1. Real-scene 3D modeling and terrain digitization comparison and acceptance: acquire multi-angle image data of the project area, process it to generate high-precision point cloud data, and then generate digital elevation model (DEM), irregular triangular network (TIN), real-scene 3D model and digital orthophoto map (DOM). Calculate the error based on the checkpoints and compare the error results with the design drawings for consistency. S2. Digital Acceptance of Spatial Layout of Water Resilient Infrastructure: Extract stormwater pipe network elements based on the Geographic Information System (GIS) platform and establish a basic model of the drainage pipe network. Combine the DEM and DOM generated in S1 to divide sub-catchment areas and extract modeling parameters. Match the actual distribution of water resilient infrastructure identified based on DOM with the design drawings and build a modeling software model. S3. Hydrological process simulation and digital acceptance of effectiveness: Calculate the accuracy of runoff-water quality simulation of the modeling software and calibrate and verify the modeling software model. After verification, input the design rainfall conditions to simulate the hydrological process, calculate key hydrological effectiveness indicators and compare them with the design target values. S4. Digital Acceptance of Water Quality Simulation and Pollution Control: Enable the water quality simulation module in the modeling software to conduct hydrological-water quality coupled simulation, calculate water quality performance indicators based on the pollutant flux data output by the simulation, and compare the water quality performance indicators with the design requirements or preset acceptance thresholds of the project area. S5. Refined simulation and digital acceptance of waterlogging risk: Based on high-precision DEM data and sub-catchment vector data, the water level of each sub-catchment is calculated and an inundation depth distribution map is generated. The maps are then merged to generate a waterlogging risk distribution map of the project area. The inundation depth information of key areas is extracted and compared with the allowable standards. S6. Digital diagnosis of the functional status of water-resilient infrastructure: Quantitative assessment of the green coverage of water-resilient infrastructure areas based on DOM; Based on the DEM, the micro-topographic catchment area of ​​the water-resilient infrastructure unit is simulated using hydrological analysis tools, and the status of permeable pavement is judged by combining DOM imagery; the 3D buffer clearance dimensions of key maintenance points are generated and measured based on the real-scene 3D model; finally, a 3D integrated diagnostic report is formed. S7. Digital accounting and pre-assessment of comprehensive benefits: Using GIS spatial analysis tools, calculate the effective service coverage rate of green space to surrounding residential areas to form a quantitative indicator of social benefits. Based on the results of refined simulation of waterlogging risk, estimate the direct economic losses from waterlogging that are avoided to form a digital estimate of economic benefits.

2. The digital acceptance method for water-resilient infrastructure based on multi-source data according to claim 1, characterized in that, The specific implementation steps for the S1 real-scene 3D modeling and terrain digital comparison and acceptance include: S1.

1. By setting up ground control points and planning flight routes, systematically acquire multi-angle oblique photogrammetric image data of the Low Impact Development Facility (LID) project area; S1.2 Perform quality checks and preprocessing on the acquired images; S1.3 Perform aerial triangulation encryption and multi-view image dense matching to generate high-precision point cloud data and complete the UAV indoor 3D reconstruction; S1.4 Apply point cloud filtering algorithm to identify and extract ground point cloud data in the project area, distinguish ground points from non-ground points, remove non-ground points to obtain clean ground point cloud data, and then generate digital elevation model (DEM) through spatial interpolation. S1.

5. Divide the clean ground point cloud data into multiple different triangular units, and construct an irregular triangular mesh model through these triangular units to simulate the shape and elevation changes of the terrain surface, thereby realizing the creation of an irregular triangular mesh (TIN). S1.

6. Based on the constructed TIN spatial location information, it is divided into blocks to form a textureless white model. The parameterized correspondence between the two-dimensional image and the three-dimensional geometric model is established. The spectral features of the multi-view image are projected onto the surface of the white model to generate a real-world three-dimensional model. S1.7 Based on the generated real-scene 3D model, digital differential correction technology is used to eliminate image distortion caused by terrain undulation and camera tilt, and generate a digital orthophoto map (DOM) with unified coordinates and scale. S1.8 Calculate the plane accuracy error of each check point. The plane accuracy error is calculated by comparing the actual measured coordinates of the check point with the plane coordinates predicted by the model. Formula for calculating the mean square error in the X direction: ； in, The mean square error of the X-axis coordinate is... Let n be the residual of the i-th checkpoint in the X direction, and n be the number of checkpoints involved in the calculation. Formula for calculating the mean square error in the Y direction: ； in, The mean square error of the Y-axis coordinate. Let be the residual in the Y direction at the i-th checkpoint; Formula for calculating plane mean square error σ: ； Substituting the actual measured coordinates of the checkpoints and the elevation coordinates measured by the model into the formula, the accuracy error of the elevation is calculated: ； in, This is the elevation error. Let be the residual of the elevation of the i-th checkpoint; S1.9 Set the plane accuracy error range and elevation accuracy error range to within ±5cm, use the DEM model to make contour lines, and compare them with the contour lines of the design drawing to complete the acceptance of the real scene 3D modeling and terrain digital comparison.

3. The digital acceptance method for water-resilient infrastructure based on multi-source data according to claim 2, characterized in that, The specific implementation steps for the digital acceptance of the spatial layout of S2 water-resilient infrastructure include: S2.1 Import the original rainwater pipeline drawings of the project area into the Geographic Information System (GIS) platform, and separate and extract the core pipeline network elements such as pipe sections, manholes and discharge outlets from the drawing data; S2.

2. The elements extracted in S2.1 are converted into standard GIS vector feature classes using data conversion tools, thereby establishing independent pipeline, manhole, and discharge outlet layers; S2.3 In the GIS platform, edit or build the attribute tables for each created feature class, and enter or associate the key attributes required for modeling for each feature, including: unique number, upstream and downstream connection relationship, pipe diameter, pipe length, elevation and facility type; S2.

4. The geometric and attribute information of each element class that has been fully constructed in S2.3 is matched and mapped according to the format and structure required by the modeling software model through the model interface tool or data conversion script, and finally exported as the input file of the modeling software to complete the digital establishment of the drainage pipe network basic model. S2.

5. Based on the DEM and DOM models generated in S1, sub-catchment areas are delineated using a combination of hydrological analysis and manual verification. S2.6 Based on the defined sub-catchments, extract and calculate the key modeling parameters of each sub-catchment, including: area, average slope, proportion of impermeable area, and feature width; organize and structure the parameters of each sub-catchment to complete the construction of the sub-catchment in the modeling software model; S2.

7. Based on the DOM model generated in step 1, identify the actual distribution of Low Impact Development Facilities (LIDs). Compare the actual calculated LID locations and areas on the GIS platform with the locations and areas on the design drawings to calculate the location matching degree, thereby completing the digital acceptance of the LID spatial layout. ； If the matching degree is ≥90%, the LID layout is accepted; if the matching degree is <90%, the LID layout is rejected. S2.

8. Add the accepted LID information, sub-catchment parameters, and rainfall data to the modeling software model for refined simulation, and complete the modeling software model construction.

4. The digital acceptance method for water-resilient infrastructure based on multi-source data according to claim 3, characterized in that, The specific implementation steps for the S3 hydrological process simulation and digital performance acceptance include: S3.1 The accuracy of the runoff-water quality simulation model in the computational modeling software is evaluated using two indicators: the Nash efficiency coefficient (NSE) and the relative error (RE). The formulas for calculating NSE and RE are as follows: ； ； in, Let be the observed value at time t, in m³ / s; The average value is the observed value, m³ / s; The simulated value at time t is m³ / s; T is the number of observation data. The closer the NSE value is to 1, the better the simulation effect and the higher the reliability of the model. The closer the RE value is to 0, the better the simulation results match the average value of the observed data; If the NSE value is much less than 0, it indicates that the model is unreliable; S3.2 Select multiple independent measured rainfall-runoff process data to calibrate and validate the modeling software model; when the NSE of multiple rainfall simulation results is greater than 0.7 and the absolute value of RE does not exceed 10%, the model is deemed to have passed the validation, its parameter settings are reasonable, and it has the ability to reliably simulate the actual hydrological process in the project area, and can be used for subsequent performance evaluation. S3.3 Using a validated model, input the design rainfall conditions, and based on the hydrodynamic principles of surface runoff generation, runoff confluence, and pipe network runoff, simulate the hydrological processes in the project area and calculate key hydrological performance indicators; S3.4 Compare the calculated index values ​​with the design target values. If they meet or exceed the design requirements, the hydrological process simulation and digital performance of the project area are deemed to have passed the acceptance test, indicating that the project has met the expected water security requirements in terms of total runoff control, pipeline drainage capacity, and urban flooding risk prevention. If the model verification fails or the performance assessment is not up to standard, the model input data, sub-catchment division, or LID parameters need to be checked and adjusted until the model is reliable and the assessment meets the standards.

5. The digital acceptance method for water-resilient infrastructure based on multi-source data according to claim 4, characterized in that, The specific implementation steps for the digital acceptance of S4 water quality simulation and pollution control include: S4.1 In the modeling software model constructed in S2, enable its water quality simulation module, assign a pollutant accumulation and scouring model to the surface of the sub-catchment area, and assign corresponding pollutant removal parameters to various LIDs. S4.2 Select at least two measured rainfall event sequences as model inputs, and set typical initial pollutant background concentrations for the project area for the model; S4.3 Run the modeling software model to perform hydrological-water quality coupled simulation, and simultaneously simulate the accumulation, flushing, transport and LID purification process of pollutants; S4.4 Based on the pollutant flux data output by the simulation, calculate the key water quality performance indicators used for assessment, including: total pollution load reduction rate and event average concentration (EMC) reduction rate. S4.

5. Compare the calculated water quality performance indicators with the design requirements or preset acceptance thresholds of the project area. If all key indicators meet or exceed the thresholds, the digital acceptance of water quality simulation and pollution control in the project area is qualified. Through the comprehensive effect of LID, the project can effectively control non-point source pollution and achieve the quantitative goal of improving water environment quality.

6. The digital acceptance method for water-resilient infrastructure based on multi-source data according to claim 5, characterized in that, The specific implementation steps for the refined simulation and digital acceptance of S5 urban flooding risk include: S5.1 Input high-precision DEM data and sub-catchment vector data including sub-catchment node overflow attributes, and use the GIS spatial clipping tool to clip the global DEM into multiple independent sub-region terrain rasters according to the boundaries of each sub-catchment. S5.2 Automated processing of the input sub-catchment terrain raster: First, obtain the minimum elevation value of the sub-catchment terrain raster; then, based on this value, generate a raster mask to identify the lowest point; subsequently, convert the raster mask into point features, and finally obtain the coordinates of the lowest point from the point features, outputting them as the water accumulation seed points of the current sub-catchment. S5.

3. Using the obtained seed point coordinates as the starting position, apply the region growing algorithm to perform spatial connectivity analysis on the sub-region terrain data to identify all potential flooded areas connected to the seed point terrain. S5.4 Within the defined connected domain, a numerical iterative algorithm is used to solve for the accurate water level by balancing the submerged water volume and the node overflow output by the modeling software. Then, a submerged depth distribution map of the sub-catchment area is generated through grid calculation. S5.

5. For each sub-catchment, execute S5.2 to S5.4 sequentially to generate the corresponding inundation depth raster file for each sub-catchment. S5.6 Merge all the sub-catchment inundation depth raster files generated in S5.5 to generate an overall urban flooding risk distribution map of the project area; based on this risk distribution map, extract the inundation depth information of key areas and compare it with the design or safety-allowed maximum inundation depth standard; if the inundation depth of all key locations does not exceed the allowable standard, the project area's refined simulation and digital acceptance of urban flooding risk is deemed qualified, indicating that the project has a reliable urban flooding prevention and control capability under the design rainfall conditions and effectively protects urban water safety; otherwise, it is deemed unqualified.

7. A digital acceptance method for water-resilient infrastructure based on multi-source data as described in claim 6, characterized in that, The specific implementation steps for digital diagnostics of the functional status of S6 water-resilient infrastructure include: S6.1 Based on the DOM model generated by S1, the normalized differential vegetation index is used to quantify the green coverage and growth vitality of the LID area, and to make preliminary calculations and graphical representations to assist the acceptance personnel in quickly identifying areas with bare, withered or insufficient vegetation. S6.2 Based on the DEM model generated in S1, the micro-topographic catchment area of ​​the LID unit is simulated using hydrological analysis tools. Combined with the DOM image within this area, the acceptance personnel are assisted in visually judging the color uniformity, texture consistency, and whether there are obvious siltation or damage marks on the permeable pavement surface. The permeability performance is also checked in conjunction with the as-built material list. S6.3 Based on the real-world 3D model generated by S1, the system automatically generates and measures the clearance dimensions within the specified radius and 3D buffer zone of key maintenance point manholes and overflow outlets, identifies whether there are fixed obstacles encroaching on the maintenance space, and outputs a quantitative analysis report. S6.4 Summarize the verification and analysis results from S6.1 to S6.3 to form a three-dimensional integrated diagnostic report that includes vegetation status diagrams, permeability observation records, and quantitative data on operation and maintenance space. This report objectively presents the functional status of LID in the early stage of completion.

8. A digital acceptance method for water-resilient infrastructure based on multi-source data according to claim 7, characterized in that, The specific implementation steps for the digital accounting and pre-assessment of S7 comprehensive benefits include: S7.1 Utilize GIS spatial analysis tools to calculate the effective service coverage rate of the newly added or renovated green space to the surrounding residential areas after the project is completed, and generate a quantitative indicator report representing the social benefits of the project. S7.2 Based on the refined simulation results of waterlogging risk generated by S5, estimate the direct economic losses from waterlogging that can be avoided under the set return period of rainfall, and form a digital estimate of economic benefits, thereby quantifying the disaster reduction benefits brought by the project in terms of water security.

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