15 results about "Ensemble Kalman filter" patented technology

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.

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.

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.

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 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.

This invention belongs to the field of flow field measurement in aerospace, and relates to a method for spatial flow field reconstruction using data assimilation of physical quantity measurement data from the wing surface. The method includes steps such as wing geometric modeling and mesh generation, solver setup and mesh convergence analysis, construction of a simulation dataset within the wind tunnel experimental range, VAE model construction and flow field dimensionality reduction, construction of an aerodynamic surrogate model using the Gaussian process regression (GPR) method, obtaining flow field priors based on experimental conditions, data assimilation using the ensemble Kalman filter method, iterative iteration and convergence judgment, and completion of flow field reconstruction. This invention combines the surrogate model with the ETKF algorithm, which improves the efficiency of the flow field reconstruction process, making it possible to quickly and accurately reconstruct the entire flow field in wind tunnel experiments. It is a powerful tool for the analysis and optimization of wing aerodynamic characteristics.

This invention discloses a real-time monitoring system for the migration front of a saline aquifer based on fiber Bragg grating sensing, belonging to the field of carbon capture, utilization, and storage. The system comprises a well monitoring unit, a surface demodulation unit, and a data inversion unit. This invention solves the problems of existing monitoring technologies, such as cross-sensitivity to temperature and pressure, poor formation coupling, poor long-term stability, and inability to actively identify the migration front. This invention establishes a direct hydraulic connection with the native formation fluid through a fish-scale-like penetrating structure, achieving high-fidelity pressure measurement. The surface demodulation unit ensures demodulation accuracy over long periods through a dual-fiber cable redundancy architecture and a dual-wavelength calibration mechanism. The data inversion unit dynamically corrects the geological model based on an ensemble Kalman filter assimilation algorithm, combining an active excitation composite monitoring mode with four-dimensional visualization technology to invert the spatiotemporal evolution of saturation and dynamically present the migration front.

This invention relates to the field of computer-aided agriculture, specifically disclosing a synergistic optimization method for carbon sequestration and yield enhancement based on the carbon and nitrogen regulation mechanism of crop root zones. The method includes: utilizing a root zone carbon and nitrogen multi-stable-state system model that integrates the mapping relationship between functional gene expression and biochemical reaction rates to diagnose the current state and plan candidate state migration paths; performing inverse sensitivity analysis through a root zone dynamic response model, identifying key regulatory windows by combining microbial functional temporal characteristics, and querying a measure-process response knowledge graph to match precise leverage measures; generating a set of synergistic regulation schemes through multi-objective optimization including functional gene expression synergy; constructing a high-fidelity digital twin for deduction and early warning, and using ensemble Kalman filtering to assimilate measured data to drive model self-evolution. This invention achieves dynamic inverse design, intelligent path planning, and continuous adaptive optimization of the complex carbon and nitrogen system in the root zone, effectively synergistically improving farmland carbon sequestration capacity and crop yield.

This invention discloses a snow ablation-optimized weak-sensitivity Kalman filter method and system for UAV state estimation, belonging to the field of UAV autonomous navigation and state estimation technology. The method includes: obtaining the posterior state estimate, posterior covariance matrix, and posterior sensitivity matrix of the previous time step; obtaining the prior statistics of the current time step based on ensemble Kalman filtering with ensemble point sampling and prediction propagation; searching online for the optimal scaling factor using the snow ablation optimization algorithm and scaling the preset sensitivity weight matrix; calculating the Kalman gain based on the weighted prior sensitivity matrix; updating the posterior state estimate using measurement data and outputting the flight state estimation result. This invention, while retaining the advantages of fast analytical calculation of the weak-sensitivity ensemble Kalman filter gain, achieves online adaptive adjustment of the sensitivity weights, improving the accuracy and robustness of state estimation in dynamic flight environments, and has low computational overhead, making it suitable for UAV airborne navigation systems.

The present application belongs to the technical field of landslide monitoring, and particularly relates to a rainfall-induced landslide deformation early warning method, comprising the following steps: constructing a landslide dynamic digital twin based on coupled physical mechanism; laying monitoring sensors to collect rainfall, displacement and underground water level data in real time; adopting ensemble Kalman filter data assimilation technology to fuse real-time monitoring data and digital twin prediction results, dynamically adjusting rock-soil mass parameters to realize adaptive calibration of the digital twin; based on the calibrated digital twin, inputting future rainfall forecast data to simulate instability scenarios, and triggering graded early warning according to the predicted stability coefficient change curve. The present application realizes the transition of landslide early warning from static threshold to dynamic prediction, and significantly improves the early warning accuracy and prediction period.

This application relates to a method, system, equipment, and medium for geological disaster early warning based on digital twins. The method includes: establishing an initial digital twin model based on multi-source monitoring data; extracting state vectors from the current digital twin model and generating a path set containing optimal and branching evolution paths through physical information deep reinforcement learning; updating the path set as a member of the set using a path trajectory similarity-weighted ensemble Kalman filter algorithm to obtain a posterior set of members and a calibrated digital twin model; extracting damage eigenstate variables from the calibrated digital twin model, calculating and classifying the instability probability, and generating graded and zoned early warning results through spatial-temporal dual constraints. This method improves the accuracy and timeliness of geological disaster early warning by integrating physical information deep reinforcement learning with path trajectory similarity-weighted ensemble Kalman filtering.

This invention discloses a method and system for coordinated optimization and control of deep coal seam storage and methane flooding based on digital twins, belonging to the field of unconventional natural gas extraction and carbon capture, utilization, and storage technology. The method constructs a high-performance digital twin and establishes a reduced-order model using a deep operator network. In each decision cycle, the twin is dynamically updated using ensemble Kalman filtering based on real-time monitoring data. The updated twin serves as the simulation environment, driving a multi-objective reinforcement learning agent to perform online rolling optimization decisions. Based on a comprehensive reward function, it outputs coordinated control commands for injection parameters and downhole intelligent equipment. Simultaneously, a risk prediction and hierarchical interlocking response mechanism based on the digital twin runs in parallel, achieving proactive safety management from risk warning to emergency response. This method realizes real-time perception, dynamic simulation, multi-objective coordinated optimization, and proactive safety control of the storage and flooding process, effectively improving storage efficiency, methane recovery rate, and engineering safety level.