180 results about "Data assimilation" patented technology

The invention relates to the technical field of oil and gas field development, and discloses an ultra-large type true triaxial hydraulic fracturing fracture evolution path prediction method and system.The prediction method comprises the steps that a coupling geomechanical model integrating microscopic, macroscopic and wellbore flow scales is constructed; initializing the model and performing crack initial expansion simulation; collecting construction data in real time, and dynamically optimizing model parameters through a data assimilation algorithm; performing fracture evolution advanced prediction by using the updated model; and generating an optimization decision based on the prediction result and feeding back to the construction site. According to the method, fusion of a multi-scale physical mechanism and real-time dynamic prediction is realized, and accurate prediction and active control can be performed on crack expansion under the ultra-large true triaxial condition.

The invention discloses a hydraulic engineering operation and management cooperative control system based on digital twinning, and the system comprises the following steps: a data assimilation module which collects observation station, remote sensing and meteorological data and assimilates the data to obtain an assimilation estimator; the dictionary and lifting module is used for generating a lifting variable sequence according to hydrodynamic force prior; the structure identification and uncertainty module is used for identifying a lifting linear model and recursively predicting uncertainty under the constraints of conservation, monotonicity, dissipation and spectral radius; the terminal security module constructs a positive invariant terminal security set according to a support function; the opportunity constraint module is used for setting a total out-of-limit probability budget and generating an opportunity constraint substitution set; the Koopman rolling optimization module is used for solving the control sequence and executing the first control quantity; and the online updating and correcting and deformation and verification module is used for executing residual error triggering low-rank correction, projection return and support surface parameter online deformation. According to the invention, risk-controllable and stable and efficient collaborative scheduling is realized.

The invention discloses an intelligent early warning method and system for snow-melting flood. The method comprises the following steps: constructing digital twin integrating snow-melting confluence and hydrological hydrodynamic force; collecting multi-source monitoring information, and forming driving data through quality control and spatio-temporal interpolation; driving data are input into digital twinning, and model parameters and states are updated through data assimilation; simulating snow-melting runoff, water level and flow to obtain snow-melting flood probability prediction; the method comprises the following steps of: solving a river reservoir gate pump scheduling scheme by using a multi-objective evolutionary algorithm by taking reservoir outlet flow, gate opening and pump station starting and stopping as decision variables, calculating a water level, flow and a submerging range in digital twinning, and selecting a target scheme according to an evaluation rule to generate a scheduling instruction and graded early warning information; monitoring data is collected during scheduling execution and compared with probability prediction, and when the deviation exceeds a threshold value, data assimilation and scheduling scheme updating are triggered. According to the invention, the snow-melting flood forecasting precision and flood control scheduling collaboration are improved.

The invention provides a river flow online measurement and calculation method based on multi-source data fusion, and belongs to the technical field of river flow measurement. A Bayesian neural network and active learning collaborative hydrological memory reconstruction model is adopted to carry out high-confidence data interpolation on a sensor failure period and quantify uncertainty, fractional calculus is introduced to model a river memory effect, and flow evolution is analyzed through a fractional order water balance equation. And selecting a steady-state or non-constant flow calculation mode according to the flow change rate, inverting an optimal flow field for the non-constant flow by adopting a four-dimensional variational data assimilation method in combination with regularization constraint and time smoothing constraint, and outputting a flow measurement result and an uncertainty quantitative index. The technical problems that flow measurement and calculation data are missing and accurate interpolation is difficult due to sensor failure under the extreme hydrological condition are solved.

The invention discloses a monthly meteorological and hydrological forecasting method and system based on multi-source data fusion, and relates to the technical field of meteorological and hydrological forecasting and data fusion, and the method comprises the steps: constructing a unified forecasting factor set through multi-source meteorological, hydrological and ecological data, and carrying out the feature screening; predicting meteorological elements based on a deep neural network, and correcting a prediction result by adopting a deviation correction method; and inputting the corrected meteorological data into the distributed hydrological model, and outputting a monthly runoff prediction result in combination with a data assimilation algorithm. According to the method, physical correlation screening and structure sparse optimization of heterogeneous data are realized, input variables are ensured to be scientific and explainable, space-time depth prediction and statistical distribution correction are combined, the precision and consistency of meteorological element prediction are improved, deep fusion of a physical model and statistical assimilation is realized, and the method is suitable for being applied to the field of meteorological element prediction. The prediction system has self-correction capability and long-term stability, and the scientificity and operability of monthly runoff prediction are improved.

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.

The invention discloses an oil reservoir history fitting method based on graph lasso and set smooth multi-data assimilation, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, acquiring to-be-optimized model parameters and observation data of a target oil reservoir, constructing an observation error covariance matrix and initializing a model parameter set, performing numerical simulation by utilizing an oil reservoir numerical simulator to obtain prediction data, constructing a joint state matrix, performing dimensionless standardization processing on the joint state matrix to obtain an empirical correlation coefficient matrix, and then, performing optimization on the joint state matrix. And executing a graph lasso algorithm combined with an extended Bayesian information criterion to obtain an optimal dimensionless sparse precision matrix, extracting correlation coefficient sub-blocks required by Kalman updating from the optimal dimensionless sparse precision matrix based on a Scherr's theorem, obtaining a robust data auto-covariance matrix through a reverse reduction physical quantity outline, calculating Kalman gain in combination with an observation error covariance matrix, and calculating a Kalman filter. And the model parameter set is updated until the preset condition is met, the reservoir history fitting model parameter set is output, and the stability and precision of reservoir automatic history fitting are improved.

The invention relates to an intelligent simulation assimilation method and device for ponding depth based on a diffusion model, and the method comprises the steps: giving full play to the advantages of the diffusion model based on a Bayesian theory and a data assimilation thought, and achieving the efficient correction of a flood model state through the limited real-time ponding monitoring data; by solving the logarithmic gradient of a posterior distribution function and guiding the sampling process, prior distribution and real-time observation information are effectively fused, so that the regional ponding state is stably estimated under the condition that monitoring points are sparse, error accumulation in model rolling calculation is remarkably inhibited, and the business precision and practicability of urban flood deduction are improved. Therefore, the problems that in the prior art, a systematic intelligent simulation-assimilation fusion system is not formed yet, and error accumulation in model rolling calculation is difficult to restrain are solved.

The invention discloses a coal mine geological data three-dimensional modeling and transparent working face construction method and system. The method comprises the steps that multi-source geological and production data are collected and preprocessed; constructing a refined three-dimensional geologic body model based on multipoint geostatistics and a collaborative Kriging method; fusing the roadway model through Boolean operation; dynamically constructing a working face model by using real-time mining data; when a model error exceeds a threshold value, dynamic updating based on data assimilation is triggered; and finally, realizing model integration and transparent display in a three-dimensional visualization engine. According to the method, multi-point geostatistics and collaborative Kriging interpolation are fused, multi-source data such as geological drilling and three-dimensional earthquakes are integrated, the depicting precision of complex geological structures such as faults and collapse columns is greatly improved, the geometrical shape of the constructed three-dimensional geologic body model is more vivid, rock mechanics parameters are integrated, and the method is suitable for large-scale popularization and application. The problem that an existing three-dimensional geologic model is limited in precision is solved.

The invention discloses an atmospheric ocean multi-source data high-precision assimilation method and device, and relates to the technical field of ocean multi-source data assimilation, and the assimilation method comprises the steps: collecting multi-source observation data of a typhoon region of the South China Sea; preprocessing the data; decomposing the observation field into different scales by using wavelet transform; acquiring a background field from the numerical model, interpolating the background field to a grid consistent with observation, projecting the background field to an observation space, and performing multi-scale decomposition to enable the background field to correspond to an observation scale; calculating a difference vector of observation and background fields on each scale, and dynamically calculating an adaptive weight of each point on each scale according to an observation error, a background error and a current deviation; and constructing a cost function, solving through an optimization algorithm to obtain an analysis field, and outputting a final analysis field. According to the method, a multi-scale decomposition and self-adaptive regularization cooperation mechanism is introduced, so that high-precision dynamic fusion of the multi-source observation data and the numerical model can be realized.

The invention relates to the technical field of ocean information processing, in particular to a multi-source ocean live data comprehensive evaluation method and system, and the method comprises the steps: receiving satellite remote sensing data, buoy array data, shore-based radar data and ship terminal data, carrying out the time-space reference unified processing and abnormal value cleaning, and generating a structured data set; calculating a dynamic credibility weight based on a data source type, a real-time transmission delay rate and an environment interference intensity factor, and constructing a data set with the weight; an ocean live fusion data field is generated through a credibility-driven variation fusion algorithm; and comparing a fusion result with independent verification data, constructing a credibility error distribution diagram, identifying a high-error region, and reversely correcting a weight calculation parameter. According to the method, standardized fusion and quality closed-loop feedback of multi-source heterogeneous observation data are realized, the precision and reliability of a fusion result are improved, and the method is suitable for scenes of ocean dynamic modeling, situation analysis, data assimilation pretreatment and the like.

The invention discloses a data assimilation water regimen prediction method based on rolling correction, and particularly relates to the technical field of hydrological prediction and data assimilation. The method comprises the following steps: constructing an initial state vector by collecting multi-source hydrological data, fusing historical similar working condition information by utilizing a set Kalman filtering algorithm to obtain an optimal estimated value and a covariance of a current state, and initializing a rolling prediction structure; in a rolling prediction process, continuously calculating a residual error between model output and actually measured data, feeding back and correcting the residual error to a previous time state, and dynamically updating a prediction state; further combining with a residual evolution trend, identifying a key abnormal water regimen position, carrying out local optimization on model parameters, and generating a dynamically optimized future prediction result; the method has the capabilities of continuous self-updating, error feedback adjustment and abnormal response identification, significantly improves the water regimen prediction precision and stability under a small-scale complex watershed, and is suitable for real-time hydrological forecasting scenes such as urban waterlogging early warning, mountain flood prediction and reservoir scheduling.

The invention relates to a red date yield and quality collaborative prediction method and device fusing a growth model and machine learning and a storage medium. The method comprises the following steps: carrying out causal discovery analysis on historical data related to red date quality, and determining key features influencing the quality and causal weights of the key features; integrating periodic remote sensing observation data into the red date growth mechanism model through a data assimilation technology to obtain an assimilated optimal state variable set; and based on the assimilated optimal state variable set, the real-time environment characteristic data and the causal characteristic adaptive weight, through a trained multi-task deep learning model, carrying out red date yield collaborative prediction processing, and generating a red date yield collaborative prediction result. By adopting the method, pseudo-periodic interference can be effectively inhibited, a physiological mechanism and a data rule are deeply fused, and collaborative prediction of the yield and the quality of the red dates is realized.

A method includes converting, via a wavelet transform, (i) data associated with a prior ocean state forecast to wavelet space prior ocean state data and (ii) ocean observations to wavelet space observation data, and then filtering the wavelet space observation data. The method includes generating a correction value based on a difference between the wavelet space prior ocean state data and the filtered observation data, and determining a wavelet space increment value based on (i) the generated correction value, (ii) an error covariance associated with the prior ocean state forecast, and (iii) an error covariance associated with the ocean observations. The method includes converting, via an inverse of the wavelet transform, the wavelet space increment value to a physical space increment value, and generating a current ocean state forecast based on (i) the converted physical space increment value and (ii) a background state associated with the prior ocean state forecast.

The present application relates to the technical field of welding, and discloses a control method and equipment for friction stir welding, which comprises the following steps: through the establishment of a multi-physical field model, real-time data acquisition, internal state estimation through data assimilation, future evolution prediction, model predictive control to determine and apply instructions, and online learning to continuously adjust model or control strategy parameters; the system comprises a model establishment module, a data acquisition module, a state estimation module, a state prediction module, a control decision and execution module, and an online learning module. The present application accurately estimates the internal state online through model and data assimilation, improves the sensing accuracy, realizes prospective adjustment based on state prediction and model predictive control, intelligently balances multi-dimensional performance by introducing a multi-objective cost function, enhances the adaptability and robustness of the system by integrating an online learning mechanism, and builds a complete intelligent closed-loop control system to improve the automation and intelligent level.

This invention belongs to the field of remote sensing technology and relates to a method for predicting canopy combustible moisture content (CFMC) by combining remote sensing CFMC products and meteorological data. Specifically, through global two-stage sensitivity analysis, key input parameters for different tree species in the MEDFATE model are identified. Using these parameters, a cost function is constructed to measure the error between the CFMC simulated by the MEDFATE ecophysical process model and the CFMC measured in the field, achieving species-based localized correction of MEDFATE. Then, through four-dimensional variational data assimilation technology, the remote sensing CFMC product is combined with the localized MEDFATE. This achieves enhanced CFMC simulation, improving the accuracy of MEDFATE CFMC simulation while also increasing the temporal resolution of remote sensing data and the spatial resolution of the MEDFATE model. With accurate meteorological forecast data, effective daily CFMC prediction of vegetation can be achieved.

It is desired to provide a technique for accurately estimating model parameters by data assimilation.SOLUTION: And a data assimilation unit configured to obtain a data assimilation result by executing sequential data assimilation on the basis of observation data obtained by observing a state of an observation target, the simulation result, and a variance of system noise in the simulation unit, in which the data assimilation unit makes a variance of system noise at an end of the sequential data assimilation smaller than a variance of system noise at a start of the sequential data assimilation.SELECTED DRAWING: Figure 1

The application discloses a LES super-high-resolution TC atmospheric flow field numerical simulation method and system, and relates to the fields of atmospheric observation, numerical assimilation and high-resolution numerical simulation. The method is characterized in that: three-dimensional dynamic-thermal information is obtained by implementing airship platform radar observation and downward sounding observation in the core area of a tropical cyclone; the radar radial wind and profile observation operators are preprocessed and constructed; a data assimilation strategy is adopted to integrate multi-source observation in a multi-nested grid numerical model to generate a three-dimensional initial / reanalysis field suitable for large eddy simulation; then, a large eddy simulation solver is configured on the innermost grid covering the boundary layer of the tropical cyclone; fine horizontal grids and boundary layer encryption vertical stratification are adopted to explicitly analyze the boundary layer vortex, small-scale convection and turbulent structure; and a super-high-resolution three-dimensional flow field product for fine evaluation of strong wind and disaster risk is output.

The invention provides a mixed three-dimensional variational assimilation method fusing a multi-scale ensemble forecast sample, and belongs to the technical field of numerical weather forecast and data assimilation, and the method comprises the steps: obtaining and processing a global ensemble forecast sample and a regional convective scale ensemble forecast sample, obtaining a large-scale ensemble disturbance sample and a small-scale ensemble disturbance sample, and carrying out the processing of the large-scale ensemble disturbance sample and the small-scale ensemble disturbance sample; inputting into a hybrid three-dimensional variational analysis system, taking a forecasting field of a global numerical weather forecasting mode of a meteorological center as a first background field, constructing a large-scale flow dependent background error covariance by using a large-scale set disturbance sample to obtain a first analysis field, taking the first analysis field as a second background field, and constructing a second analysis field by using the second background field; and constructing a small-scale flow dependent background error covariance by using the small-scale set disturbance sample to obtain a final analysis field. According to the method, the problem of inaccurate numerical weather forecast caused by insufficient information fusion of large-scale information and small-scale information, insufficient observation and static background error covariance in the existing three-dimensional variation assimilation technology is solved.

Provided herein is an integrated preprocessing system and method for observation data of an ocean data assimilation system, which can maximize efficiency in overall processes of observation data collection, quality control, data processing, and operation management, and enhance the stability and reliability of the preprocessing process. The integrated preprocessing system for observation data of an ocean data assimilation system according to the present disclosure includes: an observation data collection unit configured to collect ocean observation data required for the ocean data assimilation system; a data processing unit configured to perform quality control on the collected observation data and classify and process the collected observation data into a single executable file; and a monitoring unit configured to perform GUI-based job scheduling and monitoring using a Rose / Cylc operating system.

The invention discloses a sea-ground-air-space integrated marine intelligent collaborative three-dimensional networking observation method, which belongs to the technical field of marine environment observation, is used for intelligent collaborative three-dimensional networking observation, and comprises the following steps of: generating an optimal navigation track covering a target area of dynamic characteristics by using a multi-target optimization algorithm; each observation platform executes a measurement task according to the received collaborative observation instruction, and each observation platform carries an embedded processor to analyze the collected temperature, salinity and flow field data characteristics in real time; and the data processing center completes data assimilation and ocean mode simulation by using supercomputing parallel calculation, and generates and outputs high-resolution three-dimensional temperature, salinity and flow field analysis results of dynamic characteristics. The coverage rate and the quality of observation information are obviously improved; the observation precision and efficiency are obviously improved, and optimal configuration and high-precision observation of resources are realized; a finer three-dimensional structure is captured with less navigation mileage, data redundancy and energy consumption are reduced, and observation efficiency is improved.

The invention relates to a 4DVAR wind field inversion lambda value prediction method based on a random forest, and belongs to the technical field of atmospheric exploration and data assimilation. Real-time accurate prediction of a lambda value in a 4DVAR wind field inversion process is realized through global candidate lambda setting, batch 4DVAR inversion, data processing and random forest model training, the 4DVAR wind field inversion efficiency and precision are improved, and the method is suitable for large-scale popularization and application. The method achieves the real-time precise prediction of the lambda value, greatly shortens the obtaining time of the lambda value, improves the efficiency and precision of 4DVAR wind field inversion, and meets the real-time demands of services.

The application provides a water quality short-term prediction method based on parameter perturbation-localized ensemble data assimilation, belongs to the technical field of water quality prediction, and is characterized in that: based on the water quality state and uncertainty of a hydrodynamic-water quality mechanism model at an initial time T1, an initial state vector is constructed and converted into an initial ensemble member to input the model; sensitive water quality parameters of the model are screened, the parameters are perturbed, and the parameter perturbation set is generated together with the model driving conditions, and then the parameter perturbation set is fused with the initial ensemble member to form a perturbation set; the perturbation set is input into the model to run to the T2 time, and a water quality prediction set is obtained; the observation data and error of the water quality at the T2 time are combined, and an EnKF or other ensemble data assimilation algorithm is used to calculate and analyze the set; finally, the analysis set is used to update the initial conditions of the model at the T2 time, the above steps are repeated, and the optimal estimation at each time is continuously output, so that the water quality short-term prediction is realized. The application can improve the water quality short-term prediction precision, save the calculation cost, accurately capture the spatiotemporal heterogeneity of water bloom, and has strong interpretability and generalizability.

This invention relates to the field of remote sensing technology, specifically to remote sensing image classification methods, systems, computer equipment, and storage media. The remote sensing image classification method includes the following steps: based on raw remote sensing data, using satellite cloud image analysis and surface temperature mapping techniques to perform comprehensive analysis of microclimate characteristics; using K-means clustering analysis algorithm to classify the data; and generating a comprehensive climate characteristic dataset. The beneficial effects of this invention are that by integrating K-means clustering, adaptive histogram equalization, convolutional neural networks, long short-term memory networks, and fully convolutional network algorithms, it can effectively identify geographic patterns and monitor abnormal climate events. Simultaneously, it optimizes surface classification, combines Gaussian filtering, MODIS cloud detection algorithms, and data assimilation techniques to deeply preprocess raw data, improve data quality, enrich information content, and effectively predict and respond to climate change using long short-term memory networks and time series analysis techniques.

The invention discloses a method for predicting spatial distribution of multiple components in a transformer, and relates to the technical field of power equipment operation state monitoring, and the method comprises the steps: firstly constructing a multi-gas component transport physical simulation model of transformer oil based on fluid simulation software, and configuring an observation system mapped with the position of an actual sensor; constructing a time sequence error matrix of actual observation and simulation prediction, and dividing the time sequence error matrix into a training set and a verification set; on this basis, a dynamic B matrix is generated by using an LSTM network, simulation data and real-time sensor observation data are fused through an EnKF algorithm, and dynamic updating and error correction of a simulation model are realized; the data assimilation process is assisted by a self-adaptive step length regulation and control and exception handling mechanism; and finally, dynamically displaying the spatial distribution of each gas component in the oil tank in various forms. According to the method, the problems of space limitation of traditional monitoring, error accumulation of static simulation prediction and the like are effectively solved, and accurate and dynamic prediction of spatial distribution of multiple gas components in the transformer is realized.

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 invention discloses a knowledge-guided and process-driven forest soil layered carbon sink simulation system, which comprises the following steps of: screening a continuous and complete soil profile according to forest soil organic carbon layered observation data; optimizing process model parameters of the terrestrial ecosystem at each measuring point by adopting a Bayesian data assimilation technology; processing the current vegetation terrain soil attribute data to obtain multi-modal standardized data as an input variable of a random forest model, performing heterogeneity learning and scale expansion on optimization parameters by using the random forest model, and simulating the organic carbon reserve of the current layered soil based on steady-state hypothesis and external driving data of a lattice point scale; and based on an instantaneous hypothesis, generating a future forest soil layered carbon sink simulation result by taking scale extension parameters and future external driving data as driving and combining current layered soil organic carbon reserves, so as to realize accurate, digital and intelligent monitoring and evaluation of the forest soil carbon sink capacity.

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.