58 results about "Data assimilation" patented technology

This invention discloses a stepwise data assimilation method for a set subspace in a nonlinear inverse problem, relating to the field of data processing technology. The invention constructs an initial prior physical set based on the physical state variables to be inverted and optimized, and historical observation data. It extracts the static subspace basis anomaly matrix, initializes the latent variable set and particle weights, calculates fractional-step incremental reweighting, evaluates the current likelihood mismatch penalty using predicted data, updates and normalizes the particle weights in the logarithmic domain, obtains the effective sample number, performs system resampling operations in conjunction with a preset resampling tolerance coefficient, eliminates low-weight particles and replicates high-weight particles, synchronously updates the latent variable set and predicted data, executes a dynamic MCMC mutation loop to generate proposed latent state vectors and affinely maps them to a high-dimensional physical space, and updates the latent variable set by evaluating the annealing target energy within the latent variable subspace until all fractional steps are traversed. Finally, it outputs the latent variable set and maps it back to the physical space.

This invention provides a method, medium, and system for performance optimization of a global ocean data assimilation system, belonging to the technical field of performance optimization for global ocean data assimilation systems. This invention solves the technical problem of the inefficient parallel expression and propagation of the background error covariance matrix under high-dimensional sparse observation distribution conditions in global ocean data assimilation systems by grouping global parallel processes by communication domain and marking their active states, dynamically requesting computing resources and constructing a mapping relationship between processes and spatial data blocks, using parallel input / output interfaces to read background field and local observation data on demand, executing a Rossby wave group velocity-guided covariance propagation localization radius adaptive algorithm to update the local radius field, calling an AI-based dynamic sparse covariance assimilation increment estimation model to generate analysis increments and posterior uncertainty fields, and finally using a sparse posterior sampling algorithm constrained by physical Hamiltonian manifolds to output set analysis members.

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.

An acquisition unit (101) acquires an actual measurement value obtained by measuring changes in a data assimilation object in a predetermined environment. A calculation unit (102) uses a provisional initial state and a provisional value of an unknown parameter regarding the data assimilation object to numerically calculate the change in the data assimilation object in the predetermined environment. An update unit (103) calculates the value of an evaluation function representing an error between the actual measurement value and a value obtained from the result of the numerical calculation corresponding to the actual measurement value, and determines an acquisition function from a plurality of combinations of the initial state and unknown parameter values and the value of the evaluation function, and, on the basis of the value of the acquisition function, updates the initial state and the value of the unknown parameter that minimize the value of the evaluation function. An iterative determination unit (104) repeats the processes of the calculating unit (102) and the update unit (103) until a prescribed iteration end condition is satisfied, to thereby estimate the initial state and the value of the unknown parameter regarding the data assimilation object.

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 belongs to the field of hydrological forecasting technology, specifically relating to a flood forecasting method based on ensemble Kalman smoothing data assimilation. This method is based on a conceptual hydrological model and uses real-time flow observation information to dynamically correct the model's state variables. It is applicable to scenarios such as basin flood process simulation, real-time forecasting of reservoir inflow floods, and flood control scheduling. Specifically, based on the Xin'anjiang model, this method constructs a data assimilation framework coupling the Xin'anjiang model with ensemble Kalman smoothing. It uses real-time flow observations to dynamically backtrack and correct key state variables of the model, and combines a bias-corrected Gaussian error model to correct state disturbance deviations constrained by physical boundaries, thereby improving the accuracy of flood process simulation and real-time forecasting. This method can also be optimized by combining different state update methods, ensemble size, backtracking steps, and observation weight settings to form an optimal data assimilation scheme suitable for real-time flood forecasting.

The application provides a hydrological simulation method and system based on one-way driving of a nested model in the Yellow River Basin, and belongs to the technical field of hydrological simulation; the method comprises the following steps: taking a SHUD model as a core layer for simulating the hydrological cycle of the basin, taking a MIKE hydrodynamic model as a river channel hydrodynamic simulation layer, coupling the two models to construct a nested model, and performing one-way driving based on a spatial nesting technology; a two-way data interface is constructed, data exchange between the SHUD model and the MIKE hydrodynamic model is realized, a data assimilation algorithm is introduced, historical observation data are used to calibrate the parameters of the nested model, and key parameters are optimized; and then, the hydrological process of the basin is simulated based on the optimized nested model. The application can realize high-precision simulation of the hydrological process of the basin, and further provides scientific guidance for water resource management in the Yellow River Basin.

The application provides a cold region groundwater environment early warning method fusing digital twinning and freezing and thawing process, and the method comprises the following steps: constructing a digital twin corresponding to the state of a cold region groundwater system; collecting and assimilating multi-dimensional observation data representing the state of a cold region groundwater environment in real time through a multi-source data perception layer; in a water-heat coupling model layer, based on the multi-dimensional observation data, performing strong coupling numerical simulation of the freezing and thawing process and groundwater flow, and dynamically deducing the influence of the freezing and thawing front migration on the groundwater system; using a data assimilation algorithm, fusing the multi-dimensional observation data and the simulation output of the water-heat coupling model layer, and updating the state variables and model parameters of the digital twin in real time; in a dynamic early warning decision layer, based on the updated state of the digital twin, evaluating a groundwater environment risk index and issuing graded early warning information; through the above technical solution, the water-heat dynamics of the cold region groundwater system can be simulated and mapped in real time.

The application discloses a heterogeneous carbon sink node encryption method and system based on assimilation inversion feedback, and relates to the field of carbon sink monitoring networks.The method comprises the following steps: obtaining multi-source observation data and background driving data collected by ordinary nodes and core nodes, inputting the data into a surface carbon cycle model for data assimilation, obtaining posteriori carbon flux estimation results and a posteriori covariance matrix of each spatial grid; extracting local uncertainty and carbon flux spatial gradient based on the posteriori results, calculating a spatial heterogeneity risk index to determine candidate grids with higher monitoring risks; simulating the covariance updating process of the candidate grids after the addition of new core nodes or the upgrading of ordinary nodes, quantifying the expected reduction amount of global estimation uncertainty, and combining the risk index, the expected reduction amount and resource budget constraints to generate a heterogeneous node encryption strategy. Thus, the monitoring resources can be adaptively concentrated in high-uncertainty and high-heterogeneity areas, and the observation accuracy and configuration efficiency of the carbon sink monitoring network can be improved.

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.

This invention discloses a method for multi-scale meteorological data assimilation and spatiotemporal reconstruction of regional wind farm clusters, aiming to address the problems of insufficient accuracy in existing wind farm meteorological forecasts and difficulties in fusing multi-source data. This method achieves effective assimilation of multi-source heterogeneous meteorological data by constructing a deep convolutional variational autoencoder, combines heterogeneous graph networks and neural network differential equation techniques to achieve high-resolution spatiotemporal reconstruction of three-dimensional wind fields, and introduces physical constraints to ensure the physical consistency of the output results. Experimental results show that the method of this invention can significantly improve the accuracy of wind farm meteorological forecasts, providing reliable meteorological data support for the refined operation and management of wind farm clusters.

This invention provides an intelligent inversion method for inflow based on multi-source error constraint fusion, belonging to the field of hydrological forecasting technology. The method includes: constructing physical constraints based on long-sequence water level observation data, measured outflow sequences, and reservoir capacity-water level curves; constructing a composite optimization function including a reservoir capacity time-varying error compensation term, inflow continuity regularization constraints, and a water level observation data assimilation module; deriving the analytical inversion solution of inflow based on the Lagrange multiplier method; and automatically optimizing key parameters such as constraint weights and smoothing coefficients using intelligent optimization methods such as grid search and cooperative search algorithms to perform adaptive high-precision inversion under different hydrological scenarios. This invention, by dynamically adjusting the error weight coefficients, directly generates physically consistent inflow sequences without relying on the prior assumptions of traditional smoothing filters, significantly improving the performance of negative value rate control, water balance, and flood peak characteristic preservation.

This invention discloses a dynamic prediction method for tunnel water inflow, belonging to the fields of tunnel engineering and hydrogeology. The method first constructs an initial hydrogeological conceptual model; then establishes a dynamic database integrating geological, geophysical, monitoring, and meteorological data; furthermore, it establishes a dynamically coupled prediction model that combines a physical numerical model and a machine learning proxy model; utilizing newly revealed data during construction, it dynamically inverts and updates model parameters through a data assimilation algorithm; the updated model is then used for probabilistic water inflow prediction and risk level classification; finally, the results are visualized and a feedback loop is formed. This invention, through a dynamic update mechanism, enables the prediction model to continuously approximate real geological conditions, solving the problems of low accuracy and poor adaptability of traditional static prediction methods, and significantly improving the ability of tunnel construction to cope with water inflow risks.

The application discloses a multi-scale coupled heavy pollution weather forecasting and early warning method and system, and belongs to the technical field of weather forecasting and early warning; the method comprises the following steps: S1, receiving and standardizing multi-source heterogeneous data from each observation platform, fusing the multi-source heterogeneous data by using a data assimilation algorithm, and reconstructing a thermal-dynamic-pollution field three-dimensional analysis field; S2, based on the fused data and the analysis field, running a 'weather scale forcing W-mesoscale response M-microscale triggering mu' coupling diagnosis algorithm, quantifying key indexes of each scale, and establishing an early warning index library; S3, according to W-M-muChain concept model output, automatically generating a series of progressive early warning products from weather scale potential prediction to microscale near warning. Thus, accurate and seamless monitoring and early warning of the heavy pollution process are realized.

The application discloses a kind of spaceborne cloud radar beam conical spiral scanning forward-looking and rearview observation fast registration method, belong to meteorological radar signal processing and spaceborne observation registration technical field.The method includes: according to satellite orbit parameter and scanning geometry, the ground intersection of each pulse beam footprint is calculated, and footprint point set is constructed;According to the interval of scanning circle, the potential overlapping observation is traversed, and the candidate point pair is generated in combination with spatial distance gate;Matching cost function is constructed with footprint distance as main item and fusion observation consistency constraint, and effective registration point pair is obtained under one-to-one correspondence constraint;Registration point pair obtained by different scanning circle interval and different distance gate is accumulated and deduplicated, and global registration point set is formed.The present application makes full use of multiple overlapping observations of spiral trajectory, significantly increases the same sample, improves the reliability and precision of registration under the condition of high-speed spaceborne platform, and can provide high-quality input for subsequent three-dimensional observation data fusion, data quality control and data assimilation.

This invention relates to the fields of remote sensing and geographic information engineering technology, and particularly to a land use change identification method driven by multi-source spatial data. The method includes: acquiring multi-source spatial data covering a target area and performing standardized preprocessing to generate a priori land use map as the data assimilation background field; constructing a land use abundance change process model based on material conservation, and obtaining a spatial discrete framework through adaptive grid deformation; establishing a multi-source differentiable observation operator, and solving the core parameters through four-dimensional variational assimilation with land use conversion constraints to complete land use change identification and rolling dynamic monitoring. In this invention, land use change is modeled as a continuous physical process, overcoming the limitations of traditional discrete-time hard classification and comparison methods. This effectively improves the temporal coherence of change identification and the consistency of multi-source data fusion, meeting the needs of high-precision long-term land use dynamic monitoring, and providing stable and reliable data support for natural resource surveys, monitoring, and land spatial management.

The application discloses a three-dimensional modeling and data assimilation method for ionospheric electron density, belongs to the field of space environment monitoring and information technology, and comprises the following steps: constructing a hybrid basis function system based on spherical harmonics and empirical orthogonal functions and establishing a state space model, independently perturbing through key geophysical driving parameters, combining a physical empirical model to construct a physical-statistical dynamic model, fusing multi-source heterogeneous observation data, obtaining an analysis state vector set through a set Kalman filtering algorithm and an asynchronous assimilation window, and reconstructing a three-dimensional electron density field and uncertainty products through the hybrid basis function system based on the analysis state vector set.

The application discloses a kind of assimilation weather radar data's prediction system, specifically related to weather forecasting field, including radar data acquisition module, physical knowledge construction module, physical constraint extraction module, multi-source data assimilation module and industry risk index generation module.A kind of assimilation weather radar data's prediction system is realized to the hierarchical physical discrimination and suppression of ground clutter and superrefraction echo by radar data acquisition module, improves the data reliability of meteorological decision basis in asset dispatching management scene;Through physical constraint extraction module, the multi-objective joint optimization of microphysical process constraint, thermodynamic equilibrium constraint and non-meteorological noise suppression is realized, the credibility of meteorological element deduction result in high-reliability commercial scene is significantly improved, so that the generated feature analysis field has the physical rationality of supporting key asset management decision, improves the scientificity of administrative decision basis and the accuracy of financial risk assessment.

The application relates to the technical field of data processing, and discloses a forestry carbon sink satellite remote sensing data correction method. The method is based on a satellite transit time window to implement multi-source time synchronization collection on satellite-borne, airborne and ground measured data, combines micro-meteorological data, digital elevation model data and crown laser radar data to simulate carbon sinks at different elevations at a single time point, and completes simulation correction through remote sensing radiation transmission coupling and eddy correlation data assimilation; further, according to the deviation of measured values and simulated values at different elevations, elevation-specific correction parameters are calculated, a three-dimensional correction parameter field of space-time-elevation is constructed and is adaptively updated; and finally, the correction parameters are used to uniformly correct, weightedly fuse and optimize residuals of satellite-borne data and airborne data. The application can improve the correction accuracy, spatial adaptability and processing efficiency of multi-source forestry carbon sink detection data under complex terrain conditions.

The application discloses a regional wind power coordinated prediction method and system for center platform-wind power station data integration. The method is based on Fourier neural operator, extracts the physical space-time law and other uncertain motion law contained in the wind vector field, and uses Fourier approximation loss for constraint. Facing the space-time wind vector field simulation results and the regional wind power station power measurement, the data assimilation is carried out through the dense-sparse adaptive convolution network, and the measured true value data is used to correct the wind field simulation results. Considering the data integration demand of the regional center platform and the edge wind power station, the separated federal learning is used to realize the safe and fast solution of the center side wind field simulation and the edge side local power prediction, and the regional wind power prediction accuracy, calculation friendliness and data privacy protection ability are improved. The application can be applied to the regional power dispatching and control center platform of a large number of wind power stations, and provides 1-10 hours of regional wind power prediction results.

The application discloses a CO2 geological storage reservoir parameter inversion method combined with well-seismic multi-source data, relates to the technical field of CO2 geological storage, and obtains multi-source observation data in the CO2 geological storage process; a priori geological model set for representing reservoir heterogeneity is established; an observation error covariance matrix for comprehensively reflecting multi-source observation uncertainty is constructed; forward calculation is performed on the priori geological model to obtain predicted observation data; high-dimensional observation information is processed in a dimension reduction manner; and reservoir model parameters are iteratively updated. The application implements reservoir parameter inversion by combining well time-series data and seismic spatial data, significantly reduces the posterior uncertainty range of key parameters such as reservoir permeability, improves the precision of CO2 plume distribution and storage amount prediction, and enhances the numerical stability and physical rationality of the assimilation process. The TSVD dimension reduction strategy is introduced to effectively solve the ill-conditioned problem and set degeneration phenomenon in high-dimensional data assimilation, and the robustness of the algorithm under the condition of high-dimensional observation data is ensured.

The application discloses a kind of earth-rock dam stress deformation field time series prediction method and system based on finite element prior and monitoring data assimilation, belong to the technical field of water conservancy and hydropower engineering, method includes: multiple types of monitoring data and complete finite element field establish unified physical space registration relationship, and the prior stress deformation field of earth-rock dam is encoded into prior latent variable;Prior stress deformation field is mapped to sparse field monitoring space, and compared with field monitoring data matrix to construct monitoring residual matrix and feature extraction, correct prior latent variable in low-dimensional latent space based on sparse monitoring correction feature, and reconstruct as finite element complete stress deformation field after monitoring assimilation;Through autoregressive propagation model, online assimilation and rolling prediction are carried out.The application realizes dynamic correction and future state prediction of finite element full field prior under sparse monitoring condition, improves the spatial integrity, real-time performance and accuracy of safety state evaluation of earth-rock dam construction period and initial operation period, and has significant practicability and wide application prospect.

The application discloses a kind of based on deep learning's gas film cooling temperature field digital twinborn method and system, it is related to heat protection technical field, the method includes the following steps: constructing gas film cooling experimental device, the experimental device includes sensor, measurement and data acquisition system, complete the measurement and acquisition of experimental data;Acquisition experimental data, use infrared camera to shoot the temperature image of incomplete orifice plate and save, experimental data are used to fine-tune pre-training model, generate migration model;Training pre-training model, using CFD result as pre-training data source, generate pre-training model;Assimilate numerical data, assimilate numerical data using migration learning method based on iterative neural operator, numerical data include data from different image size and image layer;Respectively carry out space, structure and working condition deduction, update physical-virtual synchronous model.The application has data assimilation and deduction function, can support the efficient and accurate design of gas film cooling.

This application relates to a dynamic assessment method for mine slope stability based on UAV lidar. The method includes: establishing a rock mechanics numerical model of the mine slope based on the slope's geometric and geological information at the initial monitoring time from a high-precision time-series point cloud sequence; establishing a spatial coordinate mapping relationship between the computational grid of the rock mechanics numerical model and the four-dimensional geometric evolution model of the slope; based on a data assimilation algorithm, using the spatial coordinate mapping relationship, fusing the complete slope displacement observation field at the current monitoring time with the predicted state of the rock mechanics numerical model, and inverting and updating the initial mechanical parameter field to obtain the rock mechanics parameter field and the state of the rock mechanics numerical model. This drives the rock mechanics numerical model to predict the stability of the slope over a preset future period, obtaining the future deformation field and stability indicators of the slope. This method can improve the dynamic risk assessment and early warning capabilities of slopes.

The application discloses a reactor thermal-hydraulic state prediction method based on order reduction and data assimilation, and belongs to the technical field of nuclear reactor thermal-hydraulic characteristic analysis and digital twinning. In order to solve the problem that the prediction error of the existing reactor thermal-hydraulic state prediction model accumulates with time and it is difficult to maintain stable accuracy for a long time, the application first obtains the prediction value of a POD coefficient by using a machine learning model based on the operating parameters of a reactor system, linearly combines the POD mode to reconstruct a reduced snapshot, and restores the reduced snapshot to a complete snapshot through SVD orthogonal projection to obtain an observation snapshot; then, in each data assimilation cycle, the set Kalman filter is used to update the prediction state, the POD coefficient predicted by the machine learning model and the POD coefficient corrected through observation are weighted and fused based on optimal weights, and inverse mapping operation is performed to restore to a physical quantity space, so that the corrected reactor thermal-hydraulic prediction result is obtained.

The present application relates to the field of ecological environment monitoring and multi-source data fusion, and discloses a space-air-ground coordinated ecological quality multi-source data fusion method and system, comprising: collecting multi-source data, at least including satellite remote sensing image data, unmanned aerial vehicle aerial image data and ground observation time series data; standardizing and spatiotemporal aligning the multi-source data to obtain a multi-modal data set; constructing a multi-modal prediction model and training the model and data assimilation fusion using the multi-modal data set; inputting the real-time collected multi-source data of the to-be-measured region into the trained multi-modal prediction model to output the evaluation and prediction results of the ecological quality core indicators, the ecological quality core indicators including at least one of the ecosystem integrity, water conservation capacity and permafrost degradation influence degree; and the present application can improve the accuracy and generalization ability of the ecological quality index inversion as a whole.