A flood control and waterlogging prevention system and early warning method for the entire land space based on digital twins

Abstract

The present invention discloses a flood and waterlogging prevention system and early warning method for the entire domain and all elements of the national space based on digital twins. Specifically, the flood and waterlogging prevention system is constructed to realize the flood and waterlogging forecast and early warning for the entire domain and all elements of the national space, wherein the flood and waterlogging prevention system includes an intelligent perception unit for the entire domain and all elements of the national space, a digital twin system unit for the entire domain and all elements of the national space, a flood and waterlogging forecast unit for the entire domain and all elements of the national space, and a flood and waterlogging early warning and decision support unit for the entire domain and all elements of the national space. The system can construct a full-domain flood and waterlogging control work scope and full-element protection object coverage for the national space, and while covering different types of national space, it can 1:1 digitally map and fully and truly simulate, dynamically simulate, and visualize the flood and waterlogging risks faced by different elements in the national space, thereby realizing flood and waterlogging early warning and decision support for the entire domain and all elements of the national space.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin technology and flood disaster forecasting and early warning technology, and specifically relates to a national land space full-area and full-element flood control and waterlogging prevention system and early warning method based on digital twins. Background Art

[0002] In recent years, affected by the frequent occurrence of extreme heavy rainfall climate events, floods and waterlogging disasters have occurred from time to time in some cities, towns, villages and other regions in China, causing heavy losses to people's lives and production and life. Traditional flood control and waterlogging prevention work usually focuses on specific rivers, river basins or important urban construction areas, but not enough attention is paid to the flood control and waterlogging safety of the vast land space outside the river, river basin or important urban construction area. In particular, for some urban underground spaces, vast counties, towns and villages where there are weaknesses in meteorological data, hydrological forecasts and flood control and waterlogging prevention systems, the casualties and property losses caused by floods and waterlogging disasters caused by extreme heavy rainfall are particularly serious, and this is also an area of ​​work that urgently needs to be checked and filled in the flood control and waterlogging prevention of the land space. Therefore, facing the different types of land space, such as urban space, township space, village space, ground space, underground space, etc., covering the entire land space, and targeting the different protection characteristics of natural elements such as mountains, water, forests, fields, lakes, seas, grass, sand, and ice in the land space, and taking into account the flood control and waterlogging safety of different social elements such as personnel, vehicles, land, buildings, and facilities, it is particularly important and of great practical value to carry out flood control and waterlogging disaster forecasting and early warning work covering the entire land space and all elements, and to propose targeted flood disaster forecasts, early warnings, and emergency plans. Therefore, it is necessary to propose a smart forecasting and early warning method and decision support system for flood control and waterlogging prevention in the entire land space and all elements by building a digital twin system covering the entire land space and all elements.

[0003] The existing flood control and waterlogging calculation system usually focuses on specific rivers, river basins or urban concentrated construction areas, and uses HEC-RAS, MIKE and other series of models to analyze river flood evolution, flood inundation areas and waterlogging points. The above traditional commonly used methods can realize hydrodynamic simulation from one-dimensional and two-dimensional perspectives, and realize the demonstration of visual window interface. However, the analysis of traditional series models such as HEC-RAS and MIKE requires detailed rainfall data, terrain data, underlying surface characteristics, upstream and downstream boundary conditions, flow process, water level process and river roughness parameters, and also requires calibration and verification of model parameters to form fixed runoff generation and confluence simulation modules. However, the land space facing the vast city, county, township and village is usually difficult to provide detailed rainfall data and hydraulic parameters required for model construction, and it is difficult to support the construction of complex flood hydraulic models. In addition, due to the determinism of the parameters of flood models constructed by traditional methods, it is usually difficult to effectively integrate dynamic meteorological forecasts and basin boundary conditions to achieve dynamic real-time simulation of flood water conditions and flood disaster warnings. At the same time, it is impossible to connect the changes in natural and social factors caused by changes in land space development and construction, and it is difficult to achieve dynamic real-time information interaction and deep integration between physical basins and digital basins. It is impossible to efficiently and dynamically support the generation of forecast, warning, decision-making and recommendation plans in actual flood control and waterlogging prevention work. Summary of the invention

[0004] In view of the defects and problems that the construction of the existing flood control and waterlogging prevention system requires a large amount of detailed data to be implemented, and it is difficult for large areas to provide detailed parameters to meet the construction of complex flood and water conservancy models, the present invention provides a flood control and waterlogging prevention system and early warning method for the entire land space based on digital twins.

[0005] The solution adopted by the present invention to solve the technical problem is: a land space full-domain and full-element flood prevention and waterlogging early warning method based on digital twin, comprising the following steps:

[0006] Step 1: Construct a digital twin background database for the entire territory and all elements of the national land space; input precipitation data, river and lake water system data, hydrological data, and underlying surface topography data of different recurrence periods within the target national land space, and store them in the digital twin background database; at the same time, dynamically connect the meteorological data, national land space development and construction data, and the hydrological and hydraulic conditions data of the upstream and downstream of the basin around the national land space within the target national land space, and transmit them to the digital twin background database in real time;

[0007] Step 2: Construct a digital twin river basin covering the entire territory of the country: Based on the main and tributary rivers of rivers and lakes, water conservancy projects, underlying surface topography data, and urban and rural drainage systems, divide and construct river basins covering the entire territory of the country, conduct digital mapping of all elements of the physical river basins, natural elements, and economic and social information within the territory of the country, and construct a digital twin river basin system; and construct a digital flood simulator for the entire territory of the country coupled with the digital twin river basin;

[0008] Step 3: Build a precipitation database: By statistically analyzing historical precipitation data and combining regional rainstorm atlases, hydrological manuals, and multi-source precipitation forecast products, the Thiessen polygon method is used to generate a precipitation database with different return periods covering the entire national space; the precipitation database is dynamically updated and optimized in combination with meteorological forecast data to achieve dynamic synchronization between precipitation data and the digital twin basin;

[0009] Step 4: Flood and waterlogging forecast covering the entire national territory: Couple precipitation data with the constructed digital twin river basin system to build a hydrological and hydrodynamic simulation model of the national territory digital twin river basin, and simulate and forecast flood and waterlogging conditions in real time;

[0010] Step 5. Flood and waterlogging warning and decision-making support covering the entire territory and all elements of the national space: Based on the flood and waterlogging warning level standards, intelligently identify the flood and waterlogging risk levels within the national territory; based on the real-time flood water conditions and rainfall conditions, generate flood and waterlogging warning information in real time and on a rolling basis, and use visual warnings to display the flood and waterlogging risk locations and disaster conditions.

[0011] The above-mentioned digital twin-based land space full-domain and full-element flood prevention and waterlogging early warning method, step 2 of the construction of the land space full-domain and full-element digital flood simulator includes the following steps:

[0012] S1. Construct a hydrological and hydrodynamic model covering the entire national territory: Based on the digital twin basin and precipitation database data, simulate the hydrological and hydrodynamic processes of the river and lake systems and flood inundation range in the basin. By comparing the deviations between the hydrological measured data, planning, design, and construction plans and the simulation results, calibrate and optimize the parameters selected for the constructed hydrological and hydrodynamic model, intelligently perceive and identify historical flood disaster data such as remote sensing and aerial photography, and deeply learn and dynamically optimize the constructed hydrological and hydrodynamic model covering the entire national territory;

[0013] S2. Construct boundary condition module: Comprehensively consider the upstream and downstream flood control requirements of the basin where the national space is located, based on the unified flood control and waterlogging prevention standards of river and lake systems, urban spaces, township spaces, village spaces, ground spaces, and underground spaces, combined with the above-mentioned hydrological and hydrodynamic model covering the entire national space, dynamically optimize the hydraulic boundary conditions such as upstream water inflow, downstream water discharge, embankment engineering, inundation depth, and waterlogging retreat time of the river and lake systems in the model, and construct a digital boundary condition module that truly perceives and reflects the hydraulic relationship between natural elements and social elements in the national space and the digital twin basin;

[0014] S3. Construct a panoramic simulation model: Based on the simulated identification of flood overtopping, waterlogging and other dangerous conditions in river and lake systems, urban spaces, township spaces, village spaces, ground spaces and underground spaces, visualize the simulation and quantitatively present flood and waterlogging disaster scenes in the entire territory, and accurately support flood prevention and relief decisions; support the formulation of the most optimized flood control and waterlogging prevention plans through ex ante simulation to avoid flood risks to the greatest extent, and minimize the impact of flood disasters on urban and rural life, industrial production and agricultural planting through early warning.

[0015] The above-mentioned digital twin-based national land space full-domain and full-element flood prevention and waterlogging early warning method uses the SCE-UA algorithm combined with hydrological measured data, planning, design, and construction plans to calibrate and optimize the parameters selected by the hydrological and hydrodynamic model. The specific process of parameter optimization and calibration includes the following steps:

[0016] (1) Generate initial parameter set: Based on the SCE-UA algorithm, set the initial hydrological and hydrodynamic parameters, number of iterations, model convergence criteria, and likelihood function for model optimization. According to the hydrological measured data, planning, design, and construction plan data, the Nash model for flood and waterlogging is calibrated based on the moment method. n , k Parameters, determine the preferred range of parameter values ​​and generate initial values ​​of parameter groups;

[0017] Likelihood function S ( D ) and the root mean square error RMSE Can be expressed as:

[0018]

[0019] Where: D represents the shape parameter; N It indicates the total number of simulated floods and waterlogging; n Indicates the moment of simulating floods and waterlogging; Q sim,n , Q mea,n Respectively indicate time nThe hydrological simulation value, measured value and design value at that time;

[0020] (2) Solving the likelihood function and parameter optimization; Substitute the above initial parameter group into the Nash model, deduce the runoff process of flood water in the basin and solve the likelihood function, and feed the likelihood function back to the SCE-UA algorithm to optimize the parameter group; Iterate this step until the set model convergence criterion is met, and select the optimal model parameter group obtained by optimization to construct the hydrological and hydrodynamic model.

[0021] The above-mentioned digital twin-based land space full-domain and full-element flood and waterlogging early warning method, step three specifically includes the following steps:

[0022] S1. Determine the rainstorm data with different recurrence periods in the region: Based on the national, provincial, municipal and county multi-level meteorological forecast platforms and meteorological stations, statistically analyze the historical rainstorm precipitation data in the region to form a basic measured rainstorm data series; collect regional rainstorm atlases, hydrological manuals, statistical yearbooks and other materials, and use map reference, data interpolation and other methods to analyze and obtain the rainstorm data series at the data level; at the same time, compare with regional planning, design, construction and measured data, and calibrate the regional rainstorm data with different recurrence periods in the region as the basis for simulated precipitation;

[0023] S2. Determine the rainstorm data covering the entire domain: For areas without measured or statistical rainstorm data, based on the discrete points of rainstorm data with different return periods of 10min, 1h, 3h, 6h, 24h, 3d, etc. in the above-mentioned areas, the Thiessen polygon method is used to construct a polygonal network of rainstorm data covering the entire domain, and the rainstorm data covering the entire domain is obtained by drawing lines and interpolation.

[0024] The above-mentioned flood prevention and waterlogging early warning method for the entire land space based on digital twins, constructing a polygonal network of rainstorm data covering the entire area, includes the following steps:

[0025] (1) Constructing a triangulated network of global rainstorm data: Assume that the rainstorm data acquired in the region are discrete points ( X i , Y j )( i =1,2,3,……, k ; j =1,2,3,……, k ; k is the number of discrete points of heavy rain data), and the discrete points of heavy rain data in the area are divided into k adjacent triangulated networks using a set of straight line segments;

[0026] (2) Rainstorm data discrete points and triangulation network numbering: Sort the triangles adjacent to each discrete point in a clockwise or counterclockwise direction; let the discrete point be f， Find out fA triangle with vertices set to E ; Take the triangle E remove f Another vertex other than e， And at the same time find the last vertex of the triangle g ; then the next triangle must be fg The sides are triangles. G ;triangle G The other vertex of h , then the next triangle is fh Repeat this process until you return to f side;

[0027] (3) Constructing a polygonal network of global rainstorm data: Draw the center of the circumscribed circle of each rainstorm data triangle, and connect the centers of the circumscribed circles of the adjacent triangles of each rainstorm data discrete point to obtain a polygonal network of global rainstorm data.

[0028] The above-mentioned digital twin-based land space full-domain and full-element flood and waterlogging early warning method, step 4 specifically includes the following steps:

[0029] S1. Based on the precipitation data and boundary conditions of the selected return period, the digital flood simulator for the entire territory and all elements of the national space is driven to simulate the flow generation and convergence process, flood evolution and waterlogging inundation of the basin under the forecast rainstorm conditions, measured rainstorm conditions, designed precipitation conditions and excessive precipitation conditions;

[0030] S2. Combined with the simulation of flood and waterlogging, real-time forecast and visualization of flood and waterlogging conditions in urban, township, village, ground and underground spaces during different rainstorm durations are provided.

[0031] The above-mentioned digital twin-based land space full-domain and full-element flood and waterlogging early warning method, step five specifically includes the following steps:

[0032] S1. Determine flood and waterlogging warning levels: Compare the relationship between the defense water level, warning water level, guaranteed water level, flood control high water level, flood control limit water level and simulated water level of rivers, lakes, reservoirs and embankments, comprehensively consider the simulated inundation range, inundation depth and water withdrawal time of urban space, township space, village space, ground space and underground space, and combine the real-time monitoring data of flood and waterlogging to divide the flood and waterlogging warning within the national territory into four levels from light to heavy, namely blue warning, yellow warning, orange warning and red warning;

[0033] S2. Assessment of flood and waterlogging conditions covering the entire region and all elements; Combined with the flood and waterlogging conditions covering the entire region simulated in step 4, simulate the flood invasion and waterlogging inundation encountered by natural elements such as mountains, water, forests, fields, lakes, grass, and sand after different durations, as well as social elements such as people, vehicles, roads, land, buildings, and facilities, and assess the flood and waterlogging risks and potential disasters encountered by different elements;

[0034] S3. Flood and waterlogging warning: Based on the flood and waterlogging risks and potential disasters encountered by all elements in the entire region obtained through simulation and research, combined with the real-time monitoring data and disaster risks of floods and waterlogging, warnings for floods and waterlogging that may be encountered in the entire urban space, township space, village space, ground space, and underground space are issued, and warning information is issued in real time and on a rolling basis for social elements such as people, vehicles, roads, land, buildings, and facilities;

[0035] S4. Decision support for flood and waterlogging disasters: Based on simulated warnings and real-time monitoring of flood and waterlogging conditions, flood and waterlogging warning information is generated in real time for public management and decision-making departments, providing visual warnings for urban decision-making departments and displaying potential flood and waterlogging risk locations and potential disaster conditions, providing technical support for urban flood and waterlogging emergency management and scientific decision-making such as river basin flood control and waterlogging dispatching, river and lake inspection and flood control, urban traffic control, personnel transfer and resettlement, rescue force deployment, emergency plan formulation, and post-disaster recovery and reconstruction;

[0036] S5. Optimization of early warning decisions: For flood and waterlogging disaster events where the actual flood and waterlogging disaster scenarios exceed the flood and waterlogging warnings, a comprehensive comparison is made between the measured flood and waterlogging disaster scope and extent, and the parameter selection and early warning decisions of the digital flood simulator for the entire territory and all elements are corrected, updated and optimized.

[0037] The above-mentioned digital twin-based full-area and full-element flood control and waterlogging warning method for the national land space, the flood and waterlogging warning information mainly includes flood and waterlogging warning levels, and includes the areas, time, facilities, disaster-causing forms and suggestions for responding to floods and waterlogging disasters where floods and waterlogging disasters may occur.

[0038] The present invention also provides a digital twin-based intelligent forecasting and warning support system for flood and waterlogging prevention in the entire land space, the system comprising an intelligent perception unit for the entire land space, a digital twin system unit for the entire land space, a flood and waterlogging forecasting unit for the entire land space, and a flood and waterlogging warning and decision support unit for the entire land space; wherein:

[0039] The intelligent perception unit of the whole area and all elements of the national space can be based on big data and the Internet of Things to intelligently perceive and integrate meteorological data, river and lake water system data, hydrological data, underlying surface topography data, national space planning data, national space development and construction data, and social and economic data covering the whole area and all elements of the national space, dynamically perceive and transmit and store them in real time to the digital twin background database;

[0040] The digital twin system unit of the whole-domain and all-element national land space includes the digital twin background database of the whole-domain and all-element national land space and the digital twin river basin of the whole-domain and all-element national land space; among them: the digital twin background database of the whole-domain and all-element national land space is dynamically connected with the regional meteorological and hydrological data and national land space data to provide a background database for the digital twin river basin of the whole-domain and all-element national land space; the digital twin river basin of the whole-domain and all-element national land space can generate the digital mapping and structure of the natural elements, social elements, hydrological and meteorological elements, and geographical terrain of the river basin national land space based on the digital twin background database of the whole-domain and all-element national land space and relying on the digital flood simulator of the whole-domain and all-element national land space;

[0041] The flood and waterlogging forecasting unit for the entire land space and all elements can utilize the precipitation database with different return periods covering the entire land space, couple and drive the digital flood simulator of the digital twin watershed of the land space, and realize the rolling real-time forecast of flood and waterlogging conditions in urban space, township space, village space, ground space and underground space covering the entire land space;

[0042] The flood and waterlogging warning and decision-making support unit for the entire area and all elements of the national land space can assess the flood and waterlogging risks and potential disasters encountered by the entire area and all elements based on the forecast results of the flood and waterlogging forecast unit for the entire area and all elements of the national land space, and issue flood and waterlogging warning information based on the flood and waterlogging warning level.

[0043] The above-mentioned national land space all-encompassing and all-element intelligent forecasting and early warning support system for flood control and waterlogging prevention based on digital twins, the precipitation database is a high-precision precipitation database obtained by integrating historical precipitation data, regional rainstorm atlas, hydrological manuals, satellite monitoring, ground measurements, radar monitoring data, and dynamically updating and optimizing with real-time meteorological forecast data.

[0044] The above-mentioned national land space full-domain and full-element flood control and waterlogging intelligent forecasting and early warning support system based on digital twin, the national land space full-domain and full-element intelligent perception unit includes a meteorological perception and dynamic update module, a river and lake water system information perception and dynamic update module, a hydrological and hydraulic element perception and dynamic update module, an underlying surface information perception and dynamic update module, a national land space information perception and dynamic update module, a social and economic information perception and dynamic update module and a natural information perception and dynamic update module; wherein:

[0045] The meteorological perception and dynamic update module is used to obtain and update measured meteorological data, historical meteorological data, satellite meteorological forecast data, radar monitoring meteorological data, rainstorm atlas meteorological data, hydrological manual meteorological data, and statistical yearbook meteorological data;

[0046] The river and lake water system information perception and dynamic update module is used to obtain and update river and lake water system census survey data, river and lake water system planning and design data, river and lake water system remote sensing data, river and lake water system aerial photography data, national land space basic information platform and land survey data;

[0047] The hydrological and hydraulic element perception and dynamic update module is used to obtain and update flow data, water level data, upstream and downstream flow and hydrological data boundary conditions of the basin, flood depth data, levee data, pump station data, flood storage area data, flood storage area data, dam data, flood control project data, flood control project data, and cross-regional flood control data;

[0048] The underlying surface information perception and dynamic update module is used to obtain and update terrain data, geological data, soil data, vegetation data, urban space data, township space data, village space data, ground space data, and underground space data;

[0049] The national land space information perception and dynamic update module is used to obtain and update national land survey data, national land space basic information platform data, national land space planning data, natural resources survey and monitoring data, land use type data, and remote sensing data;

[0050] The social and economic information perception and dynamic update module is used to obtain and update population data, construction area data, village data, housing data, farmland data, flood control and waterlogging prevention facilities data, highway facilities data, railway facilities data, subway facilities data, airport facilities data, medical facilities data, power facilities data, water supply and drainage facilities data, sanitation facilities data, gas facilities data, communication facilities data, industrial and mining enterprise data, public service facilities data, tide prevention facilities data, and cultural relics data;

[0051] The natural information perception and dynamic update module is used to acquire and update mountain data, water data, forest data, farmland data, lake data, sea data, grassland data, desert data, ice surface data, and snow area data.

[0052] Beneficial effects of the present invention:

[0053] The system and method of the present invention can overcome the problem that traditional flood control and waterlogging prevention work focuses on a single river basin or a single key urban area, and constructs a full-area flood control work scope and full-element protection object coverage for the national land space, covering different national land space types such as urban space, township space, village space, ground space, underground space, etc., constructs a digital twin basin, integrates big data, IoT intelligent perception, and integrates meteorological data, river and lake water system data, hydrological data, underlying surface topography data, national land space planning data, national land space development and construction data, and social and economic data covering the entire national land space. 1:1 digital mapping and comprehensive and real Simulate, dynamically simulate, and visualize the flood control and waterlogging risks faced by different natural elements such as mountains, water, forests, fields, lakes, seas, grass, and sand in the national territory, as well as different social elements such as people, construction areas, villages, farmland, flood control and waterlogging prevention facilities, road facilities, railway facilities, subway facilities, airport facilities, medical facilities, power facilities, water supply and drainage facilities, sanitation facilities, housing buildings, gas facilities, communication facilities, industrial and mining enterprises, public service facilities, tidal prevention facilities, cultural relics and historical sites, to achieve flood and water situation simulation covering the entire territory and all elements of the national territory, global water situation forecast, flood disaster assessment and evaluation, and dynamic early warning of flood disasters in the entire territory and all elements.

[0054] The system and method of the present invention can overcome the problem of lack of flood and waterlogging prevention and control tools for the entire area and all elements at the administrative jurisdiction level, overcome the inability to connect the changes in natural and social elements caused by the changes in land space development and construction, and cause the problem of difficulty in realizing dynamic real-time information interaction and deep integration between physical watersheds and digital watersheds. The present invention provides a flood and waterlogging intelligent forecasting and warning decision support system for the entire area and all elements of the land space within the administrative jurisdiction, which can generate flood and waterlogging warning information for public management and decision-making departments at a certain administrative level (provincial, municipal, county, township, village) in real time, provide visual warnings and display potential flood and waterlogging risk locations and potential disaster conditions, and provide technical support for urban flood and waterlogging emergency management and policy scientific decision-making such as flood control and waterlogging dispatching, river and lake inspection and flood control, urban traffic control, personnel transfer and resettlement, rescue force deployment, emergency plan formulation, post-disaster recovery and reconstruction within the administrative jurisdiction of provinces, cities, counties, towns, and villages.

[0055] The system and method of the present invention can overcome the problem that the existing flood forecasting and warning methods rely solely on river management standards and regional flood control standards to carry out flood forecasting and warning, and focus on forecasting and warning of excessive floods caused by frequent extreme heavy rainfall and generating response strategies; it overcomes the problem that it is difficult to carry out excessive flood simulation and forecasting and warning in areas where there is a lack of meteorological and hydrological data, and constructs a database of different recurrence periods and extreme heavy rainfall precipitation covering the entire national space, and dynamically updates and optimizes the precipitation database in combination with meteorological forecast data, thereby achieving dynamic synchronization between precipitation data and the digital twin basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a principle flow chart of the present invention's method for intelligent forecasting and early warning of flood control and waterlogging prevention for the entire area and all elements of the national land space based on digital twins.

[0057] Figure 2 It is a structural schematic diagram of a national land space all-domain and all-element flood control and waterlogging intelligent forecasting and early warning decision support system based on digital twins of the present invention. DETAILED DESCRIPTION

[0058] In view of the defects and problems of the existing flood control and waterlogging prevention system that is oriented to a single river or a single urban area and cannot achieve full coverage of all ranges, all elements and all spatial types, the present invention provides a land space full-domain and all-element flood control and waterlogging prevention system and early warning method based on digital twins. The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0059] Embodiment 1: This embodiment provides a method for intelligent flood and waterlogging prevention forecasting and early warning based on digital twins for the entire land space. Figure 1 As shown, the method comprises the following steps:

[0060] Step 1. Construct a digital twin background database for the entire area and all elements of the national land space; enter precipitation data, river and lake water system data, hydrological data, and underlying surface topography data of different recurrence periods within the target national land space, and store them in the digital twin background database; and dynamically connect the meteorological data within the target national land space, national land space development and construction data, and upstream and downstream hydrological data outside the national land space, and transmit them in real time to the constructed digital twin background database, so as to realize the dynamic connection between the digital twin background database and regional meteorological data, hydrological data and national land space data.

[0061] The national land space is urban space, agricultural space, ecological space and other spaces, including urban space, township space, village space, above-ground space, underground space and other types of national land space within a certain level of administrative division (provincial, municipal, county, township and village levels). The whole area refers to the implementation of full spatial coverage of the above-mentioned national land space, including the whole area and the whole space within a certain level of administrative division, and the overall planning of urban and rural areas, land and sea, above-ground and underground spaces; all elements refer to the protection objects of flood control and waterlogging prevention are all elements within the scope of the whole national land space, including natural elements such as mountains, water, forests, fields, lakes, grass, sand, ice and social elements such as people, vehicles, roads, land, buildings and facilities within a certain level of administrative division.

[0062] Specifically: Based on big data and the intelligent perception of the Internet of Things, meteorological data covering the entire territory and all elements of the national space (meteorological data data, historical meteorological data data, satellite meteorological forecast data, radar monitoring meteorological data, rainstorm atlas meteorological data, hydrological manual meteorological data, statistical yearbook meteorological data, etc.), river and lake water system data (river and lake water system census survey data, river and lake water system planning and design data, river and lake water system remote sensing data, river and lake water system aerial photography data, national space basic information platform and land survey data, etc.), hydrological data (flow data, water level data, upstream and downstream flow and hydrological data boundary conditions of the basin, flood depth data, embankment data, pump station data, flood storage area data, flood storage area data, dam data, flood control project data, flood control project data, cross-regional flood control data, etc.), underlying surface terrain data (topography data, geological data, soil data, vegetation data, urban space data, township space data, village space data, ground space data, underground space data, etc.), national space planning data (land survey data , national land space basic information platform data, national land space planning data, natural resources survey and monitoring data, land use type data, remote sensing data, etc.), national land space development and construction data (land survey data, national land space basic information platform data, national land space planning data, natural resources survey and monitoring data, land use type data, remote sensing data, etc.), social and economic data (population data, construction area data, village data, house construction data, farmland data, flood control and waterlogging prevention facilities data, highway facilities data, railway facilities data, subway facilities data, airport facilities data, medical facilities data, power facilities data, water supply and drainage facilities data, sanitation facilities data, gas facilities data, communication facilities data, industrial and mining enterprise data, public service facilities data, anti-tide facilities data, cultural relics and historical sites data, etc.), geographical and natural data (mountain data, water data, forest data, farmland data, lake data, sea area data, grassland data, desert data, ice surface data, snow area data, etc.), dynamically sensed and transmitted and stored in real time to the digital twin background database.

[0063] Step 2. Construct a digital twin river basin covering the entire territory of the country. Based on the main and tributary rivers of rivers and lakes, water conservancy projects, underlying surface topography data, and drainage systems, divide and construct river basins covering the entire territory of the country. Digitally map all elements of the physical river basins and economic and social information within the territory of the country and construct a digital twin river basin system. Construct a digital flood simulator for the entire territory of the country coupled with the digital twin river basin.

[0064] Specifically: The digital twin basin of the entire land space and all elements is based on the digital twin background database of the entire land space, and all elements of the physical watershed and economic and social information within the land space are digitally mapped. Relying on the digital flood simulator of the entire land space and all elements, a digital map of the basin is generated covering the natural elements, social elements, hydrological and meteorological elements of the basin land space.

[0065] The construction of the digital flood simulator for the entire land area and all elements includes the following contents:

[0066] S1. Construct a hydrological and hydrodynamic model covering the entire national territory; based on the digital twin watershed and precipitation database data, simulate the hydrological and hydrodynamic processes of the river and lake systems and flood inundation range in the basin, calibrate and optimize the parameters selected for the constructed hydrological and hydrodynamic model by comparing the deviations between the hydrological measured data, planning, design, construction plans and simulation results, intelligently perceive and identify historical flood disaster data such as remote sensing and aerial photography, deeply learn and dynamically optimize the constructed hydrological and hydrodynamic model covering the entire national territory.

[0067] S2. Construct a boundary condition module; comprehensively consider the upstream and downstream flood control requirements of the basin where the national land space is located, based on the unified flood control and waterlogging prevention standards of river and lake systems, urban spaces, township spaces, village spaces, ground spaces, and underground spaces, combined with the above-mentioned hydrological and hydrodynamic model covering the entire national land space, dynamically optimize the hydraulic boundary conditions such as upstream water inflow, downstream water discharge, embankment projects, inundation depth, and inundation retreat time of rivers and lakes in the model, and construct a digital boundary condition module that truly reflects the hydraulic relationship between the natural elements and social elements in the perceived national land space and the digital twin basin.

[0068] S3. Construct a panoramic simulation model: Based on the flood overtopping and waterlogging in the river and lake systems, urban space, township space, village space, ground space, and underground space identified in the above simulation, visualize the simulation and quantitatively present the flood and waterlogging disaster scenes in the entire national space, and accurately support flood disaster prevention and disaster relief decisions. Through pre-simulation support, the optimal flood control and waterlogging prevention plan is formulated to avoid flood risks to the greatest extent, and the impact of flood disasters on urban and rural life, industrial production, and agricultural planting is minimized through early warning.

[0069] Step 3: Build a precipitation database: By statistically analyzing historical precipitation data and combining regional rainstorm atlases, hydrological manuals, and multi-source precipitation forecast products, the Thiessen polygon method is used to generate a precipitation database with different return periods covering the entire national space; the precipitation database is dynamically updated and optimized in combination with meteorological forecast data to achieve dynamic synchronization between precipitation data and the digital twin basin. Specifically, it includes the following contents:

[0070] S1. Determine the rainstorm data with different recurrence periods in the region; based on the national, provincial, municipal and county multi-level meteorological forecast platforms and meteorological stations, compile statistics on the historical rainstorm precipitation data in the region to form a basic measured rainstorm data series; collect regional rainstorm atlases, hydrological manuals, statistical yearbooks and other materials, and use map reference, data interpolation and other methods to analyze and obtain a series of rainstorm data at the data level; at the same time, compare with regional planning, design, construction and measured data, and calibrate the regional rainstorm data with different recurrence periods in the region as the basis for simulated precipitation.

[0071] S2. Determine the rainstorm data covering the entire domain. For areas without measured or statistical rainstorm data, construct a polygonal network of rainstorm data covering the entire domain based on the obtained discrete points of rainstorm data with different return periods such as 10 minutes, 1 hour, 3 hours, 6 hours, 24 hours, and 3 days, and draw lines to interpolate to obtain the rainstorm data covering the entire domain.

[0072] Step 4: Flood and waterlogging forecast covering the entire territory of the country; coupling precipitation data with the constructed digital twin watershed system, using the above-mentioned hydrological and hydrodynamic simulation model of the coupled digital twin watershed of the national territory, to achieve rolling real-time forecast of flood and waterlogging water conditions in urban space, township space, village space, ground space, and underground space covering the entire territory of the country; specifically including the following contents:

[0073] S1. Based on the precipitation data and boundary conditions of the selected return period, the digital flood simulator for the entire territory and all elements of the national space is driven to simulate the flow generation and convergence process, flood evolution and waterlogging inundation of the basin under the forecast rainstorm conditions, measured rainstorm conditions, designed precipitation conditions and excessive precipitation conditions;

[0074] The selected return periods include: once in 1 year, once in 2 years, once in 3 years, once in 5 years, once in 10 years, once in 20 years, once in 30 years, once in 50 years, once in 100 years, once in 200 years, once in 300 years, once in 500 years, once in 1000 years, and once in 2000 years.

[0075] S2. Combined with the simulation of flood and waterlogging, real-time forecast and visualization of flood and waterlogging conditions in urban space, township space, village space, ground space and underground space with different durations of heavy rain. For example, the flood and waterlogging conditions in urban space, township space, village space, ground space and underground space with different durations of heavy rain in the future can be displayed at 0.5h, 1h, 2h, 3h, 4h, 5h, 6h, 7h, 8h, 9h, 10h, 12h, 1d, 2d, 3d, 4d, 5d and 10d.

[0076] Step 5: Flood and waterlogging warning and decision-making support covering the entire territory and all elements of the national space; based on the flood and waterlogging warning level standards, intelligently identify the flood and waterlogging risk levels encountered by natural elements such as mountains, water, forests, fields, lakes, grass, and sand, as well as social elements such as people, vehicles, roads, land, buildings, and facilities within the national space; generate flood and waterlogging warning information in real time and rolling according to the real-time upstream and downstream flood water conditions and rainfall conditions, and visualize the warning to display the flood and waterlogging risk locations and disaster conditions; specifically include the following contents:

[0077] S1. Determine the flood and waterlogging warning levels; compare the relationship between the defense water level, warning water level, guaranteed water level, flood control high water level, flood control limit water level and simulated water level of rivers, lakes, reservoirs and embankments; comprehensively consider the simulated inundation range, inundation depth and water retreat time of urban space, township space, village space, ground space and underground space; combine with real-time monitoring data of floods and waterlogging, and divide the flood and waterlogging warnings within the national territory into four levels from light to heavy, namely blue warning, yellow warning, orange warning and red warning.

[0078] S2. Assessment of flood and waterlogging disasters covering the entire region and all elements; Combined with the flood and waterlogging water conditions covering the entire region simulated in step P4, simulate the flood intrusion and waterlogging inundation encountered by natural elements such as mountains, water, forests, fields, lakes, grass, sand, ice, and social elements such as people, vehicles, roads, land, buildings, and facilities after different durations, and assess the flood and waterlogging risks and potential disasters encountered by different elements.

[0079] S3. Flood and waterlogging warning: Based on the simulation and assessment of the flood and waterlogging risks and potential disasters encountered by the entire region and all elements, combined with the real-time monitoring data and disaster risks of floods and waterlogging, warnings of floods and waterlogging that may be encountered are issued for the entire urban space, township space, village space, ground space, and underground space. Warnings are issued in real time and on a rolling basis for social elements such as people, vehicles, roads, land, buildings, and facilities. The flood and waterlogging warning information mainly includes the flood and waterlogging warning level, and includes the areas, time, facilities, disaster forms, and suggestions for responding to floods and waterlogging that may occur.

[0080] S4. Decision support for floods and waterlogging: Based on simulated warnings and real-time monitoring of flood and waterlogging conditions, flood and waterlogging warning information is generated in real time for public management and decision-making departments, providing visual warnings for urban decision-making departments and displaying potential flood and waterlogging risk locations and potential disaster-causing conditions. It provides technical support for urban flood and waterlogging emergency management and scientific decision-making, such as river basin flood control and waterlogging dispatching, river and lake inspection and flood prevention, urban traffic control, personnel transfer and resettlement, rescue force deployment, emergency plan formulation, and post-disaster recovery and reconstruction.

[0081] S5. Optimization of early warning decisions: For flood and waterlogging disaster events where the actual flood and waterlogging disaster scenarios exceed the flood and waterlogging warnings, a comprehensive comparison is made between the measured flood and waterlogging disaster scope and extent, and the parameter selection and early warning decisions of the digital flood simulator for the entire territory and all elements are corrected, updated and optimized.

[0082] Example 2: The similarities between this example and Example 1 are not repeated here. The difference is that this example uses a method combining the SCE-UA algorithm with hydrological measured data, planning, design, and construction scheme to calibrate and optimize the parameters selected by the hydrological and hydrodynamic model. The specific steps include:

[0083] (1) Generate an initial parameter set; set the initial hydrological and hydrodynamic parameters, number of iterations, model convergence criteria, and likelihood function for model optimization based on the SCE-UA algorithm. Based on the hydrological measured data, planning, design, and construction plan data, the Nash model for flood and waterlogging is optimized based on the moment method. n , k Parameters; determine the preferred range of parameter values ​​and generate initial values ​​for the parameter group.

[0084] Likelihood function S ( D ) and the root mean square error RMSE Can be expressed as:

[0085]

[0086] Where: D represents the shape parameter; N Indicates the total number of simulated floods and waterlogging; n Indicates the moment of simulating floods and waterlogging; Q sim,n , Q mea,n Respectively indicate time n The hydrological simulation value at that time is compared with the measured and designed value.

[0087] (2) Solving the likelihood function and optimizing parameters: Substitute the above initial parameter group into the Nash model, deduce the flood runoff process in the basin and solve the likelihood function. Feed the likelihood function back to the SCE-UA algorithm to optimize the parameter group. Iterate this step until the set model convergence criterion is met. Select the optimal model parameter group obtained by optimization to construct the hydrological and hydrodynamic model.

[0088] Example 3: The similarities between this example and Example 1 are not repeated here. The difference is that this example uses the Thiessen polygon method to construct a polygonal network of rainstorm data covering the entire domain, which specifically includes the following steps:

[0089] (1) Constructing a triangulated network of global rainstorm data: Assume that the rainstorm data acquired in the region are discrete points ( X i , Y j )( i =1,2,3,……, k ; j =1,2,3,……, k , k is the number of discrete points of heavy rain data), and the discrete points of heavy rain data in the area are divided into k adjacent triangulated networks using a set of straight line segments;

[0090] (2) Number the rainstorm data discrete points and triangulated networks; sort the triangles adjacent to each discrete point in a clockwise or counterclockwise direction;

[0091] Let the discrete points be f , find out f A triangle with vertices E ; Take the triangle E remove f Let another vertex other than e , then another vertex can also be found, which is g ; then the next triangle must be fg The sides are triangles. G ;triangle G The other vertex of h , then the next triangle is fh Repeat this process until you return to f side;

[0092] (3) Constructing the polygonal network of global rainstorm data: Draw the center of the circumscribed circle of each rainstorm data triangle and record it; according to the adjacent triangles of each rainstorm data discrete point, connect the centers of the circumscribed circles of these adjacent triangles to obtain the polygonal network of global rainstorm data.

[0093] For the rainstorm data polygons at the edge of the triangulated network, a perpendicular bisector can be drawn to intersect with the map outline, and together with the map outline, a polygonal network of global rainstorm data with different recurrence periods can be formed.

[0094] Example 4: This example provides a smart forecasting and early warning decision support system for flood control and waterlogging prevention in the entire area and all elements of the national land space based on digital twins. The smart forecasting and early warning decision support system is used to implement the smart forecasting and early warning method for flood control and waterlogging prevention in the entire area and all elements of the national land space based on digital twins.

[0095] like Figure 2As shown, the smart forecasting, warning and decision-making support system includes an intelligent perception unit for the entire area and all elements of the national land space, a digital twin system unit for the entire area and all elements of the national land space, a flood and waterlogging forecasting unit for the entire area and all elements of the national land space, and a flood and waterlogging warning and decision-making support unit for the entire area and all elements of the national land space.

[0096] Among them: the intelligent perception unit of the whole domain and all elements of the national space is based on big data and the Internet of Things to intelligently perceive and integrate meteorological data, river and lake water system data, hydrological data, underlying surface topography data, national space planning data, national space development and construction data, and social and economic data covering the whole domain and all elements of the national space, dynamically perceive and transmit and store in real time to the digital twin background database. The whole domain national space data specifically includes different types of national space, such as urban space, township space, village space, ground space, underground space, etc. within the administrative division of a certain level (province, city, county, town, village, etc.); all elements include natural elements such as mountains, water, forests, fields, lakes, seas, grass, sand, ice, and social elements such as people, vehicles, roads, land, buildings, and facilities within the administrative division of a certain level.

[0097] Specifically, the national land space full-domain all-element intelligent perception unit specifically includes a meteorological perception and dynamic update module, a river and lake water system information perception and dynamic update module, a hydrological and hydraulic element perception and dynamic update module, an underlying surface information perception and dynamic update module, a national land space information perception and dynamic update module, a social and economic information perception and dynamic update module, and a natural information perception and dynamic update module; among which:

[0098] The meteorological perception and dynamic update module is used for measured meteorological data, historical meteorological data, satellite meteorological forecast data, radar monitoring meteorological data, rainstorm atlas meteorological data, hydrological manual meteorological data, and statistical yearbook meteorological data.

[0099] The river and lake water system information perception and dynamic update module is used to obtain and update river and lake water system census survey data, river and lake water system planning and design data, river and lake water system remote sensing data, river and lake water system aerial photography data, national land space basic information platform and land survey data.

[0100] The hydrological and hydraulic element perception and dynamic update module is used to obtain and update flow data, water level data, upstream and downstream flow and hydrological data boundary conditions of the basin, flood depth data, embankment data, pumping station data, flood storage area data, flood storage area data, dam data, flood control project data, flood control project data, and cross-regional flood control data.

[0101] The underlying surface information perception and dynamic update module is used to obtain and update terrain data, geological data, soil data, vegetation data, urban space data, township space data, village space data, ground space data, and underground space data.

[0102] The national land space information perception and dynamic update module is used to acquire and update national land survey data, national land space basic information platform data, national land space planning data, natural resources survey and monitoring data, land use type data, and remote sensing data.

[0103] The social and economic information perception and dynamic update module is used to obtain and update population data, construction area data, village data, house building data, farmland data, flood control and waterlogging prevention facilities data, highway facilities data, railway facilities data, subway facilities data, airport facilities data, medical facilities data, power facilities data, water supply and drainage facilities data, sanitation facilities data, gas facilities data, communication facilities data, industrial and mining enterprise data, public service facilities data, tide prevention facilities data, and cultural relics data.

[0104] The natural information perception and dynamic update module is used to acquire and update mountain data, water data, forest data, farmland data, lake data, sea data, grassland data, desert data, ice surface data, and snow area data.

[0105] The digital twin system unit of the whole area and all elements of the national land space mainly includes the digital twin background database of the whole area and all elements of the national land space and the digital twin watershed of the whole area and all elements of the national land space;

[0106] The digital twin background database for the entire area and all elements of the national land space enters precipitation data, river and lake water system data, hydrological data, and underlying surface topography data of different recurrence periods within the target national land space, and stores them in the digital twin background database; and dynamically connects the meteorological data within the target national land space, the national land space development and construction data, and the upstream and downstream hydrological data outside the national land space, and transmits them in real time to the constructed digital twin background database, thereby realizing the dynamic connection between the digital twin background database and regional meteorological data, hydrological data and national land space data.

[0107] The digital twin watershed of the entire territory and all elements of the national space is based on the digital twin background database of the entire territory and all elements of the national space, meteorological data, river and lake water system data, hydrological data, underlying surface topography data, national space planning data, national space development and construction data, and socio-economic data of the entire territory and all elements of the national space. It digitally maps and comprehensively simulates, dynamically simulates, and visualizes the different natural elements of the national space, such as mountains, water, forests, fields, lakes, seas, grass, sand, and ice, as well as people, construction areas, villages, farmland, flood control and waterlogging prevention facilities, road facilities, railway facilities, subway facilities, airport facilities, medical facilities, power facilities, water supply and drainage facilities, sanitation facilities, housing buildings, gas facilities, communication facilities, industrial and mining enterprises, public service facilities, tide prevention facilities, cultural relics and historical sites, and the flood control and waterlogging risks faced by different social elements.

[0108] The national land space full-domain all-element flood and waterlogging forecasting unit uses a precipitation database with different return periods covering the entire national land space, couples and drives the digital flood simulator of the digital twin watershed of the national land space, and realizes rolling real-time forecasting of flood and waterlogging conditions in urban space, township space, village space, ground space, and underground space covering the entire national land space. The main function of the national land space full-domain all-element flood and waterlogging forecasting unit is to realize the basin runoff generation and convergence process, flood evolution trend, and waterlogging inundation trend under different simulation scenarios, combined with the simulation of flood and waterlogging simulation situation, to forecast in real time and visualize the flood and waterlogging conditions in urban space, township space, village space, ground space, and underground space with different rainstorm durations.

[0109] The flood and waterlogging warning and decision-making support unit for the entire area and all elements of the national land space is used to judge the flood and waterlogging risks and potential disasters encountered in the entire area and all elements based on the forecast results of the flood and waterlogging forecast unit for the entire area and all elements of the national land space, and release flood and waterlogging warning information through the Internet, text messages and other channels according to the flood and waterlogging warning level, so as to generate flood and waterlogging warning and flood control decision-making basis for public management and decision-making departments. The main function of the flood and waterlogging warning and decision-making support unit for the entire territory and all elements of the national land space is to issue flood and waterlogging warnings for possible floods and waterlogging disasters within the scope of urban space, township space, village space, ground space and underground space based on the designated flood and waterlogging warning levels, through the assessment of flood and waterlogging disasters covering the entire territory and all elements, and to issue real-time and rolling warnings for social elements such as people, vehicles, roads, land, buildings and facilities; at the same time, based on simulated warnings and real-time monitoring of flood and waterlogging conditions, flood and waterlogging warning information is generated in real time and rolling for public management and decision-making departments, providing visual warnings for urban decision-making departments and displaying potential flood and waterlogging risk locations and potential disaster-causing conditions, providing technical support for urban flood and waterlogging emergency management and scientific decision-making such as basin flood control and waterlogging dispatching, river and lake inspection and flood control, urban traffic control, personnel transfer and resettlement, rescue force deployment, emergency plan formulation, and post-disaster recovery and reconstruction.

[0110] Example 5: This example explains in detail how to use the present invention to achieve flood and waterlogging forecasting, warning and decision support covering the entire area and all elements through a digital twin-based national land space full-area and full-element flood control and waterlogging prevention system.

[0111] 1. Build a digital twin background database of all elements of the national land space

[0112] By integrating meteorological data, river and lake water system data, hydrological data, underlying surface topography data, land space planning data, land space development and construction data, and social and economic data covering the entire land space in the intelligent perception unit of the entire land space, the data can be dynamically perceived and transmitted and stored in real time to the digital twin background database. Specifically, it includes:

[0113] 1. Meteorological perception and dynamic update module, which collects meteorological data such as precipitation and wind speed through data survey and based on big data and IoT intelligent perception, includes measured meteorological data, historical meteorological data, satellite meteorological forecast data, radar monitoring meteorological data, rainstorm atlas meteorological data, hydrological manual meteorological data, statistical yearbook meteorological data, etc.

[0114] 2. The river and lake water system information perception and dynamic update module collects, integrates and connects river and lake water system census survey data, river and lake water system planning and design data, river and lake water system remote sensing data, river and lake water system aerial photography data, national land space basic information platform and land survey data through data research and big data and Internet of Things technology.

[0115] 3. The module for sensing and dynamically updating hydrological and hydraulic elements collects, integrates and connects flow data, water level data, upstream and downstream flow and hydrological data boundary conditions, flood depth data, embankment data, pump station data, flood storage area data, flood storage area data, dam data, flood control project data, flood control project data, cross-regional flood control data, etc. through data investigation, big data and Internet of Things technology.

[0116] 4. The underlying surface information perception and dynamic update module collects, integrates and connects terrain data, geological data, soil data, vegetation data, urban space data, township space data, village space data, ground space data, underground space data, etc. through data survey, big data and Internet of Things technology.

[0117] 5. The national land space information perception and dynamic update module collects, integrates and connects national land survey data, national land space basic information platform data, national land space planning data, natural resource survey and monitoring data, land use type data, remote sensing data, etc. through data research, big data and Internet of Things technology.

[0118] 6. Social and economic information perception and dynamic update module, through data investigation and big data and Internet of Things technology, collects, integrates and connects population data, construction area data, village data, housing data, farmland data, flood control and waterlogging prevention facilities data, highway facilities data, railway facilities data, subway facilities data, airport facilities data, medical facilities data, power facilities data, water supply and drainage facilities data, sanitation facilities data, gas facilities data, communication facilities data, industrial and mining enterprise data, public service facilities data, tide prevention facilities data, cultural relics and historical sites data, etc.

[0119] 7. The natural information perception and dynamic update module collects, integrates and connects mountain data, water data, forest data, farmland data, lake data, sea data, grassland data, desert data, ice surface data, snow area data, etc. through data survey, big data and Internet of Things technology.

[0120] 2. Constructing a digital twin watershed covering all elements of the national territory

[0121] Based on the digital twin background database of the whole land space and all elements, a digital twin river basin of the whole land space and all elements is constructed. The digital mapping of all elements of the physical river basin, natural elements, social elements and economic and social information within the scope of the land space is realized, and the digital twin river basin covering the natural elements, social elements, hydrology and meteorology of the river basin land space is generated by relying on the digital flood simulator of the whole land space and all elements.

[0122] Among them, the construction of a digital flood simulator for the entire land area and all elements includes the following contents:

[0123] 1. Construct a hydrological and hydrodynamic model covering the entire national territory: Based on the digital twin basin and precipitation database data, simulate the hydrological and hydrodynamic processes of the river and lake systems and flood inundation range in the basin. By comparing the deviations between the hydrological measured data, planning, design, construction plans and simulation results, calibrate and optimize the parameters selected for the constructed hydrological and hydrodynamic model. Intelligently perceive and identify historical flood disaster data such as remote sensing and aerial photography, deeply learn and dynamically optimize the constructed hydrological and hydrodynamic model covering the entire national territory.

[0124] The parameters selected for the hydrological and hydrodynamic model are calibrated and optimized based on the SCE-UA algorithm combined with hydrological measured data, planning, design, and construction schemes. The specific process of parameter optimization and calibration includes:

[0125] 1.1 Generate an initial parameter set. Set the initial hydrological and hydrodynamic parameters, number of iterations, model convergence criteria, and likelihood function for model optimization based on the SCE-UA algorithm. According to the hydrological measured data, planning, design, and construction plan data, the Nash model for flood and waterlogging is calibrated based on the moment method. n , k Parameters; determine the preferred range of parameter values ​​and generate initial values ​​for the parameter group.

[0126] Likelihood function S ( D ) and the root mean square error RMSE Can be expressed as:

[0127]

[0128] in, D represents the shape parameter; N Indicates the total number of simulated floods and waterlogging; n Indicates the moment of simulating floods and waterlogging; Q sim,n , Qmea,n Respectively indicate time n The hydrological simulation value at that time is compared with the measured and designed value.

[0129] 1.2 Solving the likelihood function and parameter optimization: Substitute the above initial parameter group into the Nash model, deduce the flood runoff process in the basin and solve the likelihood function, feed the likelihood function back to the SCE-UA algorithm to optimize the parameter group, iterate this step until the set model convergence criterion is met, and select the optimal model parameter group obtained by optimization to construct the hydrological and hydrodynamic model.

[0130] 2. Construct a boundary condition module. Comprehensively consider the upstream and downstream flood control requirements of the basin where the national land space is located, based on the unified flood control and waterlogging prevention standards of river and lake systems, urban spaces, township spaces, village spaces, ground spaces, and underground spaces, combined with the above-mentioned hydrological and hydrodynamic model covering the entire national land space, dynamically optimize the hydraulic boundary conditions such as the upstream water inflow, downstream water discharge, embankment projects, inundation depth, and waterlogging retreat time of the river and lake systems in the model, and construct a digital boundary condition module that truly reflects the hydraulic relationship between the natural elements and social elements in the perceived national land space and the digital twin basin.

[0131] 3. Construct a panoramic simulation model: Based on the flood overtopping and waterlogging in the river and lake systems, urban space, township space, village space, ground space, and underground space identified in the above simulation, visualize the simulation and quantitatively present the flood and waterlogging disaster scenes in the entire national space, and accurately support flood disaster prevention and disaster relief decisions. Through pre-simulation support, the optimal flood control and waterlogging prevention plan can be formulated to avoid flood risks to the greatest extent, and the impact of flood disasters on urban and rural life, industrial production, and agricultural planting can be minimized through early warning.

[0132] 3. Building a precipitation database

[0133] By statistically analyzing historical precipitation data and combining regional rainstorm atlases, hydrological manuals, and multi-source precipitation forecast products, the Thiessen polygon method is used to generate a precipitation database with different return periods covering the entire national space; the precipitation database is dynamically updated and optimized in combination with meteorological forecast data to achieve dynamic synchronization between precipitation data and the digital twin basin. Specifically, the steps include the following:

[0134] 1. Determine the rainstorm data with different recurrence periods in the region: Based on the national, provincial, municipal and county multi-level meteorological forecast platforms and meteorological stations, statistically analyze the regional historical and forecasted rainstorm precipitation data to form a basic measured rainstorm data series. Collect regional rainstorm atlases, hydrological manuals, statistical yearbooks and other materials, and use map reference, data interpolation and other methods to analyze and obtain a series of rainstorm data at the data level. Through data surveys, collect and use big data, IoT intelligent sensing satellite meteorological and rainstorm forecast data, radar monitoring meteorological and rainstorm data. At the same time, compare with regional planning, design, construction and measured data, and calibrate the regional rainstorm data with different recurrence periods in the region as the basis for simulated precipitation.

[0135] 2. Determine the rainstorm data covering the entire domain: In order to obtain precipitation data covering the entire spatial domain, for areas without measured or statistical rainstorm data, based on the discrete points of rainstorm data with different recurrence periods of 10min, 1h, 3h, 6h, 24h, and 3d in the above-mentioned areas, the Thiessen polygon method is used to construct a polygonal network of rainstorm data covering the entire domain, and the rainstorm data covering the entire domain is obtained by drawing lines and interpolation.

[0136] 3. Dynamically synchronized precipitation database: Combine weather forecasts and monitoring data to update precipitation data in real time, and dynamically synchronize the global precipitation database.

[0137] IV. Flood and waterlogging forecast covering all elements of the national territory

[0138] The precipitation data is coupled with the constructed digital twin watershed system, and the hydrological and hydrodynamic simulation model of the coupled national spatial digital twin watershed is used to realize the rolling real-time forecast of flood and waterlogging conditions in urban space, township space, village space, ground space, and underground space covering the entire national spatial range. The specific steps include the following:

[0139] 1. Based on the precipitation data and boundary conditions of the selected return period, the digital flood simulator of the whole area and all elements of the national space is driven to simulate the basin runoff generation and convergence process, flood evolution trend, and waterlogging inundation trend under the forecast rainstorm conditions, measured rainstorm conditions, designed precipitation conditions, and excessive precipitation conditions. The selected return periods include: 1 year, 2 years, 3 years, 5 years, 10 years, 20 years, 30 years, 50 years, 100 years, 200 years, 300 years, 500 years, 1000 years, and 2000 years.

[0140] 2. Combined with the simulation of flood and waterlogging, real-time forecast and visualization are provided to show the flood and waterlogging conditions in urban space, township space, village space, ground space and underground space at different rainstorm durations in the future of 0.5h, 1h, 2h, 3h, 4h, 5h, 6h, 7h, 8h, 9h, 10h, 12h, 1d, 2d, 3d, 4d, 5d and 10d.

[0141] 5. Flood and waterlogging warning and decision-making support covering all elements of the national territory

[0142] According to the flood and waterlogging warning level standards, intelligent identification of the flood and waterlogging risk levels encountered by natural elements such as mountains, water, forests, fields, lakes, grass, and sand, as well as social elements such as people, vehicles, roads, land, buildings, and facilities within the scope of the national territory, and real-time rolling generation of flood and waterlogging warning information based on real-time upstream and downstream flood water conditions and rainfall conditions, and visual warnings to display flood and waterlogging risk locations and disaster conditions. The specific steps include the following:

[0143] 1. Determine flood and waterlogging warning levels: Compare the relationship between the defense water level, warning water level, guaranteed water level, flood control high water level, flood control limit water level and simulated water level of rivers, lakes, reservoirs and embankments, comprehensively consider the simulated inundation range, inundation depth and water retreat time of urban space, township space, village space, ground space and underground space, and combine the real-time monitoring data of floods and waterlogging. Divide the flood and waterlogging warnings within the national territory into four levels from light to heavy, namely blue warning, yellow warning, orange warning and red warning.

[0144] 2. Assessment of flood and waterlogging disasters covering the entire region and all elements: Combined with the flood and waterlogging water conditions covering the entire region simulated in step 4, simulate the flood attacks and waterlogging encountered by natural elements such as mountains, water, forests, fields, lakes, grass, and sand after different durations, as well as social elements such as people, vehicles, roads, land, buildings, and facilities, to determine the flood and waterlogging risks and potential disasters encountered by different elements.

[0145] 3. Flood and waterlogging warning: Based on the flood and waterlogging risks and potential disasters encountered by the entire region and all elements obtained through simulation and analysis, combined with the real-time monitoring data and disaster risks of floods and waterlogging, flood and waterlogging warnings that may be encountered within the entire urban space, township space, village space, ground space, and underground space are issued, and warnings are issued in real time and on a rolling basis for social elements such as people, vehicles, roads, land, buildings, and facilities; the flood and waterlogging warning information mainly includes the flood and waterlogging warning level, and includes the areas, time, facilities, disaster forms, and suggestions for responding to floods and waterlogging where floods and waterlogging disasters may occur.

[0146] 4. Decision support for flood and waterlogging disasters: Based on simulated warnings and real-time monitoring of flood and waterlogging conditions, flood and waterlogging warning information is generated in real time for public management and decision-making departments, providing visual warnings for urban decision-making departments and displaying potential flood and waterlogging risk locations and potential disaster-causing conditions. It also provides technical support for urban flood and waterlogging emergency management and scientific decision-making, such as river basin flood control and waterlogging dispatching, river and lake inspection and flood prevention, personnel transfer and resettlement, urban traffic control, rescue force deployment, emergency plan formulation, and post-disaster recovery and reconstruction.

[0147] 5. Optimization of early warning decisions: For flood disaster events where the actual flood and waterlogging disaster scenarios exceed the flood and waterlogging warnings, a comprehensive comparison is made between the measured flood and waterlogging disaster scope and extent, and the parameter selection and early warning decisions of the digital flood simulator for the entire territory and all elements are corrected, updated and optimized.

Claims

1. A flood and waterlogging early warning method for the entire land space based on digital twins, characterized by: The following steps are involved: Step 1: Build a digital twin background database of all elements of the national land space; Input precipitation data, river and lake water system data, hydrological data, and underlying surface topography data of different recurrence periods within the target national land space, and store them in the digital twin background database; at the same time, dynamically connect the meteorological data, national land space development and construction data, and the hydrological and hydraulic condition data of the upstream and downstream of the basin around the national land space within the target national land space, and transmit them in real time to the constructed digital twin background database; Step 2: Construct a digital twin river basin covering the entire territory of the country: Based on the main and tributary rivers of rivers and lakes, water conservancy projects, underlying surface topography data, and urban and rural drainage systems, divide and construct river basins covering the entire territory of the country, conduct digital mapping of all elements of the physical river basins, natural elements, and economic and social information within the territory of the country, and construct a digital twin river basin system; construct a digital flood simulator for the entire territory of the country coupled with the digital twin river basin; Step 3: Build a precipitation database: Generate a precipitation database with different return periods covering the entire national space by statistically analyzing historical precipitation data and combining it with regional rainstorm atlases, hydrological manuals, and multi-source precipitation forecast products; Dynamically update and optimize the precipitation database in combination with meteorological forecast data to achieve dynamic synchronization between precipitation data and the digital twin basin; Step 4: Flood and waterlogging forecast covering the entire national territory: Couple precipitation data with the constructed digital twin river basin system to build a hydrological and hydrodynamic simulation model of the national territory digital twin river basin, and simulate and forecast flood and waterlogging conditions in real time; Step 5. Flood and waterlogging warning and decision support covering the entire territory and all elements of the national space: Based on the flood and waterlogging warning level standards, intelligently identify the flood and waterlogging risk level within the national space, generate flood and waterlogging warning information in real time and rolling according to the real-time flood water conditions and rainfall conditions, and visualize the warning to display the flood and waterlogging risk location and disaster conditions; Step 2 The construction of the digital flood simulator for the entire land space includes the following steps: S1. Construct a hydrological and hydrodynamic model covering the entire national territory: Based on the digital twin basin and precipitation database data, simulate the hydrological and hydrodynamic processes of the river and lake systems and flood inundation range in the basin. By comparing the deviations between the hydrological measured data, planning, design, and construction plans and the simulation results, calibrate and optimize the parameters selected for the constructed hydrological and hydrodynamic model, intelligently perceive and identify the historical flood disaster data of remote sensing and aerial photography, and deeply learn and dynamically optimize the constructed hydrological and hydrodynamic model covering the entire national territory; S2. Construct boundary condition module: Comprehensively consider the upstream and downstream flood control requirements of the basin where the national space is located, based on the unified flood control and waterlogging prevention standards of river and lake systems, urban spaces, township spaces, village spaces, ground spaces, and underground spaces, combined with the above-mentioned hydrological and hydrodynamic model covering the entire national space, dynamically optimize the hydraulic boundary conditions of upstream water inflow, downstream water discharge, embankment engineering, inundation depth, and waterlogging retreat time of the river and lake systems in the model, and construct a digital boundary condition module that truly perceives and reflects the hydraulic relationship between natural elements and social elements in the national space and the digital twin basin; S3. Construct a panoramic simulation model: Based on the simulated identification of floods and flooding in river and lake systems, urban spaces, township spaces, village spaces, ground spaces, and underground spaces, visualize and simulate, and quantitatively present flood and waterlogging disaster scenarios in the entire national territory, and accurately support flood and waterlogging disaster prevention and relief decisions; and support the formulation of the most optimized flood control and waterlogging prevention plan through prior simulation; Step three specifically includes the following steps: S1. Determine the rainstorm data with different recurrence periods in the region: Based on the national, provincial, municipal and county multi-level meteorological forecast platforms and meteorological stations, statistically analyze the historical rainstorm precipitation data in the region to form a basic measured rainstorm data series; collect data from regional rainstorm atlases, hydrological manuals and statistical yearbooks, and analyze and obtain the rainstorm data series at the data level by means of map reference and data interpolation; at the same time, compare with regional planning, design, construction and measured data, and calibrate the regional rainstorm data with different recurrence periods as the basis for simulated precipitation; S2. Determine the rainstorm data covering the entire area: For areas without measured or statistical rainstorm data, based on the obtained rainstorm data discrete points with different return periods of 10 minutes, 1 hour, 3 hours, 6 hours, 24 hours, and 3 days in the area, use the Thiessen polygon method to construct a rainstorm data polygon network covering the entire area, and draw lines to interpolate to obtain the rainstorm data covering the entire area; The construction of a polygonal network of rainstorm data covering the entire domain includes the following steps: (1) Constructing a triangulated network of rainstorm data for the entire region: Assume that the rainstorm data acquired in the region are discrete points, and divide the discrete points of rainstorm data in the region into k Adjacent triangulated networks, k is the number of discrete points of rainstorm data; (2) Numbering of the discrete points and triangulated networks of rainstorm data: Sort the triangles adjacent to each discrete point in a clockwise or counterclockwise direction; let the discrete point be f， Find out f A triangle with vertices set to E ; Take the triangle E remove f Another vertex other than e， And at the same time find the last vertex of the triangle g ; then the next triangle must be fg The sides are triangles. G ;triangle G The other vertex of h , then the next triangle is fh Repeat this process until you return to f side; (3) Constructing a polygonal network of global rainstorm data: Draw the center of the circumscribed circle of each rainstorm data triangle, and connect the centers of the circumscribed circles of the adjacent triangles of each rainstorm data discrete point to obtain a polygonal network of global rainstorm data.

2. The method for flood and waterlogging prevention warning for the entire territory and all elements based on digital twins according to claim 1 is characterized by: The SCE-UA algorithm is combined with hydrological measured data, planning, design, and construction schemes to calibrate and optimize the parameters selected for the hydrological and hydrodynamic model. The specific process of parameter optimization and calibration includes the following steps: (1) Generate initial parameter set: Based on the SCE-UA algorithm, set the initial hydrological and hydrodynamic parameters, number of iterations, model convergence criteria, and likelihood function of model optimization. According to the hydrological measured data, planning, design, and construction plan data, the Nash model for flood and waterlogging is calibrated based on the moment method. n , k Parameters; determine the priority range of parameter values ​​and generate the initial value of the parameter group; the likelihood function S ( D ) and the root mean square error RMSE Can be expressed as: ； Where: D represents the shape parameter; N Indicates the total number of simulated floods and waterlogging; n Indicates the moment of simulating floods and waterlogging; Q sim,n , Q mea,n Respectively indicate time n The hydrological simulation value, measured value and design value at that time; (2) Solving the likelihood function and parameter optimization; Substitute the initial values ​​of the above parameter group into the Nash model, deduce the runoff process of flood water in the basin and solve the likelihood function, feed the likelihood function back to the SCE-UA algorithm to optimize the parameter group, iterate this step until the set model convergence criterion is met, and select the optimized optimal model parameter group to construct the hydrological and hydrodynamic model.

3. The method for flood and waterlogging prevention in the entire territory and in all elements of the national land space based on digital twin according to claim 1 is characterized by: Step 4 specifically includes the following steps: S1. Based on the precipitation data and boundary conditions of the selected return period, the digital flood simulator for the entire national space is driven to simulate the runoff generation and convergence process, flood evolution and waterlogging inundation of the basin under the forecast rainstorm conditions, measured rainstorm conditions, designed precipitation conditions and excessive precipitation conditions; S2. Combined with the simulation of flood and waterlogging, real-time forecast and visualization of flood and waterlogging conditions in urban, township, village, ground and underground spaces during different rainstorm durations are provided.

4. The method for flood and waterlogging prevention warning for the entire territory and all elements based on digital twins according to claim 1 is characterized by: Step 5 The specific steps include: S1. Determine flood and waterlogging warning levels: Compare the relationship between the defense water level, warning water level, guaranteed water level, flood control high water level, flood control limit water level and simulated water level of rivers, lakes, reservoirs and embankments, comprehensively consider the simulated inundation range, inundation depth and water withdrawal time of urban space, township space, village space, ground space and underground space, and combine the real-time monitoring data of flood and waterlogging. Divide the flood and waterlogging warning within the national territory into four levels from light to heavy, namely blue warning, yellow warning, orange warning and red warning; S2. Assessment of flood and waterlogging conditions covering the entire region and all elements; Combined with the flood and waterlogging conditions covering the entire region simulated in step 4, simulate the flood invasion and waterlogging inundation encountered by natural elements such as mountains, water, forests, fields, lakes, grass, and sand after different durations, and social elements such as people, vehicles, roads, land, buildings, and facilities, and assess the flood and waterlogging risks and potential disasters encountered by different elements; S3. Flood and waterlogging warning: Based on the flood and waterlogging risks and potential disasters encountered by all elements in the entire region obtained through simulation and research, combined with the real-time monitoring data and disaster risks of floods and waterlogging, warnings for floods and waterlogging that may be encountered in the entire urban space, township space, village space, above-ground space, and underground space are issued, and warning information is issued in real time and on a rolling basis for social elements such as people, vehicles, roads, land, buildings, and facilities; S4. Decision support for flood and waterlogging disasters: Based on simulated early warnings and real-time monitoring of flood and waterlogging conditions, flood and waterlogging early warning information is generated in real time for public management and decision-making departments, providing visual early warnings for urban decision-making departments and displaying potential flood and waterlogging risk locations and potential disaster conditions, providing technical support for river basin flood control and waterlogging dispatching, river and lake inspection and flood control, urban traffic control, personnel transfer and resettlement, rescue force deployment, emergency plan formulation, and post-disaster recovery and reconstruction urban flood control and waterlogging emergency management and scientific decision-making; S5. Optimization of early warning decisions: For flood and waterlogging disaster events where the actual flood and waterlogging disaster scenarios exceed the flood and waterlogging warnings, a comprehensive comparison is made between the measured flood and waterlogging disaster scope and extent, and the parameter selection and early warning decisions of the digital flood simulator for the entire territory and all elements are corrected, updated and optimized.

5. A support system for the land space all-domain all-element flood and waterlogging early warning method based on digital twins as claimed in claim 1, characterized in that: The system includes an intelligent perception unit for all elements of the national space, a digital twin system unit for all elements of the national space, a flood and waterlogging forecast unit for all elements of the national space, and a flood and waterlogging warning and decision support unit for all elements of the national space; among which: The intelligent perception unit of the whole area and all elements of the national space can be based on big data and the Internet of Things to intelligently perceive and integrate meteorological data, river and lake water system data, hydrological data, underlying surface topography data, national space planning data, national space development and construction data, and social and economic data covering the whole area and all elements of the national space, dynamically perceive and transmit and store them in real time to the digital twin background database; The digital twin system unit of the whole-domain and all-element national land space includes the digital twin background database of the whole-domain and all-element national land space and the digital twin river basin of the whole-domain and all-element national land space; among them, the digital twin background database of the whole-domain and all-element national land space can dynamically connect with the regional meteorological and hydrological data and national land space data to provide a background database for the digital twin river basin of the whole-domain and all-element national land space; the digital twin river basin of the whole-domain and all-element national land space can generate the digital mapping and structure of the natural elements, social elements, hydrological and meteorological elements, and geographical terrain of the river basin national land space based on the digital twin background database of the whole-domain and all-element national land space and relying on the digital flood simulator of the whole-domain and all-element national land space; The flood and waterlogging forecasting unit for the entire land space and all elements can utilize the precipitation database with different return periods covering the entire land space, couple and drive the digital flood simulator of the digital twin watershed of the land space, and realize the rolling real-time forecast of flood and waterlogging conditions in urban space, township space, village space, ground space and underground space covering the entire land space; The flood and waterlogging warning and decision-making support unit for the entire area and all elements of the national land space can assess the flood and waterlogging risks and potential disasters encountered by the entire area and all elements based on the forecast results of the flood and waterlogging forecast unit for the entire area and all elements of the national land space, and issue flood and waterlogging warning information based on the flood and waterlogging warning level.

6. The support system for the flood and waterlogging prevention early warning method for the entire land space based on digital twin according to claim 5 is characterized by: The precipitation database is a high-precision precipitation database obtained by integrating historical precipitation data, regional rainstorm atlas, hydrological manuals, satellite monitoring, ground measurement, radar monitoring data, and dynamically updating and optimizing with real-time weather forecast data.

7. The support system for the flood and waterlogging prevention early warning method for the entire land space based on digital twin according to claim 5 is characterized by: The national land space all-domain all-element intelligent perception unit includes a meteorological perception and dynamic update module, a river and lake water system information perception and dynamic update module, a hydrological and hydraulic element perception and dynamic update module, an underlying surface information perception and dynamic update module, a national land space information perception and dynamic update module, a social and economic information perception and dynamic update module, and a natural information perception and dynamic update module; wherein: The meteorological perception and dynamic update module is used for measured meteorological data, historical meteorological data, satellite meteorological forecast data, radar monitoring meteorological data, rainstorm atlas meteorological data, hydrological manual meteorological data, and statistical yearbook meteorological data; The river and lake water system information perception and dynamic update module is used to obtain and update river and lake water system census survey data, river and lake water system planning and design data, river and lake water system remote sensing data, river and lake water system aerial photography data, national land space basic information platform and land survey data; The hydrological and hydraulic element perception and dynamic update module is used to obtain and update flow data, water level data, upstream and downstream flow and hydrological data boundary conditions of the basin, flood depth data, levee data, pump station data, flood storage area data, flood storage area data, dam data, flood control project data, flood control project data, and cross-regional flood control data; The underlying surface information perception and dynamic update module is used to obtain and update terrain data, geological data, soil data, vegetation data, urban space data, township space data, village space data, ground space data, and underground space data; The national land space information perception and dynamic update module is used to obtain and update national land survey data, national land space basic information platform data, national land space planning data, natural resources survey and monitoring data, land use type data, and remote sensing data; The social and economic information perception and dynamic update module is used to obtain and update population data, construction area data, village data, housing data, farmland data, flood control and waterlogging prevention facilities data, highway facilities data, railway facilities data, subway facilities data, airport facilities data, medical facilities data, power facilities data, water supply and drainage facilities data, sanitation facilities data, gas facilities data, communication facilities data, industrial and mining enterprise data, public service facilities data, tide prevention facilities data, and cultural relics data; The natural information perception and dynamic update module is used to acquire and update mountain data, water data, forest data, farmland data, lake data, sea data, grassland data, desert data, ice surface data, and snow area data.

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