A flood forecasting method, device and medium based on multi-model cooperation

Abstract

The application 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 a flood forecasting model, weak visualization capability, lack of real-time interaction and non-intuitive information transmission in a traditional flood forecasting method. The method comprises the following steps: collecting water conservancy information data of a target river basin; according to the river basin characteristics of the water conservancy information data, performing interaction on water conservancy factors in the target river basin in relation to a multi-model cooperative architecture, and constructing a water professional cooperative model; through a preset UE5 engine, performing three-dimensional modeling simulation calculation on result data output by the water professional cooperative model in relation to a flood evolution process and a flood inundation range, and obtaining a three-dimensional simulation scene; based on the three-dimensional simulation scene, performing multi-dimensional information broadcasting on real-time water conservancy data, and determining multi-dimensional simulation forecasting data; and performing verification analysis on key indexes of the multi-dimensional simulation forecasting data, and obtaining flood control decision information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flood forecasting and flood disaster simulation, and in particular to a flood forecasting method based on multi-model cooperation, a device and a medium. BACKGROUND

[0002] In flood disaster prevention work, accurately and comprehensively simulating the flood evolution process is extremely critical to 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, which has the following limitations:

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

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

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

[0006] 4. Non-intuitive information transmission: Traditional methods lack intuitive information reporting functions and cannot real-time display meteorological forecast data, river water level, flow, gate opening, water conservancy engineering regulation, and downstream inundation impact, etc. In recent years, with the emergence of new generation three-dimensional engines such as UE5, their powerful rendering capability and real-time interaction function provide new technical means for flood forecasting visualization. Therefore, there is an urgent need for a full-process simulation and rehearsal system that combines multi-model coupling with UE5 three-dimensional scenes. SUMMARY

[0007] The embodiments of the present application provide a flood forecasting method based on multi-model cooperation, a device and a medium, which are used to solve the following technical problems: in the traditional flood forecasting method, the flood forecasting model has insufficient coupling, weak visualization capability, lack of real-time interaction, and non-intuitive information transmission, etc.

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

[0009] In one aspect, the embodiment of the present application provides a flood forecasting method based on multi-model cooperation, comprising: collecting water conservancy information data of a target river basin; according to the river basin characteristics of the water conservancy information data, interacting the water conservancy factors in the target river basin with the cooperation architecture under the multi-model, and constructing a water professional cooperation model; through a preset UE5 engine, performing three-dimensional modeling simulation calculation on the result data output by the water professional cooperation model about the flood evolution process and the flood inundation range, to obtain a three-dimensional simulation scene; based on the three-dimensional simulation scene, performing multi-dimensional information broadcasting on real-time water conservancy data, to determine multi-dimensional simulation forecasting data; verifying and analyzing the multi-dimensional simulation forecasting data on key indicators, to obtain flood control decision information.

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

[0011] In a feasible implementation manner, the water conservancy information data of the target river basin is collected, specifically comprising: through a GIS geographic information system, fully covering the geographic information in the target river 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 river basin; wherein the meteorological data at least includes: rainfall, evaporation, and air temperature; according to the site location of the water conservancy engineering facilities in the target river basin, performing water conservancy interrelation analysis on the water conservancy engineering facilities under the 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 engineering facilities, to obtain the water conservancy engineering facility data; wherein the water conservancy engineering facilities at least include: reservoirs, dams, and pumping stations; performing data preprocessing on the geographic information data, the meteorological data, and the water conservancy engineering facility data, integrating and obtaining the water conservancy information data.

[0012] In a feasible implementation, according to the characteristics of the water information data of the basin, the water factors in the target basin are interacted in a multi-model collaborative architecture, and a water professional collaborative model is constructed, specifically including: performing multi-level semantic analysis processing on the water information data, 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 engineering facility data static layer; the basin characteristics are all key characteristics of the target basin related to flood control factors; the basin characteristics are analyzed in relation to the flow process of the basin under the hydrological distribution, and a hydrological runoff model is generated; wherein the flow process of the basin includes: rainfall runoff process and basin section flow process; the river flood characteristics in the basin characteristics are analyzed in the evolution process, and a one-dimensional water dynamic model is constructed; the surrounding area of the river in the basin characteristics is analyzed in the flooding process, and a two-dimensional water dynamic model is constructed; and through one-two dimensional coupling technology, the one-dimensional water dynamic model and the two-dimensional water dynamic model are dynamically coupled to generate a coupled water dynamic model; the reservoir characteristic parameters in the basin characteristics are analyzed in the flood regulation process, and a reservoir flood regulation calculation model is constructed; according to the water conservancy engineering facility related data in the basin characteristics, the stations in the water conservancy engineering facilities are optimized, and a water conservancy engineering regulation model is constructed; based on the output result of the coupled water dynamic model, the flood inundation range, the inundation depth and the inundation time are calculated and processed, and a flood inundation analysis model is constructed; and through the GIS geographic information system technology, the analysis results of the flood inundation analysis model are superimposed and displayed to generate a flood risk map; through the customized standardized interface, the hydrological runoff model, the coupled water dynamic model, the reservoir flood regulation calculation model, the water conservancy engineering regulation model and the flood inundation analysis model are all dynamically simulated and interactively calculated, and the water professional collaborative model is composed.

[0013] In an available embodiment, the result data output by the water professional collaborative model is subjected to three-dimensional modeling simulation calculation on flood evolution process and flood inundation range by a preset UE5 engine, to obtain a three-dimensional simulation scene, specifically including: extracting water conservancy basic data from a database of the target watershed; wherein the water conservancy basic data includes topographic elevation data, BIM modeling data, tilt photography collection 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 water conservancy engineering facility three-dimensional model of the target watershed; generating an initial three-dimensional simulation scene based on the three-dimensional terrain model and the water conservancy engineering facility three-dimensional model; inputting the result data output by the water professional collaborative model into the initial three-dimensional simulation scene; and performing three-dimensional modeling simulation calculation on flood evolution process and flood inundation range on the initial three-dimensional simulation scene by dynamic rendering technology, to obtain three-dimensional visualization data; performing particle collision addition calculation on the three-dimensional visualization data, to obtain an optimized three-dimensional simulation scene.

[0014] In an available embodiment, the three-dimensional visualization data is subjected to particle collision addition calculation, to obtain an optimized three-dimensional simulation scene, specifically including: starting a collision module of a Niagara particle system in the UE5 engine, and configuring collision parameters of particles and terrain and buildings; configuring collision radius and collision strength of particles based on particle collision effect of a Self Collision tab; performing collision calculation on particles and static grids based on a Chaos Physics physical system, to obtain collision effect data; performing particle addition optimization calculation on the three-dimensional visualization data according to the collision parameters, the collision radius and collision strength, and the collision effect data, to obtain the three-dimensional simulation scene; wherein the three-dimensional simulation scene is visualized simulation scene data.

[0015] In an implementable embodiment, based on the three-dimensional simulation scene, multi-dimensional information of real-time water conservancy data is broadcasted to determine multi-dimensional simulation prediction data, specifically including: based on real-time water conservancy data in the three-dimensional simulation scene, a flood evolution path is determined; through a virtual water gauge, river water level and flow in the flood evolution path are calculated in real time and dynamically to broadcast river water level and flow information; based on the flood evolution path, gate opening of each station in water conservancy engineering facility data is simulated and displayed to broadcast gate opening information; water conservancy engineering facilities are simulated and calculated in relation to operating state to broadcast water conservancy engineering scheduling information; based on the three-dimensional simulation scene, dynamic downstream inundation prediction of the target river basin is performed to broadcast downstream inundation influence information; wherein the multi-dimensional simulation prediction data includes the river water level and flow information, the gate opening information, the water conservancy engineering scheduling information, the downstream inundation influence information, and real-time weather forecast information.

[0016] In an implementable embodiment, after multi-dimensional information of real-time water conservancy data is broadcasted based on the three-dimensional simulation scene to determine multi-dimensional simulation prediction data, the method further includes: determining labeled position information of each dimension prediction data in the multi-dimensional simulation prediction data; based on the labeled position information, semantic text information in each dimension prediction data is displayed correspondingly through a floating pop-up window to obtain a plurality of display pop-up window information.

[0017] In an implementable embodiment, the multi-dimensional simulation prediction data is verified and analyzed for key indicators to obtain flood control decision information, specifically including: according to a flood control plan template, key indicators in the multi-dimensional simulation prediction data are extracted; wherein the key indicators are key factors of flood control influence of each dimension prediction data; the key indicators are verified for risk early warning to obtain risk early warning information; based on the risk early warning information, a flood control scheduling strategy is optimized to obtain the flood control decision information.

[0018] In a second aspect, the embodiments of the present application further provide a flood prediction device based on multi-model cooperation, the device comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, so that the at least one processor can execute the flood prediction method based on multi-model cooperation in any of the above embodiments.

[0019] In a third aspect, the embodiments of the present application further provide a non-volatile computer storage medium, which is a non-volatile computer readable storage medium, and stores at least one program, each of which includes instructions that, when executed by a terminal, cause the terminal to perform the multi-model collaborative flood forecasting method described in any of the above embodiments.

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

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

[0022] 2. Enhanced data fusion capability: the method can effectively fuse water information data from different sources, making the prediction results more comprehensive and detailed.

[0023] 3. Optimized model interaction architecture: the construction of water professional collaborative model helps to optimize the interaction between different models and improve the overall performance of the model.

[0024] 4. Three-dimensional visualization simulation: using UE5 engine for three-dimensional modeling simulation makes the visualization of 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, the flood information can be updated in real time to provide timely data support for flood control decision-making.

[0026] 6. Multi-dimensional simulation prediction: through multi-dimensional simulation prediction data, the flood impact can be more comprehensively evaluated to provide scientific basis for flood control measures.

[0027] 7. Flood control decision information support: by providing flood control decision information, it helps relevant departments to take effective flood control measures in time to reduce flood disaster losses.

[0028] 8. Reduce the risk of decision-making: based on accurate flood forecasting, the risk of flood control decision-making can be reduced to improve the scientificity and effectiveness of decision-making.

[0029] 9. Improve emergency response capability: the method can quickly provide flood forecasting information to help improve emergency response capability and ensure people's life and property safety. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order 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 needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only represent some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort. In the drawings:

[0031] Figure 1 A flow chart of a flood forecasting method based on multi-model cooperation provided by the embodiments of the present application;

[0032] Figure 2 A schematic diagram of a multi-model deep cooperation architecture provided by the embodiments of the present application;

[0033] Figure 3 A structural schematic diagram of a flood forecasting device based on multi-model cooperation provided by the embodiments of the present application. DETAILED DESCRIPTION

[0034] In order to make the person skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the scope of protection of the present application.

[0035] It should be noted that flood disaster: also known as water damage. Refers to the flood caused by heavy rain or heavy tide in coastal areas, etc., resulting in damage to people, houses, farmland, factories in flood areas. Due to different causes (heavy rain or high tide), the nature and scale of the damage are different. Not only that, due to the differences in natural properties such as topography, vegetation in flood areas, and social factors such as land use patterns, dam and dike construction, flood control organization, flood forecasting, etc., the damage degree is also different.

[0036] GIS: Geographic Information System (GIS, Geographic Information System) is a comprehensive discipline, combining geography and cartography, as well as remote sensing and computer science, which has been widely used in different fields. It is a computer system for input, storage, query, analysis and display of geographic data. With the development of GIS, GIS is also called "Geographic Information Science" (Geographic Information Science). In recent years, GIS is also called Geographic Information Service (Geographic Information Service).

[0037] UE5: Unreal Engine 5, commonly known as UE5, is a game engine developed by Epic Games, which provides a complete set of game development tools for game developers to easily create high-quality games. UE5 is known for its powerful rendering capabilities, high flexibility and ease of use, and has become the preferred engine for many game developers and teams.

[0038] The embodiment of the application provides a flood forecasting method based on multi-model cooperation, as shown in the following formula: Figure 1 The flood forecasting method based on multi-model cooperation specifically includes steps S101-S105:

[0039] S101, collect water conservancy information data of the target basin.

[0040] Specifically, the geographic information in the target basin is fully collected by the GIS geographic information system to obtain geographic information data. The geographic information data at least includes: terrain elevation data, land use type data and soil type data.

[0041] Further, based on the preset future time period, the meteorological data of the target basin is obtained. The meteorological data at least includes: rainfall, evaporation and air temperature.

[0042] Further, according to the site position of the water conservancy facilities in the target basin, the water conservancy facilities are analyzed for water conservancy correlation under the grid connection, to obtain facility correlation data. And based on the facility correlation data, the water conservancy facilities are numerically marked for water conservancy influence degree, to obtain water conservancy facility data. The water conservancy facilities at least include: reservoir, dam and pump station.

[0043] Further, the geographic information data, meteorological data and water conservancy facility data are all preprocessed, integrated and obtained as water conservancy information data.

[0044] In one embodiment, Figure 2 A multi-model deep cooperation architecture diagram is provided for the embodiment of the application, as shown in the following formula: Figure 2 In the acquisition of multi-source data, the geographic information data (terrain elevation, land use type, soil type, etc.), meteorological data (rainfall, evaporation, air temperature, etc.) and water conservancy facility data (reservoir, dam, pump station, etc.) in the basin are collected first. The collected data also needs to be preprocessed, including data cleaning, format conversion, spatial interpolation and data fusion, to generate data format suitable for model input.

[0045] S102, according to the watershed characteristics of water conservancy information data, the water conservancy factors in the target watershed are interacted in the collaborative architecture of multiple models, and a water professional collaborative model is constructed.

[0046] Specifically, the water conservancy information data is subjected to multi-level semantic analysis processing, and the watershed characteristics are marked and determined. The multi-level includes a geographic information data basic layer, a meteorological data dynamic layer, and a water conservancy engineering facility data static layer; and the watershed characteristics are all key characteristics of the target watershed related to flood control factors.

[0047] Further, the watershed characteristics are subjected to simulation analysis of the watershed flow process under the hydrological distribution, and a hydrological runoff model is generated. The watershed flow process includes a rainfall runoff process and a watershed cross-section flow process. That is, according to the watershed characteristics, a suitable hydrological model (such as a distributed hydrological model) is selected to simulate the rainfall runoff process in the watershed, and the flow process line of the watershed outlet is output.

[0048] Further, the river flood characteristics in the watershed characteristics are subjected to evolution process analysis, and a one-dimensional water dynamic model is constructed. The surrounding area of the river in the watershed characteristics is subjected to submergence process analysis, and a two-dimensional water dynamic model is constructed. And through one-two dimensional coupling technology, the one-dimensional water dynamic model and the two-dimensional water dynamic model are dynamically coupled to generate a coupled water dynamic model. That is, the one-dimensional water dynamic model simulates the river flood evolution process, and the two-dimensional water dynamic model simulates the flood submergence process of the plain and urban area. Through one-two dimensional coupling technology, dynamic interaction simulation of flood in the river and surrounding area is realized.

[0049] Further, the reservoir characteristic parameters in the watershed characteristics are subjected to flood regulation process analysis, and a reservoir flood regulation calculation model is constructed. In combination with the reservoir characteristic parameters, the reservoir flood regulation calculation model can also be constructed to simulate the flood storage and discharge process of the reservoir. The reservoir flood regulation calculation model is coupled with the water dynamic model to dynamically simulate the influence of reservoir regulation on downstream flood.

[0050] Further, according to the water conservancy engineering facility related data in the watershed characteristics, the stations in the water conservancy engineering facilities are subjected to dispatch optimization, and a water conservancy engineering dispatch model is constructed. Moreover, in combination with the water conservancy engineering facility data in the watershed, the water conservancy engineering dispatch model is constructed to optimize the dispatch scheme of the reservoir, dam, pump station and other water conservancy engineering. The water conservancy engineering dispatch model is coupled with the water dynamic model to real-time adjust the operation parameters of the water conservancy engineering, and dynamically simulate the influence of the water conservancy engineering on the flood evolution.

[0051] Further, based on the output results of the coupled hydrodynamic model, the flood inundation range, inundation depth and inundation time are calculated and processed, and a flood inundation analysis model is constructed. Moreover, the analysis results of the flood inundation analysis model can be superimposed and displayed by using the GIS geographic information system technology, and a flood risk map is generated.

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

[0053] Further, through a customized standardized interface, the hydrological runoff model, the coupled hydrodynamic model, the reservoir flood routing model, the water conservancy engineering dispatching model and the flood inundation analysis model are all dynamically simulated and interactively calculated, and a water professional collaborative model is formed. That is, the five types of water professional models are customized with a standardized interface, and the real-time interaction of data between the models is realized through the standardized interface.

[0054] In one embodiment, as shown in Figure 2 , the real-time sharing (update frequency ≤ 5 minutes) of data between models is realized through a standardized interface protocol, for example: the flood forecasting model dynamically updates the initial conditions of the flood according to the real-time meteorological data, and transmits them to the hydrodynamic model; the reservoir flood routing model and the engineering dispatching model dynamically adjust the gate opening and other water conservancy engineering facility operation parameters according to the water level and flow results generated by the hydrodynamic model; the downstream generates the inundation water depth and inundation range according to the engineering dispatching results and the hydrodynamic model, calls the flood inundation analysis model, and then feeds back the flood impact and flood evolution process, realizes the collaborative simulation of the whole process, and greatly improves the simulation accuracy and reliability. The model coordination 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 min self.models.values()]

[0066] return self.fuse_results(results)

[0067] S103. Using the preset UE5 engine, the result data output by the water professional collaborative model is used to perform three-dimensional modeling and simulation calculations on the flood evolution process and flood inundation range to obtain a three-dimensional simulation scene.

[0068] Specifically, basic water conservancy data is extracted from the database of the target watershed. This basic water conservancy data includes: topographic elevation data, BIM modeling data, oblique photogrammetry data, 3D model data, and geographic information data.

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

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

[0071] Furthermore, the output data from the water professional collaborative model is input into the initial 3D simulation scene. Then, using dynamic rendering technology, 3D modeling and simulation calculations of the flood evolution process and flood inundation range are performed on the initial 3D simulation scene to obtain 3D visualization data.

[0072] Furthermore, particle collision calculations are added to the 3D visualization data to obtain an optimized 3D simulation scene.

[0073] As a feasible implementation, the collision module of the Niagara particle system in the UE5 engine is started, and the collision parameters of the particles with the terrain and buildings are configured. Based on the particle collision effect of the Self Collision tab, the collision radius and collision strength of the particles are configured. Based on the Chaos Physics physics system, the particles are collided with the static grid to obtain collision effect data. According to the collision parameters, the collision radius and the collision strength, and the collision effect data, the three-dimensional visualization data is calculated to add and optimize the particles, and a three-dimensional simulation scene is obtained. The three-dimensional simulation scene is visual simulation scene data.

[0074] In one embodiment, the UE5 Niagara particle system is used to increase the particle collision effect, making the interaction between the flood and the terrain, buildings, and water conservancy facilities more realistic. First, the collision module of the Niagara particle system is enabled, and the collision parameters of the particles with the terrain, buildings, and other objects are set. Then, the collision effect between particles is enabled through the Self Collision tab, and parameters such as collision radius and strength are adjusted. Then, combined with the Chaos Physics physics system, the collision effect of particles with static grids is realized, further improving the simulation realism.

[0075] In one embodiment, the powerful rendering and interaction functions of the UE5 game engine are introduced to map the analysis results of multiple models to a high-precision three-dimensional scene. The Nanite virtual micro-polygon geometry technology of UE5 is used to realize the ultra-high resolution presentation of terrain, buildings, and other geographic elements; with the help of the Lumen dynamic global lighting system, the light and shadow changes in the flood evolution process are simulated, such as water surface reflection and submerged area shadow, providing an immersive flood evolution experience for users, and facilitating intuitive and in-depth understanding of the flood situation.

[0076] 1) Ultra-high precision rendering: Nanite virtual micro-polygon technology is used to realize millimeter-level detail presentation of terrain and topography, supporting 1:1 restoration of real scenes.

[0077] 2) Physical-level particle simulation: Chaos Physics engine is used to realize the collision, splashing, and turbulence effects of flood particles, combined with Lumen dynamic global lighting, to realistically reproduce the light and shadow changes in the flood evolution process.

[0078] 3) Interactive decision-making interface: supports users to adjust engineering parameters (such as reservoir discharge flow) in real time through the mouse, and synchronously displays 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, real-time water conservancy data is broadcast in multiple dimensions to determine multi-dimensional simulation forecast data.

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

[0094] Furthermore, by using a virtual water gauge, the river level and flow rate in the flood evolution path can be dynamically calculated in real time, and the river level and flow rate information can be broadcast.

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

[0096] Furthermore, simulation calculations are performed on the operational status of water conservancy facilities, and water conservancy project scheduling information is broadcast.

[0097] Furthermore, based on the three-dimensional simulation scenario, dynamic downstream inundation prediction is performed on the target watershed, and information on the impact of downstream inundation is broadcast.

[0098] The multidimensional simulation forecast data includes: river water level and flow information, gate opening information, water conservancy project scheduling information, downstream inundation impact information, and real-time weather forecast information.

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

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

[0101] 2) River water level and flow: dynamically display the water level and flow of each section of the river channel, and update in real time through virtual water gauge and chart form. Reservoir water level and flow: real-time display of water level, flow and storage capacity of the reservoir.

[0102] 3) Gate opening situation: intuitively display the gate opening of water conservancy projects such as reservoirs, dams, etc. through three-dimensional models.

[0103] 4) Water conservancy dispatching situation: real-time broadcast of the dispatching scheme and operation state of water conservancy projects.

[0104] 5) Downstream inundation impact: dynamically display the inundation range, inundation depth, affected population, key units, villages, economic loss, transfer route and dispatch of materials, etc.

[0105] As a feasible implementation manner, after generating the multi-dimensional simulation prediction data, the labeled position information of each dimension prediction data in the multi-dimensional simulation prediction data also needs to be determined. Based on the labeled position information, and through a pop-up window, the semantic text information in each dimension prediction data is correspondingly displayed and processed to obtain a plurality of display pop-up window information.

[0106] In one embodiment, the UMG (Unreal Motion Graphics) system of UE5 is used to design an intuitive information display interface, and the above key data is displayed in real time in the three-dimensional simulation scene in the form of charts, texts, dynamic annotations, etc. In combination with the voice broadcast function, the key information can be broadcast in real time in the form of voice through an efficient voice synthesis engine. In addition, detailed information can be displayed in the form of floating annotation or pop-up window to enhance the visualization effect of the information.

[0107] As a feasible implementation manner, full-process dynamic updating and information broadcasting are used: a real-time data transmission and processing mechanism is established to continuously obtain the latest meteorological and hydrological monitoring data. The flood prediction model and other related models continuously correct the simulation parameters based on these real-time data, dynamically update the flood evolution simulation results, and simultaneously display them in the UE5 three-dimensional scene to ensure that the simulation always reflects the latest flood situation.

[0108] S105, verifying and analyzing the key indicators of the multi-dimensional simulation prediction data to obtain flood control decision information.

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

[0110] In one embodiment, key data (river water level, inundation range, etc.) can be automatically broadcast along the flood evolution path. Warning information (such as "it is recommended to immediately relocate the 3 villages downstream") is generated through semantic analysis. Multi-language broadcast (Chinese / English) is also supported. At the same time, using the risk heat map superposition method, the indicators such as inundation depth and flow rate 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 application further provides a flood prediction device based on multi-model cooperation, as shown in Figure 3 The flood prediction device based on multi-model cooperation 300 specifically includes:

[0112] at least one processor 301, and a memory 302 connected with the at least one processor 301. The memory 302 stores instructions executable by the at least one processor 301, so that the at least one processor 301 can execute:

[0113] collecting water conservancy information data of a target river basin;

[0114] According to the characteristics of the water conservancy information data of the river basin, the water conservancy factors in the target river basin are interacted in the multi-model cooperative architecture to construct a water conservancy cooperative model;

[0115] Through the preset UE5 engine, the result data output by the water conservancy cooperative model is simulated and calculated in three dimensions to obtain a three-dimensional simulation scene.

[0116] Based on the three-dimensional simulation scene, real-time water conservancy data is broadcasted in multiple dimensions to determine multi-dimensional simulation prediction data;

[0117] The multi-dimensional simulation prediction data are verified and analyzed for key indicators to obtain flood control decision information.

[0118] The embodiments of the present application improve the intuitiveness and accuracy of flood forecasting, enhance the scientificity and flexibility of flood control decision-making, and improve the performance and user experience of the system by coupling the whole process of the water professional model, three-dimensional visualization, particle collision effect, and real-time intelligent information broadcasting, and promote the innovation and development of flood control and disaster reduction technology. The 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 described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly explains the difference from other embodiments. Especially, the device and medium embodiments are described simply because they are basically similar to the method embodiments, and the related parts can be referred to the part of the method embodiments.

[0120] The device and medium provided by the embodiments of the present application are one-to-one corresponding to the method, so the device and medium also have the similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described 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 take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take 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 according to the method, device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1 The device that implements the functions specified in one or more flows and / or 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 function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of 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 such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

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

[0126] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory, etc. The memory is an example of computer readable media.

[0127] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0128] It is also to be noted that the terms "comprising", "including", and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0129] The above description is merely illustrative of the application, and not restrictive. Various modifications and changes can become apparent to those skilled in the art. Incorporating any modification, equivalent substitution, improvement, etc. within the spirit and principle of the application, shall be included in the scope of the description.

Claims

1. A flood forecasting method based on multi-model collaboration, characterized in that, The method includes: Collect water conservancy information data for the target watershed; Based on the watershed characteristics of the water conservancy information data, a collaborative architecture interaction under multiple models is carried out on the water conservancy factors in the target watershed to construct a water conservancy professional collaborative model. Using the pre-set UE5 engine, the output data of the water professional collaborative model is used for three-dimensional modeling and simulation calculations related to the flood evolution process and flood inundation range to obtain a three-dimensional simulation scene, specifically including: Basic water conservancy data are extracted from the database of the target watershed; wherein, the basic water conservancy data includes: topographic elevation data, BIM modeling data, oblique photogrammetry data, 3D model data, and geographic information data; The basic water conservancy data is input into the UE5 engine to generate a three-dimensional terrain model of the target watershed and a three-dimensional model of water conservancy engineering facilities. Based on the three-dimensional terrain model and the three-dimensional model of the water conservancy engineering facilities, an initial three-dimensional simulation scene is generated; The result data output by the water professional collaborative model is input into the initial three-dimensional simulation scene; and through dynamic rendering technology, the initial three-dimensional simulation scene is used to perform three-dimensional modeling and simulation calculation of the flood evolution process and flood inundation range to obtain three-dimensional visualization data. The particle collision calculation is performed on the 3D visualization data to obtain the optimized 3D simulation scene, specifically including: Activate the collision module of the Niagara particle system in the UE5 engine and configure the collision parameters between particles and terrain and buildings; Based on the particle collision effect in the Self Collision tab, configure the collision radius and collision intensity of the particles; Based on the Chaos Physics physics system, collision calculations are performed between particles and static meshes to obtain collision effect data; Based on the collision parameters, the collision radius and collision intensity, and the collision effect data, particle addition and optimization calculations are performed on the three-dimensional visualization data to obtain the three-dimensional simulation scene; wherein, the three-dimensional simulation scene is visualized simulation scene data; Based on the three-dimensional simulation scenario, real-time water conservancy data will be broadcast in multiple dimensions to determine multi-dimensional simulation forecast data. The key indicators of the multidimensional simulation forecast data are verified and analyzed to obtain flood control decision information.

2. The flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, Collect water resources information data for the target watershed, specifically including: Geographic information data is obtained by collecting geographic information in the target watershed through a GIS geographic information system; wherein the geographic information data includes at least: topographic elevation data, land use type data and soil type data. Meteorological data for the target watershed is acquired based on a preset future time period; wherein the meteorological data includes at least: rainfall, evaporation, and temperature; Based on the location of water conservancy facilities in the target watershed, a water conservancy interrelationship analysis is performed on the water conservancy facilities along the relevant grid lines to obtain facility correlation data; and based on the facility correlation data, the degree of water conservancy impact of the water conservancy facilities is numerically labeled to obtain water conservancy facility data; wherein, the water conservancy facilities include at least: reservoirs, dams, and pumping stations. The geographic information data, meteorological data, and water conservancy engineering facility data are all preprocessed and integrated to obtain the water conservancy information data.

3. The flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, Based on the watershed characteristics of the aforementioned water conservancy information data, a collaborative architecture interaction under multiple models is performed on the water conservancy factors in the target watershed to construct a water conservancy professional collaborative model, specifically including: The water conservancy information data is subjected to multi-level semantic analysis to mark and determine the watershed characteristics; wherein, the multi-level includes: a geographic information data base layer, a meteorological data dynamic layer, and a water conservancy engineering facility data static layer; the watershed characteristics are all key features related to flood control factors under the target watershed; The watershed characteristics are analyzed by simulating the watershed flow process under the relevant hydrological distribution to generate a hydrological runoff generation model; wherein, the watershed flow process includes: rainfall runoff generation process and watershed cross-sectional flow process; An evolution process analysis of the river flood characteristics in the watershed features is performed to construct a one-dimensional hydrodynamic model; an inundation process analysis of the river periphery area in the watershed features is performed to construct a two-dimensional hydrodynamic model; and the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model are dynamically coupled using one-dimensional and two-dimensional coupling technology to generate a coupled hydrodynamic model. The reservoir characteristic parameters in the watershed are analyzed for flood control process, and a reservoir flood control calculation model is constructed. Based on the associated data of water conservancy facilities in the watershed characteristics, the scheduling of each station in the water conservancy facilities is optimized, and a water conservancy project scheduling model is constructed. Based on the output of the coupled hydrodynamic model, the flood inundation range, inundation depth, and inundation time are calculated and processed to construct a flood inundation analysis model; and the analysis results of the flood inundation analysis model are overlaid and displayed using GIS geographic information system technology to generate an inundation risk map. Through a customized standardized interface, the hydrological runoff generation model, the coupled hydrodynamic model, the reservoir flood control calculation model, the water conservancy project scheduling model, and the flood inundation analysis model are all dynamically simulated and interactively calculated using data results, and then combined to form the water professional collaborative model.

4. The flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, Based on the aforementioned three-dimensional simulation scenario, real-time water conservancy data is broadcast in multiple dimensions to determine multi-dimensional simulation forecast data, specifically including: Based on the real-time water conservancy data in the three-dimensional simulation scenario, the flood evolution path is determined; The river level and flow rate in the flood evolution path are dynamically calculated in real time using a virtual water gauge, and the river level and flow rate information is broadcast. Based on the aforementioned flood evolution path, the gate opening degree of each station in the water conservancy engineering facility data is simulated and displayed, and the gate opening degree information is broadcast. Simulation calculations of the operational status of water conservancy engineering facilities are performed, and water conservancy project scheduling information is broadcast. Based on the three-dimensional simulation scenario, dynamic downstream inundation prediction is performed on the target watershed, and downstream inundation impact information is broadcast. The multidimensional simulation forecast data includes: river water level and flow information, gate opening information, water conservancy project scheduling information, downstream inundation impact information, and real-time weather forecast information.

5. The flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, After determining the multidimensional simulation forecast data by broadcasting real-time water conservancy data based on the three-dimensional simulation scenario, the method further includes: The annotation location information of each dimension of the forecast data in the multidimensional simulation forecast data is determined; Based on the labeled location information, and through a floating pop-up window, the semantic text information in each dimension of the forecast data is displayed accordingly, resulting in several display pop-up window messages.

6. The flood forecasting method based on multi-model collaboration according to claim 1, characterized in that, The multidimensional simulation forecast data is used to verify and analyze key indicators to obtain flood control decision information, specifically including: Based on the flood control plan template, key indicators are extracted from the multidimensional simulation forecast data; wherein, the key indicators are the key factors affecting flood control in each dimension of the forecast data; The key indicators are verified for risk warning to obtain risk warning information; Based on the risk warning information, the flood control scheduling strategy is optimized to obtain the flood control decision information.

7. 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; wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to execute a multi-model collaborative flood forecasting method according to any one of claims 1-6.

8. A non-volatile computer storage medium, characterized in that, The storage medium is a non-volatile computer-readable storage medium that stores at least one program, each program including instructions that, when executed by a terminal, cause the terminal to perform a flood forecasting method based on multi-model collaboration according to any one of claims 1-6.

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