1326 results about "Flood control" patented technology

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

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 provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention discloses a reservoir scheduling decision support system for multi-objective optimization, and relates to the technical field of hydraulic engineering scheduling. According to the system, comprehensive optimization of flood control, power generation and ecological targets is realized through cooperative work of the multi-source dynamic constraint analysis module, the collaborative optimization module, the scheduling module and the correction module. A dynamic constraint boundary table is generated by collecting meteorological, hydrological and ecological data in real time. And matching a multi-target coordination rule through a physical constraint double-depth network algorithm, generating a Pareto feasible solution set, and mapping the Pareto feasible solution set into a coordination strategy of flood discharge flow and power generation output. In combination with a hydraulic model, flood control and power generation weights are adjusted, an executable dispatching instruction is generated, it is ensured that the ecological flow reaches the standard, and the influence of a downstream submerged area is optimized. And correcting the dynamic constraint boundary table according to actual monitoring data, and continuously optimizing a scheduling decision. Accurate management and multi-objective optimization of reservoir dispatching are realized, and the utilization efficiency of water resources is improved.

The invention discloses a cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

The invention relates to the technical field of flood early warning, and discloses an urban flood disaster early warning method and system based on artificial intelligence, and the method comprises the steps: collecting five types of information, i.e., meteorological perception, hydrological monitoring, geographic space, urban operation and social perception in real time, and obtaining multi-source data with precise space-time coordinates; through preprocessing, gridding space-time alignment and key feature screening, rainfall accumulation and confluence evolution related features are extracted; constructing a physically constrained space-time fusion deep learning model, and outputting a future ponding depth prediction result in combination with a multi-head attention mechanism; environmental changes such as urban terrains and drainage facilities are adapted through incremental updating and transfer learning; and fusing the ponding depth, the influence range and the regional vulnerability characteristics to generate multi-level early warning, and synchronously outputting a spatial distribution map, a time evolution trend and affected object evaluation information. According to the invention, urban flood control and disaster reduction decision making and public accurate risk avoiding can be effectively supported.

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 multi-target optimization scheduling scheme optimization method and system based on multi-party coupling, and belongs to the field of reservoir scheduling. The system comprises the steps that firstly, boundary conditions of a simulation system are determined, and then a reservoir multi-target optimization scheduling model is constructed; and then solving the reservoir multi-objective optimization scheduling model by using a non-dominated sorting genetic algorithm of the reference points, verifying the effectiveness of the solving method by using a standard multi-objective test function, and evaluating and optimizing a reservoir scheduling scheme by using a method of combining a grey target theory, a cumulative foreground theory and a fuzzy mathematical method. According to the system, the multi-objective optimization problem among flood control, power generation and ecology of the reservoir is coordinated, decision support is provided for formulating a reservoir dispatching scheme of multi-objective optimization dispatching of the reservoir, the contradiction among flood control, power generation and downstream river ecology can be effectively coordinated, and the flood control and power generation benefits are increased on the basis of considering the downstream ecological flow demand as much as possible.

The invention provides a hydro-junction water level adjusting method and system. Wherein the sediment deposition space-time atlas containing the sedimentary gradient is constructed and periodically corrected through scanning of an underwater robot and synchronous acquisition of satellite remote sensing data. A radar array is deployed at a bend section of a flood discharge channel, and vector distribution of a three-dimensional sediment flow velocity field is analyzed by using multi-frequency pulse signals. Through correlation analysis of the dynamic coupling sedimentary gradient and the flow velocity field, a correlation area of flow velocity sudden change and sediment deposition is accurately identified, and a constraint condition set including coordinate positioning and a flow-sediment matching rule is established. And in combination with real-time water level monitoring data, calculating and generating a dynamic adjustment parameter sequence of the opening degree of the flood discharge gate by using a constraint condition set, thereby realizing accurate regulation and control of the water level. According to the technical scheme, the flood discharge parameters are intelligently optimized, the water level of the hydro-junction can be accurately regulated and controlled, the sediment deposition problem of a high-sediment-content water area is effectively relieved, and safe operation and flood control efficiency of the junction are guaranteed.

The invention provides a river levee flood control pile foundation stress analysis method and system, and relates to the technical field of river levee flood control pile foundation stress analysis. Multi-channel sensors are arranged on the pile top, the pile side and the pile end, and the sampling frequency is automatically switched along with the flood stage; inverting the soil saturation based on displacement and pore pressure change, dynamically correcting the permeability coefficient and the effective stress, and outputting a space-time coupling parameter matrix; determining a hazard source for the monitoring point, constructing an elliptical search domain by taking the hazard source as a center, and recursively incorporating abnormal points to form a risk area; a seepage-structure full-coupling finite element model is established in the risk area, the outside of the area is simplified through an equivalent spring boundary, and a pile foundation stress-strain field and a safety coefficient are calculated through self-adaptive time step control; and finally, generating a risk report according to safety factor classification, marking a high-risk area, automatically improving the sampling frequency and triggering early warning.

The invention discloses a reservoir water regimen analysis method and system based on artificial intelligence, and the method comprises the steps: collecting original signals from multi-source monitoring indexes, such as water level, flow, rainfall and water quality, carrying out the standardized conversion through employing distributed calculation nodes, and forming a unified multi-source data set; based on this, using a feature extraction network to fuse upstream rainfall and reservoir flow, extracting space-time correlation features, and determining a short-term water level change trend; historical water quality abnormal data are integrated through a sequence prediction network, a time sequence is modeled, and potential pollution risks are judged; when the risk exceeds a threshold value, dynamically adjusting the weight of the prediction model, and generating an optimized water regimen simulation scene; finally, resource scheduling logic is fused, multi-scene risks are evaluated, and an optimization management strategy is output. Through deep fusion of spatial-temporal feature extraction and dynamic prediction, accurate water regimen prediction and flood control water supply decision support are realized, and the water resource management efficiency and the pollution prevention and control capability are improved.

The invention discloses a flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting the original data of a flood detention area site, and forming a monitoring array; a monitoring array is divided into a plurality of monitoring units according to geographic grids, a rainfall permeation model is established according to rainfall data and soil humidity data, a surface runoff field is constructed in combination with terrain elevation data, and the confluence direction of the runoff field is calibrated through water level data; based on a rainfall penetration model and a surface runoff field, generating a flood detention area inundation evolution sequence, segmenting into an inundation grade map according to a time step length, planning a flood detention area emergency evacuation path, matching with a road network in a geographic information system, generating an optimal evacuation path, and issuing graded early warning information to an early warning area. And sending a withdrawal instruction to residents in the flood detention area through the emergency broadcasting system. According to the invention, by establishing a partition early warning mechanism and a differential early warning strategy, accurate delivery and efficient transmission of early warning information are realized.

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 discloses a reservoir management optimization method and system based on digital twinning. The method comprises the steps of multi-source data acquisition and virtualization mapping, digital twinning modeling, reservoir risk early warning and reservoir management decision optimization. The invention relates to the technical field of reservoir intelligent management, in particular to a reservoir management optimization method and system based on digital twinning, and the method comprises the steps: constructing a virtualization data field through multi-Bezi wavelets and fractal interpolation, and carrying out the self-learning of dynamic weight fusion through a time sequence convolution network; constructing a macroscopic twinborn body and a microcosmic twinborn body, and coupling through an asynchronous message bus; constructing multi-source risk vectors of water pressure gradient, over-overflow water level, dam body stress and seepage flow, adopting clustering and spatial diffusion, generating a dynamic risk thermodynamic diagram, and performing early warning; by taking flood control safety, water supply guarantee and ecological environment as multiple targets, a multi-target particle swarm optimization model and flow constraints are established, an optimal scheduling scheme is solved and executed in real time, and high-precision simulation, precise early warning and intelligent scheduling are realized.

The invention provides an urban subway network toughness pre-disaster and post-disaster dual-stage optimization method under flood disasters. The method comprises the following steps: acquiring urban subway network information, road network information and flood disaster information; constructing an urban subway topology network and a road topology network; simulating flood depth space-time change in the subway and road network during the duration of the flood disaster, and determining a subway network failure station set based on the accumulated water depth; calculating flood control resource demand quantity based on the platform number, the platform type and the flood depth of each station in the failure station set; establishing and solving a pre-disaster-post-disaster dual-stage optimization model of urban subway network toughness to obtain an optimal pre-disaster site closing decision, an optimal flood control resource warehouse site selection decision and an optimal post-disaster restoration decision; wherein the double-stage optimization model is a double-layer planning model; and the upper-layer model and the lower-layer model are mutually coupled and optimized through a pre-disaster closing site set and a post-disaster repairing process.

The invention discloses a water network dam overtopping flooding simulation and disaster damage assessment method, and belongs to the technical field of flood control and disaster reduction. The method comprises the following steps: performing hydrodynamic analysis based on hydrological geographic data, and determining a potential overtopping area; for the potential overtopping area, simulating breach evolution based on a cumulative scouring energy criterion, and generating a breach flow process; simulating flood routing according to the breach flow process, and generating a spatialized disaster intensity field; and in combination with the disaster intensity field and social economic data, direct and indirect economic losses are evaluated, and a comprehensive disaster damage result is generated. According to the method, a physical criterion based on accumulated scouring energy is introduced, a comprehensive strength index, a three-dimensional vulnerability curved surface and an application graph neural network are adopted to simulate cascade failure, and the problems of breach trigger mechanism deficiency, single disaster strength measurement and disaster damage assessment staticization are solved.

The invention discloses a sponge city high-terrain rainwater management method and system, particularly relates to the technical field of rainwater prediction management, and is used for solving the problem of poor high-terrain rainwater prediction management scheduling. For a high terrain small watershed, data such as elevation, permeability coefficient, soil layer thickness and vegetation coverage are collected and preprocessed, a continuous physical field is generated, a facility pipe network is mapped, and then grid units are divided; a multivariate predictor is constructed based on grid cell center attributes, edge node increment fine adjustment is issued after cloud training, and a model is optimized through scene matching and federal learning; after rainstorm, automatically extracting observation and prediction residual increment to train a neural network and generating a confidence interval; and the edge nodes divide risks according to the confidence interval, the flood control water depth and the minimum infiltration threshold value and output gate opening and pump set power suggestions, so that the high-terrain small-watershed extreme rainstorm peak prediction precision and response time efficiency are remarkably improved, and refined risk perception scheduling and rainwater resource utilization are realized.

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 relates to the technical field of reservoir flood control dispatching, and discloses a dam hydraulic opening and closing control method, device and equipment and a storage medium. The method comprises the steps that multi-source hydrological data, weather forecast information and unit operation states of a current reservoir are collected; inputting the multi-source hydrological data and the weather forecast information into a preset hydrological prediction model, and outputting a hydrological prediction result; based on the hydrological prediction result and the real-time reservoir area water level and the unit operation state in the multi-source hydrological data, a gate opening control instruction is generated through a reservoir optimization scheduling model; and a hydraulic opening and closing system is adjusted according to the gate opening control instruction, and the current reservoir flood discharge gate opening is controlled. According to the method, data such as upstream incoming water, reservoir area water level and water consumption are utilized, weather forecast information is combined, a hydrological prediction model is constructed, a reservoir dispatching scheme is optimized according to a hydrological model prediction result, the reservoir area water level and unit operation conditions, and on the premise that flood prevention safety is guaranteed, reservoir dispatching is optimized, the reservoir area water level is automatically adjusted, and power generation benefits are improved.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention provides a flood control risk identification and early warning system and method for a reservoir group, and belongs to the technical field of flood prevention and fighting. According to the method, a standard data set is generated by collecting rainfall, reservoir water level, flood discharge flow and landform data and performing data processing. And based on the standardized data set, constructing a hydrological model of the reservoir group in combination with GIS data, simulating flood discharge capacities under different rainfall scenes, calculating a flood discharge capacity index, a flood control risk index, a downstream vulnerability index, an upstream flood formation index and a midstream flood propagation index, and carrying out weighted fusion to generate a comprehensive risk index. And according to comparison between the comprehensive risk index and preset low, medium and high early warning threshold values, determining a current risk level and issuing early warning information. Meanwhile, user feedback is received, and model parameters and an early warning strategy are dynamically adjusted. According to the invention, through a multi-source data fusion technology, a deep learning algorithm and a dynamic feedback mechanism, accurate identification and timely early warning of the flood control risk of the reservoir group are realized.

The invention provides an urban water network drainage joint scheduling method and system, and relates to the technical field of urban flood control and drainage, and the method comprises the steps: carrying out the fusion processing of multi-source monitoring data, and obtaining a feature vector; inputting the feature vector into a runoff prediction model for runoff prediction to obtain a comprehensive runoff prediction result; based on the comprehensive runoff prediction result, pipe network water flow and surface accumulated water simulation is conducted through a drainage hydraulic model, and a target simulation result is obtained; performing waterlogging risk assessment based on the target simulation result and the monitoring data to obtain a waterlogging risk level; selecting a corresponding scheduling generation mode based on the weather forecast data, the monitoring data and the waterlogging risk level; and determining a corresponding scheduling strategy based on the selected scheduling generation mode and the waterlogging risk level. According to the urban water network drainage joint scheduling method and system provided by the invention, accurate prediction simulation and scientific scheduling are realized, and the drainage efficiency is further improved.

The invention discloses a reservoir scheduling method based on a deep learning adaptive dynamic network and reinforcement learning, which relates to the technical field of reservoir scheduling and comprises the following steps: acquiring real-time water level data, meteorological data and radar image data of regional cloud and earth surface characteristics of a reservoir and performing data fusion on the three data; the fused data are input into a preset self-adaptive dynamic Transform network model, prediction output is obtained, and the prediction output is a reservoir water level sequence, a reservoir inflow sequence and a discharged flow sequence; constructing a reinforcement learning state space; the reinforcement learning state space is input into a preset reinforcement learning network, an action space of the reinforcement learning network comprises flood discharge, power generation flow and ecological flow, an optimization target of the reinforcement learning network is maximized accumulated discount rewards, and a reward function is a weighting function of a flood prevention reward item, a power generation reward item and an ecological reward item. According to the invention, the cooperative management efficiency of multiple targets such as reservoir flood control safety, power generation benefits and ecological protection is 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.

A cascade reservoir optimization dynamic control method based on different scheduling objectives of flood season staging comprises the following steps: step 1, performing flood season staging on a cascade reservoir based on rainstorm flood characteristics, and establishing a pre / main flood season'flood control-power generation 'and post flood season'flood control-water storage' optimization scheduling model; step 2, constructing a cascade reservoir dynamic control domain; step 3, based on the flood season staging result in the step 1 and the cascade reservoir dynamic control domain constructed in the step 2, constructing an optimization model taking the maximum cascade generating capacity and the minimum flood control risk as targets; setting constraint conditions of the optimization model; and solving the optimization model through a multi-target intelligent optimization algorithm to obtain a dynamic scheduling scheme. According to the method, optimal scheduling methods such as flood season staging and'aggregation-decomposition 'are adopted, flood control risks of drainage basin reservoir group scheduling are fully considered, and efficient utilization of cascade reservoir water resources is realized by utilizing a cascade reservoir flood control capacity complementation mechanism.

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 flood control and emergency rescue plan generation method, which comprises the steps of S1, collecting flood control and emergency rescue original data, performing preprocessing and feature extraction, and labeling and confirming extracted feature data to serve as a training set; s2, performing LoRA fine tuning of a low-rank decomposition matrix on the basis of a pre-trained large language model, and performing large language model training operation through the training set; s3, constructing a flood control and emergency rescue field ontology model, learning distributed representation of the knowledge graph by adopting a graph neural network technology, and dynamically updating the knowledge graph and executing reasoning query according to real-time dangerous case information by establishing a dynamic reasoning mechanism; and S4, constructing a multi-modal information fusion framework which is used for carrying out information fusion processing on the knowledge graph reasoning result and then guiding the big language model to generate an emergency plan as a control signal. According to the method, the technical bottlenecks of low plan generation quality, difficulty in knowledge updating, insufficient multi-source information fusion and the like of a traditional method in a complex dangerous case scene are effectively solved.

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.