222 results about "Data assimilation" patented technology

The invention provides a method for assimilating remote sensing data of soil humidity in a watershed scale. The method comprises the following steps of: improving a watershed runoff producing calculation module and developing a distributed hydrological model which is suitable for assimilating remote sensing soil humidity information and describes a soil hydrodynamic process; introducing a particle filtering sequence data assimilation method of information science, and continuously merging and assimilating new remote sensing observation data in a dynamic operation process of distributed hydrological process numerical simulation so as to acquire updated watershed soil humidity assimilated data during sequential assimilation; feeding the updated watershed soil humidity assimilated data back to a distributed hydrological model platform; and gradually estimating the time and space distribution pattern of watershed soil moisture content. Practices prove that by the method, not only high-precision and physically consistent watershed soil humidity data can be provided for research on hydrology, zoology, environment and agriculture, but also the foundation is laid for performing four-dimensional data assimilation processing on soil humidity data of an upper soil layer acquired by using remote sensing retrieval, and improving the precision of the model.

The invention provides a dynamic optimization based neural network flood warning device and a method. The device comprises an information processing unit, an information collection module, a GPS satellite positioning module, a GIS geographic information module, a wireless network module and a client, wherein the information collection module, the GPS satellite positioning module, the GIS geographic information module and the wireless network module are connected with the information processing unit, and the client is connected with the wireless network module. The information processing unit is used for carrying out data assimilation on matched information, assimilated data comprises historical information data and implementation information data, a network flood forecasting model is established by using the historical information data, the established network flood forecasting model is corrected by using implementation information so as to acquire a dynamic network flood forecasting model, and a disaster evaluation result is outputted by using the network flood forecasting model; and the wireless network module sends the disaster evaluation result to the client. According to the invention, mountain flood disaster analysis and judgment are carried out timely and accurately through the network flood forecasting model, thereby providing a reliable basis for making and selecting defense solutions in real time.

The invention belongs to the field of agricultural remote sensing, and relates to a crop yield estimation method based on scale transformation and data assimilation and application of the crop yield estimation method to the guiding of crop production. The method comprises the following concrete steps that: 1, parameters are collected for completing spatialization of a WOFOST crop model; 2, a crop type distribution map and a purity percentage map of the crops to be tested are obtained; 3, TMLAI in a range of 30m is obtained; 4, a secondary scale conversion model is built, and a time sequence scale regulating LAI is generated; 5, the crop model error and the remote sensing observation error in a key phonological period of the crops to be tested are obtained; 6, a four-dimensional variation cost function is built, and an optimized crop model parameter is obtained; and 7, the per unit yield of the crops to be tested in a county region is output. The method provided by the invention has the advantages that the problem of scale mismatching between a remote sensing observation picture element and a crop model simulation unit is solved; the precision of a data assimilation model is improved; and the method is suitable for the crop yield estimation in the county region scale, and is particularly suitable for the winter wheat yield estimation in the county region scale.

The invention discloses a data assimilation method for monitoring soil moisture. The method provided by the invention at least comprises an assimilation cycle, and each assimilation cycle comprises the following steps: (1) an ecological process model serves as a dynamic model to stimulate the space distribution state of soil moisture every day, the initial value in the dynamic model is an initial parameter and an initial soil moisture data, and the dynamic model is operated to output simulative soil moisture data; remotely sensed data on the soil surface is performed with inversion to obtain surface layer soil moisture data; the simulative soil moisture data in the remotely sensed data corresponding date is combined with the surface layer soil moisture data obtained by remotely sensed data inversion for data assimilation so as to obtain an optimized ecological process model parameter; (2) the optimized parameter is substituted into the dynamic model in step (1) to operate the dynamic model so as to obtain simulative soil moisture data every day. The method provided by the invention can connect soil moisture variety with vegetable physiological response mechanism by water deficit and stress, which improves the precision of field soil moisture monitoring and drought evaluation and has important application prospect.

The invention discloses a predication method of a maritime searching and rescuing target drifting path. A maritime searching and rescuing model is constructed based on an ocean dynamic environment field numerical forecasting technique of ocean surface wind, ocean current, ocean waves and the like to forecast a drifting path of overboard personnel in danger and maritime wrecked ships to give information including positions of a searching and rescuing target of all moment, searching and rescuing radiuses of different possibilities and the like, so that a searching and rescuing commander can give the optimal manner for rescuing in the field in the shortest time and the searching and rescuing movement is safely and rapidly carried out. The predication method specifically comprises the steps: collecting historical weather data; utilizing an atmosphere model to calculate to obtain a wind field of a sea area on the periphery of a maritime dangerous accident happening place; obtaining field wind real-time observation data of the sea area on the periphery of the dangerous accident happening place; carrying out data assimilation on the field wind observation data; adopting an ocean wave numerical value model to forecast a wave field of the sea area on the periphery of the dangerous accident happening place; utilizing the ocean wave numerical value model to forecast a three-dimensional flow field containing wind driven current and tidal current and realize the predication of a dynamic drifting track of the maritime personnel or ships in danger; and directly displaying a predicated value of the searching and rescuing target drifting path.

The invention discloses a meteorological information service system based on an intelligent mobile phone participating in perception and an implementing method thereof. Personalized meteorological service of a user is met by combining perception information acquired by the intelligent mobile phone of the user with the conventional meteorological data and adopting a data fusion algorithm, so that the user is effectively blended into a forecasting and early warning system, becomes an important part for dynamically feeding information back and participates in the whole process from observation, data assimilation to application. The system comprises a perception layer, a data transmission layer, a data processing layer and an application layer, wherein the perception layer is used for perceiving the environment by using an intelligent mobile terminal held by the user and sensing equipment of the intelligent mobile terminal and acquiring real-time meteorological data; the data transmission layer is used for transmitting the data acquired by the perception layer to the data processing layer; the data processing layer is used for performing operational fusion on the received data; and the application layer is used for issuing meteorological fused data operated by the data processing layer through the user terminal, receiving the demand information of the user and providing feedback.

The invention discloses a coupled numerical meteorological and hydrological aggregate forecasting reservoir scheduling risk decision method comprising the steps that the numerical meteorological and hydrological aggregate forecasting model of the reservoir basin is established, and the flood process of the basin is forecasted in a rolling way; the uncertainty of hydrological aggregate forecastingis assessed by using the Bayes model averaging method, the Bayes posteriori probability is reckoned and the probability of the flood scene in the future is updated in real time; a flood scene tree isconstructed by using the method based on the probability distance on the basis of the hydrological aggregate forecasting result and branch cutting of the flood scene tree is performed; and a reservoiroptimal scheduling random change constrained programming model is established, the optimal scheduling decision of the reservoir is solved by using the optimization method and the decision risk is assessed. With application of the method, the forecast period of hydrological forecasting can be effectively extended and the forecast precision can be enhanced, and the uncertainty between modes and different data assimilation schemes is comprehensively considered; and the method is suitable for medium-and-short-term real-time reservoir scheduling and can significantly enhance the reliability of thereservoir scheduling decision.

The invention discloses a bayesian fitering-based general data assimilation method. The method comprises the steps of: inputting an initial value set into an analysis model in a prediction step so as to obtain a prediction set value; calculating prediction error covariance matrix by using set kalman filtering in an updating step, and updating each prediction set according to the observation value and kalman gain matrix; or, calculating importance weight of each set sample by adopting particle filtering through set prediction value, calculating the number of effective particles by utilizing normalization importance, resampling the set according to the weight to obtain updated analysis value and analysis set; or, calculating prediction error covariance matrix by adopting unscented kalman filtering, and updating each prediction set according to the observation value and kalman gain matrix; conducting next prediction and assimilation by taking the updated analysis set as the initial values of the analysis model, and repeating the prediction step and the updating step. The method can enable Earth remote-sensing observation information and land surface process model information to be effectively integrated, thus forming a land surface process information prediction system with small errors.

The invention discloses a measuring method for inversing reservoir feeding flow procedure, comprising the steps: step I, performing observation and sampling to obtain the information of reservoir water level and discharging flow via a water level in front of the reservoir dam; step II, building a data assimilative state model and an observation model according to the information obtained in the step I; and step III, ascertaining the reservoir feeding flow procedures by a data assimilation algorithm according to the state model and the observation model obtained in the step II. The method disclosed by the invention can be used for ascertaining the reservoir feeding flow; compared with the prior art, the method disclosed by the invention can ensure smooth reservoir feeding flow, and avoid exceptional data like negative values and the like of the flow; the method also can supply the reservoir feeding flow procedure of minute time step, so as to reflect the flood peak information more truthfully; in addition, the method disclosed by the invention has low precision requirement to the water level gauge on the reservoir dam, so that the building and management costs of water level measurement stations are saved.

The present invention discloses a spaceborne radar and ground-based radar reflectivity factor data three dimensional fusion method. According to the present invention, by using the precipitation radar (PR) data of high vertical resolution as the output of a neural network and the ground radar (GR) data of high horizontal resolution and low vertical resolution as the input, a non-linear relationship of a PR and a GR is established, thereby fusing into the three dimensional reflectivity factor data having the high vertical and horizontal resolution. Based on a neural network algorithm, the PR and GR reflectivity factor data three dimensional fusion is carried out to obtain a fine three dimensional structure of an intensity echo which is very important for the later applications, such as the fine detection and tracking of a middle and small-scale convective weather process, the subsequent quantitative rainfall measurement, the radar data assimilation in the middle-scale forecast, etc.

The invention relates to a river channel water and sediment real-time prediction method based on data assimilation, and belongs to the technical field of water conservancy projects. The method includes the steps of firstly, collecting topographic data of a water channel to be predicated, upstream boundary condition data, downstream boundary condition data and fracture surface data of the river channel, setting up a one-dimensional non-steady-flow and non-balance sediment transport model, and solving the model; secondly, conducting water and sediment model assimilation on real-time observation data while receiving the real-time observation data, and enabling the assimilation value to serve as an initial field for calculation; thirdly, calculating the changes of the future water level, the further flow and the future sediment concentration according to information of the initial field and the boundary condition data. According to the method, the water level and flow speed prediction function, the flood forecast function and other functions are achieved on the basis of data such as the assimilation real-time water level, the flow speed and the sediment concentration, and the method has the advantages of being strong in pertinency, comprehensive in function, convenient to use, practical and the like, can be applied to river channel flood real-time forecasting of great rivers, and provides the decision-making support for the practical flood prevention command work.

The invention relates to a rapid collective Kalman filtering assimilating method for real-time data of high-frequency observation data. The method comprises: collecting the high-frequency observation data and controlling the quality; calculating an observation error covariance matrix; obtaining the error covariance matrix of background fields by calculating a forecast trend, i.e. the difference value of the adjacent background fields; utilizing the covariance matrix, the error covariance matrix of the background fields, the observation data and the background fields currently obtained by the calculation of a marine numerical model so as to carry out the real-time assimilation on the observation data of different moments, assigning the updated analysis field to the initial field of the next-moment integral and continuously forecasting forwards; and repeating the operations, thus realizing the real-time assimilation on the high-frequency observation data of different moments in the integral course. The assimilating method has the advantages that the real-time assimilation of the high-frequency observation data is realized; the assimilation efficiency of the data is enhanced; the defect that a large amount of collective models are simultaneously operated in the implementation course of the traditional EnKF (ensemble kalman filter) is overcome; the problem of non-convergence is avoided; and the purposes of accurate numerical simulation and marine forecasting are reached.

The invention discloses a method of acquiring all-weather evapotranspiration by combining remote sensing and meteorological data, which comprises the following steps: remote sensing data are acquired; meteorological data are acquired; the CLDAS (China Meteorological Administration Land Data Assimilation System) data grid position to which each MODIS (Moderate Resolution Imaging Spectroradiometer) remote sensing pixel belongs is calculated for longitude and latitude matching; MODIS remote sensing pixels are traversed, whether a MODIS land surface temperature pixel contains a cloud is judged, and according to a judgment result, evapotranspiration of an MODIS cloud pixel and evapotranspiration of an MODIS cloudless pixel are estimated. The existing optical and thermal infrared remote sensing data are made full use of, the grid meteorological data are used, the defect that evapotranspiration can not be inversed in a cloud condition by purely using remote sensing data can be remedied, and all-weather evapotranspiration data can be provided.

The present invention discloses a remote sensing image area segmentation method integrating an MRF and a Bayesian network, mainly for solving the problem that a conventional MRF method can not describe the directed information effectively. The method comprises the steps of firstly segmenting a remote sensing image, then dividing the areas, the boundaries, the vertexes, the semantic features and the relationships among the features which are extracted from the image into undirected and directed two forms, and then modeling the undirected relationships, such as the spatial mutual influence of the neighborhood pixels labels, etc., by a typical undirected graphical model-MRF, and modeling the directed relationships that the two edges of the boundary do not belong to the same kind generally, the vertexes are the cross points of two or more boundaries, etc., by the Bayesian network, thereby overcoming the disadvantage that the single layer MRF is not convenient to describe the directed relationships. Finally, the MRF and the BN are integrated by the data assimilation thought in the meteorological field, thereby improving the segmentation effect. A segmentation result obtained by the present invention possesses the better precision and area consistency, and can be used for the segmentation of a high resolution remote sensing image.

The present invention relates to an initial disturbance method based on an ensemble data assimilation technology, which comprises the following steps of: 1, constructing three ensemble components of WRFDA 3D-Var by disturbance information, minimizing a cost function by an iterating method so as to acquire a statistically optimal estimated value X of a real atmospheric condition; 2, setting irrelevant global ensemble forecast initial conditions to form a new cost function; 3, adding new observation into a WDF3DVAR assimilation system; 4, extracting a plurality of vertical profiles comprising each meteorological elements from the global scale ensemble forecast of ECMWF; and 5, operating the WRF3DVAR assimilation system with the new observation for each ensemble member, assimilating an observation set and a large-scale mode field set and generating final ensemble members for carrying out ensemble forecasting. The initial disturbance method based on the ensemble data assimilation technology has the beneficial effects that the ensemble members obtained by adopting the ensemble variational assimilation method can organically combine the storm scale with large-scale disturbance information; the cost function uses a mode as the dynamic constraint, so that ensemble disturbance has the physical and power harmony; and moreover, initial disturbance and lateral boundary disturbance are mutually coordinated.

The invention provide a water and sand prediction method and system for a strong alluvial river based on data assimilation. The method particularly comprises the steps of using terrain elevation scatter data obtained by sampling to generate an irregular triangular terrain grid unit; obtaining water-sand information and initial field information of strong alluvial river inlet and outlet boundaries;taking triangular elements of terrain grids as control bodies, discretizing a two-dimensional water-sand model of the strong alluvial river, selecting a solution method for calculation, and obtainingcalculated values of the control bodes; acquiring riverway water and sand state information of the strong alluvial river in real time, establishing a real-time prediction model based on the data assimilation according to the water and sand state information, and obtaining assimilation state variables and parameter variables; finally, using a two-dimensional water and sand data assimilation modelto conduct water and sand prediction on the strong alluvial river. Through the above treatment, the real-time status of water and sand of the strong alluvial river can be effectively predicted, so that water and sand regulation can be regulated, and a basis is provided for flood control and disaster alleviation, water environmental protection and water resource management.

The invention discloses a multi-source environment ecological information data assimilation method, which relates to the field of environment ecological information merging and the field of data assimilation. The invention is characterized in that the multi-source environment ecological information data assimilation method comprises the following steps: obtaining multi-source environment ecological information data; using space deduction tool software (SET) with the proprietary intellectual property rights for carrying out data missing value supplementation and carrying out normalization processing on a multi-source data set; beginning the operation on the standardized data set by using a data assimilation model; and outputting a data layer expressing the integral environment ecological information. The method can insert the observation values from irregular observation points into regular grid points, and fully utilizes multi-object, multi-scale and multi-time-phase observation data for organically merging the observation values to obtain the data set with the physical and space-time consistency. Particularly, the environment ecological information on the analysis region can be fast obtained.

The invention belongs to the agricultural remote sensing field and relates to a crop yield estimation method based on dual-polarized synthetic aperture radar and crop model data assimilation. The method includes the following specific steps that: S1, the satellite data of a dual-polarized synthetic aperture radar are acquired; S2, polarization decomposition is performed on the pre-processed data of the dual-polarized synthetic aperture radar; S3, an LAI inversion model of a scattering component relation combination with the highest precision is selected, so that a remote sensing observation LAI can be obtained; S4, the WOFOST model of crops in a study area is calibrated, so that a WOFOST model simulation LAI is obtained; S5, the two kinds of LAIs are assimilated with a particle filter algorithm; and S6, the S5 is executed on crop grids one by one, an optimized crop growth period LAI trajectory is adopted to re-drive the WOFOST model, so that spatial mapping can be carried out. According to the method of the invention, the advantages of the remote sensing data of the SAR and the crop model are combined, and abundant information provided by the data of the multi-polarized SAR is fully utilized, and therefore, the problem of the missing of optical remote sensing data in the key growth period of corns can be solved, the yield simulation of the crop model is improved, the LAI trajectory during the crop growth period is optimized accurately, and crop yields can be estimated on a regional scale.

The invention provides systems and methods for transferring cash to and from branch banks. The method may include the steps of inputting first data relating to cash usage at a plurality of branch banks; inputting second data relating to cash usage at the particular branch bank, the second data including a known ending balance at the particular branch bank; and assimilating the first data and the second data to constitute forecasting data; and performing forecast processing to determine an amount of cash to be transferred to or from the branch bank. The forecast processing may include (1) forecasting a cash in amount and forecasting a cash out amount based on the forecasting data, and generating a projected ending balance based on the forecasted cash in amount, the forecasted cash out amount, and the known ending balance; (2) generating a forecasted ending balance; and (3) comparing the projected ending balance with the forecasted ending balance to determine a cash shipment amount. The method may also include generating a cash transfer request based on the cash shipment amount; and effecting a transfer of cash with the branch bank based on the cash transfer request.

The invention discloses a dam safety early warning police eliminating method and system based on digital twinning. The method includes the following steps that through self-adaptive numerical simulation, a comprehensive evaluation model, a dynamic recursion data driving model, an online data assimilation model, an abnormal diagnosis reasoning model, a behavior understanding model, a real scene and XR model and a data and mechanism hybrid driving control model, the safety state of the dam is the same as that of an objective physical dam, influence factors are consistent, measurable responses are equivalent, and the safety of the dam is ensured. The invention discloses a digital twin dam with multi-dimensional scene fidelity. Information perception and optimization, information abnormity diagnosis, structural safety and system working state online evaluation, dam safety state accurate forecasting, wreck consequence effective early warning, dam safety state reasonable regulation and control and subsequent measure suggestion targeted recommendation are carried out on an objective physical dam through a digital twin dam. The method is complete in system, outstanding in innovation and practicability and good in application and popularization value.

The invention relates to an ensemble forecast flood forecasting method for improving forecast rainfall based on data assimilation. The ensemble forecast flood forecasting method comprises the following steps: step 1, data preparation; step 2, rainfall ensemble forecast based on the data assimilation; step 3, rainfall data post-processing; step 4, ensemble flood forecast; step 5, flood forecast result publishing and evaluation. The ensemble flood forecasting method provided by the invention has the benefits that not only is the forecast period of the flood forecast prolonged, but also the forecast accuracy of the flood forecast is improved; compared with traditional flood forecast performed by selecting through fall, a system selects a data assimilation technology to correct a numerical atmosphere mode, so that the high-resolution accurate forecast rainfall is obtained in advance and is taken as an input to a hydrological model for the flood forecast; in addition, in order to reduce the forecast uncertainty, an idea of the ensemble forecast is introduced for the rainfall forecast and the flood forecast.

The invention discloses a climate prediction system, which is characterized by comprising the observation data; a data assimilation system; a climate model operation subsystem; a prediction subsystem;a product subsystem; and a high-performance computer. The data assimilation system comprises a data assimilation and support subsystem; the climate model operation subsystem comprises a global general circulation model, a global ocean circulation model, a high-resolution East Asian regional climate model and a simplified ENSO prediction model. The prediction subsystem comprises a prediction correction and detection subsystem; the product subsystem comprises a product generation and distribution subsystem, wherein the coupling of the global atmospheric circulation model and the global ocean circulation model constitutes an air-sea coupled model. By combining the statistical method with the dynamic method, and combining the multiple initial values with the multiple models, the climate prediction system has the advantages of long prediction time, high accuracy, fast computation speed and strong regional pertinence.

The invention provides a river network water flow quality real-time prediction method and device based on data assimilation. According to the technical scheme, on the basis of real-time monitoring data such as the water level, the flow and the water quality of the river network, real-time monitoring data are assimilated into the river network water flow and quality model through the ensemble Kalman filter or the improved algorithm of the ensemble Kalman filter, the river network water flow and quality data assimilation model is constructed, and the computing efficiency of the data assimilationmodel is improved through a parallel computing architecture. Due to the data assimilation technology, water flow and quality model structure errors, inlet and outlet boundary errors and observation value errors are comprehensively considered; real-time observation data are fused to dynamically correct water level, flow and water quality concentration state variables and roughness and water quality parameters of the water quality model, so that the water level, flow and water quality concentration of a complex river network system can be dynamically predicted by adopting a river network waterquality data assimilation model and a parallel computing architecture, and the model prediction precision can be improved.

The invention discloses an SPSA meteorological parameter solution method based on assimilation of navigation satellite occultation data. The method is characterized by comprising the following steps that a cost function of occultation data assimilation is established, the first-order approximation gradient of the cost function is calculated, the atmosphere state vector is updated by utilizing the approximation gradient, the cost function value under the present atmosphere state vector is solved, whether an iteration stop condition is met is determined, and if yes, the solution of the atmosphere state vector is obtained. It is not required to design a tangent operator and an adjoint operator of an observation operator, the solution algorithm of the cost function is greatly simplified, the efficiency and feasibility for solving the atmosphere state vector are improved, and the method can be widely applied.

The invention discloses a tide forecasting method based on a global positioning system. The method comprises the specific steps that first, according to a tidal station, satellite altimeter data and water depth data, high-precision tidal harmonic constants of all tidal constituents are calculated through a data assimilation method and based on a two-dimensional tidal wave adjoint assimilation model; second, high-precision satellite positioning information is acquired in real time; third, according to the tidal harmonic constant data obtained in the first step and the satellite positioning information obtained in the second step, interpolating calculation is carried out through a distance weighting inverse ratio method, and the tidal harmonic constants of arbitrary points and tide forecasting data at arbitrary time are obtained; fourth, the tide data obtained in the third step is forecasted and output. The high-precision tidal harmonic constants can be acquired according to the two-dimensional tidal wave adjoint assimilation model, the high-precision satellite positioning information can be acquired in real time according to the global positioning system, and the tide forecasting data at arbitrary positions and time can be obtained based on the tidal harmonic constants and the satellite positioning information.

This invention relates to a network dynamic issuance system of applied products of satellite remote sensing data including a data storage subsystem and an issuance subsystem connected by high speed network, in which, the storage subsystem includes a parallel data channel module, a data change monitor module, a register table information operation module, a satellite remote sensing meta-information pick-up module and a network connection management module, in which, the data issuance subsystem is connected with an archiving remote sensing data device in a database management system via the network and file management system and the database management system, communicates with the database management system based on the network protocol via a database access interface and the file system access interface, provides WWW service and FTP service via internet, communicates with the customer end browser and also includes a data selecting module, a data pre-process module, a data assimilation module, a data counter-operation module, a statistic analysis module, a report generation module, a result picture generation module and a result curve generation module.