A method and system for constructing a watershed digital twin based on multi-source data fusion

Abstract

The present application provides a method and system for constructing a digital twin of a river basin based on multi-source data fusion. Among them, the method is applied to the technical field of constructing a digital twin of a river basin, and includes obtaining water level monitoring data, surface flood evolution path data, meteorological forecast rainfall data, and terrain slope change data; fusing rainfall and terrain slope data to generate a runoff distribution relationship, and combining the water level mutation amplitude to identify high-risk areas. Establish a preliminary digital twin framework, and use the reverse feedback of flood path and water level data to generate associated characteristic parameters. Input meteorological data and construct a twin model based on the catchment boundary conditions to simulate flood direction anomalies and inundation warning signals. According to the warning spatio-temporal distribution and terrain elevation data, generate emergency plans such as flood diversion sluice scheduling and activation of flood storage and detention areas to achieve risk classification and control. The technical solution provided by the present application improves the accuracy of flood inundation prediction and the efficiency of emergency response, and provides precise decision-making support for river basin flood control.

Description

Technical Field

[0001] This application relates to the technical field of basin digital twin construction, and particularly to a method and system for constructing a basin digital twin based on multi-source data fusion. Background Art

[0002] In basin flood control and water resources management, it is necessary to grasp the flood evolution trend in real time, accurately predict the inundation range, and optimize the emergency dispatch. There is an urgent need to construct a high-precision digital twin system that can fuse multi-source heterogeneous data (such as meteorology, terrain, hydrology), dynamically simulate the interaction process between the river channel and surface runoff, and quickly generate adaptive control strategies to cope with the spatio-temporal uncertainty of floods under extreme weather.

[0003] Currently, the mainstream solution uses a satellite remote sensing data-driven static hydrological model. By integrating multi-spectral satellite images (such as Sentinel-2) and historical hydrological databases, a two-dimensional flood diffusion model with fixed parameters is constructed. Based on the inundation range retrieved by remote sensing and rainfall forecast data, the hydrodynamic equations (such as the shallow water equations) are used to simulate flood evolution, and the prediction results are visualized through a GIS platform. Some systems introduce machine learning algorithms (such as LSTM) to optimize the rainfall-runoff relationship and improve the short-term forecasting accuracy.

[0004] This solution has three core defects. First, it relies on the periodic update of satellite images (usually 6 - 12 hours), making it difficult to capture the rapid changes in floods (such as the sudden change in flow velocity at the moment of levee breach). Second, the fixed parameters cannot dynamically adapt to the topographic micro-geomorphology (such as river channel siltation, vegetation cover changes), resulting in the accumulation of simulation errors. Third, there is a lack of a real-time monitoring data feedback mechanism, making it impossible to achieve a closed-loop of "monitoring, simulation, regulation", and the generation of emergency plans lags behind the actual disaster process. Summary of the Invention

[0005] This application provides a method and system for constructing a basin digital twin based on multi-source data fusion to solve the problem of the lack of dynamic fusion of multi-source data and accurate early warning ability in the prior art.

[0006] In a first aspect, this application provides a method for constructing a basin digital twin based on multi-source data fusion, including:

[0007] Based on the interaction relationship between the river channel structure and surface runoff in the river hydrological interaction scenario, obtain the water level monitoring data of the river channel, the surface flood evolution path data, the meteorological forecast rainfall data, and the terrain slope change data;

[0008] Fuse the meteorological forecast rainfall data with the terrain slope change data to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and determine the high-risk areas of flood evolution in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data. The abnormal water level change characteristics include the amplitude of water level mutation at key monitoring sections in the river channel.

[0009] Establish a preliminary framework of the watershed digital twin model corresponding to the high-risk areas of flood evolution, generate associated characteristic parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjust the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated characteristic parameters.

[0010] Input the meteorological forecast rainfall data into the adjusted preliminary framework, and combine the flood interaction parameters with the watershed catchment boundary conditions in the terrain slope change data to construct a watershed digital twin model including abnormal change data of surface flood evolution direction and flood inundation warning signals in the river channel.

[0011] Generate a flood emergency regulation plan according to the spatio-temporal distribution characteristics of the flood inundation warning signals in the watershed digital twin model, in combination with the river channel terrain elevation data.

[0012] Optionally, the establishment of the preliminary framework of the watershed digital twin model corresponding to the high-risk areas of flood evolution, generating associated characteristic parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjusting the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated characteristic parameters includes:

[0013] Based on the spatial distribution range of the high-risk areas of flood evolution, establish a preliminary framework of the watershed digital twin model including the river channel topological structure and the surface flood evolution path.

[0014] Trace back the continuous change process of the flood evolution path data in reverse chronological order, and at the same time match the spatial distribution state of the water level monitoring data at the river channel monitoring sections. According to the reverse tracing results and matching results, construct the spatio-temporal association characteristics between the flood velocity mutation points in the flood evolution path data and the abnormal fluctuation points in the water level monitoring data.

[0015] Perform multi-scale combination of the distribution density of the flood velocity mutation points and the water level fluctuation amplitude in the spatio-temporal association characteristics, establish an associated characteristic parameter reflecting the correlation between the flood evolution path change pattern and the water level response intensity, and map the associated characteristic parameter to the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework.

[0016] According to the mapping result, in combination with the change rate of the flood evolution path data at the boundary of the high-risk flood area and the continuous fluctuation amplitude of the water level monitoring data, the flood interaction parameters are dynamically updated, and spatial compensation is performed on the updated flood interaction parameters through the spatial distribution range to adjust the preliminary framework.

[0017] Optionally, the fusion of the meteorological forecast rainfall data and the terrain slope change data to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and determining the high-risk flood evolution area in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data, includes:

[0018] Dynamically allocate the rainfall intensity values in the meteorological forecast rainfall data according to the spatial distribution law of the terrain slope change data to form the rainfall spatial coverage density with the slope change rate as the weight factor;

[0019] Based on the guiding effect of the slope change rate on the surface flood flow direction, migrate the rainfall intensity values in the high slope change rate areas in the rainfall spatial coverage density to the low-lying confluence areas, and combine the distribution density of the tributary confluence nodes in the surface flood evolution path to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change;

[0020] Calculate the difference between the water level mutation amplitude in the abnormal water level change characteristics and the flood runoff intensity of the corresponding geographical area in the distribution relationship, mark the abnormal areas in the distribution relationship where the slope change rate does not match the flood runoff intensity, and perform gradient compensation on the flood runoff intensity values in the abnormal areas according to the slope change rate based on the product coefficient of the difference and the slope change rate;

[0021] Overlay the compensated flood runoff intensity values with the spatial distribution of the abnormal fluctuation points in the water level monitoring data, and screen out the overlapping areas that simultaneously meet the slope change rate threshold, the flood runoff intensity compensation value, and the water level anomaly amplitude as the high-risk flood evolution areas.

[0022] Optionally, inputting the meteorological forecast rainfall data into the adjusted preliminary framework, and combining the flood interaction parameters and the basin catchment boundary conditions in the terrain slope change data to construct a basin digital twin model including surface flood evolution direction abnormal change data and river flood inundation warning signals, includes:

[0023] Based on the basin catchment boundary conditions in the terrain slope change data, perform spatial cutting on the adjusted preliminary framework to divide the surface flood evolution direction calculation units based on the terrain slope change rate;

[0024] Within the surface flood evolution direction calculation unit, based on the distribution characteristics of slope turning points in the terrain slope change data, determine the initial offset angle of the surface flood evolution direction, and correct the initial offset angle in combination with the flood interaction parameters to generate a corrected path for abnormal changes in the flood evolution direction;

[0025] Couple the critical threshold of the river channel water level in the flood interaction parameters with the distribution density of the river channel monitoring sections in the terrain slope change data. When the distribution density of the monitoring sections exceeds the critical threshold corresponding to the slope change rate, generate a flood inundation warning level based on the coupling result;

[0026] Perform a spatial overlay of the corrected path, the flood inundation warning level, and the meteorological forecast rainfall data, and perform a three-dimensional flood evolution topology reconstruction on the spatial overlay result according to the boundary conditions of the basin catchment area to generate a basin digital twin model that simultaneously includes the mutation trajectory of the surface flood evolution direction and the flood inundation warning hot area of the river channel.

[0027] Optionally, trace back the continuous change process of the flood evolution path data in reverse chronological order, and simultaneously match the spatial distribution state of the water level monitoring data at the river channel monitoring sections. According to the reverse tracing result and the matching result, construct the spatio-temporal correlation characteristics between the flood velocity mutation points in the flood evolution path data and the abnormal fluctuation points in the water level monitoring data, including:

[0028] Traverse the continuous records of the flood velocity in the flood evolution path data in reverse chronological order, identify the mutation points where the change amplitude of the flood velocity exceeds the preset mutation threshold within adjacent timestamps, and record the occurrence time of each mutation point and the corresponding river channel position coordinates as the reverse tracing result;

[0029] Screen the monitoring sections in the water level monitoring data that are closest to the river channel position coordinates in the reverse tracing result, extract the cross-section water level change data within a preset time range before and after the occurrence time, and mark the abnormal fluctuation points where the water level change amplitude exceeds the historical mean as the matching result;

[0030] Align and match the occurrence time in the reverse tracing result with the maximum fluctuation time of the abnormal fluctuation points in the matching result to construct a set of spatio-temporal correlation pairs between the mutation points and the abnormal fluctuation points;

[0031] Combine the velocity change amount of the mutation points, the water level fluctuation amplitude of the abnormal fluctuation points, and the time interval between the two in each correlation pair in the spatio-temporal correlation pair set into spatio-temporal correlation characteristics.

[0032] Optionally, coupling the critical threshold of the river water level in the flood interaction parameters with the distribution density of the river monitoring sections in the terrain slope change data, and when the distribution density of the monitoring sections exceeds the critical threshold corresponding to the slope change rate, generating a flood inundation warning level based on the coupling result, including:

[0033] Dividing the terrain slope change data into regional units according to the slope change rate, counting the total number of monitoring sections in each regional unit, and calculating the section distribution density per unit area;

[0034] According to the correlation law between the slope change rate and the section distribution density, setting the critical values of the section density corresponding to different slope change rate intervals;

[0035] In the regional unit, coupling the critical threshold of the river water level in the flood interaction parameters with the section distribution density according to a preset ratio. If the section distribution density exceeds the critical value of the section density, calculating the product of the water level difference between this region and the adjacent region and the slope change rate based on the coupling result;

[0036] Dividing into three levels of flood inundation warning levels according to the product value, where the first-level warning corresponds to the highest interval of the product value, the second-level warning corresponds to the medium interval, and the third-level warning corresponds to the lowest interval.

[0037] Optionally, generating a flood emergency regulation plan based on the spatio-temporal distribution characteristics of the flood inundation warning signals in the basin digital twin model and combining the river channel terrain elevation data, including:

[0038] In the basin digital twin model, screening the continuous regions where the signal density of the flood inundation warning signals exceeds a preset threshold, marking them as high-risk flood inundation areas, and extracting the time duration of the high-risk areas;

[0039] Matching the spatial range of the high-risk flood inundation areas with the river channel terrain elevation data, identifying the vulnerable sections within the high-risk areas where the elevation is lower than the flood warning water level, and marking them as key sections for flood diversion and discharge;

[0040] Based on the elevation gradient direction of the key sections for flood diversion and discharge, generating a multi-level flood diversion channel plan covering the upstream and downstream, where the capacity of the flood diversion channel is positively correlated with the elevation difference;

[0041] Adjusting the control instructions of the flood diversion gates at the key river channel sections within the high-risk areas through the time duration and the capacity change trend of the multi-level flood diversion channel plan, and generating a flood emergency regulation plan including the enabling rules of the flood diversion channels and the gate regulation time sequence according to the adjusted control instructions.

[0042] In a second aspect, the present application provides a system for constructing a digital twin of a river basin based on multi-source data fusion, including:

[0043] An acquisition module, configured to acquire water level monitoring data of the river channel, surface flood evolution path data, meteorological forecast rainfall data, and terrain slope change data based on the interaction relationship between the river channel structure and surface runoff in the river hydrological interaction scenario;

[0044] A fusion module, configured to fuse the meteorological forecast rainfall data with the terrain slope change data to generate a distribution relationship between rainfall and flood runoff based on the terrain slope change, and determine high-risk flood evolution areas in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data, where the abnormal water level change characteristics include the amplitude of water level mutation at key monitoring sections in the river channel;

[0045] An adjustment module, configured to establish a preliminary framework of the digital twin model of the river basin corresponding to the high-risk flood evolution area, generate associated feature parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjust the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated feature parameters;

[0046] A construction module, configured to input the meteorological forecast rainfall data into the adjusted preliminary framework, and construct a digital twin model of the river basin including abnormal change data of surface flood evolution direction and flood inundation warning signals of the river channel in combination with the flood interaction parameters and the basin catchment boundary conditions in the terrain slope change data;

[0047] A generation module, configured to generate a flood emergency regulation plan according to the spatio-temporal distribution characteristics of the flood inundation warning signals in the digital twin model of the river basin, in combination with the river channel terrain elevation data.

[0048] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for constructing a digital twin of a river basin based on multi-source data fusion as described in the first aspect above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium, storing a computer program, where when the computer program is executed by a computer, it implements a method for constructing a digital twin of a river basin based on multi-source data fusion as described in the first aspect.

[0050] In an embodiment of the present application, based on the interactive relationship between the river channel structure and surface runoff in the river hydrological interaction scenario, water level monitoring data of the river channel, surface flood evolution path data, meteorological forecast rainfall data and terrain slope change data are obtained; the meteorological forecast rainfall data and the terrain slope change data are fused to generate a distribution relationship between rainfall and flood runoff based on terrain slope changes, and the high-risk area for flood evolution in the distribution relationship is determined according to the abnormal water level change characteristics of the water level monitoring data, and the abnormal water level change characteristics include the amplitude of water level mutation at the key monitoring section in the river channel; a digital twin model of the watershed corresponding to the high-risk area for flood evolution is established. A preliminary framework is provided, and associated characteristic parameters are generated through the reverse feedback relationship between the flood evolution path data and the water level monitoring data. The flood interaction parameters between the river structure and the surface runoff in the preliminary framework are adjusted based on the associated characteristic parameters; the meteorological forecast rainfall data is input into the adjusted preliminary framework, and a watershed digital twin model including abnormal change data of the surface flood evolution direction and river flood inundation warning signals is constructed by combining the flood interaction parameters with the watershed catchment boundary conditions in the terrain slope change data; a flood emergency control plan is generated according to the spatiotemporal distribution characteristics of the flood inundation warning signals in the watershed digital twin model and the river terrain elevation data.

[0051] The technical solution of this application has the following beneficial effects:

[0052] Through the coordinated collection of water level sensors, satellite remote sensing and meteorological models, a high-frequency updated three-dimensional monitoring network is built to achieve real-time perception of the river's flood-carrying capacity and flood diffusion trends, ensuring high-precision alignment of data in time and space dimensions; rainfall intensity is dynamically allocated based on terrain slope, and high-risk areas are identified in combination with abnormal water level mutation characteristics, significantly improving the spatial resolution of runoff simulation and the positioning accuracy of risk areas; through the reverse feedback mechanism of flood path and water level data, key parameters such as river roughness and floodplain storage capacity are dynamically optimized to enhance the model's dynamic adaptability to complex hydrological interaction processes; the catchment area boundary and meteorological data are integrated to accurately capture abnormal changes in flood flow direction, build a three-dimensional visual early warning system, and significantly shorten the response time and decision-making delay of inundation range prediction; based on spatiotemporal clustering and multi-objective optimization algorithms, a hierarchical control strategy for the linkage of diversion gates and flood storage areas is generated to effectively improve the water level control efficiency after the execution of emergency commands.

[0053] Furthermore, a digital twin framework is constructed based on the spatial distribution of flood high-risk areas. By reverse-tracking the spatio-temporal correlation between the flood evolution path and water level fluctuations, multi-scale feature parameters are extracted to dynamically adjust the model interaction parameters, and the global consistency of the framework is optimized in combination with a spatial compensation mechanism. Through dynamic parameter optimization and spatial compensation, the accuracy of floodplain flood storage capacity prediction and channel roughness calibration is significantly improved, the simulation time of the flood evolution path in high-risk areas is greatly shortened, high-timeliness and highly adaptable decision-making basis are provided for flood diversion scheduling, and the flood control response efficiency of the basin is comprehensively enhanced.

[0054] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 The flowchart of a method for constructing a basin digital twin based on multi-source data fusion provided by the present application is shown;

[0057] Figure 2 The structural schematic diagram of a system for constructing a basin digital twin based on multi-source data fusion provided by the present application is shown;

[0058] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0060] In some processes described in the specification, claims and the above drawings of the present application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0061] Researchers found that current flood simulation technologies in river basins generally face bottleneck problems such as insufficient collaboration of multi-source data, static and rigid model parameters, and lagging emergency decision-making, resulting in low accuracy of flood evolution prediction, large deviation in the identification of high-risk areas, and weak adaptability of regulation schemes. Based on this, a method for constructing a digital twin of a river basin based on multi-source data fusion is provided. Specifically, by fusing meteorological forecast rainfall and terrain slope data to generate a dynamic runoff distribution relationship, high-risk areas are locked in combination with the characteristics of water level mutations; a digital twin framework is constructed and the model parameters are dynamically optimized based on the reverse feedback of flood path and water level data; finally, a hierarchical emergency plan is generated by combining three-dimensional warning signals and terrain elevation. This method can achieve centimeter-level water level perception (sensor accuracy ±1 cm) in the flood evolution process, hour-level dynamic calibration of the model (iterating once every 2 hours), and hour-level warning response (delay from data collection to warning release ≤60 minutes), meeting the actual technical conditions of wide-area monitoring scenarios and significantly improving the reliability of flood inundation prediction and the timeliness of emergency regulation under complex terrain.

[0062] The technical solution of this application can be applied to the scenario of constructing a digital twin model of a river basin.

[0063] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0064] Figure 1 The flowchart of a method for constructing a digital twin of a river basin based on multi-source data fusion is provided for the embodiments of the present application, as Figure 1 shown, and the method includes:

[0065] 101. Based on the interaction relationship between the river channel structure and surface runoff in the river hydrological interaction scenario, obtain the water level monitoring data of the river channel, the surface flood evolution path data, the meteorological forecast rainfall data, and the terrain slope change data;

[0066] In this step, the water level monitoring data is the water level elevation data (updated once per second, for example) collected in real time by water level sensors deployed at river cross-sections, reflecting the real-time flood conveyance capacity of the river. The surface flood evolution path data is the flood inundation range and flow direction data (updated once per hour, for example) obtained through satellite remote sensing or UAV aerial photography, characterizing the flood diffusion trend. The meteorological forecast rainfall data is the predicted data of rainfall intensity, duration, and spatial distribution in the next 6 hours within the basin, generated by a numerical weather prediction model. The terrain slope change data is a raster map of the slope change rate calculated based on the basin surface elevation data (resolution 1m×1m) obtained by airborne LiDAR.

[0067] In the embodiment of the present application, first, water level sensors (sampling frequency 1Hz) are deployed at key river cross-sections (such as tributary confluences, downstream of reservoirs), the flood evolution path is monitored collaboratively by a synthetic aperture radar satellite (SAR) and a multi-rotor UAV (spatial resolution 0.5m), the rainfall forecast data output by the WRF meteorological model is accessed (time resolution 15 minutes), and the terrain slope change rate is calculated by calling the basin digital elevation model (DEM); secondly, the water level data is processed by Kalman filtering to eliminate wave interference, the flood path data is processed by an image segmentation algorithm to extract the inundation boundary, and the rainfall data and terrain data are aligned to a unified grid (100m×100m) through spatial interpolation; finally, the above data is associated and stored in the basin database according to the spatio-temporal tags (UTC time + geographical coordinates) to support dynamic query and analysis.

[0068] A rainstorm flood event occurred in a certain river basin. Water level sensors were deployed at the main stream cross-section S1 (coordinates X: 120, Y: 350), and the water level was recorded in real time from 5.2m to 7.8m (warning water level 7.5m); the Sentinel-1 satellite monitoring showed that the flood evolved from northwest to southeast, and the inundation range reached 12 km²; the meteorological model predicted that the basin-wide rainfall would reach 50mm in the next 3 hours, and the maximum point rainfall would be 80mm / h; a slope change map was generated based on 1m resolution LiDAR data, showing a sharp drop in the slope in the downstream area (8%→2%). All data was associated through a spatio-temporal index and marked as "2023-07-15T14:00Z / Basin A".

[0069] 102. Fuse the meteorological forecast rainfall data and the terrain slope change data to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and determine the high-risk flood evolution area in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data, where the abnormal water level change characteristics include the water level mutation amplitude at the key monitoring cross-section in the river.

[0070] In this step, the distribution relationship between rainfall and flood runoff is based on the spatial distribution law of rainfall generated by changes in terrain slope into surface runoff (e.g., areas with slopes above 8% have fast runoff convergence speed, and depressions with slopes below 2% are prone to form temporary flood storage areas). The characteristic of abnormal water level changes is that the water level of key monitoring sections rises by more than 2 standard deviations of the historical mean for the same period in a short period of time (e.g., within 1 hour) (e.g., the water level suddenly rises by 1.5 meters), reflecting abnormal flood discharge capacity of the river. High-risk areas for flood evolution are overlapping areas with high runoff intensity and frequent water level mutations (e.g., river sections with >2 water level mutations per hour).

[0071] In the embodiments of the present application, firstly, the meteorological forecast rainfall data and the terrain slope change data are fused through the watershed hydrological model (SWAT) to generate a slope-oriented runoff distribution grid map (such as for every 5% increase in slope, the runoff velocity increases by 0.3 m / s); secondly, the sliding window Z-Score detection method (window length 1 hour) is applied to the water level monitoring data to mark the sections where the water level mutation amplitude exceeds the threshold (such as Z>3); finally, the river sections with runoff intensity>50 mm / h and water level mutation frequency>2 times / hour are screened through spatial hotspot analysis (Getis-Ord Gi*) to determine them as high-risk areas for flood evolution (such as the main stream S1-S2 section).

[0072] For example, continuing with the above example, first, the SWAT model integrates rainfall and terrain data to predict that the runoff intensity in the downstream depression area (slope 1.5%-2%) will reach 95mm / h, forming a temporary flood storage area; secondly, the Z-Score detection found that the water level of section S3 suddenly rose by 2.6m (Z=4.2) within 1 hour, far exceeding the historical average; thirdly, the hotspot analysis locked the downstream X:200-500 area (covering sections S1-S3) as a high-risk area, which is characterized by: runoff intensity>90mm / h, water level mutation frequency 3 times / hour, and satellites show that floods are rapidly evolving towards this area; finally, the X:200-500 river section is marked as a red high-risk area and pushed to the emergency command system, triggering the downstream reservoir pre-discharge and resident evacuation instructions.

[0073] 103. Establish a preliminary framework of the watershed digital twin model corresponding to the high-risk area of ​​flood evolution, generate associated characteristic parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjust the flood interaction parameters between the river structure and the surface runoff in the preliminary framework based on the associated characteristic parameters;

[0074] In this step, the reverse feedback relationship is established by analyzing the temporal correlation between the changes in flood evolution paths and sudden water level changes, and a dynamic impact link between surface runoff and river channel flood carrying capacity is constructed. The correlation characteristic parameters quantify the spatio-temporal correlation intensity between the changes in flood evolution speed and the amplitude of water level fluctuations. The flood interaction parameters are dynamic parameters defined in the model, such as channel roughness and flood storage capacity on floodplains, which are used to adjust the interaction intensity between surface runoff and the river channel.

[0075] In the embodiment of the present application, first, based on the spatial range of high-risk flood evolution areas (such as the X:200 - 500 river section), a preliminary framework including river channel cross-sections, levee structures, and surface runoff paths is constructed using a hydrodynamic modeling engine (such as HEC-RAS); second, the flood evolution path data is parsed in reverse chronological order, and a sliding window alignment mechanism (window length of 15 minutes) is used to perform time interpolation matching on satellite remote sensing flood evolution data (hourly level) and water level monitoring data (second level). Combining with a lightweight temporal convolutional network (TCN) to extract spatio-temporal correlation features, reducing the algorithm complexity and improving real-time performance; third, the matched spatio-temporal correlation features are input into a random forest regression model, and an online learning mechanism is introduced. Combining real-time flood evolution path data and hydrodynamic equations (such as the Saint-Venant equations) in the hydrodynamic modeling engine for hybrid calibration, and generating characteristic parameters reflecting the correlation intensity between flow velocity and water level according to the calibration results (such as when the flow velocity drops by 1 m / s, the water level rises by 0.8 m); finally, based on the gradient descent optimization algorithm, the characteristic parameters are mapped to the flood interaction parameters in the preliminary framework (such as adjusting the channel roughness coefficient from 0.025 to 0.035, and the flood storage capacity on the floodplain increases by 15%).

[0076] For example, continuing with the above example, first, a digital twin framework including the main river channel (width 80 m) and the floodplains on both sides is constructed based on the LiDAR topography and river channel cross-section data of the X:200 - 500 river section; second, the flood evolution data shows that at 15:00, the flow velocity suddenly drops from 3.2 m / s to 1.0 m / s at X:250, and the water level at cross-section S2 suddenly rises by 1.8 m (Z = 4.0) at 15:05. The DTW algorithm confirms that the time difference between the two is 5 minutes (less than the hydraulic conduction time of 6 minutes); third, the random forest model calculates the correlation intensity between flow velocity and water level to be 0.75 (when the flow velocity drops by 1 m / s, the water level rises by 0.75 m), adjusts the flood storage capacity on the floodplain from 500,000 m³ to 575,000 m³ (+15%), and the channel roughness from 0.028 to 0.032; finally, the updated digital twin framework is pushed to the flood forecasting system for subsequent emergency dispatching.

[0077] 104. Input the meteorological forecast rainfall data into the adjusted preliminary framework, and combine the flood interaction parameters with the basin catchment boundary conditions in the terrain slope change data to construct a basin digital twin model including data on abnormal changes in the direction of surface flood evolution and river channel flood inundation warning signals;

[0078] In this step, the boundary conditions of the watershed catchment are the watershed boundaries of sub-watersheds divided based on the Digital Elevation Model (DEM), which are used to constrain the flood simulation scope. The flood inundation warning signal is a risk alert triggered when the model predicts that the water level exceeds the dike design standard or the flow velocity exceeds the safety threshold.

[0079] In the embodiment of the present application, first, the real-time meteorological rainfall data is input into the adjusted digital twin framework, and the surface flood evolution direction is simulated based on the two-dimensional shallow water equations (SWE) to detect abnormal areas where the deviation from the terrain slope direction is >15°; second, according to the catchment boundary conditions, the adaptive mesh refinement technology (AMR) is used to divide the computational cells to ensure that the grid resolution at the boundary reaches 10m×10m; third, combined with the floodplain flood storage capacity in the flood interaction parameters, the water level and inundation range of the river channel are calculated through a hydrodynamic coupling model (such as MIKE FLOOD), and the inundation warning is triggered when the predicted water level exceeds the warning line or the flow velocity exceeds the threshold; finally, the abnormal flood direction trajectory (such as blue vector arrows) and the warning hotspots (such as red inundated blocks) are fused through a GPU-accelerated rendering engine to generate an interactive three-dimensional twin model.

[0080] For example, continuing with the above example, first, input the meteorological forecast data (rainfall peak of 60 mm / h in the next 2 hours), and the SWE model simulation shows that the flood direction in the river section X:300 - 400 deviates from the main river channel axis by 25° (abnormal change); second, based on the catchment boundary (sub-watershed W-05), grids are encrypted and generated, and it is detected that the floodplain flood storage capacity in this area has reached 575,000 m³ after adjustment (critical value of 600,000 m³); third, the MIKE FLOOD model predicts that the water level at section S4 will exceed the warning line by 1.2 m after 1 hour (design standard +1.0 m), triggering a red inundation warning; finally, the three-dimensional model marks the river section X:300 - 400 as an abnormal evolution area (blue arrow) and a high inundation risk area (red block), and pushes it to the flood control command platform to initiate the flood diversion sluice operation.

[0081] 105. Generate a flood emergency control plan based on the spatio-temporal distribution characteristics of the flood inundation warning signal in the watershed digital twin model and in combination with the river channel topographic elevation data.

[0082] In this step, the spatio-temporal distribution characteristics are the aggregation density of the flood inundation warning signal in space and the persistence in time. The river channel topographic elevation data is the river channel cross-section and surrounding topographic elevation data obtained through airborne LiDAR or underwater sounding, which is used to analyze the flood evolution trend and the flood diversion path planning. The flood emergency control plan is an integrated plan including instructions such as flood diversion sluice operation, activation of flood storage and detention areas, and allocation of emergency rescue materials, which is used to reduce the flood risk.

[0083] In the embodiments of the present application, first, a spatio-temporal clustering algorithm (ST-DBSCAN) is used to analyze early warning signals, and high-risk river sections with the density of early warning points exceeding the threshold (such as 8 per 5 square kilometers) and lasting for more than 2 hours are screened; secondly, based on the river channel terrain elevation data, a flood diversion path is generated through a hydrodynamic optimization model (such as NSGA-II) (such as opening the flood diversion sluice at X:300 and diverting 30% of the flow to the flood storage and detention area); thirdly, in combination with the early warning level (red / yellow / blue), an optimal regulation combination is dynamically selected through a multi-objective decision-making system (such as activating 2 flood storage and detention areas + deploying 3 emergency rescue teams during a red early warning); finally, the flood diversion path, the flood storage and detention area dispatching and the emergency rescue instructions are integrated to generate an emergency regulation plan with time and space coordination.

[0084] For example, continuing with the above example, first, ST-DBSCAN clustering shows that the early warning density in the river section of X:300-500 reaches 12 per 5 km² and lasts for 3 hours, which is marked as a red risk area; secondly, based on the LiDAR terrain data (the main river channel elevation is 5.2 m and the flood diversion channel elevation is 4.8 m), the NSGA-II model generates an optimal flood diversion plan: open the flood diversion sluice at X:350 and divert 25% of the flow (about 500 m³ / s) to the flood storage and detention area A; thirdly, according to the red early warning level, the multi-objective decision-making system synchronously issues the following instructions: open the floodgate of the flood storage and detention area A for flood storage (60% of the remaining capacity); deploy the emergency rescue team to the weak section of the dike at X:400; issue a secondary evacuation alert to the downstream residential area; finally, after the generated emergency regulation plan is executed for 2 hours, the water level in the river section of X:300-500 drops by 0.8 m, and the early warning is downgraded to yellow.

[0085] In steps 101-105, by constructing an integrated space-air-ground hydrological monitoring network, integrating multi-source information such as high-frequency water level data, satellite remote sensing images, weather forecasts and terrain slopes, a dynamically coupled digital twin model is established. Machine learning algorithms are used to optimize river channel parameters in real time, accurately identify high-risk areas of flood evolution, and based on three-dimensional visualization technology, intuitively present abnormal flow directions and inundation ranges. An optimal flood diversion scheduling plan is generated through intelligent optimization algorithms, significantly improving the timeliness of flood early warning and the accuracy of emergency regulation, and finally forming a closed-loop management from real-time monitoring, risk prediction to intelligent decision-making.

[0086] In some embodiments, to establish a preliminary framework of the basin digital twin model corresponding to the high-risk area of flood evolution, correlation feature parameters are generated through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and based on the correlation feature parameters, the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework are adjusted, including:

[0087] 201. Based on the spatial distribution range of the high-risk area of flood evolution, establish a preliminary framework of the basin digital twin model including the river channel topological structure and the surface flood evolution path;

[0088] In step 201, the river channel topology refers to the spatial connection relationship of the main river channel, tributaries, and river network nodes, while the surface flood evolution path represents the dynamic diffusion trajectory of floods within the basin.

[0089] In the embodiments of the present application, first, based on LiDAR terrain data and river channel cross-section measurement results, the spatial boundary of the high-risk area is delimited through a GIS platform; secondly, the Delaunay triangulation algorithm is used to construct the topological connection relationship of the river network; thirdly, the flood evolution path data obtained from satellite remote sensing is converted into vector trajectories; finally, through spatial overlay and fusion of the river channel topology and the surface path, a preliminary digital twin framework including three-dimensional terrain, river channel structure, and flood diffusion direction is formed.

[0090] 202. Reverse-trace the continuous change process of the flood evolution path data in chronological order, and at the same time match the spatial distribution state of the water level monitoring data at the river channel monitoring cross-sections. According to the reverse-tracing results and the matching results, construct the spatio-temporal correlation characteristics between the flood velocity mutation points in the flood evolution path data and the abnormal fluctuation points in the water level monitoring data;

[0091] In step 202, the spatio-temporal correlation characteristics refer to the quantitative relationship between flood velocity mutation and water level abnormal fluctuation in terms of time synchronization and spatial proximity.

[0092] In the embodiments of the present application, first, starting from the latest inundation boundary point, extract the velocity mutation points in the SAR satellite images in reverse chronological order (such as the velocity drops from 3.0 m / s to 1.5 m / s at X:200); secondly, synchronously extract the water level mutation data of cross-sections S1 - S3 during the corresponding period (such as the water level at S2 suddenly rises by 1.8 m at 15:05); thirdly, match the time difference (<5 minutes) and spatial distance (<100 m) between the two through the dynamic time warping (DTW) algorithm; finally, generate the spatio-temporal correlation characteristics and mark the strong correlation area between the velocity and the water level (such as the correlation strength R²>0.8 in the X:200 - 250 river section).

[0093] 203. Perform multi-scale combination of the distribution density of the flood velocity mutation points and the water level fluctuation amplitude in the spatio-temporal correlation characteristics, establish a correlation characteristic parameter reflecting the correlation between the flood evolution path change pattern and the water level response intensity, and map the correlation characteristic parameter to the flood interaction parameter between the river channel structure and the surface runoff in the preliminary framework;

[0094] In step 203, the correlation characteristic parameter is a coupling index quantifying the velocity mutation frequency and the water level fluctuation amplitude, and the flood interaction parameters include key hydraulic factors such as river channel roughness and flood storage capacity on the floodplain.

[0095] In the embodiments of the present application, first, the spatio-temporal correlation features are divided into grids of 500m×500m, and the flow velocity mutation density (such as 5 times / h) and the mean water level fluctuation (such as ±1.2m) within each grid are statistically calculated; secondly, the mutation density and the mean water level fluctuation are trained through a random forest regression model to generate the response coefficients of runoff and water level (such as when the flow velocity drops by 1m / s, the water level rises by 0.75m); thirdly, based on the watershed hydrological model (HEC-RAS), the response coefficients are mapped to the channel roughness (0.025→0.032) and the floodplain capacity (50→575,000 m³); finally, the updated parameters are mapped into the flood interaction parameters between the channel structure and the surface runoff in the preliminary framework through the API interface.

[0096] 204. According to the mapping results, in combination with the change rate of the flood evolution path data at the boundary of the high flood risk area and the continuous fluctuation amplitude of the water level monitoring data, the flood interaction parameters are dynamically updated, and spatial compensation is performed on the updated flood interaction parameters through the spatial distribution range to adjust the preliminary framework.

[0097] In step 204, dynamic update means iteratively optimizing the model parameters based on real-time data, and spatial compensation eliminates local distortion caused by parameter mutation through an interpolation algorithm.

[0098] In the embodiments of the present application, first, the change gradient of the flow velocity at the boundary of the high-risk area (such as a decrease of 1.2m / s / h at X:300) and the continuous water level fluctuation (±1.5m at section S3 for 1h) are detected; secondly, after removing the noise by using the Kalman filter, the floodplain capacity is adjusted by the gradient descent method (575,000→600,000 m³); thirdly, Kriging interpolation is used to smooth the global parameters to ensure continuous transition of the channel roughness between 0.030 and 0.035; finally, the optimized preliminary framework is output.

[0099] The following is a specific example:

[0100] Suppose a rainstorm flood occurs in a river basin. In step 201, a digital twin framework is constructed based on LiDAR topography and SAR images (0.5m resolution), including the main stream cross-sections S1 - S3 (width 80m) and the floodplain areas on both sides (slope 1.5% - 8%). The flood evolution path monitored by Sentinel-1 (12km² inundation area) is superimposed. In step 202, reverse tracking shows that the flow velocity suddenly drops from 3.2m / s to 1.0m / s at X:250 at 15:00, and it is matched that the water level at cross-section S2 suddenly rises by 1.8m at 15:05 (Z = 4.0), generating spatio-temporal correlation features (time difference 5 minutes, distance difference 50m, water level rise 0.75m corresponding to a 1m / s drop in flow velocity). In step 203, the flow velocity mutation density (4 / km²) and water level fluctuation amplitude (1.2m) in the X:300 - 350 section are statistically analyzed within a 100m grid, and correlation parameters are generated through random forest regression (floodplain flood storage coefficient 0.73), and the roughness coefficient of this section is updated from 0.028 to 0.032. In step 204, an abnormal flow velocity gradient (1.8m / s / 100m) is detected in the X:400 - 500 section. Combining with the continuous fluctuation of cross-section S3 (±0.6m / 30 minutes), the floodplain coefficient is dynamically adjusted to 0.68, and parameter smooth transition is achieved through Kriging interpolation. Finally, the error of the inundation range predicted by the model is reduced from 15% to 3.5%, providing accurate decision-making support for flood diversion sluice scheduling.

[0101] Steps 201 - 204 construct a dynamically feedback digital twin model by integrating river channel topology, flood path, and water level monitoring data. Based on spatio-temporal correlation features, hydraulic parameters are reversely optimized to achieve accurate mapping of the flow velocity - water level coupling relationship; combined with multi-scale analysis and spatial compensation mechanism, the simulation reliability of the model in high-risk areas is significantly improved.

[0102] In some embodiments, the meteorological forecast rainfall data and the terrain slope change data are fused to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and according to the abnormal water level change characteristics of the water level monitoring data, the high-risk flood evolution area in the distribution relationship is determined. The abnormal water level change characteristics include the water level mutation amplitude at key monitoring cross-sections in the river channel, including:

[0103] 301. Dynamically allocate the rainfall intensity values in the meteorological forecast rainfall data according to the spatial distribution law of the terrain slope change data to form the rainfall spatial coverage density with the slope change rate as the weight factor;

[0104] In step 301, the rainfall spatial coverage density is the redistribution result of the rainfall intensity under the terrain slope difference. The steep slope area is given a higher rainfall weight due to the fast runoff velocity, and the depression area needs to receive the migrated rainfall due to its weak water storage capacity.

[0105] In the embodiments of the present application, first, the rainfall forecast data (at 15-minute intervals) output by the WRF model is spatially registered with the 1m-resolution LiDAR slope raster. Secondly, the inverse distance weighting (IDW) interpolation algorithm is used to establish the mapping relationship between the slope change rate (such as 8% / 100m) and the rainfall weight (1.0 - 1.5 times). Thirdly, a rainfall weight of 1.4 times is assigned to the steep slope area in the upper reaches of the main stream (slope change rate > 10%). Finally, the rainfall spatial coverage density with a grid accuracy of 100m is generated (for example, the rainfall intensity in the area of X:150 - 200 is adjusted from 50mm / h to 70mm / h).

[0106] Optionally, when performing step 301, for different scenarios, different data can be dynamically allocated. For example, if the high-slope area is covered by forests, its runoff velocity will be significantly lower than that of the bare ground surface. At this time, the rainfall intensity value in the meteorological forecast rainfall data can also be dynamically allocated according to the terrain slope change data, soil permeability (based on the soil type database), vegetation cover index (based on multi-spectral remote sensing data), and land use type (such as the proportion of urban impervious surfaces), and the SCS-CN comprehensive weight model is used to generate the rainfall spatial coverage density, where the slope change rate is the core weight factor, and the soil permeability and vegetation cover coefficient are used as correction factors.

[0107] It should be noted that the above step 301 dynamically allocates the rainfall intensity value in the meteorological forecast rainfall data according to the spatial distribution law of the terrain slope change data to form the rainfall spatial coverage density with the slope change rate as the weight factor, which is only an example. Dynamically allocating different data according to different scenarios to form the rainfall spatial coverage density with different weight factors is within the protection scope of the present application.

[0108] 302. Based on the guiding effect of the slope change rate on the surface flood flow direction, the rainfall intensity value in the high-slope change rate area in the rainfall spatial coverage density is directionally migrated to the low-lying confluence area, and combined with the distribution density of the tributary confluence nodes in the surface flood evolution path, the distribution relationship between rainfall and flood runoff based on the terrain slope change is generated;

[0109] In step 302, the guiding effect refers to the control effect of the slope gradient on the main flood flow direction. The distribution density of the tributary confluence nodes is identified by unmanned aerial vehicle (UAV) aerial photography.

[0110] In the embodiments of the present application, first, the dominant runoff direction is determined based on the D8 algorithm (for example, the flow direction of section S1 is 145°). Second, the slope-directed migration (SDM) algorithm is used to transfer 30% of the rainfall intensity in the upstream steep area (X: 100 - 150) to the downstream depression (X: 300 - 350). Third, the tributary confluence hotspots are calculated through kernel density estimation (KDE) (for example, the density at X: 250 reaches 5 per km²). Finally, the rainfall intensity after migration (85 mm / h) and the confluence point density are fused according to a 7:3 weight to generate the distribution relationship between rainfall and flood runoff (for example, the comprehensive runoff intensity at X: 250 is marked as 92 mm / h).

[0111] 303. Calculate the difference between the water level mutation amplitude in the abnormal water level change characteristics and the flood runoff intensity in the corresponding geographical area in the distribution relationship. Mark the abnormal area where the slope change rate does not match the flood runoff intensity in the distribution relationship, and based on the product coefficient of the difference and the slope change rate, perform gradient compensation on the flood runoff intensity value in the abnormal area according to the slope change rate;

[0112] Among them, in combination with the SCS-CN curve number correction, based on the product coefficient of the difference and the slope change rate, perform gradient compensation on the flood runoff intensity value in the abnormal area, and introduce an urbanization correction factor (the compensation amount increases by 20% when the impervious surface ratio ≥ 30%).

[0113] In step 303, the water level mutation amplitude refers to the value by which the water level at the river channel monitoring section rises within a short period (usually 1 hour) exceeding the historical normal fluctuation range, reflecting the abnormal change of the river channel's flood discharge capacity.

[0114] In the embodiments of the present application, first, locate the runoff intensity (80 mm / h) corresponding to the water level mutation point (rising 1.5 m) at X: 200. Second, calculate the theoretical runoff intensity difference (80 - 65 = 15). Third, based on the product coefficient (15 × 6% = 0.9), compensate the runoff intensity to 80 + (0.9 × 10) = 89 mm / h. Finally, mark X: 200 - 220 as the abnormal area where runoff and slope do not match (red warning area) in the model.

[0115] 304. Superimpose the compensated flood runoff intensity value on the spatial distribution of the abnormal fluctuation points in the water level monitoring data, and screen out the overlapping areas that simultaneously meet the slope change rate threshold, the flood runoff intensity compensation value, and the water level abnormal amplitude as the high-risk areas for flood evolution.

[0116] In step 304, the high-risk areas for flood evolution: the spatial overlapping areas that simultaneously meet the sudden steep slope, the runoff intensity exceeding the threshold, and the abnormal water level fluctuation, usually located at the tributary confluence or the river channel turning section, have the dual risks of rapid flood accumulation and diffusion.

[0117] In the embodiments of the present application, first, the post-compensation flood runoff intensity value (89 mm / h at X:200) is superimposed with the water level anomaly fluctuation point (section S2). Secondly, grids that simultaneously meet the conditions of slope > 5%, runoff > 85 mm / h, and water level rise > 1 m in the superimposed result are screened (such as X:180 - 250). Thirdly, flood evolution high-risk areas (with an area of 3.2 km²) are generated for the grids through spatial clustering (DBSCAN algorithm). Finally, this area is compared with the actually measured inundation range of Sentinel-1 (coincidence rate 89%) to verify the accuracy of the model.

[0118] The following is a specific example:

[0119] Suppose during a rainstorm in a certain basin, in step 301, the predicted rainfall peak (80 mm / h) is fused with the LiDAR slope data, and a 1.5-fold weight (actual 120 mm / h) is assigned to the upstream steep slope area (X:100 - 200, slope change rate 12%). In step 302, 30% of the rainfall (36 mm / h) in this area is migrated to the downstream depression (X:300 - 350) through the SDM algorithm, and a runoff distribution map (comprehensive value 105 mm / h at X:300) is generated in combination with the tributary confluence density (4 per km²). In step 303, it is detected that the water level at section S3 suddenly rises by 2.1 m (Z = 4.5). After calculating the runoff difference (105 - 82 = 23), compensation is performed based on the slope change rate of 8% (23×8%×8 = 14.7), and the runoff at X:300 is updated to 119.7 mm / h. In step 304, the area of X:280 - 350 (slope 7%, runoff > 110 mm / h, water level rise > 1.5 m) is screened, and a 2.8 km² high-risk area is determined through morphological processing. Comparing with satellite images shows that 85% of the area has been covered by the flood front, verifying the effectiveness of the model warning.

[0120] Steps 301 - 304 construct a high-precision flood runoff distribution model by dynamically allocating rainfall intensity to different slope areas and combining a terrain-guided runoff migration algorithm. Based on the correlation analysis of water level mutations and slope changes, areas of mismatch between runoff and terrain are automatically identified and compensated. Finally, high-risk areas of flood evolution are accurately locked through multi-threshold spatial superposition, realizing a full-chain closed loop from rainfall prediction to risk warning.

[0121] In some embodiments, inputting the meteorological forecast rainfall data into the adjusted preliminary framework, and combining the flood interaction parameters with the basin catchment boundary conditions in the terrain slope change data to construct a basin digital twin model including abnormal change data of surface flood evolution direction and river channel flood inundation warning signals includes:

[0122] 401. Based on the watershed catchment boundary conditions in the terrain slope change data, spatially cut the adjusted preliminary framework to divide surface flood evolution direction calculation units based on the terrain slope change rate.

[0123] In step 401, the watershed catchment boundary condition refers to the closed watershed boundary extracted based on the Digital Elevation Model (DEM), which is used to define independent hydrological response units.

[0124] In the embodiment of this application, first, the D8 flow direction algorithm is used to extract the watershed boundary from 1m resolution LiDAR data. Secondly, the digital twin framework is cut into several sub-watersheds (such as the upper reaches of the main stream, the confluence area of tributaries, etc.) through the constrained Voronoi diagram. Thirdly, each sub-watershed is subdivided into 100m×100m surface flood evolution direction calculation units according to the slope change rate grid (graded from 0 to 15%). Finally, the adaptive grid encryption technology is applied to ensure that the slope variation coefficient within the unit is less than 5%.

[0125] 402. Within the surface flood evolution direction calculation unit, based on the distribution characteristics of slope turning points in the terrain slope change data, determine the initial offset angle of the surface flood evolution direction, and correct the initial offset angle in combination with the flood interaction parameters to generate a corrected path for abnormal changes in the flood evolution direction.

[0126] In step 402, the distribution characteristics of slope turning points refer to the slope mutation positions of the river channel longitudinal profile (such as steep banks, drop waters, etc.). The initial offset angle is the angle between the theoretical water flow direction calculated by the D8 algorithm and the main river channel axis.

[0127] In the embodiment of this application, first, curvature analysis is used to identify slope turning points (such as the slope drops from 8% to 3% at X:120). Secondly, the least squares method is used to fit the initial offset angle of the water flow direction within the unit (such as the S1 section deflects 18°). Thirdly, based on flood interaction parameters such as the channel roughness coefficient (0.025 - 0.035) and the flood storage capacity of the floodplain (500,000 - 600,000 m³), the offset angle is corrected through the Bayesian optimization algorithm (such as adjusted to 22°). Finally, a corrected path for bypassing river bends is generated (the path curvature radius ≥ 3 times the river width).

[0128] 403. Couple the channel water level critical threshold in the flood interaction parameters with the distribution density of river channel monitoring sections in the terrain slope change data. When the distribution density of the monitoring sections exceeds the critical threshold corresponding to the slope change rate, generate a flood inundation warning level based on the coupling result.

[0129] In step 403, the water level critical threshold refers to the warning water level corresponding to the dike design standard. The distribution density of the monitoring sections reflects the number of monitoring points per unit river length (such as 2 sections per kilometer).

[0130] In the embodiments of the present application, first, an association matrix between the water level threshold and the slope change rate is established (for example, a slope of 8% corresponds to a water level threshold of 7.2 m). Second, the cross-section density index is calculated (for example, in the X: 200 - 300 river section, it reaches 3 per km). Third, when the density index exceeds the limit (> 2.5 per km) and the real-time water level exceeds the associated threshold. Finally, the flood inundation warning level is output through a logistic regression model (for example, red warning: water level exceeding the threshold + density exceeding the limit + slope > 10%).

[0131] 404. Perform a spatial overlay of the corrected path, the flood inundation warning level, and the meteorological forecast rainfall data, and perform a three-dimensional flood evolution topology reconstruction on the spatial overlay result according to the basin catchment boundary conditions to generate a basin digital twin model that simultaneously includes the sudden change trajectory of the surface flood evolution direction and the flood inundation warning hot area of the river channel.

[0132] In step 404, the three-dimensional topology reconstruction refers to fusing multi-source data to construct a three-dimensional model of the basin with hydraulic connectivity.

[0133] In the embodiments of the present application, first, the corrected path (in GeoJSON format), the warning level (raster data), and the WRF rainfall forecast (in NetCDF format) are uniformly projected onto the UTM coordinate system. Second, a Delaunay triangulation skeleton (node spacing ≤ 50 m) is generated based on the sub-basin boundary. Third, the sudden change trajectory of the evolution direction is simulated through a GPU-accelerated water particle system (the particle size changes with the flow velocity). Finally, the warning hot area is rendered using the ray casting algorithm (the transparency is positively correlated with the warning level) to form an interactive basin digital twin model.

[0134] The following is a specific example:

[0135] Suppose during a rainstorm in a certain river basin, in step 401, 8 sub-basins are divided based on a 30 km² catchment boundary (elevation difference ≥ 50 m), and 572 calculation units are generated (slope variation coefficient ≤ 4.8%). In step 402, a slope turning point (5% → 1%) is identified in the tributary confluence area (X: 180), and the initial offset angle of 23° is corrected to 27° through the roughness coefficient (0.032) to form a corrected path for bypassing the alluvial fan (curvature radius 280 m). In step 403, it is detected that the cross-section density in the middle reaches of the main stream is 3.2 per km (the threshold for a slope of 12% is 2.8 per km), and the water level of 7.9 m exceeds the associated threshold (7.6 m), triggering a red warning. In step 404, by fusing the corrected path (27° deflection), the red warning area (X: 150 - 250), and the forecast rainfall (60 mm in the next 3 h), the constructed basin digital twin model accurately predicts the inundation range in this area after 6 hours (error rate < 5%), supporting the reservoir operation decision-making.

[0136] Steps 401 - 404 achieve high - precision digital twin modeling through the coupling of watershed terrain dynamic zoning and flood evolution parameters. Based on the intelligent correction of water flow direction using slope turning characteristics, combined with water level thresholds and section density to generate hierarchical warnings, and finally integrating multi - source data to construct a 3D visualization model, which can accurately predict the flood evolution path and inundation risk, providing full - process intelligent support for watershed flood control decision - making.

[0137] In some embodiments, the method of tracing the continuous change process of the flood evolution path data in reverse chronological order, while matching the spatial distribution state of the water level monitoring data at the river channel monitoring sections, and constructing the spatio - temporal correlation characteristics between the sudden change points of the flood flow velocity in the flood evolution path data and the abnormal fluctuation points in the water level monitoring data according to the reverse tracing result and the matching result, includes:

[0138] 501. Traverse the continuous records of the flood flow velocity in the flood evolution path data in reverse chronological order, identify the sudden change points where the change amplitude of the flood flow velocity exceeds a preset sudden change threshold within adjacent timestamps, and record the occurrence time and the corresponding river channel position coordinates of each sudden change point as the reverse tracing result;

[0139] In step 501, the reverse tracing result refers to the result of analyzing the historical change process of the flow velocity in reverse from the current flood front position. The sudden change points reflect the abnormal changes in the flood - carrying capacity of the river channel (such as levee breaches or tributary backwater).

[0140] In the embodiments of the present application, first, the dynamic time warping (DTW) algorithm is used to match the Sentinel - 1 satellite flow velocity sequence (at 30 - minute intervals) in reverse chronological order. Secondly, identify the sudden change points where the flow velocity drops suddenly by more than a threshold (such as from 3.0 m / s to 1.0 m / s). Thirdly, merge adjacent sudden change points through spatial clustering (DBSCAN algorithm) (for example, 3 sudden change points at X:250 are merged into a cluster). Finally, generate a structured reverse tracing result (timestamp 15:00, coordinate X:250, flow velocity change amount 2.0 m / s).

[0141] 502. Screen the monitoring sections in the water level monitoring data that are closest to the river channel position coordinates in the reverse tracing result, extract the cross - section water level change data within a preset time range before and after the occurrence time, and mark the abnormal fluctuation points where the water level change amplitude exceeds the historical average as the matching result;

[0142] In step 502, the abnormal fluctuation points refer to the monitoring sections where the water level rises by more than 2 times the standard deviation of the historical same period within 1 hour (such as the sudden rise of 1.8 m at section S2, Z = 4.0).

[0143] In the embodiments of the present application, first, based on the Voronoi diagram, the monitoring section closest to the mutation point is located (e.g., X: 250 → section S2, distance 80 m). Secondly, the water level data for 1 hour before and after the mutation time (14:30 - 15:30) is extracted. Thirdly, the abnormal fluctuation points are marked through the sliding Z-Score detection (window length: data of the same period in 1 year) (the water level rises by 1.8 m, Z = 4.2). Finally, the matching result is recorded (at section S2, the water level suddenly rises at 15:05, with an amplitude of 1.8 m).

[0144] 503. Align and match the occurrence time in the reverse tracking result with the maximum fluctuation time of the abnormal fluctuation point in the matching result to construct a set of spatio-temporal correlation pairs between the mutation point and the abnormal fluctuation point;

[0145] In step 503, the set of spatio-temporal correlation pairs includes the quantitative relationships of flow velocity mutation, water level fluctuation, and their spatio-temporal deviation.

[0146] In the embodiments of the present application, first, the time difference (5 minutes) between the mutation point (15:00) and the water level fluctuation (15:05) is calculated. Secondly, based on the Saint-Venant equation, the flood wave propagation time is calculated (distance 80 m / wave velocity 1.6 m / s ≈ 50 seconds). Thirdly, the correlation strength is evaluated through the spatio-temporal coupling index (STCI) (e.g., if the time difference < conduction time × 1.2, the correlation is valid). Finally, a set of spatio-temporal correlation pairs with high confidence is generated (X: 250 flow velocity mutation ↔ S2 water level sudden rise, R² = 0.89).

[0147] 504. Combine the velocity change amount of the mutation point, the water level fluctuation amplitude of the abnormal fluctuation point, and the time interval between the two in each correlation pair in the set of spatio-temporal correlation pairs into spatio-temporal correlation features.

[0148] In step 504, the spatio-temporal correlation features are multi-dimensional vectors (such as [flow velocity change amount, water level amplitude, time difference]) that quantify the dynamic relationship between "flow velocity and water level" and are used for optimizing the model parameters.

[0149] In the embodiments of the present application, first, the correlation pair features are extracted (at X: 250, the flow velocity drops by 2.0 m / s, at S2, the water level rises by 1.8 m, time difference 5 minutes). Secondly, through min-max normalization, it is converted to [1.0, 0.9, 0.2]. Thirdly, based on the random forest feature weights (flow velocity 0.6, water level 0.3, time difference 0.1), the spatio-temporal correlation feature vector [0.6, 0.27, 0.02] is generated through weighting. Finally, it is stored in the feature library for real-time correction of the flood storage parameters in the digital twin model (e.g., the weight 0.6 corresponds to an increase in the flood storage capacity by 15%).

[0150] The following is a specific example:

[0151] Suppose during a flood in the main stream of a certain river, at step 501, a sudden drop in the flow velocity from 2.8 m / s to 0.9 m / s (threshold 1.5 m / s) is detected at X:300 at 15:30. After spatial clustering, it is confirmed as a tributary backwater event. At step 502, the nearest cross-section S3 (distance 120 m) is matched, and it is found that the water level suddenly rises by 2.1 m (Z = 4.5) at 15:35, exceeding the historical extreme value. At step 503, the time difference of 5 minutes is calculated, the flood wave propagation time is 4 minutes (120 m / 3.0 m / s), and the STCI index is 0.92, which is determined to be strongly correlated. At step 504, a feature vector [1.9, 1.0, 0.25] is generated. After weight calculation, it triggers the update of the model parameters (the floodplain roughness coefficient is adjusted from 0.028 to 0.034), reducing the inundation prediction error of the downstream section from X:400 - 500 from 12% to 3%.

[0152] Steps 501 - 504 construct a high-precision dynamic response model for basin floods by retroactively tracking the spatio-temporal correlation between sudden changes in flood flow velocity and water level fluctuations. Based on the flood wave propagation theory and random forest feature weighting, the system intelligently identifies the causal relationship between flood channel flow anomalies and sudden water level changes, generates quantified "flow velocity and water level" correlation features, and real-time optimizes the parameters of the digital twin model, significantly improving the accuracy of flood evolution prediction and providing a scientific decision-making basis for basin flood control scheduling.

[0153] In some embodiments, coupling the channel water level balance value in the hydrological interaction parameters with the pipe network cross-section distribution density in the terrain slope change data, and when the monitoring cross-section distribution density exceeds the critical threshold corresponding to the slope change rate, generating a backwater warning level based on the coupling result, includes:

[0154] 601. Divide the terrain slope change data into regional units according to the slope change rate, count the total number of monitoring cross-sections in each regional unit, and calculate the cross-section distribution density per unit area;

[0155] In step 601, the cross-section distribution density reflects the spatial coverage intensity of water level monitoring cross-sections per unit basin area and is used to evaluate the risk of monitoring blind spots in the flood channel flow capacity.

[0156] In the embodiments of the present application, first, based on LiDAR slope data (1 m resolution), the slope units are divided using the Jenks natural break method (such as 0 - 5%, 5 - 10%, > 10%). Second, the number of cross-sections in each unit is counted through spatial join analysis (such as the upstream unit of the main stream contains 8 cross-sections). Finally, the cross-section distribution density is calculated (such as the density corresponding to a unit area of 5 km² is 1.6 / km²), and low-density units (< 1 / km²) are identified as monitoring weak areas.

[0157] 602. Set the critical values of section density corresponding to different intervals of slope change rate according to the correlation law between the slope change rate and the section distribution density.

[0158] In step 602, the critical value of section density refers to the minimum section configuration requirement to ensure the accuracy of flood monitoring. A higher density is required in the steep slope area to capture rapid water level changes.

[0159] In the embodiment of the present application, first, analyze the correlation between slope and density in historical flood events (for example, the ideal density in areas with a slope > 10% is ≥ 2 per km²). Second, use quantile regression to establish a dynamic threshold model (the density threshold increases by 0.5 per km² for every 5% increase in slope). Finally, generate a critical value table for slope classification to set the critical values of section density corresponding to different intervals of slope change rate (for example, 0 - 5%: 1 per km², 5 - 10%: 1.5 per km², > 10%: 2 per km²).

[0160] 603. In the regional unit, couple the critical threshold of river water level in the flood interaction parameters with the section distribution density according to a preset ratio. If the section distribution density exceeds the critical value of section density, calculate the product of the water level difference between this area and the adjacent area and the slope change rate based on the coupling result.

[0161] In step 603, the product of the water level difference and the slope change rate quantifies the prediction error of flood evolution caused by unreasonable section layout. The larger the product value, the higher the risk of monitoring blind spots.

[0162] In the embodiment of the present application, first, extract the critical threshold of river water level in the unit (for example, the warning water level of section S2 is 7.5m) and the section distribution density (for example, 1.2 per km²). Second, when the density is lower than the critical value (for example, the requirement in the area with a slope > 10% is 2 per km²), third, calculate the product of the water level difference between adjacent units (for example, the water level of the upstream unit is 7.8m vs the water level of this unit is 7.2m, the difference is 0.6m) and the slope change rate (8%) (0.6 × 8 = 4.8). Finally, mark the units with a product > 3 as high - risk monitoring blind spots.

[0163] 604. Divide three - level flood inundation warning levels according to the product value, where the first - level warning corresponds to the highest interval of the product value, the second - level warning corresponds to the medium interval, and the third - level warning corresponds to the lowest interval.

[0164] In step 604, the three - level flood inundation warning levels reflect the impact degree of monitoring blind spots on flood forecasting. For the first - level warning, temporary monitoring facilities need to be supplemented immediately.

[0165] In the embodiment of the present application, based on the historical disaster database (flood events in the past 10 years), a logistic regression model is used to train the correlation between the product value and the actual flooding depth, and the three-level warning threshold is redefined: level one (product value>8, corresponding to flooding depth≥2m), level two (5-8, 1-2m), level three (<5, <1m), and the classification accuracy is verified by the confusion matrix (F1-score≥0.85).

[0166] Here is a specific example:

[0167] Assume that during a rainstorm in a river basin, step 601 divides the tributary confluence unit (slope 12%, area 3km²) into only 4 sections (density 1.3 / km²), which is lower than the critical value (2 / km²). Step 603 calculates the product of the water level difference (0.8m) between this unit and the main river unit and the slope change rate (7%) as 5.6. Step 604 determines that it is a first-level warning, and the system automatically dispatches 2 mobile monitoring stations to strengthen data collection, reducing the water level prediction error in the area from 15% to 6%.

[0168] Steps 601-604 construct an intelligent monitoring and early warning system through slope and density coupling analysis, dynamically identify flood prediction blind spots caused by insufficient section layout, quantify risk levels based on the product of water level difference and slope change, achieve precise scheduling of monitoring resources and real-time calibration of flood evolution models, improve water level prediction accuracy in key areas, and provide data quality assurance for basin flood control.

[0169] In some embodiments, the flood emergency control plan is generated according to the spatiotemporal distribution characteristics of the flood inundation warning signal in the watershed digital twin model, combined with the river terrain elevation data, and the spatiotemporal distribution characteristics include signal density, including:

[0170] 701. Filtering, in the watershed digital twin model, continuous areas where the signal density of the flood warning signal exceeds a preset threshold, marking them as high-risk areas for flooding, and extracting the time duration of the high-risk areas;

[0171] In step 701, the high-risk area for flood inundation refers to the river section where the warning signals are continuously clustered in space and the duration exceeds the limit, reflecting the core area of ​​the continuous threat of floods.

[0172] In the embodiment of the present application, spatiotemporal hot spot analysis (Getis-Ord Gi*) is first used to identify cluster areas with a warning signal density of >15 / 5km², then the discrete warning points are merged through a morphological expansion algorithm (3×3 kernel), and finally the duration of the risk zone from the start to the release is extracted (such as the X:200-300 river section continuously exceeding the warning for 6 hours).

[0173] 702. Match the spatial range of the high-risk flood inundation area with the river channel topographic elevation data, identify the vulnerable sections within the high-risk area where the elevation is lower than the flood warning level, and mark them as the key sections for flood diversion and discharge.

[0174] In step 702, the key sections for flood diversion and discharge refer to the river reaches where the levee elevation is lower than the design flood level or there are obvious topographic depressions, and engineering interventions need to be implemented preferentially.

[0175] In the embodiment of the present application, first, the risk area boundary is overlaid with 1m LiDAR elevation data. Second, the levee sections with elevations lower than the warning level (such as 7.5m) are extracted (such as the elevation at X:250 is 7.2m). Third, topographic depressions are detected through curvature analysis (curvature > 0.05). Finally, X:240 - 260 is marked as the key section for flood diversion and discharge.

[0176] 703. Generate a multi-level flood diversion channel plan covering the upstream and downstream based on the elevation gradient direction of the key sections for flood diversion and discharge, where the capacity of the flood diversion channel is positively correlated with the elevation difference.

[0177] In step 703, the multi-level flood diversion channels refer to the stepped flood discharge paths designed according to the elevation difference between the upstream and downstream, and achieve staged flood detention through natural gravity.

[0178] In the embodiment of the present application, first, the elevation gradient direction is determined (such as the elevation rises by 3m from X:250 to X:280). Second, the designed discharge of the flood diversion channel is calculated based on the elevation gradient direction through a hydraulic model (the discharge increases by 50m³ / s for every 1m of elevation difference). Third, a three-level flood diversion channel plan (main channel 800m³ / s + auxiliary channel 300m³ / s) is generated in combination with the distribution of flood detention areas. Finally, the effectiveness of the channel is verified through HEC-RAS simulation.

[0179] 704. Adjust the flood diversion gate control instructions for the key river cross-sections within the high-risk area based on the time duration and the capacity change trend of the multi-level flood diversion channel plan, and generate a flood emergency control plan including the flood diversion channel activation rules and the gate regulation time sequence according to the adjusted control instructions.

[0180] Among them, in combination with a real-time social and economic impact assessment model (such as the vulnerability index of disaster-bearing bodies), the flood diversion gate control instructions are dynamically adjusted based on the time duration and the capacity change trend of the flood diversion channel, giving priority to ensuring the safety of densely populated areas and key infrastructure.

[0181] In step 704, the gate regulation time sequence refers to the strategy of dynamically adjusting the opening of the flood diversion gate according to the flood evolution speed to achieve flood peak staggering control.

[0182] In the embodiments of the present application, first, an association model between the time duration and the capacity of the multi-level flood diversion channel scheme is established (for example, when the water level exceeds the warning level by 6 hours, 60% of the total reservoir capacity needs to be released). Secondly, the model predictive control (MPC) algorithm is used to generate a gate command sequence based on the association model (for example, for gate X:250, the opening is 50% in the first 2 hours and 75% in the next 4 hours). Finally, the flood diversion channel activation rules (main first, then auxiliary) and the gate time sequence are integrated to form a complete regulation scheme.

[0183] The following is a specific example:

[0184] Suppose the section X:200 - 400 of the main stream of a certain basin is marked as a high-risk area (lasting for 8 hours). In step 702, it is identified that the section X:300 - 320 among them is the key section for flood diversion and discharge (elevation 7.1m < 7.5m). In step 703, a three-level flood diversion channel is designed (main channel 1000m³ / s + two auxiliary channels each with 400m³ / s). In step 704, an instruction is generated through the MPC algorithm: the main channel is opened in the first hour (gate opening 30%), and the auxiliary channels are opened starting from the 3rd hour (opening 50%). The cumulative flood diversion volume within 8 hours reaches 70% of the designed reservoir capacity, successfully controlling the downstream water level below the warning line.

[0185] Steps 701 - 704 accurately locate the key flood diversion areas through spatio-temporal hot spot identification and terrain matching, design multi-level flood diversion channels based on the elevation gradient, and dynamically optimize the gate scheduling in combination with model predictive control, realizing the scientific management and control of flood risks, improving the flood diversion efficiency, and providing intelligent decision-making support for the flood control scheduling of the basin.

[0186] Figure 2 The following is a schematic structural diagram of a system for constructing a digital twin of a basin based on multi-source data fusion provided by the embodiments of the present application, as Figure 2 shown. The system includes:

[0187] An acquisition module 21, configured to acquire water level monitoring data of the river channel, surface flood evolution path data, meteorological forecast rainfall data, and terrain slope change data based on the interaction relationship between the river channel structure and surface runoff in the river hydrological interaction scenario;

[0188] A fusion module 22, configured to fuse the meteorological forecast rainfall data and the terrain slope change data to generate a distribution relationship between rainfall and flood runoff based on the terrain slope change, and determine the high-risk area of flood evolution in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data, where the abnormal water level change characteristics include the water level mutation amplitude at the key monitoring section in the river channel;

[0189] An adjustment module 23 is configured to establish a preliminary framework of the basin digital twin model corresponding to the high-risk area of flood evolution, generate associated feature parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjust the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated feature parameters;

[0190] A construction module 24 is configured to input the meteorological forecast rainfall data into the adjusted preliminary framework, and construct a basin digital twin model including abnormal change data of surface flood evolution direction and river channel flood inundation warning signals in combination with the flood interaction parameters and the basin catchment boundary conditions in the terrain slope change data;

[0191] A generation module 25 is configured to generate a flood emergency regulation plan according to the spatio-temporal distribution characteristics of the flood inundation warning signals in the basin digital twin model and in combination with the river channel topographic elevation data.

[0192] Figure 2 The described basin digital twin construction system based on multi-source data fusion can execute Figure 1 The described basin digital twin construction method based on multi-source data fusion in the illustrated embodiment, and its implementation principle and technical effects will not be elaborated further. For the basin digital twin construction system based on multi-source data fusion in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment of the method, and will not be elaborated here.

[0193] In a possible design, Figure 2 The basin digital twin construction system based on multi-source data fusion in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, and this computing device may include a storage component 31 and a processing component 32;

[0194] The storage component 31 stores one or more computer instructions, and among them, the one or more computer instructions are called and executed by the processing component 32.

[0195] The processing component 32 is used for the Figure 1 basin digital twin construction method based on multi-source data fusion in the above

[0196] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-mentioned method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above-mentioned method.

[0197] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0198] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0199] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module may be an output device, an input device, etc.

[0200] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0201] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0202] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 A method for constructing a basin digital twin based on multi-source data fusion shown in the above embodiment.

[0203] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0204] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0205] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing a digital twin of a river basin based on multi-source data fusion, characterized in that, Including: Based on the interaction relationship between the river channel structure and surface runoff in the river hydrological interaction scenario, obtain the water level monitoring data of the river channel, surface flood evolution path data, meteorological forecast rainfall data, and terrain slope change data; Fuse the meteorological forecast rainfall data with the terrain slope change data to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and determine the high-risk flood evolution area in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data. The abnormal water level change characteristics include the water level mutation amplitude at the key monitoring section in the river channel; Establish a preliminary framework of the watershed digital twin model corresponding to the high-risk flood evolution area, generate associated characteristic parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjust the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated characteristic parameters; Input the meteorological forecast rainfall data into the adjusted preliminary framework, and combine the flood interaction parameters with the watershed catchment boundary conditions in the terrain slope change data to construct a watershed digital twin model including abnormal change data of surface flood evolution direction and river channel flood inundation warning signals; Generate a flood emergency regulation plan according to the spatio-temporal distribution characteristics of the flood inundation warning signals in the watershed digital twin model, combined with the river channel terrain elevation data.

2. The method according to claim 1, characterized in that, The establishment of the preliminary framework of the watershed digital twin model corresponding to the high-risk flood evolution area, generating associated characteristic parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjusting the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated characteristic parameters includes: Based on the spatial distribution range of the high-risk flood evolution area, establish a preliminary framework of the watershed digital twin model including the river channel topological structure and surface flood evolution path; Reverse-track the continuous change process of the flood evolution path data in chronological order, and at the same time match the spatial distribution state of the water level monitoring data at the river channel monitoring section. According to the reverse-tracking result and the matching result, construct the spatio-temporal association characteristics between the flood flow velocity mutation points in the flood evolution path data and the abnormal fluctuation points in the water level monitoring data; Perform multi-scale combination of the distribution density of the flood flow velocity mutation points and the water level fluctuation amplitude in the spatio-temporal association characteristics, establish an associated characteristic parameter reflecting the correlation between the flood evolution path change pattern and the water level response intensity, and map the associated characteristic parameter to the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework; According to the mapping result, combined with the change rate of the flood evolution path data at the boundary of the high-risk flood area and the continuous fluctuation amplitude of the water level monitoring data, dynamically update the flood interaction parameters, and perform spatial compensation on the updated flood interaction parameters through the spatial distribution range to adjust the preliminary framework.

3. The method according to claim 1, wherein Fusing the meteorological forecast rainfall data with the terrain slope change data to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and determining the high-risk areas of flood evolution in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data, including: Dynamically distributing the rainfall intensity values in the meteorological forecast rainfall data according to the spatial distribution law of the terrain slope change data to form the rainfall spatial coverage density with the slope change rate as the weight factor; Based on the guiding effect of the slope change rate on the surface flood flow direction, migrating the rainfall intensity values in the high slope change rate areas in the rainfall spatial coverage density to the low-lying confluence areas, and combining the distribution density of the tributary confluence nodes in the surface flood evolution path to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change; Calculating the difference between the water level mutation amplitude in the abnormal water level change characteristics and the flood runoff intensity in the corresponding geographical area in the distribution relationship, marking the abnormal areas in the distribution relationship where the slope change rate does not match the flood runoff intensity, and performing gradient compensation on the flood runoff intensity values in the abnormal areas according to the slope change rate based on the product coefficient of the difference and the slope change rate; Overlaying the compensated flood runoff intensity values with the spatial distribution of the abnormal fluctuation points in the water level monitoring data, and screening out the overlapping areas that simultaneously meet the slope change rate threshold, the flood runoff intensity compensation value, and the water level anomaly amplitude as the high-risk areas of flood evolution.

4. The method according to claim 1, characterized in that, Inputting the meteorological forecast rainfall data into the adjusted preliminary framework, and combining the flood interaction parameters with the watershed catchment boundary conditions in the terrain slope change data to construct a watershed digital twin model including the abnormal change data of the surface flood evolution direction and the river flood inundation warning signal, including: Based on the watershed catchment boundary conditions in the terrain slope change data, performing spatial cutting on the adjusted preliminary framework to divide the surface flood evolution direction calculation units based on the terrain slope change rate; Within the surface flood evolution direction calculation units, determining the initial offset angle of the surface flood evolution direction according to the distribution characteristics of the slope turning points in the terrain slope change data, and correcting the initial offset angle in combination with the flood interaction parameters to generate the corrected path of the abnormal change of the flood evolution direction; Coupling the critical threshold of the river water level in the flood interaction parameters with the distribution density of the river channel monitoring sections in the terrain slope change data, and generating the flood inundation warning level based on the coupling result when the monitoring section distribution density exceeds the critical threshold corresponding to the slope change rate; Performing spatial overlay on the corrected path, the flood inundation warning level, and the meteorological forecast rainfall data, and performing three-dimensional flood evolution topology reconstruction on the spatial overlay result according to the watershed catchment boundary conditions to generate a watershed digital twin model that simultaneously includes the mutation trajectory of the surface flood evolution direction and the flood inundation warning hot area of the river channel.

5. The method according to claim 2, characterized in that, Reverse-tracking the continuous change process of the flood evolution path data in chronological order, and simultaneously matching the spatial distribution state of the water level monitoring data at the river channel monitoring sections. According to the reverse-tracking results and the matching results, construct the spatio-temporal correlation characteristics between the sudden change points of the flood flow velocity in the flood evolution path data and the abnormal fluctuation points in the water level monitoring data, including: Traverse the continuous records of the flood flow velocity in the flood evolution path data in reverse chronological order, identify the sudden change points where the change amplitude of the flood flow velocity exceeds the preset sudden change threshold within adjacent timestamps, and record the occurrence time of each sudden change point and the corresponding river channel position coordinates as the reverse-tracking results; Screen the monitoring sections in the water level monitoring data that are closest to the river channel position coordinates in the reverse-tracking results, extract the cross-section water level change data within a preset time range before and after the occurrence time, and mark the abnormal fluctuation points where the water level change amplitude exceeds the historical mean as the matching results; Align and match the occurrence time in the reverse-tracking results with the maximum fluctuation time of the abnormal fluctuation points in the matching results to construct a set of spatio-temporal correlation pairs between the sudden change points and the abnormal fluctuation points; Combine the velocity change amount of the sudden change points, the water level fluctuation amplitude of the abnormal fluctuation points, and the time interval between the two in each correlation pair in the spatio-temporal correlation pair set as the spatio-temporal correlation characteristics.

6. The method according to claim 4, wherein Couple the river channel water level critical threshold in the flood interaction parameters with the distribution density of the river channel monitoring sections in the terrain slope change data. When the distribution density of the monitoring sections exceeds the critical threshold corresponding to the slope change rate, generate a flood inundation warning level based on the coupling result, including: Divide the terrain slope change data into regional units according to the slope change rate, count the total number of monitoring sections in each regional unit, and calculate the cross-section distribution density per unit area; Set the cross-section density critical values corresponding to different slope change rate intervals according to the correlation law between the slope change rate and the cross-section distribution density; Couple the river channel water level critical threshold in the flood interaction parameters with the cross-section distribution density in the regional unit according to a preset ratio. If the cross-section distribution density exceeds the cross-section density critical value, calculate the product of the water level difference between this region and the adjacent region and the slope change rate based on the coupling result; Divide the three-level flood inundation warning levels according to the product value, where the first-level warning corresponds to the highest interval of the product value, the second-level warning corresponds to the medium interval, and the third-level warning corresponds to the lowest interval.

7. The method according to claim 1, wherein Generate a flood emergency regulation plan according to the spatio-temporal distribution characteristics of the flood inundation warning signals in the watershed digital twin model, combined with the river channel terrain elevation data, including: Screen the continuous regions in the watershed digital twin model where the signal density of the flood inundation warning signals exceeds the preset threshold, mark them as high-risk flood inundation areas, and extract the time duration of the high-risk areas; Match the spatial range of the high-risk flood inundation areas with the river channel terrain elevation data, identify the weak sections within the high-risk areas where the elevation is lower than the flood warning water level, and mark them as the key sections for flood diversion and discharge; Generate a multi-level flood diversion channel plan covering the upstream and downstream based on the elevation gradient direction of the key flood diversion and discharge section, where the capacity of the flood diversion channel is positively correlated with the elevation difference; Adjust the flood diversion gate control instructions for the key cross-section of the river channel in the high-risk area through the time duration and the capacity change trend of the multi-level flood diversion channel plan, and generate a flood emergency regulation plan including the flood diversion channel activation rules and the gate regulation time sequence according to the adjusted control instructions.

8. A basin digital twin construction system based on multi-source data fusion, characterized in that Including: An acquisition module for acquiring water level monitoring data, surface flood evolution path data, meteorological forecast rainfall data, and terrain slope change data of the river channel based on the interaction relationship between the river channel structure and surface runoff in the river hydrological interaction scenario; A fusion module for fusing the meteorological forecast rainfall data and the terrain slope change data to generate the distribution relationship between rainfall and flood runoff based on the terrain slope change, and determining the high-risk area of flood evolution in the distribution relationship according to the abnormal water level change characteristics of the water level monitoring data, where the abnormal water level change characteristics include the water level mutation amplitude at the key monitoring cross-section in the river channel; An adjustment module for establishing a preliminary framework of the watershed digital twin model corresponding to the high-risk area of flood evolution, generating associated characteristic parameters through the reverse feedback relationship between the flood evolution path data and the water level monitoring data, and adjusting the flood interaction parameters between the river channel structure and surface runoff in the preliminary framework based on the associated characteristic parameters; A construction module for inputting the meteorological forecast rainfall data into the adjusted preliminary framework, and constructing a watershed digital twin model including abnormal change data of surface flood evolution direction and river channel flood inundation warning signals in combination with the flood interaction parameters and the watershed catchment boundary conditions in the terrain slope change data; A generation module for generating a flood emergency regulation plan according to the spatio-temporal distribution characteristics of the flood inundation warning signals in the watershed digital twin model and in combination with the river channel topographic elevation data.

9. A computing device, characterized in that, Including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for constructing a watershed digital twin based on multi-source data fusion as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Stores a computer program, and when the computer program is executed by a computer, it implements a method for constructing a watershed digital twin based on multi-source data fusion as described in any one of claims 1 to 7.

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