Digital twin hydrological modeling method and system, electronic equipment and storage medium

Abstract

The invention discloses a digital twin hydrological modeling method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring earth surface point cloud data and underground pipe network point cloud data of a target area; establishing a first digital model based on the earth surface point cloud data, and establishing a second digital model based on the underground pipe network point cloud data; performing data mapping on the first digital model and the second digital model, and constructing a spatial digital twinborn model of the target area; obtaining rainfall data and water level data of a plurality of sampling points in the spatial digital twinborn model, and determining a flood risk isoline of the target area based on the rainfall data and the water level data of each sampling point; and superposing the flood risk contour line to the spatial digital twinborn model to obtain a hydrological early warning model of the target area. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of hydrological modeling is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a digital twin hydrological modeling method, system, electronic device and storage medium. Background Art

[0002] With the acceleration of urbanization, urban waterlogging has become a major problem threatening the safe operation of cities. Especially in urban areas with heavy rainfall and low terrain, accurate prediction and timely warning have become the key to urban flood prevention and disaster reduction. Therefore, effective hydrological modeling has become particularly important for the prediction and management of flood disasters.

[0003] At present, the existing hydrological modeling methods usually use the method of collecting data from surface monitoring stations and establishing hydrological models. This method can obtain surface hydrological parameters and conduct flood risk analysis based on them. However, in practical applications, urban drainage is a complex three-dimensional system above and below the ground. The existing hydrological models based on surface monitoring are difficult to fully reflect the actual hydrological conditions in urban areas. Especially in heavy rain weather, due to the inability to grasp the real-time status of the underground drainage network in time, there is often a large deviation between the warning results and the actual situation, thereby reducing the accuracy of hydrological modeling. Summary of the invention

[0004] The present application provides a digital twin hydrological modeling method, system, electronic device and storage medium, which have the effect of improving the accuracy of hydrological modeling.

[0005] In a first aspect, the present application provides a digital twin hydrological modeling method, comprising: Obtain surface point cloud data and underground pipe network point cloud data of the target area; Establishing a first digital model based on the surface point cloud data, and establishing a second digital model based on the underground pipe network point cloud data; Performing data mapping on the first digital model and the second digital model to construct a spatial digital twin model of the target area; Obtaining rainfall data and water level data of multiple sampling points in the spatial digital twin model, and determining the flood risk contour lines of the target area based on the rainfall data and water level data of each sampling point; The flood risk contour lines are superimposed on the spatial digital twin model to obtain a hydrological early warning model for the target area.

[0006] In a second aspect of the present application, a digital twin hydrological modeling system is provided, the system comprising: A data acquisition module is used to acquire surface point cloud data and underground pipe network point cloud data of the target area; A data processing module, used to establish a first digital model based on the surface point cloud data, and to establish a second digital model based on the underground pipe network point cloud data; perform data mapping on the first digital model and the second digital model to construct a spatial digital twin model of the target area; A contour line determination module is used to obtain rainfall data and water level data of multiple sampling points in the spatial digital twin model, and determine the flood risk contour line of the target area based on the rainfall data and water level data of each sampling point; The model building module is used to superimpose the flood risk contour lines onto the spatial digital twin model to obtain a hydrological early warning model for the target area.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a digital twin hydrological modeling method when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a digital twin hydrological modeling method.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, the surface point cloud data and underground pipe network point cloud data of the target area are obtained, and the first digital model and the second digital model are established based on the surface point cloud data and the underground pipe network point cloud data respectively, so that the surface and underground data elements can be included in the same modeling system at the same time, so as to more comprehensively reflect the overall hydrological environment of the target area; on this basis, the first digital model and the second digital model are data mapped to construct a spatial digital twin model of the target area, and are associated through the same coordinate system to provide an integrated three-dimensional environmental support for the subsequent acquisition of rainfall data and water level data of multiple sampling points; and then, based on the rainfall data and water level data of the sampling points, the flood risk contour lines of the target area are determined and superimposed on the spatial digital twin model, so that the risk results obtained from dynamic monitoring can be intuitively presented in the overall model that integrates the surface and underground structures. The final hydrological early warning model can help managers to grasp the distribution of potential waterlogging risks in a timely and accurate manner. This method of hydrological modeling through digital twins achieves the effect of improving the accuracy of hydrological modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flowchart of a digital twin hydrological modeling method provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a digital twin hydrological modeling system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] This application embodiment provides a digital twin hydrological modeling method. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of the digital twin hydrological modeling method provided in the embodiment of the present application. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a digital twin hydrological modeling system based on the von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain surface point cloud data and underground pipe network point cloud data of the target area.

[0016] The target area refers to any area within the urban planning area that requires flood risk warning, such as low-lying areas, flood-prone areas, important commercial areas, residential areas, etc. The scope of the target area can be defined according to actual flood control needs, usually including the complete catchment area and the corresponding underground drainage system service area.

[0017] Surface point cloud data refers to the three-dimensional coordinate information set of surface spatial elements in the target area obtained by the airborne laser radar system. These spatial elements include but are not limited to: buildings, roads, sidewalks, green spaces, water bodies and other ground entities. Each point cloud data point contains spatial coordinates (X, Y, Z) and reflection intensity values, which are used to characterize the geometric characteristics of the ground objects and the type of surface cover. These data can reflect the key factors that affect surface runoff, such as the terrain undulations, building distribution, and underlying surface characteristics of the target area.

[0018] Underground pipe network point cloud data refers to the three-dimensional spatial information set of drainage system facilities in the target area obtained by the underground pipe network detection radar system. These drainage system facilities include but are not limited to: rainwater pipes, combined pipes, inspection wells, rainwater inlets, pumping stations and other drainage facilities. Each point cloud data point also contains spatial coordinates (X, Y, Z) and reflection intensity values, which are used to characterize the spatial position and structural characteristics of underground drainage facilities. These data can reflect the spatial layout, pipe diameter, burial depth distribution and other important parameters that affect drainage capacity of the drainage network in the target area.

[0019] Specifically, it is necessary to first obtain the surface point cloud data and underground pipe network point cloud data of the target area. Since the urban hydrological system is a complex above-ground and underground three-dimensional system, it is difficult to fully reflect the actual hydrological conditions of the region by relying solely on surface monitoring data. Therefore, it is necessary to simultaneously obtain surface and underground spatial data to build a complete hydrological model. Specifically, an airborne laser radar system can be used to conduct aerial mapping of the target area to obtain three-dimensional point cloud data of surface buildings, roads, vegetation and other objects; at the same time, the underground pipe network detection radar system can be used to scan the underground drainage network to obtain spatial point cloud data of facilities such as pipelines, inspection wells, and pumping stations. In the process of data acquisition, in order to ensure data quality, it is necessary to perform repeated scanning multiple times and perform noise reduction and filtering on the acquired point cloud data to remove abnormal points and redundant data. In addition, the point cloud data needs to be registered and mosaicked to ensure that the data collected in different periods and different regions can be accurately spliced. The point cloud data obtained in this way has the characteristics of high precision and high density, which can truly reflect the surface and underground spatial characteristics of the target area and provide a reliable data basis for the subsequent establishment of digital models. These high-quality point cloud data not only contain spatial location information, but also record characteristic parameters such as the reflection intensity of the objects, which are helpful for the subsequent identification of different types of surface cover and pipe network facilities, so as to more accurately evaluate the hydrological characteristics of the region. This data collection method overcomes the limitations of traditional single surface monitoring, realizes all-round three-dimensional monitoring of urban drainage systems, and lays a solid data foundation for building an accurate hydrological early warning model.

[0020] Step 102: Establish a first digital model based on the surface point cloud data, and establish a second digital model based on the underground pipe network point cloud data.

[0021] Among them, the first digital model refers to the surface hydrological characteristic model constructed based on surface point cloud data, which comprehensively expresses the terrain characteristics and surface runoff characteristics of the target area. The first digital model includes elements such as digital elevation model, slope aspect information, catchment area boundary, water flow path and confluence node. These elements together constitute a complete surface hydrological system, which can simulate the collection and flow process of surface water during rainfall and reflect the spatial distribution law of surface runoff.

[0022] The second digital model refers to the underground drainage system characteristic model constructed based on the underground pipe network point cloud data. The model comprehensively expresses the distribution and functional characteristics of underground drainage facilities in the target area. The second digital model includes elements such as the three-dimensional pipe network layout, buried depth distribution, pipe diameter distribution, pipe network service range, inspection well location and pump station location. These elements together constitute a complete underground drainage system that can simulate the transmission process of rainwater in the pipe network and reflect the spatial distribution law of underground drainage capacity.

[0023] Specifically, firstly, the elevation information is extracted from the surface point cloud data, and the digital elevation model is generated by the spatial interpolation algorithm. The model can truly reflect the terrain undulation characteristics of the target area. Based on the digital elevation model, the slope and aspect of the target area are calculated. These parameters directly affect the movement direction and speed of the surface water flow. Then, according to the calculated slope and aspect information, the boundary of the catchment area is determined, and the spatial range of the surface runoff is clarified. Within the determined catchment area boundary, the water flow path and confluence node are determined by the hydrological analysis algorithm. These elements constitute the skeleton of the surface runoff network. Finally, these hydrological elements are integrated to generate the first digital model that can characterize the surface hydrological characteristics. When establishing the second digital model, the spatial position information of the pipeline network is first extracted from the underground pipeline network point cloud data, and a three-dimensional pipeline network layout diagram is generated. The layout diagram clearly shows the spatial structure of the underground drainage system. Based on the three-dimensional pipeline network layout diagram, the buried depth distribution and pipe diameter distribution of the pipeline network in the target area are calculated. These parameters determine the transportation capacity of the drainage system. According to the buried depth distribution and pipe diameter distribution, the actual service scope of the pipeline network is determined, and the drainage responsibility area of ​​each pipe section is clarified. Within the determined service range of the pipe network, the locations of the inspection wells and pump stations are further determined. These key nodes constitute the control hub of the underground drainage system. Finally, these drainage facility elements are integrated to generate a second digital model that can characterize the characteristics of underground drainage. This hierarchical modeling approach can not only characterize the characteristics of surface runoff and underground drainage separately, but also provide a basis for subsequent model fusion, which helps to build a more accurate hydrological early warning model. These two digital models together constitute the digital expression of the urban hydrological system, which can fully reflect the hydrological characteristics of the target area and provide reliable model support for subsequent flood risk analysis.

[0024] Based on the above embodiment, as an optional embodiment, in step 102: establishing a first digital model based on the surface point cloud data, this step may also include the following steps: Step 201: extracting elevation information from surface point cloud data and generating a digital elevation model corresponding to the elevation information.

[0025] Specifically, in order to accurately reflect the terrain characteristics of the target area, it is necessary to extract elevation information from the surface point cloud data and generate a digital elevation model. First, the surface point cloud data is preprocessed, including removing abnormal points and noise points, and retaining valid point cloud data that can reflect the real terrain. Then, the point cloud data is fitted using the least squares method to generate regular grid raster data. Next, the kriging interpolation algorithm is used to perform spatial interpolation on the raster data to generate a continuous surface elevation distribution. Finally, the interpolation result is converted into a digital elevation model in the form of a raster, which is stored in the form of a regular grid, and each grid cell records the elevation value of the corresponding position. The digital elevation model generated in this way can accurately reflect the terrain undulation characteristics of the target area and provide a terrain basis for subsequent hydrological analysis.

[0026] Step 202: Calculate the slope and aspect of the target area based on the digital elevation model; and determine the boundary of the catchment area according to the slope and aspect.

[0027] Specifically, in order to determine the catchment range of the target area, it is necessary to calculate the slope and aspect based on the digital elevation model, and determine the boundary of the catchment area accordingly. First, the finite difference method is used to calculate the slope value of each grid cell in the digital elevation model, that is, the rate of elevation change between adjacent grid cells. At the same time, the aspect value of each grid cell, that is, the direction of the maximum slope, is calculated. Then, based on the calculated slope and aspect values, the direction of the water flow is tracked by methods such as the D8 algorithm, that is, it is assumed that the water flow always flows in the direction of the steepest slope. Next, by analyzing the continuity of the water flow direction, areas with the same flow direction characteristics are determined, and these areas constitute the preliminary catchment area. Finally, the specific boundary line of the catchment area is determined by the boundary tracking algorithm. This method of water catchment area division based on terrain characteristics can accurately reflect the natural collection range of surface water during rainfall.

[0028] Step 203: Determine a water flow path and a confluence node within the boundary of the water catchment area; and generate a first digital model based on the water flow path and the confluence node.

[0029] Specifically, in order to construct a complete first digital model, it is necessary to determine the flow path and confluence nodes within the determined catchment area boundary. First, based on the aforementioned water flow direction analysis results, the cumulative flow algorithm is used to calculate the confluence of each grid unit, that is, the area of ​​the upstream area flowing through the unit. Then, a threshold is set to screen grid units with larger confluences, and these units are connected to form a water flow path. Next, confluence nodes are identified at the intersection of the water flow paths. These nodes usually correspond to depressions in the terrain or the entrance locations of drainage facilities. Finally, the water flow path and confluence node information are integrated with the digital elevation model to form a complete first digital model. The first digital model constructed in this way not only contains terrain information, but also contains the spatial distribution characteristics of surface runoff, which can effectively support subsequent flood risk analysis.

[0030] Based on the above embodiment, as an optional embodiment, in step 102: establishing a second digital model based on the underground pipe network point cloud data, this step may also include the following steps: Step 204: extracting the spatial position of the pipe network from the underground pipe network point cloud data, and generating a three-dimensional pipe network layout diagram corresponding to the spatial position of the pipe network.

[0031] Specifically, in order to accurately grasp the spatial layout of the underground drainage system, it is necessary to extract the spatial location information of the pipeline network from the underground pipeline point cloud data and generate a three-dimensional pipeline layout diagram. First, the underground pipeline point cloud data is classified and processed to identify and extract the point cloud data of different types of facilities such as pipelines and inspection wells. Then, the pipeline point cloud data is cylindrically fitted using methods such as the RANSAC algorithm to obtain the spatial direction and diameter information of the pipeline. Next, the inspection well point cloud data is cylindrically fitted using the least squares method to obtain the spatial position and size information of the inspection well. Finally, the spatial information of the pipeline and the inspection well is integrated into a unified three-dimensional coordinate system to generate a complete three-dimensional pipeline layout diagram. The three-dimensional pipeline layout diagram generated in this way can intuitively display the spatial structure of the underground drainage system and provide a basis for subsequent pipeline network analysis.

[0032] Step 205: Based on the three-dimensional pipe network layout diagram, calculate the buried depth distribution and pipe diameter distribution of the pipe network in the target area; and determine the service range of the pipe network according to the buried depth distribution and pipe diameter distribution.

[0033] Specifically, in order to evaluate the service capacity of the underground drainage system, it is necessary to calculate the buried depth distribution and pipe diameter distribution of the pipeline network based on the three-dimensional pipeline network layout diagram, and determine the service range of the pipeline network accordingly. First, by comparing the pipeline network elevation with the surface elevation, the buried depth value of each pipe section is calculated, and the interpolation algorithm is used to generate a continuous buried depth distribution map. At the same time, the pipe diameter information is extracted based on the pipeline fitting results to generate a pipe diameter distribution map. Then, combined with the buried depth distribution and pipe diameter distribution of the pipeline network, the hydraulic calculation method is used to evaluate the design flow and actual transportation capacity of each pipe section. Next, based on the transportation capacity and terrain characteristics of the pipe section, the Thiessen polygon method is used to divide the service area of ​​each pipe section. Finally, the service areas of each pipe section are integrated to determine the service range of the entire pipeline system. This service range division method based on pipeline network characteristics can accurately reflect the spatial service capacity of the underground drainage system.

[0034] Step 206: Determine the locations of inspection wells and pump stations within the service range of the pipeline network, and generate a second digital model based on the locations of inspection wells and pump stations.

[0035] Specifically, in order to construct a complete second digital model, it is necessary to determine the location of the inspection well and the pump station within the determined service range of the pipeline network. First, extract the spatial coordinates of the inspection well from the three-dimensional pipeline network layout diagram, and verify the correctness of its connection relationship with the pipeline. Then, identify the location of the pump station in the pipeline network system, and determine its working parameters, including the design head and design flow. Next, based on the location information of the inspection well and the pump station, construct the node control network of the drainage system to determine the hydraulic connection between each node. Finally, integrate the node control network with the pipeline network layout information to generate a complete second digital model. The second digital model constructed in this way not only contains the spatial information of the pipeline network, but also contains the functional characteristics of the drainage facilities, which can effectively support the subsequent drainage capacity analysis.

[0036] Step 103: Perform data mapping on the first digital model and the second digital model to construct a spatial digital twin model of the target area.

[0037] The spatial digital twin model refers to a unified model obtained by fusing the first digital model and the second digital model through data mapping. The spatial digital twin model can fully reflect the hydrological characteristics of the target area, realize the complete process simulation from rainfall confluence to drainage outflow, and provide a unified model basis for flood risk warning.

[0038] Specifically, in order to achieve the organic integration of the surface runoff system and the underground drainage system, it is necessary to map the data of the first digital model and the second digital model to construct a spatial digital twin model of the target area. First, the unified coordinate system of the two models is determined by spatial registration technology, and the control point matching method is used to correct the possible spatial position deviation. Then, based on the spatial relationship between the surface water flow path and the underground pipe network layout, the connection points between the surface confluence nodes and the underground drainage facilities are identified. These connection points are usually the locations of rainwater inlets or inspection wells. Next, a hydraulic connection model is established at each connection point, which describes the process of surface water entering the underground drainage system, including inflow capacity and water level change characteristics. On this basis, the surface hydrological elements and underground drainage elements are spatially associated to construct a unified water flow network, which can express the complete process from rainfall confluence to drainage outflow. Finally, the spatially associated elements are integrated into a unified data structure to form a spatial digital twin model of the target area. The spatial digital twin model constructed in this way realizes the integrated expression of the surface and underground hydrological systems, not only retains their respective characteristic information, but also establishes a dynamic connection between the systems, which can provide complete model support for flood risk warning. Through this data mapping method, the coupling process of surface runoff and underground drainage under rainfall conditions can be accurately simulated, providing a more reliable analysis basis for subsequent risk warnings.

[0039] Based on the above embodiment, as an optional embodiment, in step 103: data mapping is performed on the first digital model and the second digital model to construct a spatial digital twin model of the target area. This step may also include the following steps: Step 301: Acquire the coordinates of the surface feature points in the first digital model and the coordinates of the pipe network nodes in the second digital model.

[0040] Specifically, in order to achieve the spatial association between the first digital model and the second digital model, it is first necessary to obtain the key spatial position information in the two models. Specifically, the coordinates of the surface feature points are extracted from the first digital model. These feature points include confluence nodes, key turning points of the water flow path, and other location points that can characterize the surface hydrological characteristics. At the same time, the coordinates of the pipeline network nodes are extracted from the second digital model. These nodes include the location of inspection wells, the location of rainwater outlets, the location of pump stations, and other location points that can characterize the characteristics of the underground drainage system. The coordinate information obtained in this way provides basic data for subsequent spatial mapping, ensuring the accuracy of establishing spatial associations between the two models.

[0041] Step 302: Mapping the coordinates of the surface feature points and the pipe network node coordinates to a preset spatial coordinate system; dividing the preset spatial coordinate system into a plurality of grid units, and determining the vertical projection distance between the surface feature points and the pipe network nodes in each grid unit.

[0042] Specifically, in order to unify the spatial reference and establish the spatial relationship between the surface and underground facilities, it is necessary to map the two sets of coordinates into the same preset spatial coordinate system. First, select a spatial coordinate system suitable for the target area, usually using the local commonly used projection coordinate system. Then, the surface feature point coordinates and the pipeline network node coordinates are uniformly converted to the preset spatial coordinate system through coordinate transformation. Next, the preset spatial coordinate system is divided into regular grid units, and the grid size is determined according to the actual situation and accuracy requirements of the target area. Finally, for each grid unit, the vertical projection distance from the surface feature point in the unit to the pipeline network node is calculated, that is, the distance from the projection point of the surface feature point on the horizontal plane to the pipeline network node. This grid-based spatial analysis method can effectively reduce the computational complexity and improve the efficiency of spatial association.

[0043] Step 303: When the vertical projection distance is less than the distance threshold, the corresponding surface feature point and the pipe network node are determined as an associated point pair.

[0044] Specifically, in order to determine the actual connection relationship between the surface hydrological system and the underground drainage system, it is necessary to identify the associated point pairs based on the vertical projection distance. First, an appropriate distance threshold is set, which should take into account the reasonable service radius of the rainwater inlet and the confluence point in the actual project. Then, the point pairs in each grid unit are judged, and when the vertical projection distance between the surface feature point and the pipe network node is less than the set distance threshold, the pair of points is determined as an associated point pair. This distance threshold-based associated point pair identification method is consistent with actual engineering experience and can accurately reflect the spatial position relationship of surface runoff entering the underground drainage system. The associated point pairs determined in this way provide a spatial basis for the subsequent establishment of a complete hydraulic connection model, and effectively support the construction of a spatial digital twin model.

[0045] Step 304: Based on the associated point pairs, a spatial mapping matrix of the first digital model and the second digital model is constructed, and weighted calculation is performed on the first digital model and the second digital model through the spatial mapping matrix to obtain a spatial digital twin model of the target area.

[0046] Specifically, in order to achieve the effective fusion of the first digital model and the second digital model, it is necessary to construct a spatial mapping matrix based on the determined associated point pairs, and integrate the models through the matrix. First, all the associated point pairs are numbered according to the spatial distribution law to construct an n×n spatial mapping matrix, where n is the total number of associated point pairs, and each element of the matrix represents the spatial association strength between the corresponding associated point pairs. Specifically, the value of the matrix element can be obtained by comprehensively calculating factors such as the distance relationship, elevation difference and hydraulic connectivity between the associated point pairs. Then, weight coefficients are set for the two models respectively, and the size of the weight coefficient is determined according to the reliability of the model, data accuracy and actual application requirements. Next, the spatial mapping matrix is ​​used to perform weighted calculation on the two models, specifically including, for each associated point pair, weighted fusion of the surface hydrological characteristics in the first digital model and the underground drainage characteristics in the second digital model through the corresponding matrix elements and weight coefficients to obtain a unified feature expression. Finally, the fusion results of all associated point pairs are spatially interpolated to form a continuous feature field covering the entire target area, that is, a spatial digital twin model. This model fusion method based on the spatial mapping matrix not only maintains the characteristic information of the original model, but also realizes the organic fusion of features through weighted calculation, so that the constructed spatial digital twin model can more accurately reflect the overall hydrological characteristics of the target area, providing a reliable model basis for subsequent flood risk analysis.

[0047] Step 104: Obtain rainfall data and water level data of multiple sampling points in the spatial digital twin model, and determine the flood risk contour lines of the target area based on the rainfall data and water level data of each sampling point.

[0048] Among them, sampling points refer to representative locations selected in the spatial digital twin model, including key confluence nodes in the surface hydrological system (such as low-lying areas and outlets of major catchment areas) and important nodes in the underground drainage system (such as inspection wells, rainwater inlets, pumping stations, etc.). The distribution of these sampling points should be able to reflect the hydrological characteristics of the target area and the operating status of the drainage system.

[0049] Precipitation data refers to the rainfall characteristic data obtained through monitoring at each sampling point, including rainfall intensity (rainfall per unit time), cumulative rainfall (total rainfall in a certain period of time), rainfall duration (how long the rainfall lasts), and other values ​​that reflect the characteristics of the rainfall process.

[0050] Water level data refers to the water accumulation status data obtained through monitoring at each sampling point, including surface water depth (water accumulation height at surface confluence nodes) and pipe network water level (water level height in inspection wells or pipes in underground drainage systems), and other values ​​reflecting the regional drainage status.

[0051] Flood risk contours refer to closed curves formed by connecting spatial points with the same flood risk level. These contours can divide the target area into different risk areas. The points on each contour have the same risk value. The density and value of the contours can intuitively reflect the spatial distribution characteristics and changing trends of flood risks.

[0052] Specifically, in order to accurately assess the flood risk distribution in the target area, it is necessary to obtain the hydrological data of key sampling points based on the spatial digital twin model and generate risk contour lines. First, representative sampling points are selected in the spatial digital twin model. These sampling points should be evenly distributed in the target area and include key surface confluence points and underground important node locations. Then, the rainfall data of each sampling point is collected, including characteristic values ​​such as rainfall intensity, accumulated rainfall and rainfall duration, and the water level data of the sampling points is monitored, including the surface water depth and pipe network water level. Then, based on the collected rainfall data and water level data, a rainfall-water level response relationship model is established using the multivariate regression analysis method. The model can reflect the water level change characteristics of each sampling point under different rainfall conditions. On this basis, combined with the terrain characteristics and drainage capacity of the target area, the flood risk level corresponding to different water levels is determined. Subsequently, the risk level of each sampling point is spatially interpolated using the Kriging interpolation method to generate a continuous risk distribution field. Finally, based on the risk distribution field, spatial location points with equal risk values ​​are extracted, and these location points are connected to form a closed curve, namely the flood risk contour line. The risk contours generated in this way can intuitively show the spatial distribution characteristics of flood risk in the target area, provide spatial reference for flood control and drainage decision-making, and ensure the reliability of risk assessment results through the support of measured data at sampling points. Through this risk assessment method based on the spatial digital twin model, high-risk areas in the target area can be more accurately identified, providing a scientific basis for the formulation and implementation of flood control and drainage measures.

[0053] Based on the above embodiment, as an optional embodiment, in step 104: determining the flood risk contour line of the target area based on the rainfall data and water level data of each sampling point, this step may also include the following steps: Step 401: Based on the rainfall data and water level data of each sampling point, the water depth corresponding to each sampling point is calculated.

[0054] Specifically, in order to quantitatively evaluate the flood conditions at each sampling point, it is necessary to calculate the depth of waterlogging based on monitoring data. First, obtain the rainfall data of each sampling point, including rainfall intensity, accumulated rainfall and rainfall duration, and at the same time obtain the corresponding water level data, including the surface water depth and pipe network water level. Then, establish a rainfall-waterlogging response model, which takes into account factors such as rainfall, surface runoff and pipe network drainage capacity, and calculates the actual waterlogging depth of each sampling point through the water balance equation. This method of calculating the waterlogging depth based on measured data can accurately reflect the actual waterlogging conditions of each sampling point and provide a quantitative basis for subsequent risk level determination.

[0055] Based on the above embodiment, as an optional embodiment, in step 401: based on the rainfall data and water level data of each sampling point, the water accumulation depth corresponding to each sampling point is calculated. This step may also include the following steps: Step 411: Obtain the underlying surface type of each sampling point; and determine the corresponding permeability coefficient according to each underlying surface type.

[0056] Specifically, in order to accurately evaluate the actual contribution of rainfall to waterlogging, it is necessary to consider the permeability characteristics of different underlying surfaces. First, the underlying surface type of each sampling point is obtained through the spatial digital twin model, such as grass, gravel pavement, asphalt pavement, concrete pavement, etc. Then, based on engineering experience and experimental data, a corresponding relationship table between the underlying surface type and the permeability coefficient is established. For example, the permeability coefficient of grass can be set to 0.4, gravel pavement to 0.6, asphalt pavement to 0.9, concrete pavement to 0.95, etc. The permeability coefficient determined in this way can reflect the interception and infiltration of rainfall on different underlying surfaces, and improve the accuracy of waterlogging calculation.

[0057] Step 421: Multiply the rainfall data of each sampling point by the corresponding permeability coefficient to obtain the corresponding effective water accumulation volume.

[0058] Specifically, in order to calculate the actual rainfall that causes waterlogging, the influence of the permeability characteristics of the underlying surface needs to be considered. Specifically, the rainfall data of each sampling point (including rainfall intensity and accumulated rainfall) is multiplied by the permeability coefficient corresponding to the point to obtain the effective amount of waterlogging. For example, if the accumulated rainfall at a sampling point is 100 mm, the point is located on the grass, and the permeability coefficient is 0.4, then the calculated effective amount of waterlogging is 40 mm. This method of calculating the amount of waterlogging that takes into account the characteristics of the underlying surface can more accurately reflect the actual waterlogging situation and avoid the calculation deviation of the waterlogging depth caused by ignoring the permeability effect.

[0059] Step 431: Accumulate the effective water volume of each sampling point and the corresponding water level data to obtain the corresponding water depth of each sampling point.

[0060] Specifically, in order to obtain the actual water depth of the sampling point, it is necessary to comprehensively consider the effective water volume and the existing water level conditions. Specifically, the calculated effective water volume is added to the current water level data of the sampling point to obtain the actual water depth of the sampling point. For example, the effective water volume of a sampling point is 40 mm, and the current water level data shows that there is 30 mm of water. The actual water depth of the point is 70 mm. This method of calculating the water depth takes into account the impact of both the new water and the original water, and can truly reflect the water status of the sampling point, providing reliable data support for subsequent risk level determination.

[0061] Step 402: Determine the corresponding flood risk level based on the interval of each waterlogging depth in the preset flood risk level mapping table.

[0062] Specifically, in order to convert the depth of accumulated water into a risk level, it is necessary to make a judgment based on a preset flood risk level mapping table. First, a flood risk level mapping table is established. The mapping table divides the depth of accumulated water into multiple intervals based on historical data and engineering experience. Each interval corresponds to a risk level. For example, a depth of accumulated water less than 15 cm can be defined as low risk, 15-30 cm as medium risk, 30-50 cm as high risk, and greater than 50 cm as extremely high risk. Then, the calculated depth of accumulated water at each sampling point is compared with the interval in the mapping table to determine the corresponding risk level. This risk level determination method based on a mapping table has clear quantitative standards and can objectively reflect the degree of flood risk at each sampling point.

[0063] Step 403: among the sampling points, the sampling points with the same flood risk level are connected to obtain flood risk contour lines.

[0064] Specifically, in order to intuitively display the spatial distribution of flood risks, it is necessary to draw contour lines based on the risk level of the sampling points. First, mark the location of each sampling point and the corresponding risk level in the spatial coordinate system. Then, use spatial interpolation methods, such as Kriging interpolation, to generate a continuous risk distribution field between the sampling points. Next, identify spatial location points with the same risk level in the risk distribution field. Finally, connect the location points with the same risk level in sequence according to the spatial proximity relationship to form closed contour lines. The flood risk contour lines generated in this way can clearly divide areas with different risk levels, intuitively display the spatial distribution characteristics of risks, and provide a spatial reference basis for flood control and drainage decisions.

[0065] Step 105: Superimpose the flood risk contour lines onto the spatial digital twin model to obtain a hydrological warning model for the target area.

[0066] Among them, the hydrological early warning model refers to a visual early warning system formed by integrating flood risk contours with the spatial digital twin model. This hydrological early warning model displays the spatial distribution characteristics of flood risks in a visual way, and can realize dynamic early warning of risks, providing a decision-making support tool for flood control and drainage management.

[0067] Specifically, in order to achieve intuitive display and dynamic early warning of flood risk in the target area, it is necessary to organically combine flood risk contour lines with the spatial digital twin model. First, the generated flood risk contour lines are imported into the spatial digital twin model according to the spatial coordinate information to ensure that the spatial position of the contour lines corresponds accurately to the geographical elements in the model. Then, the contour areas of different risk levels are color-coded, for example, low-risk areas are marked as green, medium-risk areas are marked as yellow, high-risk areas are marked as orange, and extremely high-risk areas are marked as red. The spatial distribution of risk levels is intuitively reflected through color differences. At the same time, the contour lines are associated with the surface hydrological system and underground drainage system in the spatial digital twin model, so that the risk contour lines can be dynamically updated as the hydrological conditions change. The hydrological early warning model constructed in this way can not only clearly display the distribution of flood risk in the target area, but also provide dynamic early warning based on real-time monitoring data, providing timely and intuitive spatial information support for flood control and drainage decisions. Through the hydrological early warning model, managers can quickly identify high-risk areas, dispatch flood control and drainage resources in a timely manner, and effectively improve the scientificity and timeliness of flood control and drainage.

[0068] Based on the above embodiment, as an optional embodiment, in step 105: superimposing the flood risk contour lines onto the spatial digital twin model to obtain a hydrological early warning model for the target area, this step may also include the following steps: Step 501: Convert the flood risk contour lines into the spatial coordinate system corresponding to the spatial digital twin model, and grade and label the converted flood risk contour lines based on the preset risk level classification standard to obtain warning areas of multiple different risk levels.

[0069] Specifically, in order to accurately locate and grade the flood risk contour lines in the spatial digital twin model, coordinate system conversion and risk classification are required. First, the coordinate data of the flood risk contour lines are converted to the spatial coordinate system used by the spatial digital twin model to ensure that the contour lines correspond accurately to the positions of the geographical elements in the model. Then, according to the preset risk level classification standards, such as defining the depth of water accumulation less than 15 cm as a low-risk area, 15-30 cm as a medium-risk area, 30-50 cm as a high-risk area, and greater than 50 cm as an extremely high-risk area, the converted contour lines are graded and labeled to form warning areas with different risk level attributes. This processing method based on a unified coordinate system and standardized classification can ensure the accurate positioning of the warning area in the spatial digital twin model and the clear division of risk levels.

[0070] Step 502: Fill each warning area into the spatial digital twin model, and mark each warning area with a gradient color based on the risk level of each warning area.

[0071] Specifically, in order to enhance the visualization effect of the warning area, the warning area needs to be filled and color-marked. The graded warning areas are filled into the spatial digital twin model, and a gradient color marking scheme is adopted. For example, low-risk areas are marked with a gradient from light green to dark green, medium-risk areas are marked with a gradient from light yellow to dark yellow, high-risk areas are marked with a gradient from light orange to dark orange, and extremely high-risk areas are marked with a gradient from light red to dark red. Through gradient color marking, not only can the warning areas of different risk levels be intuitively distinguished, but also the changes in risk intensity within the same risk level can be reflected through changes in color depth, thereby improving the expression effect of warning information.

[0072] Step 503: Obtain the number of surface feature points and the number of pipe network nodes in each warning area; integrate the gradient color marking situation, the number of surface feature points and the number of pipe network nodes in each warning area to obtain a hydrological warning model for the target area.

[0073] Specifically, in order to comprehensively evaluate the risk impact range of the warning area, it is necessary to count and integrate the key node information in each warning area. First, the number of surface feature points (such as important buildings, road intersections, etc.) and the number of pipe network nodes (such as inspection wells, rainwater inlets, pumping stations, etc.) in each warning area are obtained. Then, the gradient color marking information of the warning area is integrated with the node quantity information to form a comprehensive hydrological warning model that includes risk level, spatial distribution and impact range. This warning model that integrates multi-dimensional information can not only show the spatial distribution characteristics of risks, but also quantitatively evaluate the number of infrastructure that may be affected in areas with different risk levels, providing more comprehensive information support for flood control and drainage decisions.

[0074] Reference Figure 2, is a digital twin hydrological modeling system provided in an embodiment of the present application, the system comprising: a data acquisition module, a data processing module, a contour determination module, and a model construction module, wherein: A data acquisition module is used to acquire surface point cloud data and underground pipe network point cloud data of the target area; A data processing module is used to establish a first digital model based on surface point cloud data, and to establish a second digital model based on underground pipe network point cloud data; perform data mapping between the first digital model and the second digital model to construct a spatial digital twin model of the target area; The contour line determination module is used to obtain the rainfall data and water level data of multiple sampling points in the spatial digital twin model, and determine the flood risk contour lines of the target area based on the rainfall data and water level data of each sampling point; The model building module is used to superimpose flood risk contour lines onto the spatial digital twin model to obtain a hydrological early warning model for the target area.

[0075] Based on the above embodiments, the data processing module is also used to extract elevation information from the surface point cloud data and generate a digital elevation model corresponding to the elevation information; based on the digital elevation model, calculate the slope and aspect of the target area; determine the boundary of the catchment area according to the slope and aspect; determine the water flow path and confluence node within the boundary of the catchment area; and generate a first digital model based on the water flow path and confluence node.

[0076] On the basis of the above-mentioned embodiment, the data processing module is also used to extract the spatial position of the pipeline network from the underground pipeline network point cloud data, and generate a three-dimensional pipeline network layout diagram corresponding to the spatial position of the pipeline network; based on the three-dimensional pipeline network layout diagram, calculate the buried depth distribution and pipe diameter distribution of the pipeline network in the target area; determine the service range of the pipeline network according to the buried depth distribution and pipe diameter distribution; within the pipeline network service range, determine the inspection well location and pump station location, and generate a second digital model based on the inspection well location and pump station location.

[0077] On the basis of the above embodiments, the data processing module is also used to obtain the coordinates of the surface feature points in the first digital model and the coordinates of the pipeline nodes in the second digital model; map the surface feature point coordinates and the pipeline node coordinates to a preset spatial coordinate system; divide the preset spatial coordinate system into multiple grid units, and determine the vertical projection distance between the surface feature points and the pipeline nodes in each grid unit; when the vertical projection distance is less than the distance threshold, determine the corresponding surface feature points and the pipeline nodes as associated point pairs; based on the associated point pairs, construct a spatial mapping matrix of the first digital model and the second digital model, and perform weighted calculation on the first digital model and the second digital model through the spatial mapping matrix to obtain a spatial digital twin model of the target area.

[0078] Based on the above embodiment, the contour line determination module is also used to calculate the water accumulation depth corresponding to each sampling point based on the rainfall data and water level data of each sampling point; determine the corresponding flood risk level based on the interval of each water accumulation depth in the preset flood risk level mapping table; among each sampling point, connect the sampling points of the same flood risk level to obtain the flood risk contour line.

[0079] On the basis of the above-mentioned embodiment, the contour line determination module is also used to obtain the underlying surface type of each sampling point; determine the corresponding permeability coefficient according to each underlying surface type; multiply the rainfall data of each sampling point by the corresponding permeability coefficient to obtain the corresponding effective water accumulation volume; accumulate the effective water accumulation volume of each sampling point with the corresponding water level data to obtain the corresponding water accumulation depth of each sampling point.

[0080] Based on the above embodiments, the model construction module is also used to convert flood risk contour lines into the spatial coordinate system corresponding to the spatial digital twin model, and based on the preset risk level classification standards, the converted flood risk contour lines are graded and labeled to obtain warning areas of multiple different risk levels; each warning area is filled into the spatial digital twin model, and each warning area is gradiently marked based on the risk level of each warning area; the number of surface feature points and the number of pipeline nodes in each warning area are obtained; the gradient color marking, the number of surface feature points and the number of pipeline nodes of each warning area are integrated to obtain the hydrological warning model of the target area.

[0081] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0082] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0083] The communication bus 302 is used to realize the connection and communication between these components.

[0084] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0085] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0086] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0087] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a digital twin hydrological modeling method.

[0088] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a digital twin hydrological modeling method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0089] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0094] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0095] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A digital twin hydrological modeling method, characterized in that: include: Obtain surface point cloud data and underground pipe network point cloud data of the target area; Establishing a first digital model based on the surface point cloud data, and establishing a second digital model based on the underground pipe network point cloud data; Performing data mapping on the first digital model and the second digital model to construct a spatial digital twin model of the target area; Obtaining rainfall data and water level data of multiple sampling points in the spatial digital twin model, and determining the flood risk contour lines of the target area based on the rainfall data and water level data of each sampling point; The flood risk contour lines are superimposed on the spatial digital twin model to obtain a hydrological early warning model for the target area.

2. The digital twin hydrological modeling method according to claim 1, characterized in that: The step of establishing a first digital model based on the surface point cloud data comprises: Extracting elevation information from the surface point cloud data and generating a digital elevation model corresponding to the elevation information; Based on the digital elevation model, calculating the slope and aspect of the target area; Determining a catchment area boundary according to the slope and the slope direction; Determining water flow paths and confluence nodes within the boundaries of the catchment area; A first digital model is generated based on the water flow path and the confluence node.

3. The digital twin hydrological modeling method according to claim 1, characterized in that: The step of establishing a second digital model based on the underground pipe network point cloud data comprises: Extracting the spatial position of the underground pipe network from the underground pipe network point cloud data, and generating a three-dimensional pipe network layout diagram corresponding to the spatial position of the pipe network; Based on the three-dimensional pipe network layout diagram, calculating the buried depth distribution and pipe diameter distribution of the pipe network in the target area; Determining the service scope of the pipeline network according to the buried depth distribution and the pipe diameter distribution; Within the service range of the pipeline network, the location of the inspection well and the location of the pump station are determined, and based on the location of the inspection well and the location of the pump station, a second digital model is generated.

4. The digital twin hydrological modeling method according to claim 1, characterized in that: The performing data mapping on the first digital model and the second digital model to construct a spatial digital twin model of the target area includes: Acquire the coordinates of the surface feature points in the first digital model and the coordinates of the pipe network nodes in the second digital model; Mapping the surface feature point coordinates and the pipe network node coordinates to a preset spatial coordinate system; Dividing the preset spatial coordinate system into a plurality of grid units, and determining the vertical projection distance between the surface feature point and the pipe network node in each of the grid units; When the vertical projection distance is less than the distance threshold, the corresponding surface feature point and the pipe network node are determined as an associated point pair; Based on the associated point pairs, a spatial mapping matrix of the first digital model and the second digital model is constructed, and the first digital model and the second digital model are weighted calculated using the spatial mapping matrix to obtain a spatial digital twin model of the target area.

5. The digital twin hydrological modeling method according to claim 1, characterized in that: Determining the flood risk contour line of the target area based on the rainfall data and water level data of each sampling point includes: Based on the rainfall data and water level data of each sampling point, calculating the water accumulation depth corresponding to each sampling point; Determine the corresponding flood risk level based on the interval where each of the waterlogging depths is located in a preset flood risk level mapping table; Among the sampling points, sampling points with the same flood risk level are connected to obtain flood risk contour lines.

6. The digital twin hydrological modeling method according to claim 5, characterized in that: The calculating the water accumulation depth corresponding to each sampling point based on the rainfall data and water level data of each sampling point includes: Obtaining the underlying surface type of each sampling point; Determine the corresponding permeability coefficient according to each underlying surface type; Multiplying the rainfall data of each sampling point by the corresponding permeability coefficient to obtain the corresponding effective water accumulation; The effective amount of accumulated water at each sampling point is accumulated with the corresponding water level data to obtain the accumulated water depth corresponding to each sampling point.

7. The digital twin hydrological modeling method according to claim 1, characterized in that: The flood risk contour lines are superimposed on the spatial digital twin model to obtain a hydrological early warning model for the target area, including: The flood risk contour lines are converted into the spatial coordinate system corresponding to the spatial digital twin model, and based on the preset risk level classification standard, the converted flood risk contour lines are graded and labeled to obtain a plurality of early warning areas of different risk levels; Filling each of the warning areas into the spatial digital twin model, and marking each of the warning areas with a gradient color based on the risk level of each of the warning areas; Obtaining the number of surface feature points and the number of pipe network nodes in each of the warning areas; The gradient color marking situation, the number of surface feature points and the number of pipe network nodes of each warning area are integrated to obtain a hydrological warning model for the target area.

8. A digital twin hydrological modeling system, characterized in that: The system comprises: A data acquisition module is used to acquire surface point cloud data and underground pipe network point cloud data of the target area; A data processing module, used to establish a first digital model based on the surface point cloud data, and to establish a second digital model based on the underground pipe network point cloud data; perform data mapping on the first digital model and the second digital model to construct a spatial digital twin model of the target area; A contour line determination module is used to obtain rainfall data and water level data of multiple sampling points in the spatial digital twin model, and determine the flood risk contour line of the target area based on the rainfall data and water level data of each sampling point; The model building module is used to superimpose the flood risk contour lines onto the spatial digital twin model to obtain a hydrological early warning model for the target area.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the digital twin hydrological modeling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the digital twin hydrological modeling method as described in any one of claims 1 to 7 is executed.

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