269 results about "Water dynamics" patented technology

The invention provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

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

The invention provides an optical complex water body suspended sediment monitoring method based on man-machine cooperation, and belongs to the technical field of remote sensing water environment monitoring. The method comprises the following steps: acquiring a multispectral remote sensing image of a target area, preprocessing the multispectral remote sensing image, and performing space-time fusion to obtain a spectral data set with high space-time resolution; carrying out manual labeling on the spectrum data set, constructing a suspended sediment concentration classification data set, and carrying out synthesis expansion on a minority class of samples to generate an equalization training set; training a suspended sediment concentration inversion model by using the equalized training set to obtain an initial suspended sediment concentration predicted value, and dynamically optimizing the spectral feature weight and the classification boundary of the suspended sediment concentration inversion model based on expert knowledge; and locally correcting the initial suspended sediment concentration predicted value and diffusing the initial suspended sediment concentration predicted value to a global range based on a hydrodynamic law and a space consistency constraint to obtain a global suspended sediment concentration predicted value. The method combines remote sensing image analysis, machine learning and expert knowledge to realize high-precision inversion of SSC.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention provides a highway tunnel decision-making maintenance system and a maintenance method thereof. The method comprises the steps of obtaining tunnel static and dynamic data and tunnel surrounding geological environment data; according to the geological environment data, carrying out geological risk analysis by adopting a geological statistical simulation algorithm and a time sequence decomposition algorithm to obtain a safety risk assessment result of the tunnel structure influenced by geological factors and an underground water dynamic change trend prediction result; according to the geological risk analysis result, fusing the tunnel technical condition data by adopting a heterogeneous integration model of a dynamic Bayesian network and an extreme gradient boosting tree, predicting the tunnel technical condition, and generating maintenance decision parameters; optimizing the maintenance decision parameters by adopting a cuckoo search algorithm so as to minimize the maintenance cost under the influence of the geological risk; and outputting decision results including the total maintenance cost distribution based on the geological risk, the technical condition improvement expectation, the rock stratum stability safety factor prediction value and the underground water seepage risk early warning level.

The invention discloses a water environment monitoring system and method based on big data analysis, and the system comprises a data collection and processing module which is responsible for collecting and processing original data related to a water environment, and transmits the processed data to a water flow and pollutant diffusion modeling module; and the water flow and pollutant diffusion modeling module is responsible for constructing a hydrodynamic model by utilizing a CFD simulation method based on the processed data, and predicting a pollutant diffusion trend through a convection-diffusion equation. According to the invention, the data analysis and prediction module deeply excavates potential information in acquired data and simulation results through a K-means clustering algorithm and an LSTM algorithm, and realizes accurate prediction of pollutant diffusion and water quality change time series. And the early warning and emergency response module can discover potential water quality pollution events in advance based on the prediction results and the real-time monitoring data, and give out early warning in time, so that the quick response capability of coping with the water quality pollution events is enhanced.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention provides a tidal flat evolution prediction method and system based on a hybrid model, and relates to the field of dynamic geomorphology and artificial intelligence cross technology, and the method comprises the steps: constructing a tidal flat erosion and deposition evolution database under different scenes of a target coastal zone; constructing a landform prediction model through an artificial intelligence algorithm based on the database, and taking hydrodynamic characteristic data as input and seabed erosion and deposition variation as output; hybrid simulation is executed based on the physical mechanism model and the landform prediction model, and long-duration evolution prediction is realized by updating terrain boundary conditions through loop iteration; and the configuration service application module is used for receiving user input parameters through the interactive interface and outputting a visual result. According to the method, traditional hydrodynamic force-sediment-landform coupling calculation is decoupled into a physical mechanism and AI coordinated chain process, the calculation complexity is remarkably reduced, offline rapid calculation business of tidal flat evolution is achieved, and real-time technical support is provided for coastal zone planning and disaster prevention decision making.

The invention relates to a peak shaving power station downstream non-safe navigable water area identification method and system and a medium, and the method comprises the steps: building a peak shaving power station downstream river channel one-dimensional and two-dimensional coupling hydrodynamic mathematical model; determining a combination mode of a proposed condition and a water flow boundary condition of a typical peak regulation process of the downstream of the hydropower station; simulating and calculating downstream on-way hydrodynamic conditions of the power station under different peak shaving processes; calculating a downstream non-safe navigation index of the peak regulation power station; and identifying the downstream non-safe navigable water area of the peak shaving power station. According to the method, on one hand, the change condition of downstream hydrodynamic force of the peak regulation power station can be analyzed, on the other hand, the influence condition of the peak regulation process on downstream navigation conditions can be recognized, non-safe navigation water areas of the peak regulation power station in different discharge flow processes can be judged, and a research basis is provided for hub power generation and navigation scheduling of mountain rivers.

The invention discloses a canal system gate scheduling optimization method based on distributed model predictive control and a one-dimensional hydrodynamic model, and the method comprises the steps: collecting the hydrodynamic data, irrigation demands and canal system characteristics of a canal system, and taking the data as basic data; on the basis of the basic data, various disturbance conditions of the canal system are simulated, and corresponding hydrodynamic response data are obtained; constructing a one-dimensional hydrodynamic model based on the Saint-Venant equation; based on the one-dimensional hydrodynamic model and the hydrodynamic response data, constructing a distributed model prediction control architecture, and predicting the hydrodynamic data of the canal system; based on the prediction result, taking the minimum water level deviation and the minimum control cost as objective functions, and performing dynamic scheduling on the canal system; and performing feedback updating on a dynamic scheduling result of the channel system based on a dynamic rolling optimization mechanism. The method is suitable for multi-channel pool distributed water flow regulation and control, and particularly has important application value in efficient utilization of water resources in an irrigation area, flood scheduling and irrigation canal system optimization management.

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 relates to the field of water engineering, and discloses a method for installing and positioning a prefabricated part under a complex hydrological condition, which comprises the following steps of: arranging a sensor network on the surface of the part, and establishing an initial reconstruction model of strain-hydrodynamic load mapping based on finite element analysis; acquiring sensor strain data in real time, and synchronously acquiring component pose information of an external positioning system; real-time hydrodynamic disturbance is calculated through the initial model, and sensor health diagnosis is carried out in combination with a dynamic threshold value method; the extended Kalman filter fuses the pose and disturbance data, and model parameters are updated online to generate a calibration model; and on the basis of calibration model output and state estimation, a feedforward-feedback composite control instruction is generated to drive an execution mechanism to adjust the pose. Through an online calibration model and a composite control architecture, and in combination with optimal distribution of a redundant execution mechanism, real-time offset of hydrodynamic disturbance and millimeter-level pose regulation are realized, and the technical bottlenecks of low precision and poor adaptability of a traditional method are broken through.

The invention relates to the technical field of bamboo forest ecological environment monitoring, in particular to a bamboo forest ecological environment collaborative observation method based on multi-source Internet of Things sensing, and the method comprises the steps: synchronously collecting a multi-source collaborative observation parameter set of a bamboo forest slope through an Internet of Things sensor network, and then constructing a point-surface scale fusion bamboo forest moisture dynamic model; correcting evapotranspiration point cluster data by using a random forest gradient lifting algorithm to generate terrain weighted evapotranspiration, aggregating runoff data according to terrain weight to obtain a total amount of runoff sub-regions, then executing dynamic water balance constraint calculation, optimizing infiltration capacity in combination with a variation assimilation algorithm, redistributing infiltration capacity space proportion according to soil hydraulic conductivity when a residual error exceeds a threshold value, and calculating the total amount of the runoff sub-regions. Finally, according to the residual space distribution thermodynamic diagram, the evapotranspiration point cluster is migrated to the micro-topography representative area, and observation network adaptive optimization is achieved, the problems that in the prior art, the data scale is disjointed, the observation precision is insufficient, and the network is difficult to adapt to change are solved, and the bamboo forest ecological observation precision and reliability are improved.

The invention discloses a wave-current combined simulation system and method in a low-pressure environment, and relates to the technical field of underwater vehicle hydrodynamic model tests. The defects that in the prior art, sealing and strength are difficult to guarantee in a low-air-pressure environment, and accurate flow velocity and shear gradient control of a multi-layer flow channel cannot be achieved are overcome. According to the technical scheme, the device comprises a low-pressure pool, a plurality of layers of layered water inlet galleries, a plurality of layers of layered water outlet galleries, a rectifying wall, a rectifying baffle, a flow guide grid, a wave absorbing bank, an in-cabin rocking plate type wave maker, a plurality of bottom flow making pump flow making systems, an electric butterfly valve, a high-voltage frequency conversion cabinet, a three-phase asynchronous motor, a gear box, an impeller shaft system and the like. All the devices work cooperatively, and accurate coupling simulation of a multi-layer flow field and high sea condition waves under low air pressure is achieved. The device is suitable for wave-current combined hydrodynamic characteristic simulation and test research of deep-sea navigation bodies and ocean engineering equipment in a low-pressure environment.

The invention relates to a shallow water dynamic process simulation method based on a local time step length, and the method comprises the steps: dividing a target region into a plurality of grids based on a shallow water dynamic process model, the target region comprising an ocean region and a land region adjacent to the ocean region; at least dividing the plurality of grids into a first grid set and a second grid set; determining a first time step length corresponding to the first grid set according to the first grid set; determining a second time step length corresponding to the second grid set according to the second grid set; determining a first simulation result of the shallow water dynamic process according to the first time step length and the second time step length; wherein the first grid set comprises a plurality of first grids, and the second grid set comprises a plurality of second grids; the first time step length is larger than the second time step length. The efficiency of the shallow water dynamic process simulation method is improved by respectively determining appropriate local time steps according to the precision requirements of different grid sets.

The invention discloses a device for simulating influence of ditch cleaning ice cover evolution on hydrodynamic parameters and an application method thereof, and relates to the technical field of ice water dynamics and hydraulic engineering experiments. The device comprises a low-level water tank, a high-level water tank, a centrifugal pump, a flow valve, a water tank, a steady-flow perforated plate, a ditch cleaning ice cover simulation device, a first water level control grid plate, a second water level control grid plate and a flow field measurement system. According to the invention, three key parameters of ditch cleaning length, ice cover submerging water depth and roughness of the bottom of the ice cover are independently regulated and controlled, and non-contact high-precision measurement is carried out by means of a PIV technology, so that systematic and refined research on hydrodynamic parameters of an ice cover ditch cleaning area is realized. According to the method, researchers can safely and systematically research the influence of a single variable on the ice cover ditch cleaning water power at low cost, and the fundamental bottleneck of traditional field measurement in the research is solved.

The invention discloses a flood process runoff monitoring method coupled with satellite remote sensing and hydrodynamics. The flood process runoff monitoring method comprises the following steps that unmanned aerial vehicle complete river topography measurement is carried out; river upstream and downstream section hydraulic parameters are obtained; constructing a digital river channel section; batch extraction of satellite remote sensing water surface width; flow calculation parameters are obtained based on the digital river section and the water surface width data; performing remote sensing inversion on the roughness of the complete riverway; and section flow monitoring and historical data inversion. A section of complete river channel is determined as a basic research unit, flow calculation precision is remarkably improved through multi-section synchronous monitoring in combination with river channel slope and roughness change dynamically captured in real time, the flowing process of water flow in the complete river channel is finely described, and the flow calculation precision is improved. The method provides long-sequence and high-reliability flow data for parameter calibration and result verification of a hydrological model, is particularly suitable for describing the flow condition when flood comes, and enables the flow condition to be closer to reality.

The invention discloses a steady-state boundary convection-dispersion model parameter acquisition method based on a variable separation method, and the method comprises the steps: constructing a convection-dispersion mathematical model under a one-dimensional finite-scale steady-state boundary condition, and carrying out the dimensionless processing of an original mathematical model through a dimensionless factor; secondly, analyzing and solving the new mathematical model after nondimensionalization by adopting a separation variable method, systematically deriving an analytical solution expression of a one-dimensional steady-state boundary convection-dispersion model in combination with a groundwater dynamic theory and a solute transport mechanism, and constructing a standard curve capable of reflecting different permeability parameters and solute transport related parameters; an indoor one-dimensional convection-dispersion test is carried out, water level and solute concentration measured data at different observation points are obtained, and permeability parameters and solute transport related parameters of a test medium are efficiently obtained through wiring. According to the method, the accuracy and adaptability of the convection-dispersion model in the aspect of parameter identification under the limited scale condition are enhanced; and operation is simple, parameter identification quantity is large, and precision is high.

The invention provides a historical flood inundation dynamic reconstruction method and system based on deep learning, and relates to the technical field of flood simulation, and the method comprises the steps: obtaining a standardized historical flood data set; positioning P terrain cavity deficiency; matching and multiplexing P adaptive filling models in a terrain filling model library according to the P missing feature vectors; carrying out multi-thread parallelization of P terrain cavity deletion; adopting the P corrected terrain structures to carry out pixel-level replacement of P terrain cavity missing to obtain corrected DEM data; and after a two-dimensional hydrodynamic model is constructed based on the corrected DEM data, a hydrodynamic boundary control set is set to drive the two-dimensional hydrodynamic model to execute flood simulation, and a historical flood dynamic evolution process is output. According to the flood simulation method and device, the technical problem that the flood dynamic reconstruction accuracy is affected due to the fact that the flood simulation accuracy is limited due to the fact that topographic data are incomplete and the simulation generalization ability in different areas is insufficient in the prior art is solved, and the flood simulation accuracy is improved.

The invention provides an intelligent lake and reservoir water quality forecasting method and system based on physical constraint guidance and a medium. The method comprises the following steps: constructing and verifying a hydrodynamic water quality mechanism model; constructing a deep learning water quality intelligent model: preprocessing the historical hydrological water quality monitoring data to obtain a Transform-based deep learning water quality intelligent model and an input data set; constructing a physical constraint loss function based on a simulation result of the hydrodynamic water quality mechanism model subjected to calibration verification; using an input data set as a training set, taking minimization of a total loss function as a target, training the deep learning water quality intelligent model, and using the trained model for lake and reservoir water quality forecasting. According to the method, the physical mechanism is deeply embedded into the training process of the data-driven model in a differential constraint form, so that the prediction result strictly follows the physical law of pollutant migration and transformation while conforming to observation data, and the problem that the prediction result of a pure data-driven model violates physical common knowledge is effectively solved.

The invention discloses a flood inundation range dynamic simulation method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting multi-source original data of a to-be-simulated target drainage basin; s2, extracting an initial submerging range grid according to the multi-source original data; s3, according to the initial submerging range grid, generating a river channel-beach area coupled section terrain file; s4, obtaining the dynamic water level process of each downstream section according to the section topographic file and the weather forecast rainfall field; and S5, obtaining a dynamic simulation result of the flood inundation range according to the dynamic water level process. According to the invention, through continuous driving of the distributed hydrological model and the one-dimensional hydrodynamic model, full-chain simulation from rainfall forecast to submerging dynamic state is realized, so that the submerging range can be updated in real time along with meteorological conditions and riverway water levels.

The invention relates to the technical field of underwater construction engineering, and discloses an underwater foundation scouring protection range determination method, computer equipment, a storage medium and a program product, and the method comprises the steps: obtaining ocean current and ocean wave data, and survey data of a seabed where an underwater foundation is located; calculating the undisturbed Hiltz number of the basic far field, the seabed sediment starting critical Hiltz number and the flow-wave ratio; according to the undisturbed Hiltz number of the basic far field, the seabed sediment starting critical Hiltz number and the relation between the flow-wave ratio and the scouring range, a correlation model is established; according to the method, analysis is conducted from sediment dynamics and hydrodynamics theories, the analysis method is clear in theory, high in applicability, accurate and reliable in analysis conclusion, clear in mechanism and more scientific, and an effective evaluation method can be provided for engineering designers; and the method can be used for calculating the scour protection range of the sandy soil and clay seabed under the wave-current coupling effect, and the universality is high.

The invention relates to the field of disaster prevention and management, and particularly provides an intelligent decision-making method and device for realizing flood simulation prevention in combination with Cesium, and the method comprises the following steps: S1, geographic data access; s2, realizing real-time monitoring and analysis of hydrological conditions; s3, flood simulation model construction comprises hydrodynamic model construction, model parameter setting and optimization, and mechanism real-time updating; s4, processing physical interaction of the water flow by a Chaos physical engine of Unreal Engine, and associating physical data of Chaos through modular logic; s5, integrating an LSTM time sequence prediction model and combining with hydrological real-time rainfall to realize a flood routing trend; s6, displaying key disaster areas and disaster water level conditions by HeatMap; and S7, identifying a nearby refuge point location, and scientifically and reasonably arranging an emergency path. Compared with the prior art, intelligent decision management can be provided for pre-disaster simulation prevention and post-disaster rescue recovery.

The invention relates to the technical field of water environment monitoring and water ecological protection, solves the technical problem that hydrology, water quality, vegetation and bottom mud monitoring are mutually dispersed and are difficult to correspond in the prior art, and particularly relates to a lakeside microenvironment composite monitoring pile and an ecological disturbance evaluation method. The composite monitoring piles sequentially comprise an overwater area, a variable water level zone monitoring area and an underwater area in the vertical direction, the bottoms of the composite monitoring piles are fixed to bottom mud, the multiple composite monitoring piles are arranged according to the water depth gradient of 0-20 cm, 20-50 cm and 50-100 cm, a hydrodynamic force disturbance index and a microenvironment response index are established by comparing base line and disturbance working condition monitoring results, a composite ecological disturbance index is established, and the water level zone monitoring area and the water level zone monitoring area are located in the bottom mud. The method is used for quantitatively evaluating lakeside disturbance intensity and engineering measure effects. According to the method, quantitative and graded evaluation of the ecological disturbance degree of the lakeside zone is realized, and a scientific basis is provided for lakeside zone revetment form optimization, ecological restoration measure arrangement and water function zone management.

The invention discloses a lake pollution traceability analysis method based on multi-source data assimilation and reverse diffusion, and relates to water pollution traceability analys.The lake pollution traceability analysis method comprises the steps that chemical pollution is monitored at the downstream of a lake, monitoring data are imported into a hydrodynamic force-water quality model after Kalman filtering, and spatial and temporal distribution data of pollutants in a water body are obtained; pollution source inversion is carried out based on a differential evolution Markov chain Monte Carlo method, and pollution source parameters are obtained; forward simulation is carried out, a flow velocity field and water depth are solved through a hydrodynamic force-water quality model, then a pollutant transport model is generated, pollution source parameters are used as input, a finite volume method discrete control equation is used based on the pollutant transport model to predict pollutant concentration space-time evolution, and pollution duration under natural attenuation is evaluated; and comparing real-time monitoring data with an expected effect, and evaluating a pollution traceability analysis effect. Pollution source positioning errors are reduced, pollution contribution rate calculation accuracy is improved, traceability efficiency is improved, the risk of excessive reagents is avoided, and treatment cost is reduced.

The invention discloses a Taylor flow two-phase volume fraction prediction method based on CFD and machine learning, and belongs to the field of hydrodynamics. The method aims at solving the problems that traditional CFD is low in efficiency and weak in generalization, and pure machine learning lacks explanatory and physical consistency. According to the method, a mixed CFD-ML framework integrating an interFoam solver of OpenFOAM and machine learning is constructed. The method comprises the steps that firstly, a multi-working-condition calculation example is established based on OpenFOAM, and a training data set containing velocity fields Ux and Uy and a liquid phase volume fraction field alpha. Water is generated through interFoam; training a fourier neural operator (FNO)-based model by using a training solver to obtain an optimal model; and finally, loading the model by using a prediction loop solver, and quickly predicting the volume fraction field of the next time step in CFD time marching. The method improves the prediction efficiency and precision, guarantees the physical consistency, is suitable for the fields of ocean engineering, oil and gas transmission and the like, and can be expanded to a multiphase flow scene.

The present invention discloses a method for intelligent monitoring and support optimization of slope stability based on digital twins, comprising: extracting slope, curvature and geological stratification characteristics from a holographic terrain model, calculating the preliminary change trend of the stress distribution of the rock and soil body in combination with the finite element analysis method, and determining the spatial position distribution of the potential sliding surface of the slope; adjusting the data acquisition frequency of the sensor through a time series analysis method according to the spatial coordinate set of the real-time monitoring points, and obtaining a high-frequency monitoring data set reflecting the dynamic changes of stress and groundwater; extracting stress peaks and groundwater level fluctuation characteristics from the high-frequency monitoring data set, combining the spatial characteristics of the holographic terrain model, and using a data fusion algorithm to generate a multidimensional information matrix containing terrain, stress and water dynamics; judging according to the state parameters of the digital twin, if the stress distribution exceeds a preset threshold, recalculating the force distribution of the support structure through finite element analysis to obtain an adjusted slope support design scheme.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.