Flood forecasting method and device based on multi-model cooperation and medium

Abstract

The invention discloses a flood forecasting method and device based on multi-model cooperation and a medium, belongs to the technical field of flood forecasting and flood disaster simulation, and is used for solving the technical problems of insufficient coupling of flood forecasting models, weak visualization ability, lack of real-time interaction and non-visual information transmission in a traditional flood forecasting method. The method comprises the steps of collecting water conservancy information data of a target drainage basin; according to drainage basin characteristics of the water conservancy information data, collaborative architecture interaction under related multiple models is carried out on water conservancy factors in the target drainage basin, and a water outlet professional collaborative model is constructed; through a preset UE5 engine, performing three-dimensional modeling simulation calculation related to a flood routing process and a flood inundation range on result data output by the water professional cooperation model to obtain a three-dimensional simulation scene; based on the three-dimensional simulation scene, performing multi-dimensional information broadcasting on the real-time water conservancy data, and determining multi-dimensional simulation forecast data; and performing key index verification analysis on the multi-dimensional simulation forecast data to obtain flood control decision information.

Description

Technical Field

[0001] The present application relates to the technical field of flood forecasting and flood disaster simulation, and particularly to a flood forecasting method, device and medium based on multi-model collaboration. Background Art

[0002] In flood disaster prevention and control work, accurately and comprehensively simulating the flood evolution process is extremely crucial for formulating effective flood control and disaster reduction strategies. And flood forecasting is a key link in flood control and disaster reduction, and its accuracy directly affects the effectiveness of flood control decisions. Traditional flood forecasting methods mainly rely on a single hydrological model or hydrodynamic model, and have the following limitations:

[0003] 1. Insufficient model coupling: Traditional flood forecasting models are single and cannot comprehensively reflect the complex dynamic changes of floods. There is a lack of full-process coupling of hydrological runoff generation and concentration models, one-dimensional and two-dimensional coupled hydrodynamic models, reservoir flood regulation calculation models, water conservancy project operation models, and flood inundation analysis models, making it difficult to comprehensively reflect the entire process of floods from upstream to downstream.

[0004] 2. Weak visualization ability: Traditional methods mostly display results in two dimensions, making it difficult to intuitively present the dynamic process and inundation range of flood evolution, and unable to meet the intuitive understanding needs of decision-makers for flood situations.

[0005] 3. Lack of real-time interaction: It is difficult to dynamically adjust model parameters and operation plans according to real-time data, and unable to meet the flood control needs of complex river basins and urban areas.

[0006] 4. Unintuitive information transmission: Traditional methods lack an intuitive information broadcast function and cannot display meteorological forecast data, river water levels, flows, gate openings, water conservancy project operation conditions, and downstream inundation impacts in real time. In recent years, with the emergence of new generation three-dimensional engines such as UE5, their powerful rendering capabilities and real-time interaction functions have provided new technical means for the visualization of flood forecasting. Therefore, there is an urgent need for a full-process simulation and preview system that combines multi-model coupling with UE5 three-dimensional scenes. Summary of the Invention

[0007] Embodiments of the present application provide a flood forecasting method, device and medium based on multi-model collaboration, which are used to solve the following technical problems: in traditional flood forecasting methods, problems such as insufficient coupling of flood forecasting models, weak visualization ability, lack of real-time interaction, and unintuitive information transmission.

[0008] Embodiments of the present application adopt the following technical solutions:

[0009] On the one hand, an embodiment of the present application provides a flood forecasting method based on multi-model collaboration, including: collecting water conservancy information data of the target basin; according to the basin characteristics of the water conservancy information data, performing collaborative architecture interaction on water conservancy factors in the target basin under multiple models to construct a water professional collaborative model; through a preset UE5 engine, performing three-dimensional modeling simulation calculations on the result data output by the water professional collaborative model for flood evolution process and flood inundation range to obtain a three-dimensional simulation scene; based on the three-dimensional simulation scene, performing multi-dimensional information broadcast of real-time water conservancy data to determine multi-dimensional simulation forecast data; and performing verification analysis on key indicators of the multi-dimensional simulation forecast data to obtain flood control decision-making information.

[0010] The embodiment of the present application comprehensively improves the intuitiveness and accuracy of flood forecasting, enhances the scientificity and flexibility of flood control decision-making, and improves the system performance and user experience through the full-process coupling of water professional models, three-dimensional visualization, particle collision effects, and real-time intelligent information broadcast, promoting the innovation and development of flood control and disaster reduction technologies. This method and system provide more efficient and reliable technical support for flood control and disaster reduction, with significant economic and social benefits.

[0011] In a feasible implementation manner, collecting water conservancy information data of the target basin specifically includes: through a GIS geographic information system, comprehensively collecting geographic information in the target basin to obtain geographic information data; where the geographic information data at least includes: terrain elevation data, land use type data, and soil type data; based on a preset future time period, obtaining meteorological data of the target basin; where the meteorological data at least includes: rainfall, evaporation, and temperature; according to the site locations of water conservancy project facilities in the target basin, performing water conservancy correlation analysis on the water conservancy project facilities under grid connection to obtain facility correlation data; and based on the facility correlation data, performing numerical marking on the water conservancy influence degree of the water conservancy project facilities to obtain the water conservancy project facility data; where the water conservancy project facilities at least include: reservoirs, dams, and pumping stations; performing data preprocessing on the geographic information data, the meteorological data, and the water conservancy project facility data, and integrating them to obtain the water conservancy information data.

[0012] In a feasible implementation manner, according to the basin characteristics of the water conservancy information data, the water conservancy factors in the target basin are subjected to collaborative architecture interaction under multiple models to construct a water conservancy professional collaborative model, which specifically includes: performing multi-level semantic analysis and processing on the water conservancy information data, and marking and determining the basin characteristics; wherein, the multi-level includes: a geographic information data basic layer, a meteorological data dynamic layer, and a water conservancy project facility data static layer; the basin characteristics are all key characteristics related to flood control factors in the target basin; performing simulation analysis on the basin characteristics for the basin flow process under the hydrological distribution to generate a hydrological runoff model; wherein, the basin flow process includes: a rainfall runoff process and a basin cross-section flow process; performing evolution process analysis on the river flood characteristics in the basin characteristics to construct a one-dimensional hydrodynamic model; performing inundation process analysis on the area around the river in the basin characteristics to construct a two-dimensional hydrodynamic model; and through a one-two dimensional coupling technology, dynamically coupling the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to generate a coupled hydrodynamic model; performing flood regulation process analysis on the reservoir characteristic parameters in the basin characteristics to construct a reservoir flood regulation calculation model; according to the associated data of the water conservancy project facilities in the basin characteristics, optimizing the scheduling of each site in the water conservancy project facilities to construct a water conservancy project scheduling model; based on the output results of the coupled hydrodynamic model, calculating and processing the flood inundation range, inundation depth, and inundation time to construct a flood inundation analysis model; and through the GIS geographic information system technology, superimposing and displaying the analysis results of the flood inundation analysis model to generate an inundation risk map; through a customized standardized interface, performing dynamic simulation interaction calculations on the data results of the hydrological runoff model, the coupled hydrodynamic model, the reservoir flood regulation calculation model, the water conservancy project scheduling model, and the flood inundation analysis model, and forming the water conservancy professional collaborative model.

[0013] In a feasible implementation manner, through a preset UE5 engine, three-dimensional modeling simulation calculations of the result data output by the water professional collaboration model for the flood evolution process and flood inundation range are performed to obtain a three-dimensional simulation scene, specifically including: extracting water conservancy basic data through the database of the target basin; wherein, the water conservancy basic data includes: terrain elevation data, BIM modeling data, oblique photography acquisition data, three-dimensional model data, and geographic information data; inputting the water conservancy basic data into the UE5 engine, and generating a three-dimensional terrain model and a three-dimensional model of water conservancy engineering facilities of the target basin; generating an initial three-dimensional simulation scene based on the three-dimensional terrain model and the three-dimensional model of water conservancy engineering facilities; inputting the result data output by the water professional collaboration model into the initial three-dimensional simulation scene; and performing three-dimensional modeling simulation calculations of the flood evolution process and flood inundation range on the initial three-dimensional simulation scene through dynamic rendering technology to obtain three-dimensional visualization data; performing addition calculation of particle collision on the three-dimensional visualization data to obtain the optimized three-dimensional simulation scene.

[0014] In a feasible implementation manner, performing addition calculation of particle collision on the three-dimensional visualization data to obtain the optimized three-dimensional simulation scene, specifically including: starting the collision module of the Niagara particle system in the UE5 engine, and configuring the collision parameters of particles with the terrain and buildings; configuring the collision radius and collision intensity of particles based on the particle collision effect of the Self Collision tab; performing collision calculation of particles with static meshes based on the Chaos Physics system to obtain collision effect data; performing addition optimization calculation of particles on the three-dimensional visualization data according to the collision parameters, the collision radius and collision intensity, and the collision effect data to obtain the three-dimensional simulation scene; wherein, the three-dimensional simulation scene is visual simulation scene data.

[0015] In a feasible implementation manner, based on the three-dimensional simulation scenario, multi-dimensional information broadcasting of real-time water conservancy data is performed to determine multi-dimensional simulation prediction data, which specifically includes: determining the flood evolution path based on the real-time water conservancy data in the three-dimensional simulation scenario; performing real-time dynamic calculation of the river channel water level and flow rate in the flood evolution path through a virtual water gauge, and broadcasting the river channel water level and flow rate information; based on the flood evolution path, simulating and displaying the gate opening degrees of each station in the water conservancy project facility data, and broadcasting the gate opening degree information; performing simulation calculation on the operation status of the water conservancy project facilities, and broadcasting the water conservancy project scheduling information; based on the three-dimensional simulation scenario, performing dynamic downstream inundation prediction on the target basin, and broadcasting the downstream inundation impact information; wherein, the multi-dimensional simulation prediction data includes: the river channel water level and flow rate information, the gate opening degree information, the water conservancy project scheduling information, the downstream inundation impact information, and real-time weather forecast information.

[0016] In a feasible implementation manner, after determining the multi-dimensional simulation prediction data by performing multi-dimensional information broadcasting on the real-time water conservancy data based on the three-dimensional simulation scenario, the method further includes: determining the annotation position information of each dimension prediction data in the multi-dimensional simulation prediction data; based on the annotation position information, and through a floating pop-up window, performing corresponding display processing on the semantic text information in each dimension prediction data to obtain a number of display pop-up window information.

[0017] In a feasible implementation manner, verification and analysis of key indicators of the multi-dimensional simulation prediction data are performed to obtain flood control decision-making information, which specifically includes: extracting the key indicators from the multi-dimensional simulation prediction data according to the flood control plan template; wherein, the key indicator is the key factor affecting flood control for each dimension prediction data; performing risk early warning verification on the key indicators to obtain risk early warning information; based on the risk early warning information, optimizing the flood control scheduling strategy to obtain the flood control decision-making information.

[0018] In a second aspect, an embodiment of the present application further provides a flood forecasting device based on multi-model collaboration, and the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute a flood forecasting method based on multi-model collaboration according to any one of the above implementation manners.

[0019] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium. The storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program. Each program includes instructions that, when executed by a terminal, cause the terminal to execute a flood forecasting method based on multi-model collaboration as described in any of the above embodiments.

[0020] The present application provides a flood forecasting method, device, and medium based on multi-model collaboration. Compared with the prior art, the embodiments of the present application have the following beneficial technical effects:

[0021] 1. Improve forecasting accuracy: Through multi-model collaboration and combining the prediction advantages of different models, the accuracy and reliability of flood forecasting can be significantly improved.

[0022] 2. Enhance data fusion ability: This method can effectively fuse water conservancy information data from different sources, making the forecasting results more comprehensive and detailed.

[0023] 3. Optimize the model interaction architecture: Construct a water professional collaboration model, which helps to optimize the interaction between different models and improve the overall performance of the models.

[0024] 4. Three-dimensional visualization simulation: Use the UE5 engine for three-dimensional modeling and simulation, making the visualization of the flood evolution process and inundation area more intuitive, which helps decision-makers better understand the flood situation.

[0025] 5. Real-time information broadcast: Based on the multi-dimensional information broadcast of the three-dimensional simulation scene, flood information can be updated in real time, providing timely data support for flood control decision-making.

[0026] 6. Multi-dimensional simulation forecasting: Through multi-dimensional simulation forecasting data, the flood impact can be evaluated more comprehensively, providing a scientific basis for flood control measures.

[0027] 7. Flood control decision-making information support: By providing flood control decision-making information, it helps relevant departments to take effective flood control measures in a timely manner, reducing flood disaster losses.

[0028] 8. Reduce decision-making risks: Based on accurate flood forecasting, the risks of flood control decision-making can be reduced, improving the scientificity and effectiveness of decision-making.

[0029] 9. Improve emergency response ability: This method can quickly provide flood forecasting information, which helps to improve the emergency response ability and ensure the safety of people's lives and property. Description of the Drawings

[0030] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0031] Figure 1 It is a flowchart of a flood forecasting method based on multi-model collaboration provided by an embodiment of the present application;

[0032] Figure 2 It is a schematic diagram of a multi-model deep collaboration architecture provided by an embodiment of the present application;

[0033] Figure 3 It is a schematic diagram of the structure of a flood forecasting device based on multi-model collaboration provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the technical solutions in the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] It should be noted that flood disasters: also known as water disasters. It refers to floods caused by extremely heavy rain or extremely high tides in coastal areas, resulting in damage to people, houses, cultivated land, and factories in the flood area. Due to different causes (heavy rain or high tide), the nature and scale of the damage are also different. Moreover, due to differences in the natural properties such as the terrain and vegetation of the flood area, as well as social factors such as land use patterns, the construction of dams and levees, flood prevention organizations, and flood forecasting, the degree of damage will also be different.

[0036] GIS: Geographic Information System (GIS, Geographic Information System) is a comprehensive discipline that combines geography, cartography, remote sensing, and computer science. It has been widely applied in different fields and is a computer system for inputting, storing, querying, analyzing, and displaying geographic data. With the development of GIS, it is also called "Geographic Information Science" (Geographic Information Science). In recent years, it is also called Geographic Information Service (Geographic Information service).

[0037] UE5: Unreal Engine 5 (UE5) is a game engine developed by Epic Games. It provides a complete game development toolset that allows game developers to easily create high-quality games. UE5 is known for its powerful rendering capabilities, high flexibility, and ease of use, making it the engine of choice for many game developers and teams.

[0038] The present application embodiment provides a flood forecasting method based on multi-model collaboration, such as Figure 1 As shown, the flood forecasting method based on multi-model collaboration specifically includes steps S101-S105:

[0039] S101. Collect water conservancy information data of the target river basin.

[0040] Specifically, firstly, the geographic information of the target watershed is collected through the GIS geographic information system to obtain geographic information data, which includes at least terrain elevation data, land use type data, and soil type data.

[0041] Furthermore, based on a preset future time period, meteorological data of the target basin is obtained, wherein the meteorological data includes at least rainfall, evaporation, and temperature.

[0042] Furthermore, based on the location of water conservancy facilities in the target basin, a hydraulic interrelationship analysis is performed on the relevant grid lines to obtain facility correlation data. Based on this facility correlation data, the water conservancy facilities are numerically labeled with their hydraulic impact to obtain water conservancy facility data. Water conservancy facilities include at least reservoirs, dams, and pumping stations.

[0043] Furthermore, the geographic information data, meteorological data and water conservancy project facility data are all pre-processed and integrated to obtain water conservancy information data.

[0044] In one embodiment, Figure 2 A schematic diagram of a multi-model deep collaboration architecture provided in an embodiment of the present application is shown as follows: Figure 2 As shown in the figure, when acquiring multi-source data, we first collect geographic information data (topography elevation, land use type, soil type, etc.), meteorological data (precipitation, evaporation, temperature, etc.) within the watershed, and water conservancy facility data (reservoirs, dams, pumping stations, etc.). The collected data also needs to be preprocessed, including data cleaning, format conversion, spatial interpolation, and data fusion, to generate a data format suitable for model input.

[0045] S102. According to the basin characteristics of water conservancy information data, conduct collaborative architecture interaction on water conservancy factors in the target basin under multiple models to construct a water conservancy professional collaborative model.

[0046] Specifically, conduct multi-level semantic analysis and processing on water conservancy information data, and mark and determine the basin characteristics. Among them, the multi-level includes: the basic layer of geographical information data, the dynamic layer of meteorological data, and the static layer of water conservancy project facility data; the basin characteristics are all key characteristics related to flood control factors in the target basin.

[0047] Furthermore, conduct simulation analysis on the basin flow process under the hydrological distribution of the basin characteristics to generate a hydrological runoff model. Among them, the basin flow process includes: the rainfall runoff process and the basin cross-section flow process. That is, according to the basin characteristics, select a suitable hydrological model (such as a distributed hydrological model) to simulate the rainfall runoff process in the basin and output the flow process line at the basin outlet.

[0048] Furthermore, conduct an evolution process analysis on the river flood characteristics in the basin characteristics to construct a one-dimensional hydrodynamic model. Conduct a flooding process analysis on the area around the river in the basin characteristics to construct a two-dimensional hydrodynamic model. And through one-two dimensional coupling technology, dynamically couple the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to generate a coupled hydrodynamic model. That is, construct a one-dimensional hydrodynamic model to simulate the river flood evolution process, and a two-dimensional hydrodynamic model to simulate the flood inundation process in plain and urban areas. Through one-two dimensional coupling technology, realize the dynamic interaction simulation of floods between the river and the surrounding area.

[0049] Furthermore, conduct a flood control operation process analysis on the reservoir characteristic parameters in the basin characteristics to construct a reservoir flood routing model. It is also possible to combine the reservoir characteristic parameters to construct a reservoir flood routing model to simulate the flood storage and release process of the reservoir. Couple the reservoir flood routing model with the hydrodynamic model to dynamically simulate the impact of reservoir operation on downstream floods.

[0050] Furthermore, according to the associated data of water conservancy project facilities in the basin characteristics, optimize the scheduling of each station in the water conservancy project facilities to construct a water conservancy project scheduling model. Moreover, combine the water conservancy project facility data in the basin to construct a water conservancy project scheduling model to optimize the scheduling schemes of water conservancy projects such as reservoirs, dams, and pumping stations. Couple the water conservancy project scheduling model with the hydrodynamic model to adjust the operation parameters of the water conservancy project in real time and dynamically simulate its impact on flood evolution.

[0051] Furthermore, based on the output results of the coupled hydrodynamic model, calculations are performed on the flood inundation range, inundation depth, and inundation time to construct a flood inundation analysis model. Moreover, through GIS (Geographic Information System) technology, the analysis results of the flood inundation analysis model can be overlaid and displayed to generate an inundation risk map.

[0052] In one embodiment, based on the output results of the hydrodynamic model, a flood inundation analysis model is constructed to calculate the flood inundation range, inundation depth, and inundation time. The geographic information system (GIS) technology is used to perform spatial analysis on the inundation range to generate an inundation risk map.

[0053] Furthermore, through a customized standardized interface, dynamic simulation and interactive calculations of the data results of the hydrological runoff model, coupled hydrodynamic model, reservoir flood regulation calculation model, water conservancy project scheduling model, and flood inundation analysis model are all carried out, and a water professional collaborative model is formed. That is, five types of water professional models are customized with standardized interfaces, and real-time interaction of data between each model is achieved through the standardized interfaces.

[0054] In one embodiment, as Figure 2 shown, real-time sharing of data between models is achieved through a standardized interface protocol (update frequency ≤ 5 minutes). For example, the flood forecasting model dynamically updates the initial flood conditions based on real-time meteorological data and transmits them to the hydrodynamic model; the reservoir flood regulation model and the project scheduling model dynamically adjust the gate opening and other operating parameters of water conservancy project facilities according to the water level and flow rate results generated by the hydrodynamic model. Downstream, the inundation depth and inundation range are generated based on the project scheduling results and combined with the hydrodynamic model, and the flood inundation analysis model is called to further feedback the flood impact and flood evolution process, realizing full-process collaborative simulation, greatly improving the simulation accuracy and reliability. Among them, the model collaboration interface can be:

[0055] python

[0056] class ModelCoordinator:

[0057] def__init__(self):

[0058] self.models={

[0059] 'forecast':FloodForecastModel(),

[0060] 'hydro':HydroDynamicModel()

[0061] }

[0062] def update(self, real_time_data):

[0063] for model in self.models.values():

[0064] model.update_parameters(real_time_data)

[0065] results = [m.run() for m in self.models.values()]

[0066] return self.fuse_results(results)

[0067] S103. Through a preset UE5 engine, perform three-dimensional modeling simulation calculations on the result data output by the water professional collaboration model for the flood evolution process and flood inundation area to obtain a three-dimensional simulation scene.

[0068] Specifically, extract the water conservancy basic data through the database of the target basin. Among them, the water conservancy basic data includes: terrain elevation data, BIM modeling data, oblique photography acquisition data, three-dimensional model data, and geographic information data.

[0069] Furthermore, input the water conservancy basic data into the UE5 engine and generate a three-dimensional terrain model and a three-dimensional model of water conservancy engineering facilities for the target basin.

[0070] Furthermore, generate an initial three-dimensional simulation scene based on the three-dimensional terrain model and the three-dimensional model of water conservancy engineering facilities.

[0071] Furthermore, input the result data output by the water professional collaboration model into the initial three-dimensional simulation scene. And through dynamic rendering technology, perform three-dimensional modeling simulation calculations on the initial three-dimensional simulation scene for the flood evolution process and flood inundation area to obtain three-dimensional visualization data.

[0072] Furthermore, perform additional calculations for particle collisions on the three-dimensional visualization data to obtain an optimized three-dimensional simulation scene.

[0073] As a feasible implementation, start the collision module of the Niagara particle system in the UE5 engine and configure the collision parameters of the particles with the terrain and buildings. Based on the particle - to - particle collision effect on the Self Collision tab, configure the collision radius and collision intensity of the particles. Based on the Chaos Physics system, perform collision calculations between the particles and static meshes to obtain collision effect data. According to the collision parameters, collision radius, collision intensity, and collision effect data, perform particle addition and optimization calculations on the three - dimensional visualization data to obtain a three - dimensional simulation scene. Among them, the three - dimensional simulation scene is visual simulation scene data.

[0074] In one embodiment, the Niagara particle system of UE5 is used to enhance the particle collision effect, making the interaction between floods and terrain, buildings, water conservancy facilities, etc. more realistic. First, enable the collision module of the Niagara particle system and set the collision parameters of the particles with the terrain, buildings, etc. Then, enable the particle - to - particle collision effect through the "Self Collision" tab and adjust parameters such as the collision radius and intensity. After that, combined with the Chaos Physics system, the collision effect between the particles and static meshes is achieved to further enhance the simulation realism.

[0075] In one embodiment, the powerful rendering and interaction functions of the UE5 game engine are introduced, and the analysis results of multiple models are mapped into a high - precision three - dimensional scene. Using the Nanite virtual micro - polygon geometry technology of UE5, ultra - high - resolution rendering of geographical elements such as terrain, landforms, and buildings is achieved; with the help of the Lumen dynamic global illumination system, the light and shadow changes during the flood evolution process are realistically simulated, such as water surface reflection, submerged area shadows, etc., providing users with an immersive flood evolution experience and facilitating an intuitive and in - depth understanding of the flood situation.

[0076] 1) Ultra - high - precision rendering: Use the Nanite virtual micro - polygon technology to achieve millimeter - level detail rendering of terrain and landforms, supporting a 1:1 restoration of the real scene.

[0077] 2) Physical - level particle simulation: Through the Chaos Physics engine, the collision, splashing, and turbulence effects of flood particles are achieved, combined with the Lumen dynamic global illumination to realistically reproduce the light and shadow changes during the flood evolution.

[0078] 3) Interactive decision - making interface: Support users to adjust engineering parameters (such as the reservoir discharge flow rate) in real - time through the mouse and synchronously display the adjusted flood evolution results.

[0079] Among them, the UE5 particle system configuration can be:

[0080] json

[0081] {

[0082] "particle_system": {

[0083] "density": 2000,

[0084] "viscosity": 0.01,

[0085] "collision": {

[0086] "enabled": true,

[0087] "friction": 0.3,

[0088] "restitution": 0.1

[0089] }

[0090] }

[0091] }

[0092] S104. Based on the three - dimensional simulation scenario, perform multi - dimensional information broadcast on real - time water conservancy data to determine multi - dimensional simulation and prediction data.

[0093] Specifically, based on the real - time water conservancy data in the three - dimensional simulation scenario, determine the flood evolution path.

[0094] Furthermore, through virtual water gauges, perform real - time dynamic calculations on the river channel water level and flow rate in the flood evolution path, and broadcast the river channel water level and flow rate information.

[0095] Furthermore, based on the flood evolution path, simulate and display the gate opening degrees of each station in the water conservancy project facility data, and broadcast the gate opening degree information.

[0096] Furthermore, perform simulation calculations on the operating status of water conservancy project facilities, and broadcast the water conservancy project scheduling information.

[0097] Furthermore, based on the three - dimensional simulation scenario, perform dynamic downstream inundation prediction on the target basin, and broadcast the downstream inundation impact information.

[0098] Among them, the multi - dimensional simulation and prediction data includes: river channel water level and flow rate information, gate opening degree information, water conservancy project scheduling information, downstream inundation impact information, and real - time weather forecast information.

[0099] In one embodiment, as Figure 2 shown, the information broadcast content can be:

[0100] 1) Meteorological forecast data: Real - time broadcast meteorological data such as rainfall, temperature, evaporation, etc. within the basin.

[0101] 2) River water level and flow rate: Dynamically display the water level and flow rate of each cross-section of the river, and update them in real time through virtual water gauges and charts. Reservoir water level and flow rate: Real-time display of the water level, flow rate, and water storage volume of the reservoir.

[0102] 3) Gate opening condition: Intuitively display the gate opening of water conservancy projects such as reservoirs and sluice dams through a 3D model.

[0103] 4) Water conservancy project scheduling situation: Real-time broadcast of the scheduling plan and operation status of water conservancy projects.

[0104] 5) Downstream inundation impact: Dynamically display the inundation range, inundation depth, affected population, key units, villages, economic losses, evacuation routes, and dispatching materials, etc.

[0105] As a feasible implementation method, after generating the multi-dimensional simulation prediction data, it is also necessary to determine the annotation position information of each dimension of prediction data in the multi-dimensional simulation prediction data. Based on the annotation position information, and through floating pop-ups, the semantic text information in each dimension of prediction data is correspondingly displayed and processed to obtain a number of display pop-up information.

[0106] In one embodiment, use the UMG (Unreal Motion Graphics) system of UE5 to design an intuitive information display interface, and display the above key data in the 3D simulation scene in the form of charts, texts, dynamic annotations, etc. It can also be combined with the voice broadcast function, and through an efficient voice synthesis engine, the key information is broadcast in real time in voice form. Detailed information can also be displayed in the form of floating annotations or pop-ups to enhance the visualization effect of the information.

[0107] As a feasible implementation method, utilize the full-process dynamic update and information broadcast: Establish a real-time data transmission and processing mechanism, which can continuously obtain the latest meteorological and hydrological monitoring data. The flood forecast model and other related models continuously correct the simulation parameters based on these real-time data, dynamically update the flood evolution simulation results, and synchronously display them in the UE5 3D scene to ensure that the simulation preview always reflects the latest flood situation.

[0108] S105. Conduct verification and analysis on the key indicators of the multi-dimensional simulation prediction data to obtain flood control decision-making information.

[0109] Specifically, according to the flood control plan template, key indicators in the multi-dimensional simulation and forecast data are extracted. Among them, the key indicators are the key flood control impact factors for each dimension of forecast data. The key indicators are verified for risk early warning to obtain risk early warning information. Based on the risk early warning information, the flood control scheduling strategy is optimized to obtain flood control decision-making information. That is, a flood control plan can be formulated according to the simulation results, the water conservancy project scheduling plan can be optimized, and a scientific basis can be provided for flood control decision-making. Among them, the risk early warning level can be generated based on the multi-index weighted algorithm.

[0110] In one embodiment, key data (such as river water level, inundation range, etc.) can be automatically broadcast along the flood evolution path. Warning information (such as "It is recommended to immediately evacuate 3 villages downstream") is generated through semantic analysis. Multilingual broadcasting (Chinese / English) is also supported. At the same time, by using the risk heat map overlay method, indicators such as inundation depth and flow velocity are rendered in real time in the three-dimensional scene to assist decision-makers in quickly locating high-risk areas.

[0111] In addition, the embodiment of the present application also provides a flood forecasting device based on multi-model collaboration, such as Figure 3 As shown, the flood forecasting device 300 based on multi-model collaboration specifically includes:

[0112] At least one processor 301. And a memory 302 communicatively connected to at least one processor 301. Among them, the memory 302 stores instructions that can be executed by at least one processor 301, so that at least one processor 301 can execute:

[0113] Collect water conservancy information data of the target basin;

[0114] According to the basin characteristics of the water conservancy information data, the water conservancy factors in the target basin are subjected to collaborative architecture interaction under multiple models to construct a water conservancy professional collaborative model;

[0115] Through the preset UE5 engine, the result data output by the water conservancy professional collaborative model is subjected to three-dimensional modeling simulation calculation of the flood evolution process and the flood inundation range to obtain a three-dimensional simulation scene;

[0116] Based on the three-dimensional simulation scene, the real-time water conservancy data is broadcast in multi-dimensional information to determine multi-dimensional simulation and forecast data;

[0117] Verify and analyze the key indicators of the multi-dimensional simulation and forecast data to obtain flood control decision-making information.

[0118] The embodiments of the present application comprehensively enhance the intuitiveness and accuracy of flood forecasting, strengthen the scientificity and flexibility of flood control decision-making, improve the performance of the system and the user experience, and promote the innovation and development of flood control and disaster reduction technologies through the full-process coupling of water professional models, three-dimensional visualization, particle collision effects, and real-time intelligent information broadcasting. This method and system provide more efficient and reliable technical support for flood control and disaster reduction, and have significant economic and social benefits.

[0119] The embodiments in the present application are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device and the medium, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the related parts.

[0120] The device and the medium provided by the embodiments of the present application correspond one-to-one to the method. Therefore, the device and the medium also have beneficial technical effects similar to the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and the medium will not be elaborated here.

[0121] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0123] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in one or more of the blocks and / or processes. Figure 1 one or more processes and / or blocks Figure 1 specified in the block or blocks.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the function specified in one or more of the blocks and / or processes. Figure 1 one or more processes and / or blocks Figure 1 specified in the block or blocks.

[0125] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0126] Memory may include non-permanent memory in the computer-readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory. Memory is an example of a computer-readable medium.

[0127] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0128] It should also be noted that the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0129] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the description of the present application.

Claims

1. A flood forecasting method based on multi-model collaboration, characterized in that, The method includes: Collecting water conservancy information data of the target basin; According to the basin characteristics of the water conservancy information data, conducting collaborative architecture interaction on water conservancy factors in the target basin under multiple models, and constructing a water professional collaborative model; Through a preset UE5 engine, performing three-dimensional modeling simulation calculations on the result data output by the water professional collaborative model for flood evolution process and flood inundation range, and obtaining a three-dimensional simulation scene; Based on the three-dimensional simulation scene, broadcasting multi-dimensional information of real-time water conservancy data, and determining multi-dimensional simulation forecast data; Verifying and analyzing key indicators of the multi-dimensional simulation forecast data to obtain flood control decision-making information.

2. The flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, Collecting water conservancy information data of the target basin specifically includes: Through a GIS geographic information system, comprehensively collecting geographic information in the target basin to obtain geographic information data; wherein, the geographic information data at least includes: terrain elevation data, land use type data, and soil type data; Based on a preset future time period, obtaining meteorological data of the target basin; wherein, the meteorological data at least includes: rainfall, evaporation, and temperature; According to the site locations of water conservancy project facilities in the target basin, conducting water conservancy correlation analysis on the water conservancy project facilities under grid connection to obtain facility correlation data; and based on the facility correlation data, numerically marking the water conservancy influence degree of the water conservancy project facilities to obtain the water conservancy project facility data; wherein, the water conservancy project facilities at least include: reservoirs, dams, and pumping stations; Preprocessing the geographic information data, the meteorological data, and the water conservancy project facility data, and integrating and obtaining the water conservancy information data.

3. A flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, According to the basin characteristics of the water conservancy information data, conducting collaborative architecture interaction on water conservancy factors in the target basin under multiple models, and constructing a water professional collaborative model specifically includes: Performing multi-level semantic analysis and processing on the water conservancy information data, marking and determining the basin characteristics; wherein, the multi-level includes: the geographic information data basic layer, the meteorological data dynamic layer, and the water conservancy project facility data static layer; the basin characteristics are all key characteristics related to flood control factors in the target basin; Conducting simulation analysis on the basin flow process under hydrological distribution for the basin characteristics to generate a hydrological runoff model; wherein, the basin flow process includes: rainfall runoff process and basin section flow process; Conducting evolution process analysis on the river flood characteristics in the basin characteristics to construct a one-dimensional hydrodynamic model; conducting inundation process analysis on the area around the river in the basin characteristics to construct a two-dimensional hydrodynamic model; and through one-two dimensional coupling technology, dynamically coupling the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model to generate a coupled hydrodynamic model; Conducting flood control operation process analysis on the reservoir characteristic parameters in the basin characteristics to construct a reservoir flood regulation calculation model; According to the water conservancy project facility correlation data in the basin characteristics, optimizing the scheduling of each site in the water conservancy project facilities to construct a water conservancy project scheduling model; Based on the output results of the coupled hydrodynamic model, calculations are performed on the flood inundation range, inundation depth, and inundation time to construct a flood inundation analysis model; and through GIS geographic information system technology, the analysis results of the flood inundation analysis model are superimposed and displayed to generate an inundation risk map. Through a customized standardized interface, dynamic simulation interactive calculations of the data results of the hydrological runoff model, the coupled hydrodynamic model, the reservoir flood routing model, the water conservancy project scheduling model, and the flood inundation analysis model are carried out, and the water professional collaborative model is formed.

4. A flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, Through a preset UE5 engine, three-dimensional modeling simulation calculations of the result data output by the water professional collaborative model for the flood evolution process and the flood inundation range are carried out to obtain a three-dimensional simulation scene, specifically including: Extract hydraulic basic data through the database of the target basin; among them, the hydraulic basic data includes: terrain elevation data, BIM modeling data, oblique photography acquisition data, three-dimensional model data, and geographic information data. Input the hydraulic basic data into the UE5 engine and generate a three-dimensional terrain model and a three-dimensional model of water conservancy project facilities of the target basin. Based on the three-dimensional terrain model and the three-dimensional model of water conservancy project facilities, an initial three-dimensional simulation scene is generated. Input the result data output by the water professional collaborative model into the initial three-dimensional simulation scene; and through dynamic rendering technology, three-dimensional modeling simulation calculations of the flood evolution process and the flood inundation range of the initial three-dimensional simulation scene are carried out to obtain three-dimensional visualization data. Perform additional calculations of particle collisions on the three-dimensional visualization data to obtain the optimized three-dimensional simulation scene.

5. A flood forecasting method based on multi-model collaboration according to claim 4, characterized in that, Perform additional calculations of particle collisions on the three-dimensional visualization data to obtain the optimized three-dimensional simulation scene, specifically including: Start the collision module of the Niagara particle system in the UE5 engine and configure the collision parameters of the particles with the terrain and buildings. Based on the particle collision effect of the Self Collision tab, configure the collision radius and collision intensity of the particles. Based on the Chaos Physics system, perform collision calculations between the particles and the static mesh to obtain collision effect data. According to the collision parameters, the collision radius and collision intensity, and the collision effect data, perform additional optimization calculations of the particles on the three-dimensional visualization data to obtain the three-dimensional simulation scene; among them, the three-dimensional simulation scene is visual simulation scene data.

6. A flood forecasting method based on multi-model collaboration according to claim 1, characterized in that Based on the three-dimensional simulation scene, multi-dimensional information broadcasting of real-time hydraulic data is carried out to determine multi-dimensional simulation forecast data, specifically including: Based on the real-time hydraulic data in the three-dimensional simulation scene, determine the flood evolution path. Through virtual water gauges, perform real-time dynamic calculations on the river water level and flow rate in the flood evolution path and broadcast the river water level and flow rate information. Based on the flood evolution path, simulate and display the gate openings of each station in the water conservancy project facility data and broadcast the gate opening information. Perform simulation calculations on the operating status of water conservancy project facilities and broadcast water conservancy project scheduling information; Based on the three-dimensional simulation scenario, conduct dynamic downstream inundation prediction for the target basin and broadcast downstream inundation impact information; Among them, the multi-dimensional simulation forecast data includes: the river water level and flow information, the gate opening information, the water conservancy project scheduling information, the downstream inundation impact information, and the real-time weather forecast information.

7. A flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, After performing multi-dimensional information broadcast on the real-time water conservancy data based on the three-dimensional simulation scenario and determining the multi-dimensional simulation forecast data, the method further includes: Determine the annotation position information of each dimension of forecast data in the multi-dimensional simulation forecast data; Based on the annotation position information and through floating pop-up windows, perform corresponding display processing on the semantic text information in each dimension of forecast data to obtain a number of display pop-up window information.

8. A flood forecasting method based on multi-model collaboration according to claim 1, characterized in that Conduct verification and analysis of key indicators for the multi-dimensional simulation forecast data to obtain flood control decision-making information, specifically including: Extract the key indicators from the multi-dimensional simulation forecast data according to the flood control plan template; among them, the key indicators are the key factors affecting flood control for each dimension of forecast data; Perform risk warning verification on the key indicators to obtain risk warning information; Based on the risk warning information, optimize the flood control scheduling strategy to obtain the flood control decision-making information.

9. A flood forecasting device based on multi-model collaboration, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; among them, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute a flood forecasting method based on multi-model collaboration according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that, The storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, and each program includes instructions that, when executed by the terminal, cause the terminal to execute a flood forecasting method based on multi-model collaboration according to any one of claims 1-8.

Images