893 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 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 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 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 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 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 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 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 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.

The invention discloses a dynamic topology mapping and GPU heterogeneous flood coupling model real-time early warning method, and solves the problems of inflexible coupling of a one-dimensional model and a two-dimensional model, low calculation efficiency, poor dynamic response capability and the like in traditional flood simulation. A dynamic topology mapping mechanism is introduced to respond to emergencies such as dike burst and overflow in real time, the calculation efficiency is remarkably improved by utilizing a GPU heterogeneous parallel architecture, rapid and accurate simulation of the whole process of flood formation, evolution and submerging is achieved, reliable technical support is provided for flood control dispatching and emergency decision making, and the method is suitable for large-scale popularization and application. The method comprises the following steps: constructing a two-dimensional hydrodynamic model based on an unstructured grid, establishing a dynamic topology mapping mechanism and realizing GPU parallel numerical calculation; a two-dimensional model lateral connection coupling mechanism is constructed, dam overtopping flow is calculated in real time, and breach flow dynamic calculation of an improved DAMBRK method is carried out; multi-target parameter calibration, multi-dimensional model verification and one-two-dimensional model GPU collaborative parallel computing are realized.

The invention discloses a hydrological flow monitoring method and a hydrological flow monitoring system based on a dynamic topology adaptive technology. The hydrological flow monitoring method comprises the following steps: performing hydrological flow monitoring in real time by adopting a trained online flow measurement model; the method comprises the following steps: firstly, acquiring actually measured hydrological element data in the same period of an online flow measurement data sequence, and establishing a river section hydrological element information base; then based on edge computing gateway equipment, flow velocity analysis is carried out by adopting a dynamic topology adaptive technology, parameter calibration is carried out on an online flow measurement model, and the average flow of a test section is calculated; performing comparative measurement analysis and precision evaluation on the flow online monitoring result based on the edge computing gateway equipment; and finally, outputting the finally trained online flow measurement model. According to the method, the applicability of the online flow measurement equipment under different hydraulic conditions and water flow characteristics can be further explored, and a key technical support is provided for application of flood control early warning and other aging sensitive scenes and construction of a cloud-side-end collaborative distributed hydrological monitoring network.

The invention relates to the technical field of electric digital data processing, in particular to a flood control plan generation method based on a knowledge graph and a large language model, which comprises the following steps: acquiring a flood control plan query problem; extracting water conservancy entities, relationships between the entities and keywords, and obtaining semantic vector representation of a flood prevention plan query problem; retrieving in a pre-constructed knowledge base to obtain a knowledge retrieval result; performing duplicate removal on the knowledge retrieval result to obtain a target knowledge set; and utilizing the large language model to generate a flood control plan text corresponding to the flood control plan query problem. According to the scheme, deep fusion of the structured knowledge and the semantic vector is realized, and the structured information of the knowledge graph is combined with the semantic vector, so that an information island is broken, efficient organization and accurate matching of a semantic space are realized, the breadth and depth of knowledge retrieval are improved, generation and reasoning of an intelligent plan are effectively supported, and the accuracy of knowledge retrieval is improved. And the generated plan is more comprehensive and accurate.

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'four-pre 'platform intelligent interactive grading method and system based on a big language model, belongs to the technical field of intelligent water conservancy, and can reduce the'four-pre' platform use threshold, improve the decision making efficiency and realize efficient coupling of expert experience and a data model. Comprising the following steps: receiving a natural language instruction input by a user; and dynamically selecting one of the interaction levels from L0 to L3 to execute operation according to the complexity of the user instruction and a decision autonomy demand. Compared with the prior art, the method has the following beneficial effects: 1) layered interaction: from auxiliary operation to autonomous decision, different user requirements are adapted, and the use threshold is reduced; 2) determinacy improvement: reducing large model output randomness by optimizing preset content and superposing specific problem constraints, and 3) efficiency optimization: automatically analyzing intention and calling the model, shortening a decision chain and improving flood control response real-time performance.

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 provides a river flow velocity intelligent monitoring method and system based on multi-scale optical tracking velocity measurement. A plurality of detectors with different characteristics are organically combined, and dynamic grid density adjustment based on flow velocity gradient is realized through adaptive grid adjustment; establishing a multi-dimensional track quality evaluation system through intelligent track evaluation; the real-time performance optimization realizes the performance optimization of multi-thread parallel processing, integrates an intelligent grid optical flow detection and dynamic density adjustment mechanism, and optimizes the detection precision and performance in real time according to the flow velocity gradient and the flow field complexity; a multi-track quality evaluation system is utilized, tracks are screened and fused from three dimensions of motion consistency, physical constraint and time stability, and the accuracy and reliability of data are remarkably improved. The method overcomes the problems that a traditional speed measurement method is complex in operation, limited in environment, insufficient in precision and the like, supports real-time processing, can be widely applied to the fields of water conservancy projects, environment monitoring, flood prevention early warning, scientific research and education and the like, and has remarkable advancement and practical value.

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 a cascade reservoir flood control water level combined application scheduling method and scheduling system, and relates to the technical field of cascade reservoir scheduling, and the method comprises the following steps: dividing into a plurality of sub-regions according to an administrative region, determining a sub-reservoir corresponding to each sub-region, and collecting the rainfall and historical flood discharge data; analyzing the water storage capacity of each sub-region at the moment corresponding to the historical data to form a training data set; constructing a flood season flood discharge prediction model, and obtaining the target flood discharge flowing into the sub-reservoir by using the prediction model; analyzing the water level change in the flood season according to the target flood discharge amount, and judging whether scheduling is needed; and if scheduling is needed, determining a drainage flow combination of each sub-reservoir, setting a constraint condition, and performing optimization through a multi-target genetic algorithm to obtain an optimal drainage flow scheme meeting constraints. According to the scheme, the scientificity, the accuracy and the intelligent level of combined dispatching of the cascade reservoirs are remarkably improved.

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 provides a physical attribute-external environment fused single building flood vulnerability assessment method and device. According to the method, the physical attribute vulnerability characteristics of the single building are automatically and efficiently extracted based on the deep learning model; adopting an entropy weight method to quantify external environment vulnerability indexes of the single building; a four-quadrant coupling analysis model fusing physical attributes of a single building and an external environment is constructed, a multi-dimensional building flood grading system is established, and visualization and accurate positioning of vulnerability grades are realized in combination with a GIS spatial analysis technology. According to the method, the technical level of urban flood vulnerability assessment is improved, the assessment result is more accurate and efficient, and a scientific basis and technical support are provided for urban flood control and disaster reduction decisions.

The invention discloses a real-time flood control scheduling method for a water project group. The method comprises the following steps: constructing a component system of project entities, disaster-bearing targets and law and regulation knowledge; real-time water and rain work conditions are obtained, the five-dimensional indexes are fused and combined with the aging correction factor, and a composite risk index is calculated; based on the real-time water and rain work condition and the assembly system, combining a marginal benefit ratio, and dynamically identifying an effective water engineering group; according to the threshold value of the composite risk index, implementing a low-level layered activation strategy, a middle-level layered activation strategy and a high-level layered activation strategy on the effective water project group; and performing multi-scene simulation optimization on the activated project by adopting a dual-mode workflow combining an agent model and a high-precision physical model, and outputting a final flood control scheduling scheme. According to the invention, accurate perception of risks, dynamic optimization of projects and fine configuration of scheduling strategies are realized.