2216 results about "Flood control" patented technology

The invention discloses a reservoir flood control monitoring system and method based on digital twinning, and relates to the technical field of reservoir flood control monitoring, and the system comprises a multi-source data collection module which is used for building a digital twinning model according to the real-time monitoring data of the multi-source data collection module, and transmitting the digital twinning model to a reservoir flood control monitoring module. The coupling module is used for receiving the fusion data set provided by the digital twin module, performing dynamic simulation on the fusion data set, and providing dynamic prediction results of dam seepage, deformation and flood routing; the dynamic early warning module comprises a dynamic threshold adjusting unit and an early warning unit; compared with a traditional early warning system with a fixed threshold value, the system can adapt to changes of dam body states and environmental conditions, identify potential abnormal events in advance, provide sufficient time for flood control of a reservoir to perform emergency response and scheduling decision making, and improve the flood control efficiency. Therefore, the disaster risk is effectively reduced, and the safety of reservoir operation is ensured.

The invention provides a drainage basin digital twinning construction method and system based on multi-source data fusion. The method is applied to the technical field of drainage basin digital twinning construction, and comprises the following steps: acquiring water level monitoring data, surface flood routing path data, weather forecast rainfall data and terrain slope change data; rainfall and topographic slope data are fused to generate a runoff distribution relation, and a high-risk area is identified in combination with a water level sudden change amplitude. A digital twinning preliminary framework is established, and related characteristic parameters are generated by utilizing reverse feedback of a flood path and water level data. And inputting meteorological data, constructing a twinborn model based on the boundary condition of the catchment area, and simulating flood direction abnormity and flooding early warning signals. And according to the early warning space-time distribution and the terrain elevation data, generating emergency schemes such as flood dividing gate scheduling and flood storage and detention area starting, and realizing risk hierarchical management and control. According to the technical scheme provided by the invention, the flood inundation prediction precision and the emergency response efficiency are improved, and precise decision support is provided for flood control of a drainage basin.

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 an urban inland inundation situation deduction method based on fusion of mechanism knowledge and deep learning, and the method comprises the steps: collecting data, such as weather, terrain and historical disaster situations, and constructing a standardized database in combination with an inland inundation production and convergence mechanism; constructing an urban inland inundation numerical twinborn model, and optimizing hyper-parameters to improve prediction precision by adopting finite element calculation and a multi-scale coupling method based on hydrological and hydrodynamic models; constructing a causal association network by using the numerical twinborn data, and identifying a core affected area; designing a situation deduction algorithm of a space-time diagram neural network, capturing rainfall waterlogging dynamic characteristics, and realizing advanced early warning and accurate deduction; based on the waterlogging intensity and the exposure and vulnerability of the disaster-bearing body, a risk assessment system is constructed, the disaster diffusion trend and the disaster risk are analyzed, and support is provided for flood prevention decision and emergency management.

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 discloses an urban flood control toughness critical state identification method and system considering multiple pressure coupling. Performing space-time unified preprocessing on original meteorological, hydrological, engineering and social economic data, and performing pressure characteristic decomposition after abnormal value detection and correction; analyzing a coupling relation between pressure elements based on mutual information entropy and Granger causality test, constructing a dynamic network and extracting network evolution characteristics; state index construction is carried out in combination with historical toughness evaluation data, and a toughness state evolution model is established through time sequence decomposition and scene simulation; critical characteristic analysis and threshold optimization are performed based on real-time monitoring data, and dynamic identification of the flood control toughness critical state is realized. According to the method, the critical state of the urban flood control system under multiple pressure coupling can be accurately identified, and the early warning accuracy is 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 a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention relates to the technical field of data evaluation processing, in particular to a rural waterlogging area waterlogging risk grade evaluation and early warning method based on multi-source data. The method comprises the following steps: acquiring geographic information, hydrological and flood data and flood control and drainage engineering data of a rural waterlogging area, and performing multi-source data fusion to obtain rural flood multi-source data; and performing river flood overflow calculation on the rural flood multi-source data to obtain river flood overflow data. According to the invention, through a data processing technology, a mode identification technology and a deep learning technology, rural multi-source feature data is subjected to fusion analysis, and risk level evaluation of a waterlogging area and a waterlogging sheet in a rural area is realized; a flood real-time forecasting and early warning model is constructed, when rainstorm occurs or a meteorological department issues rainstorm early warning, the rural flood flooding range is quickly predicted, and a risk avoiding transfer route is planned.

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 provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

The invention discloses an urban life body full-cycle monitoring system based on a digital twinborn technology, relates to the technical field of urban governance, integrates multi-source data such as GIS data, remote sensing information, surface permeability data and an underground drainage pipe network structure, quantifies a flood high-risk area, and provides an urban life body full-cycle monitoring system based on the digital twinborn technology. The AI flood prediction module deduces the ponding range and the drainage capacity under different rainfall situations by adopting extreme weather analogue simulation based on a historical rainfall trend, a current meteorological condition and a drainage network state, and dynamically adjusts a flood diffusion trend prediction model, so that the flood risk identification is more accurate; the intelligent emergency scheduling module automatically matches flood control resources, combines flood high-risk areas, ponding point locations and traffic flow analysis, optimizes a drainage pump station start-stop strategy, remotely regulates and controls an underground drainage gate, dynamically adjusts the overflow adjustment capability of a sewage treatment plant, and optimizes an emergency risk avoiding route in combination with an intelligent traffic management system. And the accuracy and the execution efficiency of the flood control scheduling scheme are improved.

The invention discloses an inspection well overflow point forecasting and early warning method and system based on digital twinborn. The system comprises a city sensing module, a digital twinborn body module, an inspection well forecasting module and a flood risk early warning module. The method comprises the following steps: firstly, perfecting a city knowledge graph to construct a city perception module; constructing a digital twin city; constructing an inspection well forecasting module; and constructing a flood risk early warning module. Based on a digital twin platform, through an Internet of Things network and a data flow processing framework, an urban hydrological hydrodynamic model is coupled, parameters of the coupled model are dynamically calibrated, and an inspection well is finely simulated; the method comprehensively displays the multi-temporal-spatial-scale urban development state flood inundation condition, tracks and reflects the actual operation state of an urban drainage system, dynamically evaluates the risk level and carries out real-time early warning, improves the urban emergency management response efficiency, and provides support for smooth development of urban flood control and drainage work.

The invention discloses a water level monitoring and management method based on multi-source information fusion, and relates to the technical field of water level monitoring and management. Comprising the following steps: acquiring a water level and water level related data by adopting a sensor, and acquiring surface feature information and meteorological data through GIS data; performing data cleaning and standardization on the collected data; a convolutional neural network is adopted to analyze infrared cloud picture data obtained by satellites to predict precipitation, a weighted average method is adopted to carry out multi-source information fusion, and water level prediction is carried out through a hydrological model; and performing real-time water level monitoring and water level management decision according to accurate water level data obtained by data fusion. Through the above steps, the system can provide a more comprehensive view angle for water level management by fusing multi-source information, and can more accurately predict the water level change trend, thereby optimizing the water resource management strategy and flood control measures.

The invention discloses a reservoir flood control dispatching system and method, and belongs to the technical field of reservoir flood control, and the dispatching system comprises an incoming water forecasting module which employs a reservoir incoming water forecasting model to forecast flood or runoff incoming water; the flood control dispatching model is used for carrying out flood control dispatching under the control of water balance and power balance; the reservoir real-time scheduling module comprises an incoming water forecast result correction unit which is used for accessing flood or runoff incoming water forecast results in real time and correcting the forecast results in real time; the scheduling model unit is used for selecting a scheduling scheme or model according to the scheduling model configuration and the incoming water forecast result, and correcting the scheduling model at a fixed time period; the scheduling result unit is used for calculating a scheduling result in real time according to the real-time incoming water forecast result and a real-time scheduling model or scheme; and the scheduling instruction unit is used for generating a scheduling instruction according to the scheduling result in a fixed format. The system improves the digitization level of reservoir dispatching and enhances the flood control capacity of the reservoir.

The invention discloses a UE5-based dynamic simulation method for digital twin flood inundation of small and medium-sized watersheds. The method comprises the following steps: 1, constructing a two-dimensional hydrodynamic numerical model; step 2, creating a flood inundation simulation UE5 plug-in project; step 3, creating a flood role class; 4, constructing a method for drawing triangular mesh primitives of the flood model; 5, constructing a flood hydrological feature and flood flow state rendering method; and 6, realizing a dynamic simulation process of digital twinborn flood submerging. According to the invention, dynamic rendering and high-fidelity visualization of an ultra-large data volume flood model in a small and medium-sized basin digital twinning three-dimensional scene are realized, and multi-dimensional flood inundation numerical simulation results of water level, water depth, flow velocity, flow direction and the like and complex flood flow states of backflow, convergent flow, bifurcated flow, overflat flow, turbulent flow and the like are expressed; the authenticity and accuracy of flood inundation simulation are improved, and the flood risk assessment and flood control plan decision support capability of small and medium-sized drainage basins can be enhanced.

The invention relates to the technical field of flood storage and detention area regulation and control, in particular to a flood storage and detention area risk-benefit dynamic game regulation and control method based on digital twinborn. Performing dynamic simulation on the real-time flood event based on a digital twinborn model to obtain probability distribution data; performing multi-target iterative optimization solution on the historical flood diversion regulation and control strategy based on the probability distribution data to generate a multi-target optimal balance set; generating a real-time controllable gate regulation and control instruction set on the basis of the digital twinborn model and the optimal tradeoff set; and in the process of real-time gate regulation and control through the gate regulation and control instruction set, carrying out quantitative evaluation on the regulation and control risk and benefit of the flood storage and detention area. According to the method, high-precision, dynamic and intelligent regulation and control of the flood storage and detention area are realized, accurate quantitative evaluation of risks and benefits of the flood storage and detention area is realized, a scientific basis is provided for flood control decision, and flood disaster loss and ecological influence are effectively reduced.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

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 drainage basin flood risk assessment method and system based on a particle recognition algorithm, and belongs to the technical field of flood early warning. Although the real-time flow of a river can be measured through an existing scheme, how to deduce and evaluate the flood inundation condition is not involved, and consequently the flood inundation condition around a river bank cannot be rapidly known. According to the drainage basin flood risk assessment method based on the particle recognition algorithm, the drainage basin spatial discrete model, the flow velocity calculation model, the flow simulation model, the two-dimensional hydrodynamic model and the flood risk assessment model are constructed, so that the section flow of the drainage basin can be calculated, and the flood inundation condition can be deduced and assessed; therefore, the flood inundation risk and inundation data around the water area can be quickly known, the requirement of flood risk analysis of the drainage basin can be met, the accuracy of flood disaster early warning is improved, and more data supplementation and management bases are provided for flood control of the drainage basin.

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 discloses an urban inland inundation simulation method based on an SWMM and SIMWE coupling model. The method comprises the steps that basic data and historical heavy rainfall data of a research area are collected and processed; rainstorm events in different recurrence periods are designed; an SWMM-SIMWE coupling model is constructed and verified; and simulating the urban inland inundation condition in a certain period of time in the future based on the designed rainstorm. According to the method, the SWMM model and the SIMWE model are combined and used for simulating an underground drainage system and surface runoff respectively, and water flow movement and accumulated water distribution under the complex urban terrain can be reflected more accurately and efficiently. According to the method, through a reasonable coupling mechanism, the problem that in the prior art, the urban drainage system and the surface runoff dynamic state are difficult to accurately and efficiently simulate is solved, and the water exchange efficiency and precision between the underground drainage system and the surface water flow are remarkably improved. The simulation result provides a scientific basis for urban inland inundation risk assessment and flood control and drainage system optimization, and is helpful for improving the disaster defense capability of cities.

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 discloses a multi-project flood control and disaster reduction method based on a four-pre-coupling model, and the method comprises the steps: constructing a reservoir-river channel-dike multi-project system flood control and disaster reduction forecasting model, constructing a reservoir-river channel-dike multi-project system flood control and disaster reduction early warning model, and constructing a reservoir-river channel-dike multi-project system flood control and disaster reduction rehearsal model. A flood control and disaster reduction plan model of a reservoir-river channel-dike multi-project system is constructed, a monitoring network is planned and designed according to a certain rule, and four-pre dynamic coupling including forecasting, early warning, rehearsal and pre-warning is carried out. Compared with the prior art, the method comprehensively considers the synergistic effect and influence restriction relation of flood control processes of a reservoir, a river channel and a dike, and realizes chain type dynamic connection of four pre-links of flood control and disaster reduction through a multi-engineering system coupling flood control four-pre dynamic coupling model; and flood forecasting precision, flood control and disaster reduction decision scientization and flood risk management refinement are realized.

The present invention discloses a reservoir flood control risk analysis and disposal plan generation system based on AI big model, including a data processing module: extracting valuable information from water conservancy plans and reports in various formats, converting it into structured data, and performing cleaning and quality improvement to form a data set for model training; a model fine-tuning and training module: selecting a basic big model, and training an intelligent generation model for reservoir flood control risk analysis and emergency disposal through fine-tuning technology according to needs; a human-computer interaction module: realizing effective interaction between users and the system, including a question-and-answer system, allowing users to query reservoir information, input flood event data for risk analysis, and obtain disposal plans; the system solves the problem that the existing technology of flood control disposal plan generation uses virtual reality technology and case reasoning technology. Although these methods can improve the accuracy and effectiveness of the plan, they can only reason for specific reservoirs and events and lack universality and generalization capabilities.

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.