590 results about "Flood control" patented technology

The invention discloses a reservoir real-time scheduling simulation system based on a deep learning algorithm, and belongs to the technical field of intelligent water conservancy and artificial intelligence. Aiming at the problems of low prediction precision, poor multi-target coordination capability, weak coping uncertainty and the like of a traditional scheduling system, the system is designed to acquire hydrological, meteorological, water quality and engineering safety data through a multi-source data acquisition unit, and a multi-dimensional feature tensor is generated after preprocessing and fusion; the dispatching center server adopts an STGCN-LSTM mixed model to achieve high-precision prediction and uncertainty quantification of the water inflow process in the future 7-30 days, a reservoir hydrodynamic model and an MO-PPO algorithm are combined to complete multi-scene simulation and multi-target optimization decision, and an AF-DT mechanism dynamically adjusts the dispatching rule priority. According to the system, a sensing-decision-execution-feedback closed loop is constructed, the scheduling adaptive capacity and robustness are improved, the synergistic interaction of flood control, water supply, power generation and ecological protection is realized, and the system is suitable for real-time intelligent scheduling of large and medium reservoirs.

The invention discloses a reservoir group-lake system flood control risk adaptive analysis method and system. The method comprises the following steps: acquiring a basin hydrological sequence and a pre-configured physical causal constraint matrix, and constructing a VineCopula joint distribution and initial Bayesian network conforming to physical topology according to the basin hydrological sequence and the pre-configured physical causal constraint matrix; risk sensitive weights of the sample points are calculated based on the Bayesian network, and weighted sampling density inclining towards the high-risk area is constructed; generating a self-adaptive scene set based on the density, solving an optimal scheduling scheme, feeding back a scheduling result to the Bayesian network to update parameters, carrying out closed-loop iteration of perception-sampling-scheduling-learning until risk assessment is converged, and determining a comprehensive flood control risk assessment result of the system based on the converged risk factor Bayesian network. According to the method, the problems of poor physical interpretability and scarcity of extreme risk samples of a traditional model are solved, and accurate capture and quantification of extreme risks of a complex flood control system are realized.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

The invention discloses an intelligent early warning method and system for snow-melting flood. The method comprises the following steps: constructing digital twin integrating snow-melting confluence and hydrological hydrodynamic force; collecting multi-source monitoring information, and forming driving data through quality control and spatio-temporal interpolation; driving data are input into digital twinning, and model parameters and states are updated through data assimilation; simulating snow-melting runoff, water level and flow to obtain snow-melting flood probability prediction; the method comprises the following steps of: solving a river reservoir gate pump scheduling scheme by using a multi-objective evolutionary algorithm by taking reservoir outlet flow, gate opening and pump station starting and stopping as decision variables, calculating a water level, flow and a submerging range in digital twinning, and selecting a target scheme according to an evaluation rule to generate a scheduling instruction and graded early warning information; monitoring data is collected during scheduling execution and compared with probability prediction, and when the deviation exceeds a threshold value, data assimilation and scheduling scheme updating are triggered. According to the invention, the snow-melting flood forecasting precision and flood control scheduling collaboration are improved.

The invention discloses a flood control decision-making method and system based on multi-agent cooperation, and relates to the technical field of artificial intelligence and water conservancy information.The flood control decision-making method comprises the steps that a decision-making thinking chain template guided by domain business logic is constructed, and the flood control decision-making process originally depending on artificial experience is converted into an interpretable and traceable structured reasoning process; modularized disassembly is carried out on complex tasks in flood control business to form a standardized decision sequence, and a business framework is provided for the reasoning process of a large language model, so that each step of reasoning must be aligned with a preset decision target and call a specified knowledge demand; therefore, the common logic jump and constraint omission problems of a general large model in a professional scene are completely eradicated from the working principle, when a system processes user query, automatic matching is carried out, step-by-step advancing is carried out along the thinking chain template, and an intermediate result highly consistent with business operation is generated; effective alignment of artificial intelligence reasoning and domain expert thinking modes is realized.

The invention relates to the technical field of data processing, in particular to an intelligent supervision method and system based on a data visualization platform, and the method comprises the steps: collecting multi-source water conservancy data through dual-channel redundancy check, converting the multi-source water conservancy data into a standardized space-time matrix, carrying out the data cleaning through a containerization adaptation and variational auto-encoder, and marking the confidence; hierarchically storing the data based on a confidence threshold, and generating a routing strategy through reinforcement learning to distribute data fragments; calculating a flood peak evolution prediction result by using space-time diagram convolution, calculating an output risk assessment value by combining a Bayesian network and long and short-term memory, and activating federal learning parameter updating when the confidence coefficient is insufficient; the digital twin model loads physical constraint parameters to deduce a flood control scene, and a scheduling instruction set and a rehearsal animation are generated through multi-objective optimization; and dynamically rendering to generate an interactive visual interface, associating a causal deduction path, and triggering federal model parameter updating by user feedback, thereby realizing dynamic collaboration of the forecasting and early warning rehearsal plan function chain.

The invention discloses a drainage basin real-time flood control scheduling risk dynamic early warning and emergency scheme making method, which comprises the steps of obtaining a real-time total element information tensor and an engineering flood control design parameter set, and calculating to obtain a real-time hierarchical risk entropy time sequence front section based on safety margin distribution, and inputting real-time data and pre-constructed entropy flux Markov graph structure data into the trained entropy perception double-flow prediction model for reasoning to obtain a future risk prediction set. And a dynamic risk early warning result set is generated in combination with a preset entropy level threshold and a future failure chain probability, and an emergency scheduling scheme set is generated on the basis of meeting safety margin constraints and entropy reduction targets. According to the invention, dynamic early warning and decision making of strong physical-risk coupling are realized.

The invention discloses a rainfall intensity rapid identification method and system based on multi-sensor data fusion, and relates to the technical field of hydro-meteorological monitoring and urban drainage scheduling, and the method comprises the steps: obtaining a historical rainfall data set, drawing a peak curve, and extracting an evolution sequence feature before a peak value; correlation analysis is carried out on the data and complete rainfall process characteristics to establish a historical rule model; rainfall intensity data are collected in real time through multiple sensors, and space-time dimension features are fused; matching the fusion features with historical features, and screening target historical events; constructing a rainfall reproduction prediction model to reconstruct a current rainfall evolution process; and finally, calculating an inflow load based on a reconstruction result, and generating a dynamic drainage scheduling scheme. According to the invention, accurate prediction of the rainfall process and intelligent scheduling of the drainage system are realized, and the urban waterlogging prevention capability is improved.

The invention discloses a multi-dimensional flood risk assessment method and system oriented to personnel, traffic and economy. The method comprises the following steps: acquiring multi-source data, and generating flood spatio-temporal evolution data through hydrodynamic simulation; constructing a time-dependent traffic network driven by flood power, wherein the road traffic capacity is dynamically attenuated along with hydrodynamic parameters; on the basis of a space-time racing mechanism, calculating the time difference between the personnel danger arrival time and the shortest evacuation time consumption, and constructing an evacuation time margin and evacuation success probability model so as to dynamically correct the static personnel exposure risk and obtain a personnel safety risk index; and in combination with physical state attenuation and asset exposure characteristics, traffic operation and economic loss risk indexes are calculated respectively, and multi-dimensional fusion evaluation is carried out. According to the method, the problem that dynamic interaction between flood routing and personnel evacuation is neglected in traditional static assessment is solved, accurate calculation of personnel trapped probability and critical road section rush-through marginal contribution is realized, and scientific decision support is provided for flood control command and emergency rescue.

The invention discloses an urban inland inundation dynamic early warning method fusing multi-source data and a high-performance numerical model, and belongs to the technical field of urban flood control and disaster reduction and disaster early warning, and the method comprises the following steps: 1, obtaining and preprocessing multi-source data; 2, constructing a high-performance urban flood coupling model; step 3, carrying out assimilation correction on the ensemble Kalman filter; 4, performing rolling prediction and dynamic updating; and step 5, risk identification and early warning release. According to the method, the advantages of multi-source data can be fully fused, and the problems of inaccurate rain condition and unclear waterlogging condition in rapid early warning of urban waterlogging are solved; through multi-source data fusion and high-performance numerical model real-time assimilation correction, minute-level rolling simulation and dynamic correction are achieved, the regional refined ponding risk can be forecasted in advance, the problems that traditional early warning is low in precision, updating lags behind, and the model lacks the self-updating capacity are solved, and the practical value is remarkable.

The invention relates to a swarm intelligence-driven cascade reservoir autonomous negotiation scheduling method. The method comprises the following steps: firstly, generating a scheduling basic data set; respectively packaging each reservoir node of the cascade reservoir group into an independent reservoir unit body; each reservoir unit carries out parallel computing and distributed negotiation through a group fusion negotiation strategy based on interaction data of a local scheduling basic data set and a neighborhood reservoir unit, and a preliminary scheduling scheme is generated; the group fusion negotiation strategy is based on the function types of the reservoir units, adopts a distributed negotiation mode of a contract network protocol or a bidding mechanism, takes the minimum transaction cost among the reservoir units as a core objective function, and combines flood control, power generation and ecological multi-objective weight coefficients to determine a preliminary scheduling scheme; and carrying out digital twinborn simulation verification and fine tuning to obtain a final scheme. In case of exception, preferential intra-group coordination is realized, and in case of invalidation, cross-group linkage is realized. The method improves the scheduling efficiency, guarantees the multi-target balance of flood control, power generation and the like, and enhances the anti-risk capability.

The invention discloses a hydrological forecasting method based on multi-feature combination and a Transform model. The method comprises the following steps: acquiring measured data of a target watershed hydrological station, building a physical hydrological model, acquiring output data and derivative feature data of the physical hydrological model, and integrating to obtain basic hydrological data; creating enhanced hydrological physical features, and forming a multi-dimensional original feature pool; constructing a plurality of combination strategies based on the basic hydrological data and the multi-dimensional original feature pool; capturing a long-term dependency relationship of the hydrological time sequence by using an improved Transform model; and the model performance is improved through automatic hyper-parameter optimization. According to the method, the influence of different input feature combinations on the flood forecasting precision is highlighted, and effective technical support is provided for water resource management and flood control and disaster reduction.

The invention discloses a flood forecast evaluation method adapted to reservoir dispatching characteristics, which comprises the following steps: respectively driving the same hydrological model by using historical observation rainfall and forecast rainfall, quantifying the error contribution of the hydrological model and rainfall forecast through double-track difference, and constructing a sample evaluation weight in combination with a rainfall score; constructing a weighting condition error distribution model based on the weight, and generating a reservoir flow forecasting scene set containing physical cause characteristics; and inputting the scene set into a reservoir scheduling model containing flood control and benefit-making rules, and outputting a comprehensive scheduling risk index containing a downstream over-alarm risk and water abandoning loss through water balance and downstream evolution calculation. According to the method, the statistical weight is corrected through error attribution, the abstract forecast error is converted into specific engineering risk and economic loss, the hidden risk working condition which is difficult to find by a traditional statistical index is effectively identified, and a quantitative basis is provided for risk decision making of reservoir dispatching and targeted optimization of a forecast system.

The invention discloses a distributed waterlogging and ponding digital map prediction system based on edge calculation, and relates to the technical field of urban flood prevention intelligent decision making. The system comprises a distributed intelligent acquisition module, an edge integrated processing module, a multi-stage collaborative analysis module and a visualization and decision output module. Collecting multi-source heterogeneous data through multiple types of sensing equipment; calling a local digital elevation map slice and a lightweight hydrological model, generating local waterlogging state information and self-calibrating model parameters; key information is screened based on double threshold values of information entropy change and accumulated water depth change rate, multi-monitoring-point data and a drainage pipe network topological relation are fused, and regional-level inland inundation situation analysis and accumulated water and recessive water trend prediction are achieved; and generating and updating a whole-region distributed ponding digital map, and outputting graded early warning and auxiliary decision information. According to the method, distributed acquisition and edge calculation are combined, the monitoring real-time performance and precision are improved, and the problems that traditional monitoring is high in delay, low in precision, fuzzy in decision and the like are effectively solved.

The invention discloses an urban inland inundation prediction method and system based on space attention and depth fusion, and relates to the technical field of urban inland inundation prediction, and the method comprises the steps: laying monitoring points in a waterlogging-prone risk area, and constructing a dense perception network; constructing a dynamic space attention network, quantifying rainfall-ponding space correlation, calculating a dynamic influence weight through a learnable weight matrix, and generating an attention weight matrix to represent the influence of rainfall points on ponding points; training a long-short-term memory network model, and fusing the feature representation of the spatial correlation information and a historical ponding depth sequence to carry out ponding depth time sequence prediction; and based on topographic feature extraction, physical guide diffusion and point time sequence prediction data conversion, expanding topographic-guided point-surface data, and generating a surface region ponding risk map. Through fusion of a spatial attention mechanism and deep learning, rainfall-ponding spatial heterogeneity association is effectively captured, the accuracy and timeliness of urban inland inundation prediction are improved, and scientific decision support is provided for urban flood control and disaster reduction.

The invention relates to the technical field of hydrological prediction, and discloses a flood prediction method and system based on machine learning, and the method comprises the steps: constructing a multi-source data probability representation layer, and converting multi-source hydrological data into probability distribution representation through a variational auto-encoder; a Bayesian depth feature extraction layer is constructed, and a random weight neural network is adopted to extract hydrological features from data represented by probability distribution; constructing a causal structure learning layer, and learning a causal relationship between variables from hydrological features by using a causal discovery algorithm; a probability inference prediction layer is constructed, and prediction distribution of hydrological variables is generated through a Markov chain Monte Carlo method; an interpretable anti-fact analysis layer is constructed, and hypothesis analysis and decision evaluation of a flood scene are supported; according to the method, the transformation from determinacy prediction to probability prediction is realized, the interpretability is enhanced through causal learning and symbol probability hybrid reasoning, and reliable technical support is provided for flood control decision making.

The invention discloses a double-layer optimization method and system for real-time flood control scheduling of a series reservoir group. The method comprises the steps that the maximum feasible impounding boundary of each time period is calculated based on the discharge capacity and storage capacity constraint of each reservoir; constructing a space-time risk response kernel matrix, encoding lag response and attenuation intensity of flood routing, constructing a dynamic risk potential energy index in combination with real-time reservoir capacity, and solving space distribution water quantity of each reservoir by taking minimization of the index as a target under physical boundary constraint; the space distribution water quantity is used as a guide target, an evolutionary algorithm embedded with a physical consistent projection operator is adopted, and iterative optimization is carried out on hourly output flow of each reservoir. According to the method, through matrix coding of the space-time risk and introduction of manifold projection solution, the problem that upstream and downstream hydraulic space-time coupling and strong physical constraint are difficult to solve is effectively solved, and the physical consistency and peak clipping efficiency of a scheduling scheme are improved.

The invention discloses a watershed water level missing data interpolation method, which belongs to the technical field of water level data interpolation, and comprises the following steps: (1) data preprocessing: collecting hourly water level data and hydrological data of a plurality of stations of a target river reach, carrying out integrity and consistency inspection, and evaluating the data quality grade of each station; (2) model construction: respectively constructing an interpolation model based on a time sequence, an interpolation model based on spatial features and an interpolation model based on an interpolation algorithm based on the data quality grade; (3) sub-scene model selection: formulating an interpolation model selection strategy according to a data missing type and missing duration, and performing data interpolation; and (4) verifying interpolation precision through evaluation indexes, and outputting repair data meeting requirements. According to the method, the problems of single adaptive scene and insufficient precision in the prior art are solved, high-precision and stable interpolation of the water level data under multiple missing scenes is realized, and the requirements of hydrological analysis, flood control scheduling and shipping safety are met.

The utility model belongs to the technical field of flood control devices, and particularly relates to a flood control device convenient and fast to fill, which comprises a flood control device body, the flood control device body comprises a flood control blocking box, a filling groove is reserved at the top of the flood control blocking box, and a handle is fixedly connected to the inner wall of the filling groove. The top of the flood prevention blocking box is provided with a splicing hole, the bottom of the flood prevention blocking box is in threaded connection with an insertion rod, the surface of the flood prevention blocking box is fixedly connected with a sealing mechanism, the sealing mechanism comprises a bottom sealing gasket, and the surface of the bottom sealing gasket is fixedly connected with the bottom of the flood prevention blocking box; according to the anti-flood wall, a user can conveniently and rapidly fill the interiors of the filling grooves through the arranged anti-flood blocking boxes, the anti-flood blocking boxes can be rapidly spliced into the anti-flood wall body through the splicing holes, the inserting rods, the clamping grooves and the clamping blocks, meanwhile, splicing seams are sealed through the bottom sealing gaskets and the side face sealing gaskets, and therefore the anti-flood wall body is formed. And the use convenience of the flood control device is improved.

The invention relates to the technical field of hydraulic engineering digital twinning, and discloses an avionics hub dynamic flood digital twinning deduction method and system fusing a hydrodynamic model, and the method comprises the steps: collecting multi-source observation data to form a space-time coding original data set, constructing a hydrodynamic boundary parameter set, and carrying out the digital twinning deduction of the dynamic flood of an avionics hub; constructing a two-dimensional unsteady state hydrodynamic calculation model based on the parameter set, assimilating newest monitoring data online to update model parameters, mapping a hydrodynamic simulation state field to a three-dimensional terrain grid to generate a three-dimensional twin rendering scene, and calculating a submerging index and a scheduling benefit index based on the scene; and a scheduling optimization scheme set is generated and an execution control instruction is issued, so that dynamic deduction, multi-target optimization scheduling and closed-loop control of the flood process of the avionics hub are realized, and the accuracy and timeliness of flood control decision are improved.

The invention discloses a calculation method of a compound section river channel beach and groove excess flow ratio considering vegetation influence. According to the method, the section of the riverway is divided into the vegetation area and the non-vegetation area based on measured data, the vegetation density, the stalk diameter, the underwater height and other characteristic parameters are obtained, and the roughness of the riverway under the vegetation condition is deduced by introducing the vegetation additional water blocking area. After a river section is divided into vegetation areas on the two sides and a non-vegetation area in the middle, the water passing area and the average water depth of each area under the typical water level are calculated respectively, then the excess flow of each subarea is calculated, and finally, the excess flow ratio of the flood plain to the main channel is expressed through the ratio of the excess flow of the vegetation areas to the excess flow of the non-vegetation areas. The method is simple and feasible, and can be used for river flood control and treatment.

The invention provides a reservoir water rain condition monitoring system and a monitoring method, the system constructs a dual monitoring system of physical observation reference and electronic automatic acquisition, a customized stainless steel water gauge and a multi-element acquisition device are deployed on key sections of a reservoir dam and a drainage basin, and a front-end edge calculation module is utilized to invert the flow in real time. And after the data is transmitted to the cloud platform, intelligent management and control are carried out through the four-pre functional module. The system displays the watershed situation based on a GIS graph, integrates an online flow measurement algorithm to generate a water and rain condition process line in real time, and realizes visual supervision by combining video monitoring and OSD data superposition. The core multi-target optimization scheduling model comprehensively considers flood control safety and power generation benefits, generates an optimal scheduling scheme through simulation deduction, and provides a closed-loop control interface. The problems of low monitoring precision, scheduling decision lag and the like in the prior art are solved, and the automatic management level and the water resource utilization rate of the reservoir group are remarkably improved.

A flood similarity intelligent analysis method based on multi-mode Transform and comparative learning comprises the steps that firstly, static features are processed through a grouping full-connection network, and the characterization capacity is enhanced through feature specific transformation; a CNN-Transform-Attention hybrid network is constructed to extract dynamic process line features, a convolutional layer captures local morphological features, a Transform encoder captures a global time sequence dependency relationship through a self-attention mechanism, key hydrological stages are adaptively focused through an attention weight, multi-modal features are adaptively integrated by adopting a gating fusion mechanism to generate unified embedded representation, and the dynamic process line features are extracted by adopting a convolutional neural network (CNN)-Transform-Attention hybrid network. A comparison learning and difficult sample mining strategy is introduced, discriminative characterization is learned in a low-dimensional embedding space through a comparison loss function training model based on the Euclidean distance, and the similarity is mapped into an interpretable probability of a [0, 1] interval; the technical problem that multi-source heterogeneous features are insufficient in utilization is effectively solved, accurate quantification and interpretable evaluation of flood similarity are achieved, and technical support is provided for flood forecasting, historical flood matching and flood control dispatching decision making.

The invention provides a flood inundation situation intelligent perception and lightweight analysis model construction method, and relates to the technical field of hydrological monitoring and flood control and disaster mitigation. Unmanned aerial vehicle LiDAR point cloud data and satellite multispectral image data are acquired, a differential manifold topographic representation model is constructed, the earth surface is regarded as a Riemannian manifold, and complex topographic features are accurately described; a lightweight submerging calculation model is constructed based on a manifold diffusion theory, and a Laplace-Beltrami operator and a multi-scale solving strategy are adopted to realize rapid and accurate prediction of a flood submerging range and water depth; through a multi-terminal early warning information pushing mechanism, full-process intelligent support is provided, the simulation time is shortened to be within 2 hours from traditional 24 hours, the submerging range prediction goodness of fit reaches 89.7%, the water depth prediction root-mean-square error is controlled to be 0.35 m, and the flow velocity prediction error in a gradient sudden change area is reduced by 12%.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

This invention discloses an ecological scheduling method and its application based on fish reproductive needs, belonging to the field of environmental protection technology in water conservancy and hydropower engineering. This invention specifically addresses the challenge of protecting fish that lay adhesive eggs. By establishing a population reproductive characteristic adaptation domain model (Ω(pi)), it quantifies for the first time the dynamic response relationship between water level fluctuations (proximal factor) and water temperature and flow velocity (terminal factor), breaking through the limitations of traditional ecological scheduling that only focuses on a single hydrological indicator. It achieves multi-objective collaborative optimization, balancing engineering needs such as flood control, power generation, and water supply with ecological protection through a dynamic adjustment model of ΔH(t). Through proximal factor monitoring and terminal factor feedback, it corrects the water level adjustment ΔH(t) in real time, improving the response speed compared to traditional methods. Furthermore, by dividing the control period according to the key processes of the fish life history, the water level fluctuation limit during the spawning period is more precise, providing an innovative technical method for ecological scheduling in water conservancy projects.

The invention discloses a gate distributed operation scheduling method and system. According to the method provided by the invention, a'global-regional-device 'three-level hierarchical collaborative architecture is constructed, and the overall operation condition of the drainage basin is identified and the global scheduling instruction with maximized revenue is generated through the global optimization server based on the reinforcement learning model under the driving of the dynamic reward function; and the achievement of high-level targets such as water resource utilization efficiency or flood control safety is ensured. And the cooperative control gateway responds to the global scheduling instruction so as to carry out data fusion and cooperative analysis on data in the region through a supply and demand balance model, and the global instruction is converted into a cooperative scheduling strategy adaptive to the region characteristics. And finally, the control device accurately executes the localized instruction to form a sensing-decision-execution closed loop. And self-learning and self-optimization of the scheduling strategy are realized, and gate number expansion and scheduling target dynamic adjustment are supported.

The invention provides a GNSS (Global Navigation Satellite System) water vapor chromatography and deep learning-based flood forecasting method. The method comprises the following steps: S1, collecting GNSS, meteorological, flood and geographic data; s2, the PWV is estimated through a PPP equation and a Saastamoinen model; s3, grid water vapor density is calculated based on the three-dimensional water vapor chromatography technology; s4, constructing a Transform model, and performing rainfall prediction by taking PWV, water vapor density and the like as input; and S5, constructing a CNN-LSTM model based on rainfall prediction data to carry out flood forecasting. According to the rainfall and flood forecasting method established by the invention, GNSS multi-source and multi-mode navigation positioning signals are effectively utilized, the rainfall and flood forecasting method is not influenced by cloud and rain, all-weather observation can be realized, the water vapor gathering and conveying process in a three-dimensional space before rainfall can be carefully captured, the rainfall inversion process is optimized, the flood forecasting precision is improved, and the rainfall and flood forecasting period is prolonged; and a new thought is provided for flood control early warning of the drainage basin.

The invention provides a synthetic aperture radar (SAR) image-based water level extraction method, system and equipment. The method is realized based on a dihedral angle reflection mechanism formed by a vertical column-shaped target and a water surface, and comprises the following steps: obtaining a time sequence SAR image of a monitoring area and three-dimensional space information of the vertical column-shaped target; establishing a mapping relation between SAR image coordinates and geographic coordinates; determining a water surface secondary scattering imaging area based on the target three-dimensional information, SAR imaging geometric parameters and a dihedral angle reflection mechanism; extracting distance direction coordinates of actual secondary scattering feature points in the area through oversampling and maximum value searching; and finally, according to the characteristic point coordinate change, the distance Doppler principle and the geometrical relationship, the water level height and the time sequence change value thereof are calculated. All-weather, high-precision and large-range water level monitoring is achieved, the defect that a traditional method is limited by weather, terrains and equipment is overcome, and the method is suitable for the fields of water conservancy management, flood control, disaster reduction and the like.

The invention discloses a water conservancy flood prevention decision-oriented key element identification and causal modeling method and system, relates to the technical field of water conservancy flood prevention scheduling decisions, and aims to solve the problems of difficult element identification and complex causal relationship in current flood prevention decisions. Comprising the following steps: constructing a flood prevention three-dimensional element system of basic elements-target elements-control elements; performing structured processing on the three-dimensional element data to generate a decision modeling data set in a unified format; identifying credible key elements through a three-stage algorithm of relevance preliminary screening, causal modeling and expert verification; constructing an IMaGES-DBN fusion causal model, and depicting the dynamic change of the causal relationship in flood evolution; introducing bounded linear sequential logic to optimize key element weights; and verifying and iteratively optimizing the model by adopting a statistical model. According to the invention, flood risk rapid identification and early warning are realized, flood prevention command automation, intelligence and high reliability are improved, and the method is suitable for scenes such as drainage basin flood control scheduling and hydraulic engineering safe operation.