26 results about "Empirical orthogonal functions" patented technology

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention belongs to the technical field of marine environment monitoring and numerical simulation, and relates to a three-dimensional marine element field reconstruction method based on multivariable empirical orthogonal function decomposition. Comprising the steps of S1, multi-source data collaborative preprocessing; S2, MEOF joint modal extraction; S3, vertical projection operator optimization construction; S4, three-dimensional element field dynamic reconstruction; according to the method, through organic combination of multivariable modal decomposition and physical constraint projection, limitation of precision, efficiency and adaptability in three-dimensional field reconstruction in the prior art is broken through, key technical support is provided for real-time monitoring of the ocean dynamic environment, and the method can be widely applied to the fields of ocean disaster early warning, underwater engineering safety guarantee and the like.

The invention relates to a rainfall objective prediction method based on an intelligent algorithm, and the method comprises the steps: decomposing the midsummer rainfall observation data of a time sequence based on an empirical orthogonal function, obtaining a spatial mode and a time coefficient, calculating a circulation field mean value of a target time period based on an atmospheric circulation field monthly value data set predicted by a climate mode, and obtaining the rainfall objective prediction result. The method comprises the following steps: obtaining a circulation abnormal field through a difference value with a same-period climate state circulation field, carrying out correlation analysis on a time coefficient and the circulation abnormal field, identifying and extracting a first correlation region with high correlation, carrying out correlation analysis on the time coefficient and atmosphere reanalysis data, obtaining a second correlation region with high correlation, and identifying and extracting a second correlation region with high correlation. And extracting a circulation anomaly average value of a similar region in the first related region and the second related region as an input feature, training a prediction model according to the input feature and a corresponding time coefficient, obtaining the corresponding time coefficient according to the prediction model and circulation anomaly feature data in the second time period, and performing reduction to obtain rainfall prediction data. And the accuracy of rainfall prediction is improved.

The invention relates to the technical field of ocean remote sensing, in particular to an offshore water quality remote sensing inversion and classification method based on small sample deep learning, which comprises the following steps: acquiring offshore in-situ observation, satellite remote sensing and ocean reanalysis data; carrying out atmospheric correction, carrying out space-time reconstruction on the missing remote sensing reflectivity by adopting a data interpolation empirical orthogonal function, carrying out mathematical transformation on input features, and screening out an optimal feature subset by adopting a strategy based on an average absolute percentage error; constructing a deep learning network based on a generative small sample to invert the offshore soluble inorganic nitrogen and inorganic phosphorus concentration, and determining the water quality grade; and analyzing an inversion mechanism by using an SHAP method. According to the method, the problem of data space-time discontinuity is solved through the data interpolation empirical orthogonal function, the capture capability and inversion precision of the nonlinear relation under the small sample condition are remarkably improved by utilizing the generative network, the model interpretability is realized in combination with SHAP analysis, and scientific support is provided for offshore water quality fine management.

The invention discloses a method for extracting characteristic parameters of a low-frequency oscillation mode of a power system, and in the power system, a time sequence signal generally refers to records of parameters such as voltage, frequency and the like in the system along with time change. In order to effectively analyze the signals, singular spectrum analysis is firstly carried out, the singular spectrum analysis comprises the steps of converting the time sequence signals into a track matrix, then carrying out singular value decomposition on the track matrix, and reconstructing a time empirical orthogonal function obtained after singular value matrix decomposition, and at the moment, a denoised reconstructed signal is obtained; and performing matrix pencil analysis on the reconstructed signal, and extracting characteristic parameters of the low-frequency oscillation mode. In this way, the characteristic parameters of the low-frequency oscillation mode can be accurately extracted under the condition that the identification integrity is guaranteed.

The present invention provides a method for evaluating the efficiency of wide-area distributed photovoltaic power generation, which relates to the technical field of photovoltaic power generation efficiency evaluation, and includes: obtaining the reanalysis time-series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the eigenvectors by using the empirical orthogonal function decomposition method, and screening out the set of meteorological resource representative points for the final site selection in combination with the site selection conditions; collecting the measured data of the total solar radiation on the surface of the set of meteorological resource representative points, and performing correction processing on the reanalysis time-series data by using the singular value decomposition method; matching the solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluating the efficiency of distributed photovoltaic power generation by using the physical method and the statistical method based on the corrected reanalysis time-series data to obtain the evaluation value. The present invention has wide applicability, and only needs to use the publicly available global meteorological reanalysis data set to realize the resource distribution analysis.

The application provides a method for vortex-uplift flow coordinated regulation chlorophyll-a concentration variability, relates to the field of ocean remote sensing and environmental monitoring technology, obtains multi-source satellite and reanalysis data set, constructs smooth time series; identifies dominant variability period in chlorophyll-a concentration, sea surface temperature and sea surface height anomaly, extracts monthly anomaly value sequence; applies complex empirical orthogonal function analysis to sea surface height anomaly, extracts its dominant spatial variability mode, identifies vortex distribution position and shape; according to vortex type and season, classifies and filters target vortex, carries out normalization processing to chlorophyll-a concentration and aligns to vortex center, analyzes spatial response relationship between vortex and chlorophyll-a concentration; in combination with surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, density time-depth profile graph and CHL-SLA composite graph are drawn, and the influence mechanism of vortex-uplift flow on chlorophyll-a horizontal distribution is analyzed.

The application discloses a kind of air temperature change driving factor attribution method, device, medium and equipment.Therein, method includes: the historical data obtained is re-sampled according to preset grid scale, and pretreatment data is obtained, wherein pretreatment data includes pretreatment air temperature data and pretreatment driving factor data;Respectively on pretreatment air temperature data and pretreatment driving factor data are carried out empirical orthogonal function decomposition, and air temperature decomposition data and driving factor decomposition data are obtained;XGBoot regression fitting is carried out to air temperature sequence of air temperature decomposition data and driving factor sequence of driving factor decomposition data, and XGBoost model is obtained;Based on air temperature spatial pattern of air temperature decomposition data, XGBoost model and the SHAP explainability decomposition of each driving factor to air temperature change for sample data for explanation, and the attribution result of each driving factor affecting air temperature change is obtained.

The invention belongs to the field of airborne interference imaging altimeter error correction, and particularly relates to a platform and baseline error correction method based on an airborne interference imaging altimeter, which comprises the following steps of: acquiring a height measurement data sequence X1 continuously acquired along a flight path; preprocessing the X1 to obtain a preprocessed data matrix X2; performing empirical orthogonal function decomposition on the X2 along the rail direction, and extracting a spatial mode, a time coefficient and a characteristic value; determining a main characteristic mode according to a variance contribution rate corresponding to the characteristic value; identifying and separating a platform error component, a baseline error component and a real height signal component by combining distribution characteristics of a space mode and a time coefficient; and reconstructing a real height signal based on a separation result to realize comprehensive correction of a platform error and a baseline error. According to the method, the platform error and the baseline error which are different from real signals in space-time characteristics are effectively separated through EOF decomposition, and the height measurement precision of the airborne interference imaging altimeter is remarkably improved.

The application relates to the technical field of marine meteorological data assimilation, in particular to a marine meteorological data assimilation method based on self-adaptive scale decomposition. An original data set is acquired and preprocessed to obtain a two-dimensional matrix. Based on an empirical orthogonal function analysis method EOF, dimension reduction is carried out on the initial matrix to obtain a spatial mode which does not change with time. A spatial self-adaptive scale reduction model is constructed by using the correlation of points on the spatial mode. Grid setting is carried out on the spatial self-adaptive scale reduction model to obtain an optimized multiple network. A target functional is determined and scale-by-scale assimilation is carried out to obtain an analysis field. An accuracy calculation formula and an accuracy threshold value are determined. Sample observation values and sample analysis values of a sample to be analyzed are acquired, and the accuracy is calculated. The two-dimensional matrix is extracted and processed by the EOF, the spatial self-adaptive scale reduction model is obtained, and scale-by-scale assimilation of adaptive matching of spatial characteristics is realized.

This invention discloses a method for predicting the spatial distribution of algal blooms in lakes based on near-surface wind vectors and a Bi-GRU-Attention model. The invention employs Empirical Orthogonal Function (EOF) analysis to extract the dominant mode of algal bloom spatial distribution and time coefficients that represent the temporal variation characteristics of algal bloom spatial patterns. Then, a bidirectional gated cyclic unit model based on an attention mechanism is constructed to extract key dynamic features from the wind vector time series and identify wind periods that significantly contribute to the formation of algal bloom spatial distribution patterns. Finally, the time coefficients of the dominant mode of algal bloom spatial distribution are predicted using the wind vector time series state, and the spatial distribution of algal blooms is reconstructed by combining the corresponding spatial modes. This invention provides a new approach for the study of wind-driven algal bloom spatial distribution.

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 black carbon emission inversion method, system, equipment and medium, it is related to carbon emission inventory inversion technical field, comprising: using empirical orthogonal function model to black carbon emission inventory is carried out spatial and temporal decomposition dimension reduction, obtain the multiple emission source regions of global scale indicating black carbon emission source;In each emission source region, consider the linear and nonlinear interaction between multiple influence factors, establish the nonlinear regression model between black carbon emission under the interaction of multiple influence factors;Satellite remote sensing observation true value and reanalysis meteorological field data of multiple influence factors are input nonlinear regression model to obtain the black carbon emission inventory in emission source region.The application can obtain accurate black carbon emission, and then obtain accurate black carbon emission inventory data.

The invention provides an optimal solution solving algorithm and system for a nonlinear problem, and the optimal solution solving algorithm for the nonlinear problem comprises the steps: constructing a corresponding nonlinear problem according to an actual demand; selecting at least one state variable from a plurality of state variables of a partial differential equation corresponding to the nonlinear problem as a disturbance variable; constructing a first objective function corresponding to the nonlinear problem according to the disturbance variable and the limited amplitude of the disturbance variable; based on an empirical orthogonal function EOF decomposition method, determining an optimal solution of the first objective function, taking the optimal solution of the first objective function as the optimal solution of the nonlinear problem, and performing dimension reduction on the objective function by adopting the EOF decomposition method, so that the optimal solution is approximately solved in a subspace approaching a specific physical problem, the calculation efficiency is improved, and the calculation cost is reduced. The high requirement for computing resources is reduced, and the hardware cost is reduced.

The invention discloses a marine environment medium and long term prediction method based on a multivariable extended empirical orthogonal function, and belongs to the technical field of marine environment prediction and forecasting, and the method comprises the following steps: S1, obtaining marine environment multi-element data, and selecting sea level abnormal data and sea surface temperature data to construct a prediction model; s2, ocean multi-element decomposition is carried out, extended empirical orthogonal function decomposition is carried out on the obtained ocean environment multi-element data, and corresponding space-time orthogonal modes and time principal components are obtained through decomposition; and S3, carrying out ocean multi-factor medium and long term prediction, and carrying out prediction by using the space-time orthogonal mode obtained in the S2 to obtain a future ocean multi-factor prediction result. By the adoption of the marine environment medium and long term prediction method based on the multivariable extended experience orthogonal function, medium and long term prediction of marine environment elements can be achieved, the calculation cost of a prediction model can be reduced, and the method has the application effects of miniaturization and light weight and wide application prospects.

The invention relates to the technical field of satellite data processing, and provides a gravity satellite vacancy data interpolation method and device, and the method comprises the steps: obtaining a plurality of pieces of historical satellite gravity data and corresponding multi-source hydrometeorological auxiliary data in a preset time period; performing empirical orthogonal function decomposition on the plurality of historical satellite gravity data to obtain a plurality of spatial modes; acquiring a plurality of continuous time coefficient sequences according to the plurality of historical satellite gravity data and the plurality of spatial modes; constructing a preliminary reconstruction field according to the plurality of spatial modals and the plurality of continuous time coefficient sequences; and determining a to-be-predicted residual error matrix according to the plurality of historical satellite gravity data and the preliminary reconstruction field, and obtaining a corresponding to-be-predicted residual error coding matrix. And according to the preliminary reconstruction field, the multi-source hydro meteorological auxiliary data, the to-be-predicted residual matrix and the to-be-predicted residual coding matrix, predicting a prediction result value of the to-be-interpolated satellite gravity data through a prediction model. And according to the prediction result value and the preliminary reconstruction field, performing interpolation processing on the to-be-interpolated satellite gravity data.

The invention relates to the technical field of precipitation analysis, and discloses a basin precipitation space-time evolution characteristic and development trend analysis method, which comprises the following steps of: selecting precipitation data and foundation laser radar water vapor data of a plurality of ground meteorological stations in a target basin, and performing monthly correlation analysis on the precipitation data and precipitation data parameters respectively; and analyzing the variation trend of the four-season precipitable water in the recent N years along with the years, analyzing the evolution rule of the four-season precipitable water in the recent N years along with the years by utilizing a Mangkendall test method, and analyzing the evolution trend of the precipitable water in the recent N years by utilizing an empirical orthogonal function analysis method. According to the invention, through analyzing the annual, latitude and longitude and seasonal spatial-temporal distribution of the precipitable water amount of the target drainage basin in recent 20 years, the spatial-temporal distribution evolution characteristics of the precipitable water amount of the target drainage basin in recent 20 years and the long-time sequence development change trend of the target drainage basin can be obtained; therefore, important reference can be provided for the climate and natural disaster early warning of the target drainage basin.

The present application provides a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, including the steps of: preprocessing the original image data corresponding to the spatial and temporal remote sensing monitoring, identifying and marking missing value pixels and non-missing value pixels, and performing outlier tests to identify and mark outlier pixels; using a filtering algorithm to replace the data of the outlier pixels in the original image; performing reconstruction processing on the preprocessed original image data by using the data empirical orthogonal function decomposition method; performing outlier tests on the data of the reconstructed image to identify and mark outlier pixels, and using a filtering algorithm to replace the data of the outlier pixels in the reconstructed image; reprocessing the data of the reconstructed image to obtain spatially and temporally complete chlorophyll a concentration data. This method can fill in the gaps and reconstruct the satellite remote sensing observation values of chlorophyll a concentration, and achieve continuous coverage monitoring of global chlorophyll a concentration in the time and space domains.

A kind of multi-modal quantitative inversion method of paleoatmospheric circulation based on spatiotemporal deep learning network, using the powerful non-euclidean space feature extraction capability of spatiotemporal graph neural network (ST-GNN), captures the nonlinear spatiotemporal lag effect of vegetation response to climate; At the same time, coupling empirical orthogonal function decomposition (EOF) technology, orthogonal mode is extracted in potential feature space to capture the macroscopic atmospheric teleconnection pattern in geological record. The present application provides a quantitative inversion paradigm that can integrate micro-ecological mechanism and macro-dynamic mode, solves the problem of mixed signals in paleoclimate records of Qinghai-Tibet Plateau and surrounding transition zone, and realizes high-precision and high-resolution restoration of the evolution process of paleoatmospheric circulation intensity. In the multi-source stratigraphic record verification, not only can the mixed signals be effectively separated, but also the superior robustness and generalization ability are shown when dealing with geological age error and data loss.

The invention discloses an offshore area nutritive salt concentration inversion method and device based on remote sensing and machine learning, and relates to the technical field of ocean water quality remote sensing monitoring, and the method comprises the steps: carrying out the space-time matching processing and feature extraction processing of original data in a historical time period, training a multi-branch space-time feature fusion network in combination with a loss function, and obtaining a multi-branch spatial-time feature fusion network; a nutritive salt parameter inversion model corresponding to each kind of nutritive salt is obtained; and according to the current original data sequence, performing nutrition concentration inversion on the target sea area by using the target nutritive salt parameter inversion model, and performing interpolation processing on an inversion result by using a data interpolation algorithm based on empirical orthogonal function decomposition to obtain a reconstructed concentration distribution diagram sequence of the target nutritive salt type. Through the multi-branch spatial-temporal feature fusion network and the data interpolation algorithm based on empirical orthogonal function decomposition, the inversion precision of the nutritive salt concentration of the offshore area is improved.

This invention belongs to the technical field of seawater sound velocity profile inversion, and particularly relates to a method and apparatus for seawater sound velocity profile inversion based on the HMC algorithm. The method includes the following steps: interpolating and preprocessing historical sound velocity profile data and decomposing it using empirical orthogonal functions to obtain empirical orthogonal function expressions; calculating the time required for sound velocity propagation at different depths using the empirical orthogonal function expressions; using time as observation data, modeling the inversion algorithm based on the HMC sampling algorithm principle to obtain a result dataset; calculating the mean and standard deviation of the result dataset to obtain empirical orthogonal function values; and using the HMC algorithm to invert the empirical orthogonal function values ​​to reconstruct the seawater sound velocity profile, thus completing the inversion of the seawater sound velocity profile. This invention can accurately calculate the relationship between parameters using the constructed target distribution function and efficiently complete the inversion of seawater sound velocity profiles using the HMC sampling algorithm, providing a new method for seawater sound velocity profile inversion.

This invention discloses a method and system for enhancing the resolution of water vapor products using multimodal features and deep learning. The invention first acquires a first atmospheric precipitable water vapor fusion field generated using a fusion method, along with multimodal feature data related to atmospheric water vapor. Then, it uses an empirical orthogonal function method to extract common modes from both, obtaining common variation patterns. Finally, it inputs the common variation patterns into a pre-trained AutoResNet model to generate a second atmospheric precipitable water vapor product with a higher spatiotemporal resolution than the first fusion field. The AutoResNet model is a neural network model based on an autoencoder and a residual network, which has established a nonlinear mapping relationship between multimodal features and atmospheric precipitable water vapor. This invention effectively breaks through the resolution limit of the original water vapor data source, significantly improving the temporal and spatial resolution of the fusion product, and can be applied to severe weather monitoring, regional water cycle research, and high-resolution water vapor inversion operations.

The application provides a kind of meteorological element space-time representative calculation method and device, it is related to the technical field of meteorological element processing, including: obtaining meteorological element data of meteorological element guarantee target area preset period, and based on meteorological element data and preset clustering algorithm, meteorological element guarantee target area is divided into air mass;Based on meteorological element data, the spatial heterogeneity coefficient of each grid point in air mass is calculated;Based on the spatial heterogeneity coefficient of each grid point in air mass and preset meteorological guarantee precision, the spatial representative range of meteorological element data in air mass is calculated;Based on meteorological element data and EOF empirical orthogonal function, the time representative range of meteorological element data in air mass is calculated, the technical problems that the accuracy of existing meteorological element space-time representative calculation method is poor and space-time dimension is missing are solved.

The invention relates to a mirror image type acoustic tomography step-by-step three-dimensional field inversion method based on an empirical orthogonal function. The method comprises the following steps: collecting marine environment data; performing modal extraction and dimension reduction characterization, and establishing a field characterization system; constructing an acoustic propagation forward modeling model; constructing a core observed quantity, carrying out joint inversion, and carrying out robust solution by adopting a regularization algorithm to obtain a time coefficient of a section vertical structure; parameterizing a three-dimensional sound velocity field inversion problem, and assimilating discrete section sound velocity inversion into globally optimal field estimation; parameterizing a three-dimensional flow velocity field inversion problem, and assimilating discrete section flow velocity inversion into global optimal field estimation; solving and reconstructing a three-dimensional sound velocity field inversion problem, estimating a global coefficient for describing a sound velocity abnormal field, and finally generating a quantitative ocean sound velocity field product; and solving and reconstructing a three-dimensional flow velocity field inversion problem, estimating a global coefficient for describing an east component and north component abnormal field, and finally reconstructing a three-dimensional flow velocity field.

The invention discloses a regional medium-and-long-term wind power generation probability prediction method, which comprises the following specific steps: step 1, acquiring medium-and-long-term weather prediction data in a target region, and acquiring week-by-week weather field data from the future 4-26 weeks from a global climate prediction system; and 2, constructing a regional wind power plant group spatial topology network, and constructing a weighted spatial adjacency matrix based on geographic coordinates and installed capacities of all wind power plants in a target region. According to the regional medium-and-long-term wind power generation capacity probability prediction method, large-scale meteorological field data output by a global climate prediction system is fused, and an empirical orthogonal function dominant mode is extracted as a medium-and-long-term driving factor, so that the time scale limitation that a traditional method is limited to short-term weather forecast is broken through; effective tracking of wind resource evolution trends from several weeks to several months in the future is realized, and a weighted spatial topology network based on geographic distance and installed capacity is constructed.

The application belongs to the field of airborne interferometric imaging altimeter error correction, and particularly relates to a platform and baseline error correction method based on an airborne interferometric imaging altimeter, which comprises the following steps: obtaining a height measurement data sequence X1 continuously collected along a flight track; pre-processing X1 to obtain a pre-processed data matrix X2; performing empirical orthogonal function decomposition on X2 along a track direction to extract spatial modes, time coefficients and eigenvalues; determining main characteristic modes according to the variance contribution rate corresponding to the eigenvalues; combining the distribution characteristics of the spatial modes and the time coefficients to identify and separate platform error components, baseline error components and real height signal components; and reconstructing the real height signal based on the separation result to realize comprehensive correction of the platform error and the baseline error. The application effectively separates the platform error and the baseline error which are different from the real signal in the time and space characteristics through EOF decomposition, and significantly improves the height measurement accuracy of the airborne interferometric imaging altimeter.