Rainwater garden construction control method and system based on BIM dynamic rainfall flood simulation

Abstract

The invention relates to the technical field of intelligent construction. The invention provides a rainwater garden construction control method and system based on BIM dynamic rainfall flood simulation. The method comprises the following steps: integrating target area data and real-time weather forecast, and constructing a fusion model; importing the fusion model into a coupling engine, simulating an extreme rainfall scene, and generating a dynamic hydrological load map; according to the dynamic hydrological load map, reverse iterative calculation is carried out, and a three-dimensional parameterized design model is output; decomposing the three-dimensional parameterized design model into a layered construction instruction set, and constructing a real-time twinborn body on a BIM platform; field data are collected and compared with real-time twinborn bodies, and when the soil layer permeability coefficient deviation is larger than a preset threshold value or the process conflict risk, a re-optimization instruction is triggered, and the mechanical operation sequence is adjusted. The problems that due to the fact that existing rainwater garden static design and experience construction cannot dynamically respond to the complex hydrological environment and construction disturbance, facility efficiency is low, failure is prone to occurring, and the construction rework rate is high are solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction technology, and in particular to a rain garden construction control method and system based on BIM dynamic rain and flood simulation. Background Art

[0002] With the advancement of sponge city construction, rain gardens, as key low-impact development (LID) facilities, play an important role in alleviating urban waterlogging, purifying rainwater runoff and replenishing groundwater resources.

[0003] Existing designs primarily rely on static specifications (such as the "Regulations for the Compilation of Urban Storm Intensity Formulas" GB / T50892-2013) and general empirical models (such as SWMM), which struggle to accurately respond to dynamic regional stormwater processes. Particularly in areas with complex topography and variable climates (such as the high-altitude, cold, and windy environment of Changzhi City), traditional models cannot fully simulate complex hydrological interactions such as water exchange and building shielding. This results in rain garden storage capacity, layout, and soil structure design deviating from actual requirements. Rain garden performance (such as peak flood reduction rate and rainwater infiltration efficiency) is dynamically influenced by multiple factors, including precipitation intensity, soil permeability, and the state of the drainage system. Static designs can easily lead to facility failure under extreme rainfall or low daily utilization. During the construction phase, reliance on two-dimensional drawings and manual experience makes it difficult to anticipate construction process conflicts, material scheduling bottlenecks, and environmental risks (such as increased soil salinization), resulting in high rework rates and project delays. There is a lack of quantitative tools to assess the impact of construction plans on the function of rain gardens. For example, problems such as excavation disturbing the native soil permeability and improper compaction of the structural layer reducing the water storage capacity may weaken the ultimate effectiveness of the facility. Summary of the Invention

[0004] The purpose of the present invention is to provide a rain garden construction control method and system based on BIM dynamic rainwater simulation, aiming to solve the problem that the static design and empirical construction of existing rain gardens cannot dynamically respond to complex hydrological environments and construction disturbances, resulting in low facility efficiency, easy failure and high construction rework rate.

[0005] The present invention is achieved through the following technical solutions: A rain garden construction control method based on BIM dynamic rainwater simulation includes the following steps: Integrate geological exploration data, building 3D models, historical rainfall data, and real-time weather forecasts in the target area to build a BIM-GIS fusion model and embed dynamic soil permeability parameters; The BIM-GIS fusion model is imported into the hydrodynamic-hydrological coupling engine, and the regional rainstorm intensity formula and building shielding factor are loaded to simulate the surface runoff path, rain garden infiltration efficiency and overflow risk points under extreme rainfall scenarios, generating a dynamic hydrological load map. According to the dynamic hydrological load map, the aquifer thickness, filler gradation ratio and overflow outlet elevation of the rain garden are calculated by reverse iteration, and a three-dimensional parametric design model that meets the flood peak reduction rate threshold is output; Decomposing the three-dimensional parametric design model into a hierarchical construction instruction set, linking construction machinery path planning, earthwork scheduling lists, and environmental monitoring probe placement, to build a real-time twin of construction progress, resource consumption, and soil disturbance on the BIM platform; The IoT terminal collects data on on-site soil compaction, groundwater level fluctuations, and equipment placement status, and compares them with the real-time twin of construction progress, resource consumption, and soil disturbance. When the deviation of the soil layer permeability coefficient is monitored to be greater than the preset threshold or there is a risk of process conflict, the adaptive construction plan re-optimization instruction is triggered and the mechanical operation sequence is adjusted.

[0006] Optionally, the specific process of integrating geological exploration data, building 3D models, historical rainfall data and real-time weather forecasts of the target area, constructing a BIM-GIS fusion model, and embedding dynamic soil permeability parameters is as follows: The stratigraphic structure, drilling points, and soil physical property parameters in the geological exploration data are mapped to the GIS spatial database through a 3D geocoding matrix. At the same time, the roof outline, underground pipelines, and obstruction elevation information in the 3D building model are converted into BIM physical components. Based on historical rainfall data, the attenuation function of soil permeability with precipitation frequency is fitted. Combined with the temperature and humidity data of real-time meteorological forecasts, the freeze-thaw cycle correction coefficient and saturation threshold are introduced to generate a dynamic soil permeability parameter set with temporal and spatial discretization. A geographic coordinate datum was established in the BIM platform. Boolean operations were performed on the stratigraphic topological relationships in the GIS spatial database and the BIM entity components. The geological data of the building-occupied area was eliminated, and the dynamic soil permeability parameter set was bound to the exposed surface and rain garden design domain to form a BIM-GIS fusion model with hydrological attributes.

[0007] Optionally, the specific process of generating the dynamic hydrological load map is: Based on the BIM-GIS fusion model with hydrological attributes, it is imported into the hydrodynamic-hydrological coupling engine, and the regional rainstorm intensity formula and building shielding factor are loaded; Combined with the dynamic soil permeability parameter set bound to the BIM-GIS fusion model, the precipitation intensity, duration, and time step of the extreme rainfall scenario are set, and the building shielding factor is applied to correct the spatial distribution of surface rainfall. Perform hydrodynamic-hydrological coupling calculations in the engine to simulate dynamic stormwater processes, including the spatial evolution of surface runoff paths, real-time changes in rain garden infiltration efficiency, and the spatiotemporal distribution of overflow risk points; Based on the simulation results, a dynamic hydrological load map based on geographic coordinates is quantitatively output. The dynamic hydrological load map integrates runoff flow heat map, infiltration rate contour lines and overflow probability raster data.

[0008] Optionally, the specific process of reversely iterating and calculating the aquifer thickness, filler gradation ratio, and overflow outlet elevation of the rain garden based on the dynamic hydrological load map, and outputting a three-dimensional parametric design model that meets the flood peak reduction rate threshold is as follows: Analyze the runoff flow heat map, infiltration rate contour lines, and overflow probability grid data in the dynamic hydrological load map to extract the maximum runoff load, critical infiltration rate threshold, and overflow risk spatial coordinates within the rain garden design domain; Using the flood peak reduction rate threshold as the constraint target, parameter sensitivity equations for aquifer thickness, filler gradation ratio, and overflow outlet elevation were established. The aquifer thickness was initially calculated using Darcy's law inversion, based on the critical infiltration rate threshold and maximum runoff load. The filler gradation ratio dynamically matched the gravel-sand-humus mass ratio according to the soil permeability attenuation function and overflow risk distribution. The overflow outlet elevation was set based on the spatial coordinate elevation extreme value in the overflow probability grid data, with a safety margin factor superimposed to set the benchmark height. The initial parameters corresponding to the aquifer thickness, filler gradation ratio, and overflow outlet elevation obtained through reverse iterative calculation are input into the hydrodynamic-hydrological coupling engine for forward performance verification. If the flood peak reduction rate does not reach the threshold, the parameters are adjusted according to the sensitivity equation and re-verified until the performance constraints are met. The iteratively optimized parameter set is mapped to the BIM platform, including: converting the aquifer thickness into the 3D solid volume of the fill area; linking the fill gradation ratio to the material database to generate a hierarchical structural family; and binding the overflow outlet elevation to the spatial coordinates of the drainage pipe fittings. The integrated output is a three-dimensional parametric design model with hydrological performance parameters. The geometric topology of the three-dimensional parametric design model is compatible with the decomposition logic of the construction instruction set.

[0009] Optionally, the specific process of decomposing the three-dimensional parametric design model into a hierarchical construction instruction set, associating it with construction machinery path planning, earthwork scheduling list, and environmental monitoring probe layout, and constructing a real-time twin of construction progress, resource consumption, and soil disturbance on the BIM platform is as follows: The three-dimensional solid volume of the fill area, the layered structural family, and the spatial coordinates of the drainage pipe fittings in the three-dimensional parametric design model are analyzed and decomposed into a set of construction instructions for the base treatment layer, the fill laying layer, and the overflow facility layer according to the elevation gradient. An earthwork scheduling list is automatically generated based on the material ratio of the layered structural family and the volume of the fill area, and the construction machinery path planning is optimized based on the building obstruction factor. The earthwork scheduling list includes the mass ratio and transportation batches of gravel, sand, and humus soil. Combining the spatial coordinates of overflow risk points in the dynamic hydrological load map with the soil permeability parameter set, groundwater level monitoring probes and soil compaction sensors were deployed at the boundaries of the rain garden, at the interface of the fill layer, and around the overflow outlet. The BIM platform integrates the timing logic of the construction instruction set, the topological relationship of the mechanical path, the quality threshold of the earthwork scheduling list, and the spatial coordinates of the monitoring probe, and dynamically renders the resource consumption heat map and soil disturbance coefficient cloud map during the construction process, forming a real-time twin of construction progress-resource consumption-soil disturbance.

[0010] Optionally, the specific process of collecting on-site soil compaction, groundwater level fluctuation and equipment installation status data through the Internet of Things terminal is: Groundwater level monitoring probes and soil compaction sensors deployed at the boundaries of the rain garden, the interface of the fill layer, and the perimeter of the overflow outlet are used to collect real-time soil pore pressure change data at the boundaries of the rain garden, the interface of the fill layer, and the perimeter of the overflow outlet. The data is converted into a soil compaction gradient distribution through a dynamic calibration algorithm. Synchronously acquire data from water level sensors embedded in the filler layer interface, and combine it with evaporation parameters from real-time weather forecasts to calculate the groundwater level fluctuation vector; Based on the topological relationship of the machine path, RFID tags are embedded in the construction machinery positioning module to collect and feedback construction equipment status data in real time. This construction equipment status data includes the excavator scraper elevation, the vibration frequency of the vibratory roller, and the positioning status of the earthmoving transport vehicle. The soil compaction gradient distribution, groundwater level fluctuation vector, and construction equipment status data are encapsulated into an IoT data stream according to the construction instruction set timing logic, which serves as the input of the real-time twin of construction progress-resource consumption-soil disturbance.

[0011] Optionally, when it is monitored that the deviation of the soil layer permeability coefficient is greater than a preset threshold or there is a risk of process conflict, the specific process of triggering the adaptive construction plan re-optimization instruction and adjusting the mechanical operation sequence is as follows: Comparing the soil compaction gradient distribution in the IoT data stream with the preset compaction standard value in the construction progress-resource consumption-soil disturbance real-time twin in real time, and inverting the on-site soil permeability coefficient using Darcy's law; By coupling analysis of groundwater level fluctuation vectors and real-time meteorological evaporation data, an infiltration efficiency warning signal is generated when the inverted permeability coefficient exceeds ±15% of the design threshold or the water level fluctuation rate exceeds the safety margin threshold. Synchronously detect the relationship between construction equipment status data and mechanical path topology. If the combined value of the excavator scraper elevation and the vibratory roller excitation frequency interferes with the paving sequence of the layered structure family, or if the delay in the earthmoving vehicle positioning causes a break in the process logic, the corresponding process conflict risk point will be marked. Based on the infiltration efficiency warning signal, the parameter sensitivity equations of aquifer thickness, filler gradation ratio, and overflow outlet elevation are reversed to dynamically adjust the filler gradation ratio and compaction control parameters. For process conflict risk points, the mechanical operation sequence is reconstructed based on the quality threshold of the earthwork scheduling list. Output adjustment instructions to the BIM platform, update the construction instruction set timing logic and redraw the mechanical path topology diagram, drive the graded control of the vibration roller excitation frequency and the scheduling of earthwork transportation vehicles, until the soil disturbance coefficient cloud map in the real-time twin of construction progress-resource consumption-soil disturbance matches the infiltration rate contour line of the dynamic hydrological load map.

[0012] Based on the same inventive concept, the present invention also provides a rain garden construction control system based on BIM dynamic rainwater simulation, which is used to implement the rain garden construction control method based on BIM dynamic rainwater simulation, including: The data integration module is used to integrate geological exploration data, building 3D models, historical rainfall data, and real-time weather forecasts of the target area to build a BIM-GIS fusion model and embed dynamic soil permeability parameters; A simulation engine module is used to import the BIM-GIS fusion model into the hydrodynamic-hydrological coupling engine, load the regional rainstorm intensity formula and building shielding factors, simulate the surface runoff path, rain garden infiltration efficiency and overflow risk points under extreme rainfall scenarios, and generate a dynamic hydrological load map; a parameter optimization module for reversely iterating and calculating the aquifer thickness, filler gradation ratio, and overflow outlet elevation of the rain garden based on the dynamic hydrological load map, and outputting a three-dimensional parametric design model that meets a flood peak reduction rate threshold; A construction instruction generation module is used to decompose the three-dimensional parametric design model into a layered construction instruction set, which is associated with construction machinery path planning, earthwork scheduling list and environmental monitoring probe placement; A twin construction module is used to construct a real-time twin of construction progress, resource consumption, and soil disturbance based on the hierarchical construction instruction set, construction machinery path planning, earthwork scheduling list, and environmental monitoring probe placement in the BIM platform; The monitoring and optimization module is used to collect on-site soil compaction, groundwater level fluctuations and equipment placement status data through the Internet of Things terminal, and compare them with the real-time twin of construction progress-resource consumption-soil disturbance. When the deviation of the soil layer permeability coefficient is monitored to be greater than the preset threshold or there is a risk of process conflict, the module triggers the adaptive construction plan re-optimization instruction and adjusts the mechanical operation sequence.

[0013] Based on the same inventive concept, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned rain garden construction control method based on BIM dynamic rainwater simulation.

[0014] Based on the same inventive concept, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned rain garden construction control method based on BIM dynamic rainwater flood simulation is implemented.

[0015] The technical solution of the present invention has at least the following advantages and beneficial effects: By integrating the BIM-GIS dynamic model with the hydrodynamic coupling engine, real-time meteorological data, soil permeability changes, and building shielding effects are integrated to achieve accurate simulation of runoff paths, infiltration efficiency, and overflow risks under extreme rainfall scenarios. This breaks through the limitations of traditional static specifications, allowing the rain garden's aquifer thickness, filler gradation, and overflow outlet elevation design to dynamically respond to regional hydrological loads, significantly improving the reliability of the flood peak reduction rate meeting standards and avoiding failure in extreme rainfall or low daily utilization.

[0016] The parametric design model is automatically decomposed into a layered construction instruction set, and the mechanical path, earthwork scheduling and monitoring probe layout are linked to build a real-time digital twin of construction progress, resources and environment. Through the Internet of Things, data such as soil compaction and groundwater level are collected in real time, and the twin model is dynamically compared to provide timely warnings of permeability coefficient deviations or process conflicts, triggering self-optimization of the construction plan, effectively preventing and controlling the attenuation of water storage capacity caused by earthwork excavation disturbances, improper compaction of structural layers, etc., and reducing the rework rate.

[0017] By reversely iterating design parameters based on dynamic hydrological load maps, material waste caused by redundant or insufficient capacity can be avoided at the source. By linking intelligent planning of construction machinery paths with earthwork scheduling lists, equipment idleness and secondary transfers can be reduced. The process conflict prediction and adaptive adjustment mechanism can shorten the construction period and significantly improve construction efficiency.

[0018] For special environments such as high altitude, cold and windy areas, dynamic soil permeability parameters and building shielding factors are embedded to accurately simulate scenarios such as superposition of runoff during the snowmelt period and blockage of pores by wind and sand; overflow risk points are located in advance and the structural design is optimized to avoid derivative disasters such as aggravated salinization and frost heave damage, thereby improving the operational resilience of facilities in extreme climates.

[0019] From the generation of dynamic hydrological maps in the design phase, to soil disturbance monitoring during the construction period, and then to the feedback on seepage performance after completion, a "simulation-construction-monitoring" closed-loop data chain is formed, providing a scientific basis for the long-term performance evaluation of rain gardens (such as infiltration efficiency attenuation warning and filler replacement cycle), and promoting LID facilities from experience-driven operation and maintenance to data-driven operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of a flow chart of a rain garden construction control method based on BIM dynamic rainwater simulation according to an embodiment of the present invention; Figure 2 This is a structural schematic diagram of a rain garden construction control system based on BIM dynamic rainwater simulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following is a specific implementation method with reference to the accompanying drawings.

[0022] Reference Figure 1 A rain garden construction control method based on BIM dynamic rainwater simulation includes the following steps: Step 1: Integrate the geological exploration data, building 3D model, historical rainfall data and real-time weather forecast of the target area to build a BIM-GIS fusion model and embed dynamic soil permeability parameters.

[0023] In some embodiments, the specific process of integrating geological exploration data, building 3D models, historical rainfall data, and real-time weather forecasts of the target area, constructing a BIM-GIS fusion model, and embedding dynamic soil permeability parameters is as follows: Map the stratigraphic structure, borehole locations, and soil physical properties from geological exploration data to a GIS spatial database using a three-dimensional geocoding matrix. Geological exploration data, including borehole location (spatial coordinates), stratigraphic layer data (e.g., soil layer depth and thickness), and soil physical properties (e.g., permeability, porosity, and saturation), are collected. The data is standardized: borehole locations are unified into a geodetic coordinate system (e.g., WGS-84 or a local coordinate system), stratigraphic structure is encoded by depth, and soil parameters are normalized to the range [0, 1]. A three-dimensional geocoding matrix is ​​defined as the data carrier. This matrix maps borehole locations to three-dimensional spatial points and associates stratigraphic and soil parameters. Spatial interpolation algorithms (e.g., Kriging) are used to convert the discrete borehole data into a continuous spatial grid or TIN (Triangulated Irregular Network) for storage in the GIS spatial database. In a GIS platform (such as ArcGIS or QGIS), create a point feature layer to store the drill hole locations and a raster layer or voxel grid to store the strata and soil parameters; use a GIS API (such as ArcPy or GDAL) to automate the import process, ensuring that the data has a spatial reference (such as EPSG code).

[0024] At the same time, the roof outline, underground pipelines and obstruction elevation information in the building 3D model are converted into BIM entity components. Extract building model information from input sources (such as CAD files or point cloud data): Roof profile: as a polygonal boundary (roof plane); Underground pipelines: as linear paths (pipeline centerlines); Occluder elevation information: as an elevation point cloud or mesh (e.g., wall height).

[0025] Unified coordinate system: Align with geological data and use the same geodetic coordinate system.

[0026] In a BIM platform (such as Revit or Bentley), create parametric components through an API (such as the Revit API): Roof profiles are converted to IfcSlab or IfcRoof entities.

[0027] Underground pipelines are converted to IfcPipeSegment entities.

[0028] Occluding objects (such as walls or trees) are converted to IfcWall or IfcBuildingElementProxy entities.

[0029] Component property binding: Add dynamic properties (such as material and permeability) to facilitate subsequent Boolean operations. Define component shapes using boundary representation (B-rep) or constructive solid geometry (CSG).

[0030] Based on historical rainfall data, the attenuation function of soil permeability with precipitation frequency is fitted. Combined with the temperature and humidity data of real-time meteorological forecasts, the freeze-thaw cycle correction coefficient and saturation threshold are introduced to generate a dynamic soil permeability parameter set with temporal and spatial discretization. The permeability attenuation function is shown in the following formula (1):

[0031] in, represents the dynamic permeability; represents the initial permeability; express Rainfall intensity at each moment; represents the attenuation coefficient, which is obtained by regressing historical data. The infiltration attenuation function quantifies the exponential decay of soil infiltration rate with increasing rainfall intensity during rainfall, reflecting the phenomenon that rainwater saturates the soil and causes the infiltration capacity to decrease.

[0032] The freeze-thaw cycle correction is shown in the following formula (2):

[0033] in, Represents the freeze-thaw correction factor, which is used to adjust the impact of low temperature environment on permeability; represents the average daily temperature; Indicates the critical freezing temperature; the freeze-thaw cycle correction simulates the periodic effect of soil freeze-thaw cycle on permeability in alpine areas. When the permeability is significantly reduced by ice crystal blocking (coefficient range 0.5-0.8); No correction (coefficient is 1) is applied.

[0034] The saturation threshold is set as shown in the following formula (3):

[0035] in, represents the saturation correction factor, which is used to describe the inhibitory effect of soil moisture on permeability; Indicates the volumetric moisture content of soil; Indicates the critical saturation, the water content threshold at which the permeability drops sharply, which is determined by the soil type; the Sigmoid function is used to characterize the nonlinear attenuation characteristics of the permeability when the soil is close to saturation. Much greater than hour, (Full saturation leads to stagnation of penetration), Much smaller than hour, (No inhibition).

[0036] The dynamic permeability output is shown in the following formula (4):

[0037] in, represents the spatiotemporal dynamic permeability, i.e., the position exist Final penetration rate at the moment; is the dynamic permeability calculated by formula (1); is the freeze-thaw correction coefficient calculated by formula (2); is the saturation correction factor calculated by formula (3). The dynamic effects of rainfall, temperature and soil moisture are integrated through dynamic permeability to output the spatial location and time The actual permeability on the surface is used as input to the hydrological simulation engine.

[0038] Generate space-time discrete parameter sets ,in, is the gridded dynamic permeability, representing the spatial grid unit In time The value of ; and is the spatial grid index, is the row number, is the column number; and Represents GIS spatial grid in and Directional resolution; and The spatiotemporal discretization parameter set discretizes the continuous dynamic permeability into spatiotemporal grid data for numerical calculations by the hydrodynamic-hydrological coupling engine.

[0039] A geographic coordinate datum was established in the BIM platform. Boolean operations were performed on the stratigraphic topological relationships in the GIS spatial database and the BIM entity components. The geological data of the building-occupied area was eliminated, and the dynamic soil permeability parameter set was bound to the exposed surface and rain garden design domain to form a BIM-GIS fusion model with hydrological attributes.

[0040] Step 2: Import the BIM-GIS fusion model into the hydrodynamic-hydrological coupling engine, load the regional rainstorm intensity formula and building shielding factor, simulate the surface runoff path, rain garden infiltration efficiency and overflow risk points under extreme rainfall scenarios, and generate a dynamic hydrological load map.

[0041] In some embodiments, the specific process of generating the dynamic hydrological load map is as follows: Based on the BIM-GIS fusion model with hydrological attributes, it is imported into the hydrodynamic-hydrological coupling engine, and the regional rainstorm intensity formula and building shielding factor are loaded; Combined with the dynamic soil permeability parameter set bound to the BIM-GIS fusion model, the precipitation intensity, duration, and time step of the extreme rainfall scenario are set, and the building shielding factor is applied to correct the spatial distribution of surface rainfall. Perform hydrodynamic-hydrological coupling calculations in the engine to simulate dynamic stormwater processes, including the spatial evolution of surface runoff paths, real-time changes in rain garden infiltration efficiency, and the spatiotemporal distribution of overflow risk points; Based on the simulation results, a dynamic hydrological load map based on geographic coordinates is quantitatively output. The dynamic hydrological load map integrates runoff flow heat map, infiltration rate contour lines and overflow probability raster data.

[0042] Step 3: Based on the dynamic hydrological load map, reverse iteratively calculate the aquifer thickness, filler gradation ratio and overflow outlet elevation of the rain garden, and output a three-dimensional parametric design model that meets the flood peak reduction rate threshold.

[0043] In some embodiments, the specific process of reversely iterating and calculating the aquifer thickness, filler gradation ratio, and overflow outlet elevation of the rain garden based on the dynamic hydrological load map, and outputting a three-dimensional parametric design model that meets the flood peak reduction rate threshold is as follows: Analyze the runoff flow heat map, infiltration rate contour lines, and overflow probability grid data in the dynamic hydrological load map to extract the maximum runoff load, critical infiltration rate threshold, and overflow risk spatial coordinates within the rain garden design domain; Using the flood peak reduction rate threshold as the constraint target, parameter sensitivity equations for aquifer thickness, filler gradation ratio, and overflow outlet elevation were established. The aquifer thickness was initially calculated using Darcy's law inversion, based on the critical infiltration rate threshold and maximum runoff load. The filler gradation ratio dynamically matched the gravel-sand-humus mass ratio according to the soil permeability attenuation function and overflow risk distribution. The overflow outlet elevation was set based on the spatial coordinate elevation extreme value in the overflow probability grid data, with a safety margin factor superimposed to set the benchmark height. The initial parameters corresponding to the aquifer thickness, filler gradation ratio, and overflow outlet elevation obtained through reverse iterative calculation are input into the hydrodynamic-hydrological coupling engine for forward performance verification. If the flood peak reduction rate does not reach the threshold, the parameters are adjusted according to the sensitivity equation and re-verified until the performance constraints are met. The iteratively optimized parameter set is mapped to the BIM platform, including: converting the aquifer thickness into the 3D solid volume of the fill area; linking the fill gradation ratio to the material database to generate a hierarchical structural family; and binding the overflow outlet elevation to the spatial coordinates of the drainage pipe fittings. The integrated output is a three-dimensional parametric design model with hydrological performance parameters. The geometric topology of the three-dimensional parametric design model is compatible with the decomposition logic of the construction instruction set.

[0044] Step 4: Decompose the three-dimensional parametric design model into a layered construction instruction set, associate it with construction machinery path planning, earthwork scheduling list and environmental monitoring probe layout, and build a real-time twin of construction progress-resource consumption-soil disturbance on the BIM platform.

[0045] In some embodiments, the specific process of decomposing the three-dimensional parametric design model into a hierarchical construction instruction set, associating it with construction machinery path planning, earthwork scheduling list, and environmental monitoring probe layout, and building a real-time twin of construction progress, resource consumption, and soil disturbance on the BIM platform is as follows: The three-dimensional solid volume of the fill area, the layered structural family, and the spatial coordinates of the drainage pipe fittings in the three-dimensional parametric design model are analyzed and decomposed into a set of construction instructions for the base treatment layer, the fill laying layer, and the overflow facility layer according to the elevation gradient. An earthwork scheduling list is automatically generated based on the material ratio of the layered structural family and the volume of the fill area, and the construction machinery path planning is optimized based on the building obstruction factor. The earthwork scheduling list includes the mass ratio and transportation batches of gravel, sand, and humus soil. Combining the spatial coordinates of overflow risk points in the dynamic hydrological load map with the soil permeability parameter set, groundwater level monitoring probes and soil compaction sensors were deployed at the boundaries of the rain garden, at the interface of the fill layer, and around the overflow outlet. The BIM platform integrates the timing logic of the construction instruction set, the topological relationship of the mechanical path, the quality threshold of the earthwork scheduling list, and the spatial coordinates of the monitoring probe, and dynamically renders the resource consumption heat map and soil disturbance coefficient cloud map during the construction process, forming a real-time twin of construction progress-resource consumption-soil disturbance.

[0046] Step 5: Use the IoT terminal to collect on-site soil compaction, groundwater level fluctuations, and equipment installation status data, and compare them with the real-time twin of construction progress-resource consumption-soil disturbance. When the monitored soil layer permeability coefficient deviation is greater than the preset threshold or there is a risk of process conflict, the adaptive construction plan re-optimization instruction is triggered and the mechanical operation sequence is adjusted.

[0047] In some embodiments, the specific process of collecting on-site soil compaction, groundwater level fluctuation, and equipment installation status data through the Internet of Things terminal is as follows: Groundwater level monitoring probes and soil compaction sensors deployed at the boundaries of the rain garden, the interface of the fill layer, and the perimeter of the overflow outlet are used to collect real-time soil pore pressure change data at the boundaries of the rain garden, the interface of the fill layer, and the perimeter of the overflow outlet. The data is converted into a soil compaction gradient distribution through a dynamic calibration algorithm. Synchronously acquire data from water level sensors embedded in the filler layer interface, and combine it with evaporation parameters from real-time weather forecasts to calculate the groundwater level fluctuation vector; Based on the topological relationship of the machine path, RFID tags are embedded in the construction machinery positioning module to collect and feedback construction equipment status data in real time. This construction equipment status data includes the excavator scraper elevation, the vibration frequency of the vibratory roller, and the positioning status of the earthmoving transport vehicle. The soil compaction gradient distribution, groundwater level fluctuation vector, and construction equipment status data are encapsulated into an IoT data stream according to the construction instruction set timing logic, which serves as the input of the real-time twin of construction progress-resource consumption-soil disturbance.

[0048] In some embodiments, when the deviation of the soil layer permeability coefficient is detected to be greater than a preset threshold or there is a risk of process conflict, the specific process of triggering the adaptive construction plan re-optimization instruction and adjusting the mechanical operation sequence is as follows: Comparing the soil compaction gradient distribution in the IoT data stream with the preset compaction standard value in the construction progress-resource consumption-soil disturbance real-time twin in real time, and inverting the on-site soil permeability coefficient using Darcy's law; By coupling analysis of groundwater level fluctuation vectors and real-time meteorological evaporation data, an infiltration efficiency warning signal is generated when the inverted permeability coefficient exceeds ±15% of the design threshold or the water level fluctuation rate exceeds the safety margin threshold. Synchronously detect the relationship between construction equipment status data and mechanical path topology. If the combined value of the excavator scraper elevation and the vibratory roller excitation frequency interferes with the paving sequence of the layered structure family, or if the delay in the earthmoving vehicle positioning causes a break in the process logic, the corresponding process conflict risk point will be marked. Based on the infiltration efficiency warning signal, the parameter sensitivity equations of aquifer thickness, filler gradation ratio, and overflow outlet elevation are reversed to dynamically adjust the filler gradation ratio and compaction control parameters. For process conflict risk points, the mechanical operation sequence is reconstructed based on the quality threshold of the earthwork scheduling list. Output adjustment instructions to the BIM platform, update the construction instruction set timing logic and redraw the mechanical path topology diagram, drive the graded control of the vibration roller excitation frequency and the scheduling of earthwork transportation vehicles, until the soil disturbance coefficient cloud map in the real-time twin of construction progress-resource consumption-soil disturbance matches the infiltration rate contour line of the dynamic hydrological load map.

[0049] Based on the same inventive concept, corresponding to any of the above embodiments, refer to Figure 2 The present invention provides a rain garden construction control system based on BIM dynamic rainwater simulation, which is used to implement the aforementioned rain garden construction control method based on BIM dynamic rainwater simulation, including: The data integration module is used to integrate geological exploration data, building 3D models, historical rainfall data, and real-time weather forecasts of the target area to build a BIM-GIS fusion model and embed dynamic soil permeability parameters; A simulation engine module is used to import the BIM-GIS fusion model into the hydrodynamic-hydrological coupling engine, load the regional rainstorm intensity formula and building shielding factors, simulate the surface runoff path, rain garden infiltration efficiency and overflow risk points under extreme rainfall scenarios, and generate a dynamic hydrological load map; a parameter optimization module for reversely iterating and calculating the aquifer thickness, filler gradation ratio, and overflow outlet elevation of the rain garden based on the dynamic hydrological load map, and outputting a three-dimensional parametric design model that meets a flood peak reduction rate threshold; A construction instruction generation module is used to decompose the three-dimensional parametric design model into a layered construction instruction set, which is associated with construction machinery path planning, earthwork scheduling list and environmental monitoring probe placement; A twin construction module is used to construct a real-time twin of construction progress, resource consumption, and soil disturbance based on the hierarchical construction instruction set, construction machinery path planning, earthwork scheduling list, and environmental monitoring probe placement in the BIM platform; The monitoring and optimization module is used to collect on-site soil compaction, groundwater level fluctuations and equipment placement status data through the Internet of Things terminal, and compare them with the real-time twin of construction progress-resource consumption-soil disturbance. When the deviation of the soil layer permeability coefficient is monitored to be greater than the preset threshold or there is a risk of process conflict, the module triggers the adaptive construction plan re-optimization instruction and adjusts the mechanical operation sequence.

[0050] Based on the same inventive concept, corresponding to any of the above embodiments, the present invention provides an electronic device, including a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the rain garden construction control method based on BIM dynamic rainwater simulation of the embodiment.

[0051] Optionally, the above-mentioned electronic device may be a server.

[0052] In addition, this embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the rain garden construction control method based on BIM dynamic rainwater flood simulation of the embodiment is implemented.

[0053] It is understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0054] The method steps in the embodiments of the present invention can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC.

[0055] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a storage medium or transmitted via a storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

Claims

1. A rain garden construction control method based on BIM dynamic rainwater simulation, characterized in that: The following steps are involved: Integrate geological exploration data, building 3D models, historical rainfall data, and real-time weather forecasts in the target area to build a BIM-GIS fusion model and embed dynamic soil permeability parameters; The BIM-GIS fusion model is imported into the hydrodynamic-hydrological coupling engine, and the regional rainstorm intensity formula and building shielding factor are loaded to simulate the surface runoff path, rain garden infiltration efficiency and overflow risk points under extreme rainfall scenarios, generating a dynamic hydrological load map. According to the dynamic hydrological load map, the aquifer thickness, filler gradation ratio and overflow outlet elevation of the rain garden are calculated by reverse iteration, and a three-dimensional parametric design model that meets the flood peak reduction rate threshold is output; Decomposing the three-dimensional parametric design model into a hierarchical construction instruction set, linking construction machinery path planning, earthwork scheduling lists, and environmental monitoring probe placement, to build a real-time twin of construction progress, resource consumption, and soil disturbance on the BIM platform; The IoT terminal collects data on on-site soil compaction, groundwater level fluctuations, and equipment placement status, and compares them with the real-time twin of construction progress, resource consumption, and soil disturbance. When the deviation of the soil layer permeability coefficient is monitored to be greater than the preset threshold or there is a risk of process conflict, the adaptive construction plan re-optimization instruction is triggered and the mechanical operation sequence is adjusted.

2. The rain garden construction control method based on BIM dynamic rainwater simulation as claimed in claim 1 is characterized in that: The specific process of integrating the geological exploration data, building 3D model, historical rainfall data and real-time weather forecast of the target area, building a BIM-GIS fusion model, and embedding dynamic soil permeability parameters is as follows: The stratigraphic structure, drilling points, and soil physical property parameters in the geological exploration data are mapped to the GIS spatial database through a 3D geocoding matrix. At the same time, the roof outline, underground pipelines, and obstruction elevation information in the 3D building model are converted into BIM physical components. Based on historical rainfall data, the attenuation function of soil permeability with precipitation frequency is fitted. Combined with the temperature and humidity data of real-time meteorological forecasts, the freeze-thaw cycle correction coefficient and saturation threshold are introduced to generate a dynamic soil permeability parameter set with temporal and spatial discretization. A geographic coordinate datum was established in the BIM platform. Boolean operations were performed on the stratigraphic topological relationships in the GIS spatial database and the BIM entity components. The geological data of the building-occupied area was eliminated, and the dynamic soil permeability parameter set was bound to the exposed surface and rain garden design domain to form a BIM-GIS fusion model with hydrological attributes.

3. The rain garden construction control method based on BIM dynamic rainwater simulation as claimed in claim 1 is characterized in that: The specific process of generating the dynamic hydrological load map is as follows: Based on the BIM-GIS fusion model with hydrological attributes, it is imported into the hydrodynamic-hydrological coupling engine, and the regional rainstorm intensity formula and building shielding factor are loaded; Combined with the dynamic soil permeability parameter set bound to the BIM-GIS fusion model, the precipitation intensity, duration, and time step of the extreme rainfall scenario are set, and the building shielding factor is applied to correct the spatial distribution of surface rainfall. Perform hydrodynamic-hydrological coupling calculations in the engine to simulate dynamic stormwater processes, including the spatial evolution of surface runoff paths, real-time changes in rain garden infiltration efficiency, and the spatiotemporal distribution of overflow risk points; Based on the simulation results, a dynamic hydrological load map based on geographic coordinates is quantitatively output. The dynamic hydrological load map integrates runoff flow heat map, infiltration rate contour lines and overflow probability raster data.

4. The rain garden construction control method based on BIM dynamic rainwater simulation as claimed in claim 1 is characterized in that: The specific process of reversely iterating and calculating the aquifer thickness, filler gradation ratio, and overflow outlet elevation of the rain garden based on the dynamic hydrological load map, and outputting a three-dimensional parametric design model that meets the flood peak reduction rate threshold is as follows: Analyze the runoff flow heat map, infiltration rate contour lines, and overflow probability grid data in the dynamic hydrological load map to extract the maximum runoff load, critical infiltration rate threshold, and overflow risk spatial coordinates within the rain garden design domain; Using the flood peak reduction rate threshold as the constraint target, parameter sensitivity equations for aquifer thickness, filler gradation ratio, and overflow outlet elevation were established. The aquifer thickness was initially calculated using Darcy's law inversion, based on the critical infiltration rate threshold and maximum runoff load. The filler gradation ratio dynamically matched the gravel-sand-humus mass ratio according to the soil permeability attenuation function and overflow risk distribution. The overflow outlet elevation was set based on the spatial coordinate elevation extreme value in the overflow probability grid data, with a safety margin factor superimposed to set the benchmark height. The initial parameters corresponding to the aquifer thickness, filler gradation ratio, and overflow outlet elevation obtained through reverse iterative calculation are input into the hydrodynamic-hydrological coupling engine for forward performance verification. If the flood peak reduction rate does not reach the threshold, the parameters are adjusted according to the sensitivity equation and re-verified until the performance constraints are met. The iteratively optimized parameter set is mapped to the BIM platform, including: converting the aquifer thickness into the 3D solid volume of the fill area; linking the fill gradation ratio to the material database to generate a hierarchical structural family; and binding the overflow outlet elevation to the spatial coordinates of the drainage pipe fittings. The integrated output is a three-dimensional parametric design model with hydrological performance parameters. The geometric topology of the three-dimensional parametric design model is compatible with the decomposition logic of the construction instruction set.

5. The rain garden construction control method based on BIM dynamic rainwater simulation as claimed in claim 4 is characterized in that: The specific process of decomposing the three-dimensional parametric design model into a hierarchical construction instruction set, associating it with construction machinery path planning, earthwork scheduling list, and environmental monitoring probe layout, and building a real-time twin of construction progress, resource consumption, and soil disturbance on the BIM platform is as follows: The three-dimensional solid volume of the fill area, the layered structural family, and the spatial coordinates of the drainage pipe fittings in the three-dimensional parametric design model are analyzed and decomposed into a set of construction instructions for the base treatment layer, the fill laying layer, and the overflow facility layer according to the elevation gradient. An earthwork scheduling list is automatically generated based on the material ratio of the layered structural family and the volume of the fill area, and the construction machinery path planning is optimized based on the building obstruction factor. The earthwork scheduling list includes the mass ratio and transportation batches of gravel, sand, and humus soil. Combining the spatial coordinates of overflow risk points in the dynamic hydrological load map with the soil permeability parameter set, groundwater level monitoring probes and soil compaction sensors were deployed at the boundaries of the rain garden, at the interface of the fill layer, and around the overflow outlet. The BIM platform integrates the timing logic of the construction instruction set, the topological relationship of the mechanical path, the quality threshold of the earthwork scheduling list, and the spatial coordinates of the monitoring probe, and dynamically renders the resource consumption heat map and soil disturbance coefficient cloud map during the construction process, forming a real-time twin of construction progress-resource consumption-soil disturbance.

6. The rain garden construction control method based on BIM dynamic rainwater simulation as claimed in claim 5 is characterized in that: The specific process of collecting on-site soil compaction, groundwater level fluctuation and equipment installation status data through the Internet of Things terminal is as follows: Groundwater level monitoring probes and soil compaction sensors deployed at the boundaries of the rain garden, the interface of the fill layer, and the perimeter of the overflow outlet are used to collect real-time soil pore pressure change data at the boundaries of the rain garden, the interface of the fill layer, and the perimeter of the overflow outlet. The data is converted into a soil compaction gradient distribution through a dynamic calibration algorithm. Synchronously acquire data from water level sensors embedded in the filler layer interface, and combine it with evaporation parameters from real-time weather forecasts to calculate the groundwater level fluctuation vector; Based on the topological relationship of the machine path, RFID tags are embedded in the construction machinery positioning module to collect and feedback construction equipment status data in real time. This construction equipment status data includes the excavator scraper elevation, the vibration frequency of the vibratory roller, and the positioning status of the earthmoving transport vehicle. The soil compaction gradient distribution, groundwater level fluctuation vector, and construction equipment status data are encapsulated into an IoT data stream according to the construction instruction set timing logic, which serves as the input of the real-time twin of construction progress-resource consumption-soil disturbance.

7. The rain garden construction control method based on BIM dynamic rainwater simulation as claimed in claim 6 is characterized in that: When the deviation of the soil layer permeability coefficient is detected to be greater than a preset threshold or there is a risk of process conflict, the specific process of triggering the adaptive construction plan re-optimization instruction and adjusting the mechanical operation sequence is as follows: Comparing the soil compaction gradient distribution in the IoT data stream with the preset compaction standard value in the construction progress-resource consumption-soil disturbance real-time twin in real time, and inverting the on-site soil permeability coefficient using Darcy's law; By coupling analysis of groundwater level fluctuation vectors and real-time meteorological evaporation data, an infiltration efficiency warning signal is generated when the inverted permeability coefficient exceeds ±15% of the design threshold or the water level fluctuation rate exceeds the safety margin threshold. Synchronously detect the relationship between construction equipment status data and mechanical path topology. If the combined value of the excavator scraper elevation and the vibratory roller excitation frequency interferes with the paving sequence of the layered structure family, or if the delay in the earthmoving vehicle positioning causes a break in the process logic, the corresponding process conflict risk point will be marked. Based on the infiltration efficiency warning signal, the parameter sensitivity equations of aquifer thickness, filler gradation ratio, and overflow outlet elevation are reversed to dynamically adjust the filler gradation ratio and compaction control parameters. For process conflict risk points, the mechanical operation sequence is reconstructed based on the quality threshold of the earthwork scheduling list. Output adjustment instructions to the BIM platform, update the construction instruction set timing logic and redraw the mechanical path topology diagram, drive the graded control of the vibration roller excitation frequency and the scheduling of earthwork transportation vehicles, until the soil disturbance coefficient cloud map in the real-time twin of construction progress-resource consumption-soil disturbance matches the infiltration rate contour line of the dynamic hydrological load map.

8. A rain garden construction control system based on BIM dynamic rainwater simulation, used to implement the rain garden construction control method based on BIM dynamic rainwater simulation according to any one of claims 1 to 7, characterized in that: include: The data integration module is used to integrate geological exploration data, building 3D models, historical rainfall data, and real-time weather forecasts of the target area to build a BIM-GIS fusion model and embed dynamic soil permeability parameters; A simulation engine module is used to import the BIM-GIS fusion model into the hydrodynamic-hydrological coupling engine, load the regional rainstorm intensity formula and building shielding factors, simulate the surface runoff path, rain garden infiltration efficiency and overflow risk points under extreme rainfall scenarios, and generate a dynamic hydrological load map; a parameter optimization module for reversely iterating and calculating the aquifer thickness, filler gradation ratio, and overflow outlet elevation of the rain garden based on the dynamic hydrological load map, and outputting a three-dimensional parametric design model that meets a flood peak reduction rate threshold; A construction instruction generation module is used to decompose the three-dimensional parametric design model into a layered construction instruction set, which is associated with construction machinery path planning, earthwork scheduling list and environmental monitoring probe placement; A twin construction module is used to construct a real-time twin of construction progress, resource consumption, and soil disturbance based on the hierarchical construction instruction set, construction machinery path planning, earthwork scheduling list, and environmental monitoring probe placement in the BIM platform; The monitoring and optimization module is used to collect on-site soil compaction, groundwater level fluctuations and equipment placement status data through the Internet of Things terminal, and compare them with the real-time twin of construction progress-resource consumption-soil disturbance. When the deviation of the soil layer permeability coefficient is monitored to be greater than the preset threshold or there is a risk of process conflict, the module triggers the adaptive construction plan re-optimization instruction and adjusts the mechanical operation sequence.

9. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the rain garden construction control method based on BIM dynamic rainwater simulation according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the rain garden construction control method based on BIM dynamic rainwater flood simulation described in any one of claims 1 to 7 is implemented.

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