71 results about "Ensemble Kalman filter" patented technology

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention belongs to the technical field of tunnel engineering, and provides a multi-physics field real-time assimilation simulation and regulation method and system in a tunnel grouting process in order to solve the problem that real-time dynamic simulation and automatic regulation are lacked in existing tunnel construction, and the real-time assimilation simulation and regulation method and system in the tunnel grouting process are provided by utilizing ensemble Kalman filtering and combining real-time monitoring data in the tunnel grouting process. Dynamically correcting parameters of the multi-physical model; time correlation in the slurry condensation process is considered, a time-varying condensation model depicting physical property changes of slurry evolving along with time is integrated, the time-varying condensation model serves as an external function in the time step length to be embedded into the multi-physical field model in correction, and the slurry flowing state is adjusted in a self-adaptive mode through numerical simulation; and generating control parameters of tunnel grouting according to a dynamic simulation result, and realizing closed-loop regulation and control of tunnel grouting. Synchronous linkage of numerical simulation and on-site working conditions is realized.

The invention discloses a die temperature field coordinated regulation and control method based on digital twinning, and relates to the technical field of die temperature control. The method comprises the following steps: constructing a mold temperature field digital twin model covering macroscopic, mesoscopic and microscopic scales; a distributed optical fiber temperature sensor is deployed to realize continuous temperature field sensing; physical-digital real-time synchronous mapping is established through ensemble Kalman filtering; performing model prediction control based on the digital twinborn body; three-dimensional visualization and multi-modal man-machine interaction of the temperature field are realized by adopting an augmented reality technology; and reinforcement learning is used to enable the digital twinborn body to have an autonomous optimization capability. Through deep fusion of a physical space and a digital space, precise sensing, real-time mapping and intelligent control of a mold temperature field are realized, and the temperature control precision and the self-adaptive capability of the system 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 discloses a Xinanjiang model correction method based on CLDAS soil humidity assimilation. Soil moisture constants such as field capacity FC and wilting point WP are calculated; corresponding and mapping of a CLDAS observation layer and a model three-layer soil structure (WU, WL and WD) are realized through layered interpolation. For the problem of missing measurement of the CLDAS, a reconstruction model based on space-time Transform is introduced for filling, pixel-level uncertainty is output, and an EnKF observation covariance matrix is constructed based on the pixel-level uncertainty. And then dynamically updating the three-layer soil water content state of the model by using ensemble Kalman filtering, thereby reducing the influence of soil humidity initial estimation and simulation deviation on flood simulation. The flood simulation and real-time forecasting precision can be effectively improved, the method has the advantages of being stable in data source, clear in physical significance and high in applicability, and a new technical approach is provided for state initialization and digital hydrology research of a distributed hydrological model.

The invention provides a multi-scheme collaborative yield forecasting method based on data assimilation and model parameter optimization, and belongs to the technical field of agricultural information, and the method comprises the steps: obtaining historical and real-time data of a target region and a target crop growth season; constructing a plurality of combined simulation schemes of the WOFOST model; carrying out data assimilation on the leaf area index and the soil humidity by utilizing an ensemble Kalman filter (EnKF) method and combining a Gaussian disturbance strategy; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination and operating the model; improving a water stress function; constructing a rolling updating yield prediction framework, and dynamically optimizing a yield prediction result; and dynamically selecting an optimal simulation strategy to simulate and forecast the yield. According to the method, the yield simulation precision and forecasting stability of the crop model under different moisture years are remarkably improved, and a reference is provided for developing a new meteorological year adaptive dynamic simulation framework of the crop model.

The invention discloses a buried gas pipeline multi-leakage parameter identification and concentration distribution rapid prediction method, and belongs to the technical field of buried gas pipeline safety detection. The method comprises the following steps: firstly, based on a porous medium flow control equation and a component transport equation, establishing a leakage diffusion physical model by utilizing computational fluid mechanics and generating a multi-working-condition sample data set; training a neural network model by using the sample data, and constructing a rapid concentration prediction agent model; in combination with measured data of a monitoring area, an ensemble Kalman filtering algorithm is adopted, the agent model serves as a forward prediction operator, and collaborative inversion of parameters such as the soil type, the leakage direction and the flow is achieved through iterative updating; and finally, outputting complete underground gas concentration distribution in the monitoring area based on the leakage parameters obtained through identification. According to the method, rapid identification of multiple leakage parameters and accurate prediction of concentration distribution are realized under the condition of limited measuring points, and the method has remarkable disaster early warning and engineering application values.

The invention relates to the technical field of computer data processing and artificial intelligence, in particular to a data processing method and device for aircraft atmospheric turbulence early warning and a medium, and the method comprises the steps: carrying out the offline training of a first-scale meteorological risk assessment model and a second-scale turbulence disturbance model; in the flight process of the aircraft, time sequence flight state data and corresponding first-scale meteorological data are obtained in real time, and first-scale meteorological features and second-scale turbulence features are extracted; based on an ensemble Kalman filtering algorithm, taking the output of the first-scale meteorological risk assessment model as a background trend term, generating a nonlinear correction term from real-time data based on a key indication factor determined by the second-scale turbulence disturbance model, and fusing the background trend term and the nonlinear correction term to generate a forecast result of atmospheric turbulence intensity; and generating a turbulence early warning signal according to the forecast result. According to the invention, through multi-scale information fusion, accurate and early warning of atmospheric turbulence is realized, and the flight safety is effectively improved.

The invention relates to the technical field of agricultural non-point source pollution load evaluation, and provides an agricultural non-point source pollution real-time simulation method, device and equipment for an event rainfall. The method comprises the following steps: acquiring preset basic data of a target area, corresponding forecast rainfall of each hydrological response unit under a time scale, and initial conditions of each hydrological response unit before a rainfall event occurs; according to the forecast rainfall capacity, preset basic data and initial conditions of the model, prediction is carried out in combination with an agricultural non-point source pollution model of the rainfall event, and the flow and nitrogen and phosphorus load simulation values at the hydrological monitoring node at the current time point are obtained; and real-time correction simulation of the session event process is carried out based on an ensemble Kalman filtering algorithm and real-time monitored and transmitted flow and nitrogen and phosphorus load observation values. According to the method, the precision of the initial condition of the agricultural non-point source pollution model of the session rainfall event can be effectively improved, and real-time simulation of the process of the agricultural non-point source pollution model of the session rainfall event is realized.

The application relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraints, and specifically comprises the following steps: step 1: synchronously collecting data such as spectrum information, DO, COD, temperature and pH at key monitoring sites, constructing a water dynamics-water quality coupling equation, and simulating the space-time dynamic distribution of water quality parameters; step 2: outputting a water quality sensitive area through a water dynamics-water quality model, screening sensor layout points by combining information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution map by adopting a spatial interpolation method; step 3: analyzing main pollution sources according to the synchronous collection of spectrum information at key monitoring sites, and adopting principal component analysis and an attention mechanism neural network; step 4: based on real-time optical characteristic value-DO data, combining a spatial topological network, a water quality gradient and a cross-region covariance to capture the spatial correlation of water quality parameters between different points, and constructing an optical characteristic value-DO-COD dynamic prediction model; combining pollution tracing and spectrum characteristic correction to correct the COD prediction value, adopting ensemble Kalman filtering to assimilate multi-source data, and improving the model accuracy.

The application relates to a river flood discharge capacity dynamic evaluation and hydrological early warning method. The method comprises the following steps: based on a parameter sensitive observation subset, sequentially updating an initial hidden variable in a low-dimensional hidden space through an unscented particle filter algorithm to obtain an optimized hidden variable, and inputting the optimized hidden variable into a river dynamic resistance field generator based on an initial dynamic roughness field to perform decoding processing to obtain a dynamic roughness field of a current period; based on the dynamic roughness field and a state sensitive observation subset, performing analysis and update processing on a state variable field of a hydrodynamic model through a deterministic ensemble Kalman filter algorithm to obtain an assimilated water level field and a flow velocity field; according to the water level field, the flow velocity field and the dynamic roughness field, the maximum flood discharge capacity of a section is calculated in combination with dike top elevation data, and a flood discharge capacity dynamic index is calculated based on the maximum flood discharge capacity of the section and a current actual flow. The method can realize real-time dynamic quantification of river flood discharge capacity and predictive evaluation of flood risk.

A set Kalman filtering turbulence model assimilation method based on full field similarity criterion is disclosed, which comprises the following steps: sensitivity analysis is performed on the coefficients of the candidate turbulence model to obtain sensitive coefficients and their sampling range for Latin hypercube sampling, i.e., sampling interval; model constant sample combinations are extracted from the sampling interval by using a directional Latin hypercube sampling method, and target flow field results are calculated; a data assimilation algorithm based on the set Kalman filtering algorithm is used for the target flow field results and the model constant sample combinations, and after obtaining the optimized model constant combination, the model constant combination is updated and verified in cycles to obtain the optimal model parameter combination; finally, the improved turbulence model is used to simulate the internal flow field of the fuel assembly in the core of the pressurized water reactor with high similarity. The present application can provide an objective evaluation standard for the full physical field similarity of the data assimilation method, significantly improve the convergence speed of the algorithm, strengthen the robustness of the algorithm, and reduce the calculation cost of the algorithm.

The invention relates to a gamma radiation field reconstruction method based on Lagrange particle model parameter assimilation, and the method comprises the steps: determining the spatial positions of a plurality of radioactive aerosol particles based on a Lagrange particle model; constructing state parameters based on the plurality of spatial positions and the source intensity of each aerosol particle; forming a background state vector by using the state parameters, and dynamically updating the background state vector by adopting an ensemble Kalman filtering algorithm to obtain a corrected assimilation state; determining an optimized transportation track based on the spatial position of the radioactive aerosol particles in the corrected assimilation state; constructing a local concentration field corresponding to the radioactive aerosol particles by adopting a Gaussian kernel function; and determining a gamma radiation field corresponding to the radioactive aerosol particles. The three-dimensional radiation field reconstruction is carried out based on the background state vector and the real-time monitoring data by taking the parameters of the radioactive aerosol particles as the core, and the purposes of correcting the background state variable, obtaining the corrected assimilation variable and improving the accuracy of the three-dimensional radiation field reconstruction are achieved.

The invention discloses a data and model fusion driving-based space stack operation parameter correction and inversion method, which comprises the following steps of: 1, analyzing the influence degree of empirical constants in a space stack simulation model on an operation parameter prediction result, and determining empirical constants needing to be corrected and operation parameters; 2, predicting N groups of operation conditions containing empirical constant error disturbance by using the simulation model, obtaining N groups of simulation model estimation vectors, and forming an initial prediction matrix; 3, summarizing on-orbit actual operation data or ground test actual measurement data acquired by a space stack sensor, and providing an actual measurement data matrix for correcting space stack operation parameters; 4, fusing the characteristics of the prediction data and the actual measurement data of the simulation model by using an ensemble Kalman filtering algorithm to obtain a corrected empirical constant; 5, updating the empirical constants in the space stack simulation model into the corrected empirical constants to realize the correction of the simulation model; and 6, using the corrected simulation model to carry out operation condition analysis, and realizing operation parameter inversion calculation. The method is high in prediction efficiency and accurate in result.

The invention discloses a low-altitude three-dimensional wind field nowcasting method and device, and the method comprises the steps: firstly collecting and preprocessing wind field data through an API, constructing a spatial-temporal feature project, and dividing a data set according to a time sequence; thirdly, a LightGBM framework is adopted, and a static optimal forecasting model is trained by means of Bayesian optimization; the method is characterized in that the static optimal forecasting model and an ensemble Kalman filtering framework are deeply fused, a dynamic assimilation forecasting system is constructed, forecasting output is continuously corrected and optimized, and error accumulation is effectively restrained. And finally, after performance verification, the system is operated in a business mode, and a final wind field forecasting result containing the NaN identifier is output.

The application relates to a cloud platform-based water resource management method, device, equipment and medium. The method integrates multi-source water data through a cloud platform, constructs a hierarchical Bayesian model after data cleaning, uniformly quantizes model parameters, input and posterior distribution of structural errors, drives ensemble Kalman filtering dynamic assimilation of real-time observation data based on the same, generates a prediction distribution fusing multi-source uncertainty, further analyzes the contribution source of prediction variance in real time through a Sobol' index, identifies dominant uncertainty factors, constructs a coupled error model according to the same, feeds the coupled error model back to the assimilation cycle as a state variable for online correction, and finally forms an improved prediction model capable of dynamically tracking and quantifying the uncertainty coupling propagation mechanism, thereby significantly improving the accuracy and reliability of hydrological prediction, and providing risk quantification basis and optimization scheme for flood control scheduling, water resource allocation and urban drainage management decision-making.

The application provides a multi-scheme cooperative yield prediction method based on data assimilation and model parameter optimization, and belongs to the field of agricultural information technology.The method comprises the following steps: obtaining historical and real-time data of a target region and a target crop growing season; constructing multiple combination simulation schemes of a WOFOST model; using an ensemble Kalman filter (EnKF) method combined with a Gaussian disturbance strategy to perform data assimilation on a leaf area index and soil humidity; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination, and running the model; improving a water stress function; constructing a rolling update yield prediction framework, dynamically optimizing yield prediction results; and dynamically selecting an optimal simulation strategy to perform yield simulation and prediction.The application significantly improves the yield simulation accuracy and prediction stability of the crop model under different water year types, and provides a reference for developing a new framework of crop model meteorological year type self-adaptive dynamic simulation.

This invention discloses a sound velocity profile assimilation and inversion method and system based on dual physical data driving. The method includes: performing adaptive observation and sparse characterization of the sound propagation path to obtain the sparse characterization results; performing long-term noise cross-correlation calculation and passive time delay self-calibration to obtain the virtual sound source arrival time series after absolute time delay reference correction; performing constraint verification through a physical-data dual-driven, dual-stream coupled generative inversion network; acquiring a broadband electromagnetic response time series and performing cross-medium electromagnetic-acoustic joint inversion constraints to obtain a strongly constrained calibrated full-depth sound velocity profile; and performing ensemble Kalman filtering assimilation and uncertainty propagation based on POD order reduction. This invention can quantify the uncertainty of the inversion results. As a sound velocity profile assimilation and inversion method and system based on dual physical data driving, this invention can be widely applied in the field of underwater acoustic engineering technology.

This invention discloses a method for collaborative control of mold temperature field based on digital twins, belonging to the field of mold temperature control technology. The method includes: constructing a digital twin model of the mold temperature field covering macroscopic, mesoscopic, and microscopic scales; deploying distributed fiber optic temperature sensors to achieve continuous temperature field sensing; establishing a physical-digital real-time synchronous mapping through ensemble Kalman filtering; implementing model predictive control based on the digital twin; using augmented reality technology to achieve three-dimensional visualization of the temperature field and multimodal human-computer interaction; and using reinforcement learning to enable the digital twin to have autonomous optimization capabilities. This invention, through the deep integration of physical and digital spaces, achieves accurate sensing, real-time mapping, and intelligent control of the mold temperature field, significantly improving temperature control accuracy and system adaptability.

The application provides a multiscale geological model cross-scale nesting and fusion modeling method, belonging to the geological field, including four steps of multiscale data preprocessing, data fusion, cross-scale nesting and fusion and dynamic coupling. This method can integrate multi-source heterogeneous data, realize data fusion through joint probability space, variational assimilation framework, fuzzy logic conflict factor and alternating direction multiplier method, utilize deep learning auxiliary fusion technology such as multi-scale convolutional adversarial network to improve the stability and precision of the model. At the same time, the model parameters are determined through volume average upsampling algorithm and random field conditional simulation, and the consistency of the parameters between the models is realized through dynamic adjustment of ensemble Kalman filter and localized ensemble transform Kalman filter, bidirectional feedback and iterative optimization. Finally, through the double grid strategy and the restrictive interpolation, the multiscale convergence is realized, and the convergence and dynamic balance of the model on the scale of kilometers to microns are ensured.

The present application relates to a kind of water culture crop biomass prediction methods based on stage-specific feedback evapotranspiration calculation, comprising the following steps: according to growth day or accumulated temperature, water culture crop growth is divided into three stages;For current growth stage, calculate stress factor and regulate the weight corresponding to each stage stress factor;Multiply basic crop coefficient, canopy coverage, reference evapotranspiration and stress factor and corresponding weight, calculate stage-specific evapotranspiration;Multiply water productivity adjustment value, the ratio of stage-specific evapotranspiration and reference evapotranspiration and environmental stress factor and corresponding weight, predict biomass increment, obtain water culture crop biomass prediction value;Using ensemble Kalman filter, integrated assimilation canopy coverage and biomass prediction value, carry out sustained biomass prediction in the whole growth cycle of water culture crop.Compared with prior art, the present application has the advantages of biomass prediction accuracy.

A digital-driven real-time regulation method of tunnel intelligent construction parameters, which belongs to the technical field of mine tunnel construction control, builds a tunnel construction digital twin initial model containing surrounding rock, tunneling and supporting structure, and defines adjustable construction parameters; collects and preprocesses multi-source data of tunneling, supporting equipment and surrounding rock monitoring to form stable data flow to drive virtual model evolution; uses ensemble Kalman filter algorithm for data assimilation and relies on measured data to dynamically correct model variables; uses the updated model to simulate the response of surrounding rock in advance, constructs the objective function, takes tunneling speed, anchor pre-tightening force, etc. as variables, and optimizes construction parameters with the help of genetic algorithm; converts the optimized parameters into control commands and issues them to the equipment PLC system to complete the equipment linkage regulation; each time a tunneling cycle is completed, the data collection, model updating, parameter optimization and equipment regulation process are repeated. This method can significantly improve the safety, efficiency and intelligent level of construction under complex geological conditions.

The invention relates to the technical field of underground water seepage parameter inversion, in particular to an FNO and ensemble Kalman filtering combined underground water seepage parameter inversion method, which comprises the following steps of: 1, establishing a two-dimensional Darcy flow equation seepage model, and generating a training set and a test set of permeability and a hydraulic water head; step 2, constructing a Fourier neural operator network, and performing offline training on the Fourier neural operator network to obtain a Fourier neural operator substitution model; step 3, under an ensemble Kalman filtering framework, using an adaptive correction algorithm to optimize model parameters, and obtaining an adaptive correction Fourier neural operator network based on ensemble Kalman filtering; step 4, performing ensemble Kalman filtering posterior sampling to generate a posterior sample; and step 5, extracting permeability parameters from the posterior sample. According to the method, the Fourier neural operator and the ensemble Kalman filtering are combined, and the precision and efficiency of underground water seepage parameter inversion are improved by optimizing network parameters through a self-adaptive correction algorithm.

Systems and methods for generating a phase-resolved ocean wave forecast with ensemble based data assimilation are disclosed. An example method includes receiving radar data corresponding to an ocean surface, and determining a surface elevation and a surface potential of a portion of the ocean surface. The example method also includes generating an ensemble of perturbed ocean surface data, and applying a phase-resolved nonlinear wave model to the ensemble of perturbed ocean surface data to generate a set of forecast ocean surface data. The example method also includes receiving a subsequent set of radar data corresponding to the ocean surface, and determining a subsequent surface elevation and surface potential of the portion of the ocean surface. The example method also includes combining, by applying an ensemble Kalman filter, the set of forecast ocean surface data with the subsequent surface elevation and surface potential to generate a phase-resolved ocean wave forecast.

The invention relates to the technical field of rock mechanics and engineering geology numerical analysis, in particular to a dynamic relaxation quasi-static crack path tracking method and system.The method comprises the steps that geometric information, boundary and loading information, material parameters, initial defect information and observation data are obtained, a finite element model is constructed, and a damage field is initialized; applying a load to each external load increment, and solving a balance displacement field and a current damage field by adopting dynamic relaxation to obtain a crack propagation driving force field and an energy balance amount; a crack zone is obtained based on persistent coherence, a crack tip front edge is extracted, a candidate crack propagation increment set is generated, submission increments are determined through factor graph inference and dynamic relaxation prediction, and a damage field is updated; according to the energy balance amount and the convergence state back-off increment, numerical control parameters are updated, and material parameters are updated in combination with ensemble Kalman filter assimilation. According to the invention, stable tracking of the crack path is realized and prediction consistency is improved.

The present application relates to a kind of enhanced prediction methods for inverse pressure multi-wall surface large separation flow field, belong to computational fluid dynamics technical field;Method steps include: the equivalent wall distance expression reflecting the coupling dissipation effect of multi-wall surface is constructed;Strain rate is revised turbulent generation term;The revision of equivalent wall distance and generation term strain rate of all grid points of Spalart-Allmaras turbulence model is completed;Global optimization is carried out to model parameter set and boundary parameter set using average ensemble Kalman filter;The generation term and dissipation term related to wall in standard Spalart-Allmaras turbulence model are revised by equivalent wall distance, and then the generation term related to strain rate is revised, to obtain revised Spalart-Allmaras turbulence model;Optimized parameters are substituted into the control equation of revised Spalart-Allmaras turbulence model to carry out three-dimensional flow field numerical prediction.The present application effectively reduces the prediction deviation of corner separation flow field, significantly improves the precision and robustness of inverse pressure multi-wall surface large separation flow field prediction, and shows good engineering application prospect.

This invention discloses a system for predicting the cumulative deformation of a barrel foundation under typhoon load, comprising a data acquisition module, a core prediction module, and a result output module. The core prediction module employs a prediction framework based on dynamic data assimilation. Through an integrated ensemble Kalman filter algorithm, it iteratively fuses the physical model of the barrel foundation with real-time monitoring data, dynamically updating the model state and key soil parameters, thereby achieving adaptive and high-precision prediction of future cumulative deformation. Simultaneously, the system is supplemented by a multi-source data fusion mechanism that considers data quality to ensure the robustness of the prediction process. This invention effectively solves the technical problem that traditional methods struggle to accurately predict the nonlinear cumulative deformation of barrel foundations during typhoons in real time.

The invention relates to the technical field of ocean remote sensing and intelligent ocean monitoring, in particular to a sea surface ocean current inversion method and system fusing ground wave radar and GNSS reflection, and the method comprises the steps: obtaining ground wave radar radial flow and GNSS-R original data; aI-driven space-time interpolation is carried out on the ground wave radar data so as to restore missing; constructing a wind-wave-current coupled GNSS-R forward physical model to extract weak ocean current signals; taking a regional ocean model as a framework, dynamically distributing two types of observation weights according to a shore distance, and carrying out partition self-adaptive data assimilation by adopting ensemble Kalman filtering; and finally, a high-resolution seamlessly-connected two-dimensional ocean current field product is output. According to the method, the problems of discontinuous flow field, high data missing rate and difficulty in GNSS-R ocean current signal extraction in a coastal-ocean transition zone of a traditional method are solved, and physically consistent and high-precision business ocean current monitoring from a coastal area to an open sea area is realized.

The application discloses a time-varying wind stress drag coefficient inversion method and system based on an EAKF, comprising the following steps: obtaining vertical flow velocity observation data to be assimilated by an Ekman model, and preprocessing the vertical flow velocity observation data; performing multi-scale parameterization and physical constraint mapping on a time-varying wind stress drag coefficient, constructing an Ekman model ensemble assimilation system based on the EAKF, performing ensemble data assimilation, and jointly estimating a flow velocity state and the time-varying wind stress drag coefficient; performing covariance estimation based on physical and statistical mixing to obtain a covariance matrix, performing double-channel adaptive covariance inflation and localization on the covariance matrix, and performing robust ensemble Kalman filter updating on the executed ensemble; and adopting a sequential method to optimize the Ekman model ensemble assimilation system, performing cyclic assimilation until all observation data are processed, and outputting an inversion result.

The application discloses an algae-source lake flooding early warning and prevention and control method based on precursor pattern recognition, and aims at solving the problems of low accuracy of existing lake flooding early warning, difficulty in quantifying prediction uncertainty, and lack of closed-loop adaptive update of early warning and prevention and control. The application constructs training samples by collecting historical monitoring data of target water area, trains a precursor pattern recognition model containing a physically constrained neural dynamics model and a multi-task output network, performs time alignment and space matching on an observation data sequence online to extract features, uses ensemble Kalman filtering to iteratively assimilate state vectors to obtain assimilated state sequences and their uncertainties, drives the physically constrained neural dynamics model to generate a predicted state sequence, and then performs multi-task reasoning to output a lake flooding risk level and a predicted outbreak time window warning result. Furthermore, the warning result is converted into a prevention and control instruction, and assimilation correction and incremental training update are performed in combination with effect monitoring data, so that the technical effects of high-precision and rolling optimization of lake flooding early warning and whole-process linkage prevention and control are achieved.