Flood disaster risk assessment method, device and electronic equipment

Abstract

The application provides a flood disaster risk assessment method and device and electronic equipment, and belongs to the cross technical field of hydrological forecasting and geographic information processing. The method comprises the following steps: dividing a first region into N basin units, N being an integer greater than 1; obtaining rainfall runoff data of a target basin unit in the N basin units based on meteorological information; obtaining a flow process line of an outlet point of the target basin unit based on the rainfall runoff data of the target basin unit, the outlet point being a node at which runoff of the target basin unit flows into a main stream or a branch stream in the first region, and a time sequence feature of the flow process line being consistent with a time sequence feature of a rainfall process line indicated by the meteorological information; inputting the flow process line into a hydrodynamic model to obtain water level and flow of a target river channel section; and outputting flood disaster assessment information of the target river channel section based on the water level and flow of the target river channel section. In this way, the accuracy of the flood disaster assessment information is improved.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of hydrological forecasting and geographic information processing, and in particular to a method, apparatus and electronic equipment for flood disaster risk assessment. Background Technology

[0002] Currently, disaster prevention and mitigation efforts primarily involve obtaining rainfall data within a watershed and simulating the flood evolution process based on this data, then outputting the simulation results. However, due to the spatiotemporal non-uniform and nonlinear characteristics of flood formation conditions under changing environments, the simulation results of flood evolution based solely on rainfall data contain significant errors compared to the actual flood evolution process.

[0003] It is evident that the simulation and assessment schemes for flood disasters in related technologies suffer from poor accuracy. Summary of the Invention

[0004] This application provides a flood disaster risk assessment method, apparatus, and electronic device to address the problem of poor accuracy in flood disaster simulation assessment schemes in related technologies.

[0005] To address the aforementioned problems, the first aspect of this application provides a flood disaster risk assessment method, comprising: The first region is divided into N watershed units, where N is an integer greater than 1; Rainfall and runoff data of the target watershed unit among the N watershed units are obtained based on meteorological information; Based on the rainfall and runoff data of the target watershed unit, the flow process line of the outlet point of the target watershed unit is obtained. The outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region. The time series characteristics of the flow process line are consistent with the time series characteristics of the rainfall process line indicated by the meteorological information. The flow process line is input into the hydrodynamic model to calculate the water level and flow rate of the target river section. Based on the water level and flow rate of the target river section, output flood assessment information for the target river section.

[0006] Secondly, this application provides a flood disaster risk assessment device, comprising: The partitioning module is used to divide the first region into N watershed units, where N is an integer greater than 1; The first acquisition module is used to acquire rainfall and runoff data of the target watershed unit among the N watershed units based on meteorological information; The second acquisition module is used to acquire the flow process line of the outlet point of the target watershed unit based on the rainfall and runoff data of the target watershed unit. The outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region. The time series characteristics of the flow process line are consistent with the time series characteristics of the rainfall process line indicated by the meteorological information. The calculation module is used to input the flow process line into the hydrodynamic model to calculate the water level and flow rate of the target river section; The output module is used to output flood assessment information of the target river section based on the water level and flow rate of the target river section.

[0007] Thirdly, this application provides an electronic device including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0008] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0009] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0010] In a sixth aspect, this application provides a computer program product stored in a storage medium, which is executed by at least one processor to implement the method described in the first aspect.

[0011] In this application, a first region is divided into N watershed units, where N is an integer greater than 1; rainfall-runoff data of a target watershed unit in the N watershed units are obtained based on meteorological information; based on the rainfall-runoff data of the target watershed unit, a flow process line is obtained at the outlet point of the target watershed unit, where the outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region, and the temporal characteristics of the flow process line are consistent with the temporal characteristics of the rainfall process line indicated by the meteorological information; the flow process line is input into a hydrodynamic model to calculate the water level and flow rate of the target river section; based on the water level and flow rate of the target river section, flood assessment information of the target river section is output. This method obtains rainfall-runoff data for the target watershed unit based on meteorological information, and then obtains the flow process line at the outlet point of the target watershed unit based on the rainfall-runoff data. Since the temporal characteristics of the flow process line are consistent with the temporal characteristics of the rainfall process line indicated by the meteorological information, it can effectively reduce the accumulation of errors caused by factors such as inconsistencies in spatiotemporal scales. This makes the calculation results of water level and flow at the target river section based on the flow process line more accurate, thereby improving the accuracy of flood assessment information for the target river section. Attached Figure Description

[0012] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a flood disaster risk assessment method provided in an embodiment of this application; Figure 2 A system architecture block diagram provided for one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a flood disaster risk assessment device provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] The terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Additionally, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: including A alone, B alone, C alone, and the presence of both A and B, both B and C, both A and C, and the presence of A, B, and C.

[0016] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0017] The flood disaster risk assessment method provided in this application is explained below.

[0018] See Figure 1 , Figure 1 This is a flowchart illustrating a flood disaster risk assessment method provided in one embodiment of this application. Figure 1 The flood disaster risk assessment method shown can be performed by electronic devices.

[0019] Among them, electronic devices can also be called user equipment (UE). In practical applications, electronic devices can be mobile phones, tablet personal computers, laptop computers, personal digital assistants (PDAs), mobile internet devices (MIDs), wearable devices, or in-vehicle devices, etc.

[0020] like Figure 1 As shown, the flood disaster risk assessment method provided in this application may include the following steps: Step 101: Divide the first region into N watershed units, where N is an integer greater than 1.

[0021] The aforementioned first region can be determined based on meteorological information such as weather forecasts. For example, if meteorological information indicates that a certain area will experience heavy rain or torrential rain within the next 24 hours, that area can be designated as the first region. This allows for risk assessment of flooding in that area, and based on the assessment results, emergency rescue and disaster relief actions can be carried out in advance to reduce the impact of flooding and achieve the goal of disaster prevention and mitigation.

[0022] In some embodiments, the river network information in the first region can be extracted based on high-precision digital elevation model (DEM) data using the D8 algorithm. Then, the first region can be divided based on a set catchment area threshold to obtain N independent watershed units.

[0023] The aforementioned high-precision DEM data can be understood as DEM data with a resolution of 30 meters or less.

[0024] The aforementioned catchment area can be understood as the surface area on which rainwater is collected. The catchment area threshold can be used for watershed geomorphological analysis, extracting catchment basins at different thresholds.

[0025] In some embodiments, the catchment area threshold can be set to 50 km².

[0026] The aforementioned watershed unit can be understood as a closed catchment area, and the outlet point of the watershed unit can be understood as the node where the runoff of the watershed unit flows into the main stream or tributary in the first region.

[0027] In some embodiments, the N watershed units obtained from the division can be constructed into an object set. .

[0028] Furthermore, semantic annotations can be applied to each watershed unit, and its static attributes can be bound to it.

[0029] The static attributes of a watershed unit include: watershed area. River length Average slope Soil type, land use type, and previous soil saturation Convergence time wait.

[0030] By semantically labeling each watershed unit and binding its static attributes, rapid calculation of rainfall and runoff data for the watershed unit can be achieved, improving the efficiency of flood risk assessment.

[0031] In practical applications, the soil type of a watershed unit can be determined by querying the Curve Number (CN) table, and the roughness can be determined by querying the table. The land use type of a watershed unit is determined by this method.

[0032] Step 102: Obtain rainfall and runoff data of the target watershed unit among the N watershed units based on meteorological information.

[0033] The above meteorological information can be obtained through the internet or broadcasting, such as by regularly accessing weather forecast applications or weather forecast broadcasting frequencies, to obtain accurate and real-time meteorological information.

[0034] The aforementioned target watershed unit can be understood as any one of the N watershed units, or as a watershed unit among the N watershed units that meets preset conditions. For example, the target watershed unit can be a watershed unit among the N watershed units whose rainfall-runoff data is greater than the preset runoff data.

[0035] In some embodiments, the rainfall of each watershed unit can be obtained first based on meteorological information, and then the rainfall-runoff data of each watershed unit can be calculated based on the static attributes of each watershed unit, thereby realizing the acquisition of rainfall-runoff data of the target watershed unit.

[0036] In some embodiments, a target watershed unit can be determined from N watershed units first, and then rainfall and runoff data of the target watershed unit can be obtained based on meteorological information.

[0037] In some embodiments, rainfall and runoff data for each of the N watershed units can be obtained first based on meteorological information, and then the rainfall and runoff data for the target watershed unit can be determined based on the rainfall and runoff data for each watershed unit.

[0038] Step 103: Based on the rainfall and runoff data of the target watershed unit, obtain the flow process line of the outlet point of the target watershed unit. The outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region. The time series characteristics of the flow process line are consistent with the time series characteristics of the rainfall process line indicated by the meteorological information.

[0039] The above-mentioned rainfall process line can be determined based on parameters such as rainfall amount, topography, and rainfall duration indicated by meteorological information.

[0040] In some embodiments, the temporal characteristics of the flow process line are consistent with the temporal characteristics of the rainfall process line indicated by meteorological information, which can be understood as the temporal resolution of the flow process line being consistent with the temporal resolution of the rainfall process line.

[0041] The time resolution of the above-mentioned flow process line is consistent with the rainfall time resolution indicated by meteorological information, and the flow process line has certain temporal characteristics. It can effectively reduce the error accumulation caused by factors such as inconsistency in spatiotemporal scales and improve the accuracy of the calculation results obtained based on the flow process line.

[0042] In some embodiments, the surface runoff process line of a target watershed unit can be characterized as a flow process line.

[0043] In some embodiments, a watershed-river network topological relationship can also be established, such as by constructing a topological connection between watershed units and river networks through spatial analysis techniques.

[0044] Specifically, river network raster data can be extracted from DEM data first, then the extracted raster data can be converted into vector line features, and the river network can be divided into several river segments according to the geometric features of the river channels. For example, the river network can be divided into several river segments based on the confluence points of the river channels, and a set of river segments can be established. Furthermore, each river segment can record its starting point, ending point, length, average gradient, Manning roughness, and other attributes.

[0045] In some embodiments, the coordinates of the outlet point of each watershed unit can be extracted based on DEM data.

[0046] Specifically, spatial nearest neighbor analysis using Geographic Information System (GIS) can be used to match the outlet point of each watershed unit to the nearest river segment, that is, to match the outlet point of each watershed unit to the target river segment, and calculate the linear reference position of the outlet point on the target river segment as the inflow node.

[0047] The linear reference position of the target river segment can be understood as the distance between the outlet point of the watershed unit and the starting point of the target river segment, or as the distance between the outlet point of the watershed unit and the ending point of the target river segment.

[0048] Furthermore, it can record the river segment identifier and node mileage corresponding to each watershed unit, forming a watershed-river segment comparison table.

[0049] In some embodiments, upstream and downstream topological relationships between river segments can also be established, including river segment connection relationships, river network classification, and the affiliation relationship between the main stream and tributaries.

[0050] In some embodiments, a downscaling interpolation algorithm based on topographic elevation gradient can be used to calculate the forecast precipitation process line at the outlet point of the target watershed unit, and then the flow process line at the outlet point of the target watershed unit can be generated based on the forecast precipitation process line.

[0051] The downscaling interpolation algorithm also considers the lifting effect of topography on precipitation, and the expression for the downscaling interpolation algorithm can be expressed as:

[0052] In the formula, For anti-gravity weight, for The first of the rain clouds Rainfall amount of a single rain cloud layer in the target watershed unit. The average topographic gradient of the target watershed unit. The empirical lift coefficient, Forecast rainfall process curves for the target watershed unit.

[0053] In practical applications, the time resolution of the forecast rainfall process line can be set to less than or equal to 1 hour.

[0054] After calculating the forecast rainfall process line, the surface runoff of the target watershed unit can be calculated based on the Soil Conservation Service (SCS) model. Then, the runoff can be used to calculate the confluence of the surface runoff of the target watershed unit, such as by using the Muskingan method to calculate the river segment, so as to obtain the flow process line of the outlet point of the target watershed unit.

[0055] Step 104: Input the flow process line into the hydrodynamic model to calculate the water level and flow rate of the target river section.

[0056] The aforementioned hydrodynamic model can be a mathematical model based on the principles of fluid mechanics, which can describe the motion of water flow through a set of differential equations such as the mass conservation equation and the momentum conservation equation.

[0057] The aforementioned target river section can be understood as the river section of the main stream or tributary associated with the outlet point of the target watershed unit.

[0058] In this embodiment, by inputting the flow process line into the hydrodynamic model, the water level and flow rate of the target river section can be calculated.

[0059] Step 105: Based on the water level and flow rate of the target river section, output the flood assessment information of the target river section.

[0060] In some embodiments, the flood assessment information of the target river section can be the water level and flow rate of the target river section, so that relevant personnel can determine whether there is a flood risk in the target river based on the output flood assessment information, that is, based on the water level and flow rate of the target river section.

[0061] In some embodiments, the flood assessment information of the target river section can be flood warning information generated based on the water level and flow of the target river section. This flood warning information is used to indicate that there is a flood risk in the target river, so that relevant personnel can intuitively determine the flood risk of the target river.

[0062] For example, if the water level at the target river section is higher than the preset water level, a flood warning can be generated, which indicates that there is a risk of flooding in the target river section.

[0063] In this application, rainfall-runoff data of the target watershed unit is obtained based on meteorological information, and then the flow process line of the outlet point of the target watershed unit is obtained based on the rainfall-runoff data of the target watershed unit. Since the temporal characteristics of the flow process line are consistent with the temporal characteristics of the rainfall process line indicated by the meteorological information, the accumulation of errors caused by factors such as inconsistency in spatiotemporal scales can be effectively reduced, making the calculation results of water level and flow of the target river section based on the flow process line more accurate, and improving the accuracy of flood assessment information of the target river section.

[0064] In some embodiments, the step of outputting flood assessment information for the target river section based on the water level and flow rate of the target river section includes: When the water level at the target river section is higher than the crest elevation of the target river section, the flow rate of the target river section is input into the overflow model to calculate the inundation range and water depth information associated with the target river section. Output flood assessment information for the target river section, including the inundation range and water depth information.

[0065] In this embodiment, it can be determined whether the water level of the target river section is higher than the top elevation of the target river section. If the water level of the target river section is higher than the top elevation of the target river section, the inundation range and water depth information associated with the target river section can be calculated to further improve the accuracy of the flood assessment information of the target river section.

[0066] The above-mentioned overflow model can be represented by a governing equation using an unstructured triangular mesh, and its expression is as follows:

[0067]

[0068]

[0069] In the formula, water depth For time, Represents the horizontal coordinate. They represent along The depth-average velocity component in the direction of flow. It is the acceleration due to gravity. For riverbed or ground elevation, Indicates the shear stress in the riverbed Components in direction, This indicates the density of water.

[0070] In practical applications, the Godunov scheme combined with the HLLC approximate Riemann solution can be used for numerical solutions to obtain the inundation extent. and water depth .

[0071] In some embodiments, the step of outputting flood assessment information for the target river section based on the water level and flow rate of the target river section includes: Based on the water level and flow rate of the target river section, visualized information including the water level and flow rate of the target river section is generated. The visualized information is used to characterize the flood assessment information of the target river section. The visualization information is displayed.

[0072] In this embodiment, by generating and displaying the visualization information of the target river section's water level and flow rate, and displaying the visualization information used to characterize the flood assessment information of the target river section, relevant personnel can intuitively perceive the flood assessment information of the target river section, thereby improving the efficiency of flood assessment information dissemination.

[0073] The aforementioned visualization information may be at least one of the following: tabular information, data information, image information, and dynamic view information used to characterize the water level and flow of the target river section.

[0074] By visualizing flood assessment information, relevant staff can better understand and experience the information, and the efficiency of its dissemination can be improved.

[0075] In some embodiments, displaying the visual information includes: When the water level at the target river section is higher than the preset water level, and / or the flow rate at the target river section is higher than the preset flow rate, the visualization information is displayed according to a preset display mode, which includes at least one of a highlight display mode and a blinking display mode.

[0076] In this embodiment, when the water level of the target river section is higher than the preset water level and / or the flow rate of the target river section is higher than the preset flow rate, i.e. for the target river section with flood risk, the visualization information can be displayed according to the preset display mode, such as displaying the visualization information through the highlight display mode or the flashing display mode, so that relevant personnel can capture the target river section with flood risk more quickly and further improve the efficiency of flood assessment information transmission.

[0077] In some embodiments, after outputting flood assessment information for the target river section based on the water level and flow rate of the target river section, the method further includes: Obtain the actual water level and actual flow rate of the target river section from the meteorological information; The model parameters of the hydrodynamic model are adjusted based on the actual water level and the actual flow rate.

[0078] In this embodiment, the model parameters of the hydrodynamic model can be adjusted based on the actual water level and actual flow of the target river section in meteorological information, so as to make the calculation results of the water level and flow of the target river section obtained based on the hydrodynamic model more accurate.

[0079] In some embodiments, acquiring rainfall-runoff data of the target watershed unit among the N watershed units based on meteorological information includes: Rainfall and runoff data for each of the N watershed units are obtained based on meteorological information; The watershed units whose rainfall-runoff data are greater than the runoff trigger threshold among the N watershed units are identified as target watershed units, and the rainfall-runoff data of the target watershed units are obtained.

[0080] In this embodiment, the target watershed unit can be determined by judging whether the rainfall-runoff data in the watershed unit is greater than the runoff trigger threshold, and only the flow process line corresponding to the target watershed unit is calculated. This reduces the amount of calculation in the flood disaster assessment process, avoids a large number of invalid calculations, and improves the response speed of the above-mentioned electronic equipment.

[0081] See Figure 2 , Figure 2 This is a system architecture block diagram provided for one embodiment of this application. (See diagram below.) Figure 2 As shown, the system architecture includes a data input layer, a preprocessing layer, a model calculation layer, a data flow layer, and an application layer. The flood disaster risk assessment method provided in this application can be based on... Figure 2 The system architecture shown is implemented and includes the following steps: Step S1: Multi-scale grid nesting and condition triggering mechanism based on semantic segmentation.

[0082] This step can be implemented based on a data input layer and a preprocessing layer, and specifically includes the following processes: S1.1 Geographic unit discretization and semantic annotation.

[0083] For example, the river network can be extracted using the D8 algorithm based on high-precision DEM data, and the first region can be divided into N independent watershed units based on the catchment area threshold, thus constructing a set of watershed unit objects. Each watershed unit can be understood as a closed catchment area, with its outlet point being the node where the runoff of that watershed unit flows into the main stream or tributary.

[0084] Furthermore, semantic annotation can be applied to each watershed unit, and its static attributes can be bound to it. The static attributes of a watershed unit include: watershed area. River length Average slope Soil type, land use rate, and previous soil saturation Convergence time wait.

[0085] S1.2 Establishment of watershed-river network topology.

[0086] The establishment of the watershed-river network topology can be referred to the previous descriptions, and will not be repeated here.

[0087] In this embodiment, the watershed-river network topology can be used to calculate the propagation path of flood waves in the subsequent hydrodynamic model.

[0088] S1.3, Meteorological grid downscaling mapping.

[0089] Coarse-grained gridded rainfall data G can be obtained based on weather forecast models, and the resolution of the gridded rainfall data G can be set to 0.1°×0.1°~0.25°×0.25°.

[0090] The predicted rainfall process curves for watershed units can be calculated by referring to the downscaling interpolation algorithm described earlier. .

[0091] S1.4 Condition-triggered gating.

[0092] Runoff trigger thresholds can be set for each watershed unit. ,Right now

[0093] in, This is the initial loss rainfall, and this parameter is related to land use type; This parameter represents the initial soil moisture content and can be updated dynamically. This is an empirical coefficient.

[0094] In practical applications, the forecast rainfall process line can be obtained in real time through polling. If in Moment When this occurs, the watershed unit is marked as the target watershed unit, and the rainfall process data of the target watershed unit is pushed to the hydrological calculation queue; if in Moment If the watershed unit is in a dormant state during this round of calculations, it will not participate in subsequent calculations, thereby reducing the amount of computation in the flood disaster assessment process and avoiding a large number of invalid calculations.

[0095] Step S2: Parallel computation of the distributed hydrological model and dynamic injection of runoff generation and confluence parameters.

[0096] This step can be implemented based on the model computation layer, and specifically includes the following process: S2.1 Model Instantiation and Parameter Fetching.

[0097] For the target watershed unit, hydrological model parameters that have been calibrated and validated are dynamically retrieved from a pre-built parameter library. The parameter library can be established using historical data and a multi-objective calibration algorithm to reduce the uncertainty of "different parameters having the same effect".

[0098] Instantiate independent hydrological computation threads, each thread loading parameters for the corresponding target watershed unit, including those from the SCS-CN model. Value, Muskingen method and Value, base current decay coefficient wait.

[0099] Step 2.2: Parallel solution of generation and confluence. Each thread independently solves the governing equations of the hydrological model. A distributed parallel computing framework can be used to fully utilize the computing power of multi-core central processing units (CPUs) or graphics processing units (GPUs).

[0100] Runoff calculation can be performed using either the SCS model or the Sinanjiang model. Taking the SCS model as an example:

[0101] in, For rainfall, , As surface runoff, For the maximum potential retention, The number of runoff curves (CN) can be determined by referring to a table based on soil type.

[0102] For confluence calculations, the Muskingan method can be used to calculate river segments:

[0103] in, , , From parameters and Decide, , The input flow rate of the river segment at times 1 and 2. and This represents the output flow rate of the river segment at times 1 and 2.

[0104] Then it can be based on , The input flow rate of the river segment at times 1 and 2. and The flow rate at the outlet point of the target watershed unit is calculated to represent the output flow rate of the river segment at times 1 and 2. Furthermore, its time resolution is consistent with the input rainfall.

[0105] S2.3 Boundary condition encapsulation and transmission.

[0106] The flow process line can be The boundary condition data package is encapsulated with timestamps and spatial coordinates. Its format can be JSON or Protobuf, and it contains information such as: watershed identifier, latitude and longitude of the outlet point, node number of the tributary node, and flow time series array.

[0107] The data packets can be written to a distributed message queue (such as Kafka), partitioned according to the incoming nodes, and used for real-time calculation by the downstream hydrodynamic model.

[0108] Step S3: Coupled solution of one-dimensional / two-dimensional hydrodynamic model and calculation of flood evolution.

[0109] This step can be implemented based on the model computation layer, and specifically includes the following process: S3.1 River channel topology modeling and mesh generation. A one-dimensional Saint-Venant equations solution mesh can be constructed for the main river channel, discretizing the river channel into M cross-sections with the cross-section spacing adaptively adjusted according to topographic variations (50~500m).

[0110] The inflow nodes of each target watershed unit are treated as internal source terms or lateral inflow terms in the equation system, and source terms are added to the corresponding cross sections. .

[0111] S3.2 Dynamic Boundary Coupling and Numerical Solution. The hydrodynamic model can retrieve the data generated in step S2 from the message queue in real time. Data serves as a time-varying boundary condition.

[0112] The expression for the one-dimensional Saint-Venant equations is as follows:

[0113]

[0114] In the formula, For the water flow area, For traffic, For water level, For friction slope, For lateral inflow, For time, It is the acceleration due to gravity. This represents the longitudinal distance along the river channel.

[0115] The data can be discretized using a four-point implicit difference scheme. This scheme is unconditionally stable and allows for a relatively large time step, such as 300 seconds. The discretized linear equations are then solved using the chasing method to obtain the water level at each cross-section. and traffic .

[0116] S3.3, Coupling of levee breach or overflow. At each time step, check whether the water level at each cross-section exceeds the levee crest elevation. .like If so, a two-dimensional overflow model is triggered at that cross-section.

[0117] The two-dimensional overflow model can be referred to the overflow model described above, and will not be repeated here.

[0118] Step S4: WebGL dynamic rendering and scene synchronization based on physical quantities.

[0119] This step can be implemented based on the data flow layer and the application layer, and specifically includes the following processes: S4.1 Texture Packaging of Time Series Data. To avoid frequent requests to the backend from the frontend, the time series calculation results of each target watershed unit and river cross-section are packaged into floating-point texture data.

[0120] The time series calculation results for each target watershed unit and river cross-section can include data within a preset time period. , , , , wait.

[0121] In one embodiment, the texture format of the floating-point texture data can be set to RGBA32F per pixel, with each channel storing one physical quantity. For example, the texture width can be 256, the height can be N (the number of target watershed units + the number of M cross-sections + the number of two-dimensional meshes), and the R channel of each pixel stores... The flow rate at any given moment.

[0122] It should be noted that the texture width mentioned above can be 256, or it can be set to 128 or other parameters, without any specific limitation here.

[0123] Texture data can be uploaded to GPU memory in one go via HTTP / 2 or WebSocket.

[0124] S4.2, WebGL rendering pipeline and physical quantity binding. The front end can build a WebGL rendering pipeline based on Three.js or MapboxGL, defining each river channel, each target watershed unit, and each 2D mesh as a programmable geometric object.

[0125] Vertex shader: Receives geometric vertex coordinates and timestamp uniform variables. Sample the current physical quantity value from the floating-point texture; dynamically adjust vertex attributes based on the physical quantity: river width: The widening effect is achieved by extending the line segment into a triangular band. Flow point location: based on flow velocity. Based on the direction of flow, calculate the offset of particles along the river channel.

[0126] Fragment shader: Receives the interpolated physical quantity values ​​and calculates the final color based on a predefined color map: Small watershed fill color: , output flow →Light gray Increase → Green → Blue. Two-dimensional submerged water depth: The deeper the water, the deeper the blue color, and the transparency increases with water depth.

[0127] S4.3 Dynamic Cloud Cluster and Rainfall Simulation. Based on wind field vector data output by meteorological models. , The trajectory of each cloud particle is calculated in the vertex shader to achieve a realistic physical sense of wind-driven cloud movement.

[0128] Rainfall intensity can be represented by a semi-transparent particle layer or a screen-space fog effect overlay. Positive correlation.

[0129] S4.4 Timeline Control and Animation Looping. The front-end can drive the animation loop through Request AnimationFrames (RAF), updating uniform variables each frame. (At the current simulation moment), a re-render is triggered. Users can control the time progress via a slider to achieve backtracking or prediction.

[0130] All rendering data can be read directly from the GPU memory, eliminating the need for frequent CPU-GPU communication and ensuring smooth 60fps animation.

[0131] Step S5: Interactive decision support.

[0132] This step can be implemented based on the data flow layer and the application layer, and specifically includes the following processes: S5.1 Warning Threshold Setting and Highlighting. Users can set warning thresholds for flow and water level at the front end. When the simulated value exceeds the threshold, the corresponding river or area will automatically flash or highlight.

[0133] S5.2 Scheme Comparison and Scenario Analysis. Supports loading forecast data for different rainfall scenarios (such as 50-year return period, 100-year return period), comparing flood risks under different scenarios, and displaying the results in a split-screen or layer overlay manner.

[0134] S5.3 Automatic Report Generation. A flood warning briefing containing key charts (peak flow, peak time, inundated area) and map screenshots can be generated with a single click, supporting export to PDF or image format.

[0135] The flood disaster risk assessment method provided in this application enables fully automated flood disaster risk assessment across the entire chain. Furthermore, through an event-driven chain-based call architecture, it breaks down data barriers between meteorological, hydrological, and hydrodynamic models, achieving automatic connection and data closure between models from meteorological input to flood assessment information output, without requiring manual intervention and significantly improving the efficiency of flood disaster assessment information.

[0136] By using a condition-triggered mechanism, calculations are performed only on the target watershed unit, avoiding indiscriminate calculations on all watershed units in the first region, which can reduce computational redundancy by about 40%-60%. At the same time, the parallel solution of the distributed hydrological model makes full use of hardware computing power and effectively shortens the simulation response time.

[0137] Based on physical quantity-driven real-time rendering technology, visual elements such as river width and color depth are directly driven by hydrodynamic calculation results. The visualization results are strictly synchronized with the numerical simulation results, completely eliminating the delay or distortion problems between visualization and simulation results in traditional methods.

[0138] By transforming complex flow and water level data into intuitive visual symbols (river channel thickness, color gradient), relevant personnel can quickly perceive changes in the flood situation visually, shortening the reaction time from data acquisition to decision-making and improving the timeliness of early warning response.

[0139] By using multi-source data such as runoff and remote sensing vegetation index to calibrate the hydrological model with multiple objectives, the impact of the "different parameters with the same effect" phenomenon on the model's uncertainty is effectively reduced, and the predictive reliability of the model under extreme conditions is improved.

[0140] The following section uses a river basin as the first region to provide a detailed explanation of the flood disaster risk assessment method provided in this application.

[0141] A river basin is located in the southeast of City B in Province A, with geographical coordinates of east longitude. ,north latitude The total drainage area is approximately 2,310 km². The terrain of the basin is high in the south and low in the north, with mountainous areas in the upper reaches, hilly areas in the middle reaches, and plains in the lower reaches. It has a typical subtropical monsoon climate with an average annual rainfall of 1,450 mm, with rainfall concentrated from April to September, making it prone to flash floods.

[0142] First, 30m resolution DEM data, 1:10000 land use data, soil type data, and historical hydrological and meteorological data for a certain river basin were acquired. Based on the DEM data, the D8 algorithm was used to extract the river network. Using 50km² as the control threshold, the river basin was divided into 47 watershed units, and an object set for each of the 47 watershed units was constructed. Underlying surface parameters, including soil type, land use type, average slope, and river channel length, were then bound to each watershed unit.

[0143] Daily flow data from multiple hydrological stations within a river basin were collected from 2010 to 2020, along with concurrent rainfall and evaporation data. A watershed hydrological model was constructed using the SWAT model, and the SUFI-2 algorithm was employed for parameter sensitivity analysis and calibration. The calibration period was from 2010 to 2015, and the validation period was from 2016 to 2020. Calibration results showed that the most sensitive parameters, in descending order, were CN value, effective soil water content, and baseflow decay coefficient. The Nash efficiency coefficients for both the calibration and validation periods were greater than 0.75, with relative errors less than 15%, meeting the simulation accuracy requirements.

[0144] By integrating high-resolution numerical weather prediction products, hourly rainfall forecast data for the next 72 hours can be obtained, with spatial resolution... A downscaling interpolation method based on elevation gradient was used to interpolate gridded rainfall data to 47 watershed units, generating forecast rainfall process data for each watershed unit. .

[0145] Set the flow trigger threshold Set the system to poll for the latest forecast data every 10 minutes. If a certain watershed unit's... If the target watershed unit is identified, its corresponding rainfall data is pushed to the hydrological calculation queue. If 29 watershed units are triggered in a certain polling process, the remaining 18 watershed units that are not triggered remain dormant, which can effectively reduce the amount of computation by about 38%.

[0146] A hydrological calculation thread is instantiated for each triggered watershed unit, with a maximum thread pool concurrency of 16. The SCS model is used for runoff calculation, and the Muskingan method is used for runoff calculation to obtain the flow process curves at the outlet points of each watershed unit. The time resolution is 1 hour. Parallel computation of 29 watershed units takes about 8.2 seconds, while serial computation is expected to take about 45 seconds, showing a significant speedup.

[0147] Each target watershed unit Encapsulated into boundary condition data packets, they are written to the Kafka message queue according to their inflow locations. Among them, there are 21 outflow points for watershed units that directly flow into a main stream, and 26 outflow points for watershed units that flow into a tributary.

[0148] Specifically, a one-dimensional hydrodynamic model of a certain water main stream can be constructed, dividing the main stream into 358 calculation sections with an average section spacing of about 200 meters.

[0149] The solution can be discretized using a four-point implicit difference scheme with a time step of 300 seconds. The hydrodynamic model pulls data from each sub-basin in real time from a Kafka queue. Substituting the data, as an intrinsic term, into the one-dimensional Saint-Venant equations described above, the water level at each cross-section can be calculated. and traffic .

[0150] For some low-lying river sections, a two-dimensional overflow model can be coupled simultaneously, and the shallow water equation can be solved using the Godunov scheme to calculate the potential flooding range.

[0151] Furthermore, the front-end can be developed using Three.js (a WebGL wrapper library) and MapboxGL to create a visual interface, and can achieve the following dynamic effects: 1. Dynamic simulation of cloud clusters and rainfall: Based on the wind field data output by the meteorological model, the particle trajectory is calculated in the vertex shader to simulate the movement of cloud clusters; at the same time, a semi-transparent blue layer is overlaid to represent the rainfall intensity, and the color depth is positively correlated with the forecast rainfall intensity.

[0152] 2. Small watershed runoff status rendering: Define 47 watershed unit polygons as programmable geometric objects, whose fill color is related to the real-time runoff. Establish mapping: It displays light gray at times. As the water level increases, it gradually changes from light green to dark green to blue. The color change visually reflects the runoff intensity of each small watershed.

[0153] 3. River Flood Evolution Rendering: Define a main river channel as a dynamic line element, with line width attributes and real-time flow rate. Establish mapping: (Unit: pixels). As the flow rate increases from 200 m³ / s to 2000 m³ / s, the river channel width gradually changes from 4 pixels to 22 pixels, achieving the visual effect of "the larger the flow rate, the wider the river channel." Simultaneously, dynamic flow points are superimposed on the river channel, with the flow velocity positively correlated with the flow rate, simulating the flood peak propagation process.

[0154] 4. Flooding Range Rendering: The flooding mesh calculated from the 2D model is superimposed on the terrain as a semi-transparent blue water layer. The deeper the water, the darker the color, and the flooding boundary is dynamically updated.

[0155] All rendering data is transmitted using textures: the flow and water level time series for the next 72 hours are packaged into floating-point textures and uploaded to the GPU memory. The front-end animation loops (60fps) and updates according to the timestamp, achieving smooth dynamic effects and avoiding the performance issues of requesting the backend for each frame.

[0156] For example, a risk assessment verification was conducted using an actual rainstorm and flood event in a river basin from July 2nd to 5th, 2021. The average rainfall in the basin was 156 mm, with a maximum hourly rainfall intensity of 42 mm / h. The risk assessment results showed that the simulated peak flow at a certain station was 1820 m³ / s, while the measured value was 1760 m³ / s, with a relative error of 3.4%; the peak occurrence time error was ±1 hour; and the inflow process of a certain reservoir matched the measured values ​​well. The visualization system fully presented the dynamic process of "cloud movement → increased rainfall → color change and runoff generation in small watersheds → gradual widening of the river channel → downstream propagation of the flood peak → localized inundation".

[0157] In summary, this application was applied in a specific river basin, demonstrating intuitive visualization and reliable simulation results, thus providing strong support for flood control decision-making.

[0158] The flood disaster risk assessment method provided in this application involves dividing a first region into N watershed units, where N is an integer greater than 1; acquiring rainfall-runoff data for a target watershed unit within the N watershed units based on meteorological information; obtaining the flow process line at the outlet point of the target watershed unit based on the rainfall-runoff data, where the outlet point is the node where the runoff from the target watershed unit flows into the main stream or tributary in the first region; inputting the flow process line into a hydrodynamic model to calculate the water level and flow rate of the target river section; and outputting flood assessment information for the target river section based on the water level and flow rate. This effectively reduces the accumulation of errors caused by factors such as inconsistencies in spatiotemporal scales, making the calculated water level and flow rate of the target river section based on the flow process line more accurate, thus improving the accuracy of the flood assessment information for the target river section.

[0159] See Figure 3 , Figure 3 This is a structural diagram of the flood disaster risk assessment device provided in this application. Figure 3 As shown, the flood disaster risk assessment device 300 includes: The partitioning module 301 is used to divide the first region into N watershed units, where N is an integer greater than 1; The first acquisition module 302 is used to acquire rainfall and runoff data of the target watershed unit among the N watershed units based on meteorological information; The second acquisition module 303 is used to acquire the flow process line of the outlet point of the target watershed unit based on the rainfall and runoff data of the target watershed unit. The outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region. The time series characteristics of the flow process line are consistent with the time series characteristics of the rainfall process line indicated by the meteorological information. Calculation module 304 is used to input the flow process line into the hydrodynamic model to calculate the water level and flow rate of the target river section; The output module 305 is used to output flood assessment information of the target river section based on the water level and flow rate of the target river section.

[0160] Optionally, the output module 305 is specifically used for: When the water level at the target river section is higher than the crest elevation of the target river section, the flow rate of the target river section is input into the overflow model to calculate the inundation range and water depth information associated with the target river section. Output flood assessment information for the target river section, including the inundation range and water depth information.

[0161] Optionally, the output module 305 is specifically used for: Based on the water level and flow rate of the target river section, visualized information including the water level and flow rate of the target river section is generated. The visualized information is used to characterize the flood assessment information of the target river section. The visualization information is displayed.

[0162] Optionally, the output module 305 is specifically used to display the visualization information according to a preset display mode when the water level at the target river section is higher than a preset water level and / or the flow rate at the target river section is higher than a preset flow rate. The preset display mode includes at least one of a highlight display mode and a flashing display mode.

[0163] Optionally, the flood disaster risk assessment device 300 further includes: The third acquisition module is used to acquire the actual water level and actual flow rate of the target river section in the meteorological information; The adjustment module is used to adjust the model parameters of the hydrodynamic model based on the actual water level and the actual flow rate.

[0164] Optionally, the first acquisition module 302 is specifically used for: Rainfall and runoff data for each of the N watershed units are obtained based on meteorological information; The watershed units whose rainfall-runoff data are greater than the runoff trigger threshold among the N watershed units are identified as target watershed units, and the rainfall-runoff data of the target watershed units are obtained.

[0165] The flood disaster risk assessment device 300 can achieve the functions described in this application. Figure 1 The various processes in the method embodiments, and the ways to achieve the same beneficial effects, will not be repeated here to avoid repetition.

[0166] like Figure 4 As shown, this application also provides an electronic device, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described flood disaster risk assessment method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0167] It should be noted that the electronic device in this application can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, or ultra-mobile personal computer. Mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs) can also be used as servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application does not impose specific limitations.

[0168] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described flood disaster risk assessment method embodiments and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0169] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0170] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described flood disaster risk assessment method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0171] It should be understood that the chip mentioned in this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0172] This application provides a computer program product stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-described flood disaster risk assessment method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0173] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as a read-only memory). The device includes a number of instructions in a ROM (random access memory), RAM (magnetic disk), or optical disk to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0175] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for assessing flood disaster risk, characterized in that, include: The first region is divided into N watershed units, where N is an integer greater than 1; Rainfall and runoff data of the target watershed unit among the N watershed units are obtained based on meteorological information; Based on the rainfall and runoff data of the target watershed unit, the flow process line of the outlet point of the target watershed unit is obtained. The outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region. The time series characteristics of the flow process line are consistent with the time series characteristics of the rainfall process line indicated by the meteorological information. The flow process line is input into the hydrodynamic model to calculate the water level and flow rate of the target river section. Based on the water level and flow rate of the target river section, output the flood assessment information of the target river section; The process of outputting flood assessment information for the target river section based on the water level and flow rate of the target river section includes: When the water level at the target river section is higher than the crest elevation of the target river section, the flow rate of the target river section is input into the overflow model to calculate the inundation range and water depth information associated with the target river section. Output flood assessment information for the target river section, including the inundation range and water depth information; The acquisition of rainfall and runoff data for the target watershed unit among the N watershed units based on meteorological information includes: Rainfall and runoff data for each of the N watershed units are obtained based on meteorological information; The watershed units whose rainfall-runoff data are greater than the runoff trigger threshold among the N watershed units are identified as target watershed units, and the rainfall-runoff data of the target watershed units are obtained. The process of obtaining the flow process curve at the outlet point of the target watershed unit based on the rainfall-runoff data of the target watershed unit includes: Based on the rainfall and runoff data of the target watershed unit, a downscaling interpolation algorithm based on topographic elevation gradient is used to calculate the forecast rainfall process line at the outlet point of the target watershed unit, and a flow process line at the outlet point of the target watershed unit is generated based on the forecast rainfall process line. The expression for the downscaling interpolation algorithm is as follows: In the formula, for The first of the rain clouds Rainfall amount of a single rain cloud layer in the target watershed unit. The average topographic gradient of the target watershed unit. The empirical lift coefficient, The forecast rainfall process line for the target watershed unit; The time resolution of the flow process line is consistent with the time resolution of the forecast rainfall process line.

2. The method according to claim 1, characterized in that, The process of outputting flood assessment information for the target river section based on the water level and flow rate of the target river section includes: Based on the water level and flow rate of the target river section, visualized information including the water level and flow rate of the target river section is generated. The visualized information is used to characterize the flood assessment information of the target river section. The visualization information is displayed.

3. The method according to claim 2, characterized in that, The display of the visualized information includes: When the water level at the target river section is higher than the preset water level, and / or the flow rate at the target river section is higher than the preset flow rate, the visualization information is displayed according to a preset display mode, which includes at least one of a highlight display mode and a blinking display mode.

4. The method according to any one of claims 1 to 3, characterized in that, After outputting flood assessment information for the target river section based on its water level and flow rate, the method further includes: Obtain the actual water level and actual flow rate of the target river section from the meteorological information; The model parameters of the hydrodynamic model are adjusted based on the actual water level and the actual flow rate.

5. A flood disaster risk assessment device, characterized in that, include: The partitioning module is used to divide the first region into N watershed units, where N is an integer greater than 1; The first acquisition module is used to acquire rainfall and runoff data of the target watershed unit among the N watershed units based on meteorological information; The second acquisition module is used to acquire the flow process line of the outlet point of the target watershed unit based on the rainfall and runoff data of the target watershed unit. The outlet point is the node where the runoff of the target watershed unit flows into the main stream or tributary in the first region. The time series characteristics of the flow process line are consistent with the time series characteristics of the rainfall process line indicated by the meteorological information. The calculation module is used to input the flow process line into the hydrodynamic model to calculate the water level and flow rate of the target river section; The output module is used to output flood assessment information of the target river section based on the water level and flow rate of the target river section; The output module is specifically used for: When the water level at the target river section is higher than the crest elevation of the target river section, the flow rate of the target river section is input into the overflow model to calculate the inundation range and water depth information associated with the target river section. Output flood assessment information for the target river section, including the inundation range and water depth information; The first acquisition module is specifically used for: Rainfall and runoff data for each of the N watershed units are obtained based on meteorological information; The watershed units whose rainfall-runoff data are greater than the runoff trigger threshold among the N watershed units are identified as target watershed units, and the rainfall-runoff data of the target watershed units are obtained. The second acquisition module is specifically used for: Based on the rainfall and runoff data of the target watershed unit, a downscaling interpolation algorithm based on topographic elevation gradient is used to calculate the forecast rainfall process line at the outlet point of the target watershed unit, and a flow process line at the outlet point of the target watershed unit is generated based on the forecast rainfall process line. The expression for the downscaling interpolation algorithm is as follows: In the formula, for The first of the rain clouds Rainfall amount of a single rain cloud layer in the target watershed unit. The average topographic gradient of the target watershed unit. The empirical lift coefficient, The forecast rainfall process line for the target watershed unit; The time resolution of the flow process line is consistent with the time resolution of the forecast rainfall process line.

6. The apparatus according to claim 5, characterized in that, The output module is specifically used for: Based on the water level and flow rate of the target river section, visualized information including the water level and flow rate of the target river section is generated. The visualized information is used to characterize the flood assessment information of the target river section. The visualization information is displayed.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions being executed by the processor to implement the steps of the flood disaster risk assessment method as described in any one of claims 1 to 4.

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