3324 results about "River routing" patented technology

The invention provides a flood control project virtual simulation and risk rehearsal method based on digital twinning, and the method comprises the following steps: obtaining drainage basin multi-source data collected by a space-air-ground integrated monitoring network, the multi-source data comprising a satellite remote sensing image, an unmanned aerial vehicle LiDAR point cloud and ground sensor monitoring data; inputting the multi-source data into a pre-constructed digital twin model, wherein the model comprises a spatio-temporal data fusion layer, a physical process modeling layer, a multi-scale simulation deduction layer and a risk assessment decision-making layer which are connected in sequence; the spatio-temporal data fusion layer is used for performing spatio-temporal registration and feature fusion on input multi-source data, and constructing a total element digital backplane; through the space-air-ground integrated monitoring network and the spatio-temporal data fusion technology, high-precision digital mapping of basin total elements is realized. Compared with a traditional single data source, time-space consistency of flood routing simulation is remarkably improved through multi-source data fusion, and prediction errors of key physical processes such as river channel scouring are greatly reduced.

The invention relates to the technical field of river channel environment monitoring, and discloses a river channel monitoring system based on the Internet of Things. The system comprises a multi-mode sensing module, an environment feature analysis module, a space-time coupling modeling module, a self-adaptive resource scheduling module and an intelligent cooperative control module. The multi-modal sensing module collects multi-source heterogeneous data, the environment feature analysis module extracts various feature vectors, the space-time coupling modeling module generates a coupling space-time feature matrix, and the self-adaptive resource scheduling module constructs a double-layer optimization library and stores related rule strategies. And the intelligent cooperative control module generates a real-time pollution early warning instruction and a hydrological regulation and control decision through a multi-target reinforcement learning framework based on the results. In addition, the system can carry out anomaly detection and emergency treatment on the floating objects and predict sudden change of water quality. According to the system, comprehensive monitoring, intelligent analysis and accurate regulation and control of the river environment are realized, and the efficiency and scientificity of river management are improved.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The invention relates to the technical field of river channel desilting treatment, in particular to a dynamic simulation and optimization decision-making-based river channel silt treatment method, which comprises the following steps of: acquiring and treating historical and real-time multi-dimensional environmental data of a to-be-treated river channel and an influence area of the to-be-treated river channel; a river basin process simulation system capable of simulating river water power, sediment transportation, pollutant migration and transformation and ecological response processes is constructed, decision variables forming a river regulation scheme are defined in a parameterized mode, a multi-target optimization problem is defined and set in a formalized mode, a multi-target optimization engine is adopted, and the river regulation scheme is optimized. The method comprises the following steps: embedding a drainage basin process simulation system as a core evaluation module into an optimization iterative loop, implementing an optimization scheme, and continuously monitoring the environmental state change in the implementation process and after the implementation; the method can accurately predict the long-term and short-term influence of different treatment schemes in multiple aspects of hydrodynamic force, silt, pollutant migration and transformation, ecological response and the like, and solves the problem of comprehensive treatment of sludge.

The invention relates to the technical field of digital twinning, and provides a digital twinning-based river slope stability real-time monitoring and early warning method and system, and the method comprises the steps: constructing a coupled digital twinning initial model; based on the multi-source heterogeneous monitoring data set, processing the coupled digital twinborn initial model through an inversion analysis and state estimation algorithm to obtain a digital twinborn optimization model; processing the multi-source heterogeneous monitoring data set through an edge computing node to obtain edge preprocessing data, inputting the edge preprocessing data and a digital twin optimization model into a cloud computing cluster, and processing through a multi-physics field coupling analysis algorithm to obtain a slope stability evaluation result data set; and processing the slope stability assessment result data set through a multi-index fusion algorithm to obtain a comprehensive risk score, and processing the comprehensive risk score based on a graded early warning threshold to obtain multi-level risk early warning information and an engineering disposal scheme. According to the invention, high-precision real-time monitoring, dynamic risk assessment and graded early warning of the stability of the river slope are realized.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention relates to the technical field of water resource analysis, and discloses a water resource predictive analysis method based on artificial intelligence. The method relates to the technical field of water resource analysis, and comprises the following steps: acquiring an original hydrological data set including rainfall intensity, river flow and the like through a sensing terminal, and performing multi-modal data alignment to generate a hydrological space-time tensor; constructing a dynamic water level threshold response mechanism in combination with watershed topographic features to obtain a partition water level calibration matrix; inputting the hydrological feature map into a spatial-temporal feature coupling network containing a long-short-term memory module and a spatial self-attention module to generate a hydrological feature map; constructing a multi-dimensional abnormal association tensor based on the multi-dimensional abnormal association tensor, and identifying rainfall flood event nodes by using an adaptive sliding window detection algorithm; and an optimal hydrological parameter set is obtained through genetic algorithm optimization, and the three-dimensional hydrological dynamic model is driven to establish a mapping relation chain. The method can effectively fuse hydrological data spatio-temporal characteristics, and improves the accuracy and efficiency of water resource prediction analysis.

The invention provides a digital twin water conservancy hyper-fusion method for coupling multi-source data and an all-in-one machine, and relates to the technical field of intelligent water conservancy. By constructing a unified water conservancy spatio-temporal data link protocol, efficient fusion of multi-source heterogeneous data of hydrology, meteorology, engineering operation and the like is realized, and the problem of water conservancy system data islands is solved; a coupling distributed hydrological model and a hydrodynamic model are established, and real-time accurate simulation of the watershed hydrological process and the river channel hydrodynamic process is achieved; a multi-target dynamic weight reinforcement learning algorithm is adopted to optimize a water conservancy scheduling scheme, model parameters are dynamically corrected in real time through a time sequence error regression analysis model, and closed-loop adaptive optimization is achieved; according to the method, closed-loop cooperation of water conservancy data fusion, real-time accurate simulation, intelligent scheduling optimization and dynamic parameter self-correction is realized, and the real-time performance and accuracy of water conservancy intelligent decision making are remarkably improved.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention provides a dynamic change monitoring method and system based on a multi-temporal river channel image, and relates to the technical field of image monitoring, and the method comprises the steps: obtaining a multi-temporal multispectral remote sensing image and an unmanned plane oblique photogrammetry image; identifying a riverway water body boundary and constructing a water area mask; dense point cloud data are generated and cut; generating a balanced riverbed point cloud through density compensation sampling and local curvature constraint; constructing a terrain model to generate a section sequence; calculating hydrodynamic correlation strength to construct an evolution propagation sequence; and recursively calculating the erosion and deposition change rate and performing early warning. According to the invention, high-precision monitoring and early warning of river form changes are realized.

The invention discloses a riverway three-dimensional water flow numerical simulation method, and relates to the technical field of intelligent water conservancy simulation, and the method comprises the steps: generating a longitudinal long and narrow hexahedral mesh of a main riverway in a C gradient direction through employing a mucous network growth algorithm, and outputting a hexahedral mesh with a shear layer identifier; turbulence viscosity coefficient distribution is dynamically initialized through a DDPG reinforcement learning model, and turbulence parameters are output; inputting the hexahedral mesh with the shear layer identifier and turbulence parameters into a coupling solver, performing parallel numerical solution, and outputting transient flow field data; a lambda 2 criterion is adopted to identify a vortex core area and calculate a vorticity modulus, a transverse circulation is defined, an on-way distribution cloud picture is generated, an immersive VR scene is constructed through a Unity 3D engine, and a three-dimensional visual water flow simulation scene with a physical feature label is output. Through fusion of lambda2 criterion vortex identification and a Unity 3D visualization technology, a complete solution with both calculation precision and engineering practicability is provided for river flow simulation.

The invention relates to the field of environment monitoring and ecological restoration, in particular to a farmland and river ecological collaborative monitoring and regulation system and method. The system comprises a first-stage barrage, a second-stage barrage, a gabion revetment, front-end acquisition equipment, execution equipment and a server, wherein the first-stage barrage and the second-stage barrage are arranged at intervals; the execution equipment comprises irrigation equipment, a first gate, a second gate and an ecological flocculant putting device; the server comprises a data acquisition module, a data storage module, an analysis processing module and a control execution module, inputs control parameters including a first gate opening degree, a second gate opening degree, an irrigation equipment electric valve opening degree and an ecological flocculant putting amount through an LSTM model, and predicts state variables including an upstream water level, a downstream flow, an irrigation water supply amount, a total phosphorus concentration and a total nitrogen concentration; and a multi-target optimization model including agricultural water demand, ecological flow, flood control safety and water quality purification targets is solved in combination with an optimization NSGA-II algorithm, target control parameters are acquired, and a control instruction is generated to drive execution equipment.

The invention relates to the technical field of data quality evaluation, in particular to a quality evaluation system and method based on river channel data. The method comprises the following steps: obtaining multi-source river channel data, and carrying out parallel data fusion to obtain river channel fusion data to be evaluated; performing error fluid propagation simulation based on the to-be-evaluated river fusion data to obtain an error statistical result set; performing spatial deviation multi-scale analysis according to the error statistical result set to obtain an elevation error distribution diagram; randomly selecting an underwater point cloud sample based on the to-be-evaluated river fusion data, and performing spatial interpolation point cloud precision calculation on the underwater point cloud sample to obtain a point cloud precision error matrix; performing quality detection and correction on the to-be-evaluated river fusion data by combining the point cloud precision error matrix and the elevation error distribution diagram to obtain quality optimization data; and based on the quality optimization data, carrying out river channel map profile visualization to generate a river channel optimization map profile. The method is helpful for improving the precision and consistency of the river surveying and mapping data.

The invention discloses a geological disaster risk intelligent pre-judgment method based on deep learning, and relates to the technical field of geological disaster monitoring and early warning. According to the method, high-precision alignment and feature extraction can be automatically performed on monitoring data with different temporal-spatial resolutions and different physical meanings, such as optical remote sensing, radar measurement, laser point cloud and the like, uniform and information-rich representations are generated, a solid data foundation is laid for subsequent accurate prediction, and the limitation of data splitting application in a traditional method is overcome; a space-time diagram with slope units as nodes is constructed, an attention mechanism diagram convolutional network with hydrological directivity introduced is utilized, and the model can accurately describe the spatial propagation process that slope substances migrate and accumulate along with a confluence path and block a river channel under the rainfall condition; meanwhile, the time sequence module effectively learns the influence of the past hydrological state on the future evolution trend.

The invention discloses a hydrological data prediction method based on a convolutional neural network and a physical information neural network. Comprising the following steps: 1) utilizing an HEC-RAS hydrological model to generate cross sectional area, flow and water level elevation data of a key section of a target river channel in a preset time period as training reference truth values; 2) constructing space-time coordinates corresponding to the data as an input data set; 3) space-time input is processed through a physical information neural network model based on a convolutional neural network, the convolutional neural network extracts local heterogeneity features of a spatial position of a river channel through one-dimensional convolution, and a continuity equation and a momentum equation of a Saint-Venant equation set are embedded into a training process in a residual form through physical information constraint; and 4) finally, directly outputting a hydrological parameter prediction result of the target river channel. Therefore, under the environment that the observation data is deficient or missing, the hydrological parameter prediction precision and the model generalization ability are remarkably improved by fusing the simulation data and the physical mechanism constraint.

The embodiment of the invention discloses a riverway pollution detection method based on unmanned aerial vehicle inspection, and the method comprises the steps: firstly obtaining a label data set of a target riverway region, training an initial riverway pollution detection network based on the label data set after building the initial riverway pollution detection network, and obtaining a trained riverway pollution detection network; and inputting the obtained current multi-modal image of the river channel region into a river channel pollution detection network for pollution detection to obtain a river channel pollution detection result. The river pollution detection network optimizes a multi-modal feature fusion mechanism, and can avoid information interference caused by insufficient utilization of feature scale sensitivity, thereby effectively improving pollution detection precision. Meanwhile, the network is adaptive to edge equipment such as an unmanned aerial vehicle, the current multi-mode image of the river channel can be quickly processed, real-time pollution detection and early warning are realized, and the timeliness and practicability of river channel pollution monitoring are enhanced.

A method for reducing the danger of a dammed lake formed by a debris flow blocking a river, comprising: first predicting the accumulation range of a river-blocking debris dam during occurrence of a debris flow, dividing a river boundary into multiple areas on the basis of the elevation, and then performing roughness reduction treatment on the areas of the river boundary during a dry season. The present method reduces the roughness of a river boundary within the accumulation range of a river-blocking debris dam, so as to reduce the opposite bank dam height of the river-blocking debris dam and the storage capacity of the dammed lake, thereby achieving the purpose of reducing the danger of dammed lakes formed by debris flows and reducing the impact of debris flow disasters.

The invention discloses a river sediment scouring and silting simulation method, and belongs to the technical field of hydraulic engineering and hydrological simulation. In order to solve the problem that it is difficult to accurately simulate the river sediment erosion and deposition evolution trend in the prior art, the method comprises the steps that on the basis of actually measured topographic data and channel chart data of a target river reach, a water-sediment coupling mathematical model is constructed by adopting a non-uniform sediment unbalance sediment transportation theory, and the model interacts with a water power module and a wave module in real time through an MCT coupler; actually measuring a flood and water and sediment series data calibration verification model; and obtaining future water and sediment process data predicted by a plurality of climate modes, inputting the future water and sediment process data into the verified model, dividing river reaches according to the characteristics such as riverbed gradient and river channel curvature, and calculating the erosion and deposition amount of each sub-river reach in the future 50 years. The method can simulate water flow and sediment movement of rivers, reservoirs and lakes, can be applied to the fields of riverway regulation planning, flood control and disaster reduction decision making, reasonable development and utilization of water resources and the like, and provides important data support and scientific basis for construction and management of water conservancy projects.

The invention discloses a distributed flood forecasting scheduling model construction method based on sub-basins, particularly relates to the technical field of flood forecasting model optimization, and is used for solving the problem of local prediction failure due to spatio-temporal differences caused by neglecting parameter sensitivity of an existing flood forecasting model. A target drainage basin is divided into sub-drainage basins, hydrological and geographic feature parameters are extracted, and a cross-drainage-basin water conservancy facility topology network is constructed to dynamically correct river channel evolution boundary conditions; quantifying parameter sensitivity spatial diversity based on a Sobo index method, identifying a sensitive abnormal response region in combination with historical flood event distribution, and applying spatial lag correction through a river topology network to generate parameter sensitivity grade classification; constructing a dynamic weight matrix, and performing partition parameter calibration by adopting global optimization and local adjustment strategies respectively; and the hydraulic equilibrium constraint of the calibration result is verified based on the river topological relation, and a partition and grading flood discharge scheduling scheme is generated after iterative correction, so that the forecasting capability in a sudden flood scene is remarkably improved.

The invention discloses a DEM-based reservoir flood control dispatching temporary submerging area rapid generation method and device, and relates to the field of hydraulic and hydro-power engineering temporary submerging area definition. The method comprises the following steps: dividing a river channel higher than a reservoir inundation area into a plurality of units by obtaining or generating a reservoir reach DEM, a river channel section line, a normal water storage level inundation area range and water return meters under different working conditions; calculating the water level of each unit grid by adopting an inverse distance weighting method in an interpolation manner, and comparing with the DEM elevation to determine the grid meeting the condition; and merging the units after boundary tracking and smoothing, and erasing a normal water storage level submerging area to obtain a temporary submerging area. The method solves the problems that an existing method is long in time consumption and insufficient in precision, gives consideration to efficiency and precision, and provides reliable support for flood control dispatching decision making, submerging loss evaluation and the like.

The invention discloses an RCBAM model construction method for remote sensing inversion of river flow. The method comprises the following steps: 1) collecting remote sensing image data; (2) high-precision DEM production; 3) obtaining a central line of a river channel; 4) river width and gradient acquisition; 5) river section shape extraction; 6) calculating an open-current area; and 7) estimating the river flow. According to the method, the section shape of the river channel is obtained based on a high-precision digital elevation model (DEM), and the section area of the river channel is dynamically calculated by combining instantaneous water surface information obtained by a remote sensing image, so that the problem that defects exist due to the fact that the overflowing area is obtained by depending on an empirical formula in a traditional BAM flow model is effectively solved, and river flow estimation considering the shape characteristics of the river channel is realized. And efficient and accurate river flow remote sensing inversion can be realized.

The invention relates to a hydrodynamic force and water quality coordinated regulation and control method for a river channel and multi-pond communication system, which comprises the following steps of: quantifying a trade-off relationship between HRT and pollutant removal rate by coupling hydrodynamic force and water quality models, and introducing a dynamic regulation and control strategy; the problems of insufficient model separation and eutrophication risk control, single regulation and control means, disjunction of design parameters and engineering effects and the like in the prior art are solved, and the water quality improvement effect and the operation stability of the river-pond communication system are remarkably improved.

The invention discloses a flood regulation and control two-stage reverse deduction method under a large-scale watershed complex engineering system, which comprises the following steps: S1, constructing a watershed flood deduction model: constructing a flood control engineering system joint scheduling and flood routing coupling model according to watershed water system and flood control engineering characteristics; evaluating flood influence and disaster loss based on the coupling model; s2, two-stage reverse deduction based on the flood deduction model and the intelligent optimization algorithm: constructing a calculation framework based on the flood deduction model and the intelligent optimization algorithm, iteratively optimizing and dynamically adjusting scheduling parameters such as water volume, opening degree and the like by taking the minimum overall disaster as a reverse deduction target and taking the minimum watercourse overflow and collapse and the minimum flood diversion water volume as hard constraint conditions, and calculating the overall disaster of the flood deduction model and the intelligent optimization algorithm; and reverse deduction is carried out through a reverse deduction mechanism, and an optimal scheduling scheme considering flood control safety and disaster damage control is generated. The method has the advantages that fast and refined reverse deduction can be achieved, and the actual requirements of basin flood routing refined rehearsal, engineering regulation and control scheme fast optimization generation and the like are met.

The invention relates to the technical field of water pollution and sewage discharge, in particular to a data-driven water pollution source and pollution discharge period prediction method. Comprising the following steps: step 1, establishing a spatial-temporal feature database, and unifying spatial-temporal references of data sources; 2, meteorological-hydrological coupling characteristics are obtained; step 3, quantifying the pollution transportation contribution of the upstream and downstream sub-basins to the downstream; step 4, automatically associating pollution point sources with sub-basins, feature monitoring points and the like based on spatial topology, and constructing a knowledge spectrogram; 5, pollution source analysis and emission period prediction; step 6, dynamically adjusting the monitoring network to cover the pollution high-risk area; step 7, constructing a whole watershed pollution early warning map; through data driving, map construction, pollution / pollution source analysis and prediction, monitoring dynamic adjustment and a whole-basin pollution early warning map, accurate traceability and dynamic transmission simulation of whole-basin pollutants, remote monitoring of a river leader and intelligent operation management of a river channel are realized, and the safety and health of a water ecological environment are ensured.

The invention relates to a river flood flow monitoring method based on remote sensing image and hydrodynamic model fusion, and belongs to the technical field of flood monitoring. The method comprises the following steps: dividing flood levels in a river drainage basin by combining historical rainfall and flood conditions, drawing flood hydrographs of different flood levels, determining 24-hour design rainstorm of each level of flood, establishing a river one-dimensional hydrodynamic model by utilizing section survey data, and constructing a water level-water surface width relationship of a section to be measured; solving the model to obtain a water level-flow relation curve of riverway water rising and water recession under each level of flood, determining the recurrence level of the flood, calculating the current water surface width of the to-be-measured riverway section, obtaining the corresponding water level, and calculating the water level of the to-be-measured riverway section. And selecting a corresponding water level-flow relation curve according to the flood level of the river channel and the stage of the flood flow process, and substituting into the curve to obtain the flood flow of the section to be measured in the current time period. According to the method, through water level inversion of different levels and different stages, the accuracy of inversion of the flood peak flow by the satellite remote sensing image is improved.

The invention discloses a river spill flood routing rapid simulation method based on river adaptive segmentation and strong water balance constraint, and the method comprises the steps: collecting and processing basic data of a drainage basin, building a rapid calculation model of river spill flood inundation, and carrying out the rapid prediction and risk analysis of the river spill flood. Rapid and fine simulation of watershed river overflow flood is realized, four-pre requirements of watershed flood control fine simulation, real-time early warning and forecasting, rapid scene deduction and the like are met, and the method can also serve watershed flood control planning and design.

The invention relates to the technical field of water regimen forecasting, in particular to a river channel water regimen forecasting method and system based on digital twinning. The method comprises the following steps: arranging sensor nodes along a river channel and carrying out real-time monitoring on the river channel environment water regimen along the river channel to obtain real-time data of the water level along the river channel, real-time data of the flow along the river channel and real-time data of the weather along the river channel; performing digital twinborn construction and risk estimation division along the riverway to generate a riverway water regimen flood risk estimation period and a riverway water regimen drought risk estimation period; the method comprises the following steps: performing flood risk forecast processing in a river water regimen flood risk estimation period to execute corresponding river water regimen flood risk management decision-making work; and performing drought risk forecast processing in the river channel water regimen drought risk estimation time period to execute corresponding river channel water regimen drought risk management decision-making work. According to the invention, efficient and accurate water regimen prediction and decision support can be realized.