93 results about "Ensemble Kalman filter" patented technology

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention relates to the field of environmental monitoring, and discloses a plateau region carbon emission monitoring management method and system, electronic equipment and a medium, and the method comprises the following steps: constructing an atmospheric boundary layer dynamic model for a plateau low-pressure and strong-turbulence environment, and achieving environmental adaptability modeling by correcting a turbulence diffusion coefficient and localized combustion efficiency; based on unmanned aerial vehicle group dynamic path planning and a ground station collaborative sensing network, flight parameters are adjusted in real time in combination with the concentration gradient, and time-space continuous multi-source fusion monitoring data are generated; a high-resolution carbon emission field is output through embedding a combustion efficiency correction factor and a space correlation graph structure by adopting a data assimilation algorithm with mixed ensemble Kalman filtering and a graph convolutional network; and constructing frozen soil degradation index parameters, and driving a transfer learning early warning model to realize methane flux dynamic prediction and multi-stage early warning triggering. According to the invention, the problems of insufficient carbon emission monitoring precision and lack of frozen soil degradation correlation evaluation in a plateau complex environment are solved.

The invention belongs to the technical field of tunnel engineering, and provides a multi-physics field real-time assimilation simulation and regulation method and system in a tunnel grouting process in order to solve the problem that real-time dynamic simulation and automatic regulation are lacked in existing tunnel construction, and the real-time assimilation simulation and regulation method and system in the tunnel grouting process are provided by utilizing ensemble Kalman filtering and combining real-time monitoring data in the tunnel grouting process. Dynamically correcting parameters of the multi-physical model; time correlation in the slurry condensation process is considered, a time-varying condensation model depicting physical property changes of slurry evolving along with time is integrated, the time-varying condensation model serves as an external function in the time step length to be embedded into the multi-physical field model in correction, and the slurry flowing state is adjusted in a self-adaptive mode through numerical simulation; and generating control parameters of tunnel grouting according to a dynamic simulation result, and realizing closed-loop regulation and control of tunnel grouting. Synchronous linkage of numerical simulation and on-site working conditions is realized.

The invention provides a cross-scale nesting and fusion modeling method for a multi-scale geological model, which belongs to the field of geology and comprises four steps of multi-scale data preprocessing, data fusion, cross-scale nesting and fusion and dynamic coupling. According to the method, multi-source heterogeneous data can be integrated, data fusion is realized through a joint probability space, a variational assimilation framework, a fuzzy logic conflict factor and an alternating direction multiplier method, and the stability and precision of the model are improved by using a deep learning auxiliary fusion technology, such as a multi-scale convolutional adversarial network. Meanwhile, model parameters are determined through a volume average upscaling algorithm and random field condition simulation, dynamic adjustment is carried out through set Kalman filtering and localized set transformation Kalman filtering, and consistency, bidirectional feedback and iterative optimization of the parameters between the models are achieved. And finally, multi-scale convergence is realized through a dual grid strategy and restrictive interpolation, and convergence and dynamic balance of the model on the scale from kilometer to micrometer are ensured.

The invention discloses a die temperature field coordinated regulation and control method based on digital twinning, and relates to the technical field of die temperature control. The method comprises the following steps: constructing a mold temperature field digital twin model covering macroscopic, mesoscopic and microscopic scales; a distributed optical fiber temperature sensor is deployed to realize continuous temperature field sensing; physical-digital real-time synchronous mapping is established through ensemble Kalman filtering; performing model prediction control based on the digital twinborn body; three-dimensional visualization and multi-modal man-machine interaction of the temperature field are realized by adopting an augmented reality technology; and reinforcement learning is used to enable the digital twinborn body to have an autonomous optimization capability. Through deep fusion of a physical space and a digital space, precise sensing, real-time mapping and intelligent control of a mold temperature field are realized, and the temperature control precision and the self-adaptive capability of the system are remarkably improved.

The invention discloses a remaining oil distribution prediction method and system, a control device and a storage medium, and the method comprises the steps: obtaining a multi-scale fusion feature vector according to wide-area apparent resistivity data, seismic attribute data, optical fiber time sequence signal data, nano tracer tracking data and pore network point cloud data, inputting a physical constraint neural network to obtain a residual oil saturation initial value and a pressure gradient initial value of each region of the target layer, wherein a loss function comprises data fitting item loss, Darcy law physical constraint loss and mass conservation constraint loss; the real-time water content and the bottomhole flowing pressure of the production well serve as observation data, and a static parameter correction value, a remaining oil saturation correction value and a pressure gradient correction value are obtained through correction based on an ensemble Kalman filtering algorithm; the static parameter correction value and the pressure gradient correction value are fed back to a physical constraint neural network for loss function updating; and determining a remaining oil distribution result of the target layer. According to the invention, the accuracy and timeliness of residual oil distribution prediction can be improved.

The invention discloses an urban inland inundation dynamic early warning method fusing multi-source data and a high-performance numerical model, and belongs to the technical field of urban flood control and disaster reduction and disaster early warning, and the method comprises the following steps: 1, obtaining and preprocessing multi-source data; 2, constructing a high-performance urban flood coupling model; step 3, carrying out assimilation correction on the ensemble Kalman filter; 4, performing rolling prediction and dynamic updating; and step 5, risk identification and early warning release. According to the method, the advantages of multi-source data can be fully fused, and the problems of inaccurate rain condition and unclear waterlogging condition in rapid early warning of urban waterlogging are solved; through multi-source data fusion and high-performance numerical model real-time assimilation correction, minute-level rolling simulation and dynamic correction are achieved, the regional refined ponding risk can be forecasted in advance, the problems that traditional early warning is low in precision, updating lags behind, and the model lacks the self-updating capacity are solved, and the practical value is remarkable.

The invention discloses a Xinanjiang model correction method based on CLDAS soil humidity assimilation. Soil moisture constants such as field capacity FC and wilting point WP are calculated; corresponding and mapping of a CLDAS observation layer and a model three-layer soil structure (WU, WL and WD) are realized through layered interpolation. For the problem of missing measurement of the CLDAS, a reconstruction model based on space-time Transform is introduced for filling, pixel-level uncertainty is output, and an EnKF observation covariance matrix is constructed based on the pixel-level uncertainty. And then dynamically updating the three-layer soil water content state of the model by using ensemble Kalman filtering, thereby reducing the influence of soil humidity initial estimation and simulation deviation on flood simulation. The flood simulation and real-time forecasting precision can be effectively improved, the method has the advantages of being stable in data source, clear in physical significance and high in applicability, and a new technical approach is provided for state initialization and digital hydrology research of a distributed hydrological model.

The invention belongs to the technical field of meteorological monitoring, and provides a sky-sea intelligent eye meteorological perception system for correcting airspace weather forecast in real time based on an unmanned aerial vehicle. An air-sea-land intelligent coupling mechanism is established, meteorological foundation observation, an ocean circulation field and unmanned aerial vehicle detection data are fused in real time based on an ensemble Kalman filtering algorithm, and an efficient three-dimensional wind field reconstruction engine is constructed; a physical enhancement AI correction strategy is adopted, an LSTM-Transform hybrid model is used for driving forecast updating, and meanwhile, a gradient constraint mechanism is introduced to effectively inhibit non-physical mutation of a meteorological field. The final value of the system is reflected in deep coordination of airspace management and control, a risk thermodynamic diagram can be automatically generated, an obstacle avoidance path can be planned, and an air traffic management system is linked to trigger a control instruction. The comprehensive application of the system breaks through the limitation of a traditional method in temporal-spatial resolution, and provides high temporal-spatial resolution early warning support for scenes such as port scheduling and unmanned aerial vehicle logistics.

The invention discloses a carbon footprint monitoring system suitable for a high altitude area and an energy-saving data processing method, and the method comprises the steps: firstly, through an EnKF (Ensemble Kalman Filter) technology, fusing multi-source heterogeneous data such as satellite remote sensing and ground monitoring according to a data source reliability "matching", and overcoming the data sparsity; secondly, based on the oxygen partial pressure corresponding to the local altitude and the'ratio 'of a key sensitivity coefficient # imgabs0 #, applying a specific correction function to dynamically adjust an emission factor; and finally, downscaling the coarse-resolution carbon emission data by adopting an adaptive-resolution convolutional neural network to generate a high-precision and high-resolution carbon emission spatial distribution diagram.

A parameter estimation device repeatedly executes estimation process for estimating a parameter using an ensemble Kalman filter based on measured value data a plurality of times. Further, in the estimation process, the parameter estimation device performs at least one of increasing the number of ensemble members of the ensemble Kalman filter from the number of members in the estimation process related to the previous iteration and decreasing the magnitude of the system noise of the ensemble Kalman filter from the system noise in the estimation process related to the previous iteration, and sets an initial value of the parameter using the estimation result of the parameter by the estimation process related to the previous iteration.

The invention provides a multi-scheme collaborative yield forecasting method based on data assimilation and model parameter optimization, and belongs to the technical field of agricultural information, and the method comprises the steps: obtaining historical and real-time data of a target region and a target crop growth season; constructing a plurality of combined simulation schemes of the WOFOST model; carrying out data assimilation on the leaf area index and the soil humidity by utilizing an ensemble Kalman filter (EnKF) method and combining a Gaussian disturbance strategy; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination and operating the model; improving a water stress function; constructing a rolling updating yield prediction framework, and dynamically optimizing a yield prediction result; and dynamically selecting an optimal simulation strategy to simulate and forecast the yield. According to the method, the yield simulation precision and forecasting stability of the crop model under different moisture years are remarkably improved, and a reference is provided for developing a new meteorological year adaptive dynamic simulation framework of the crop model.

The invention discloses a buried gas pipeline multi-leakage parameter identification and concentration distribution rapid prediction method, and belongs to the technical field of buried gas pipeline safety detection. The method comprises the following steps: firstly, based on a porous medium flow control equation and a component transport equation, establishing a leakage diffusion physical model by utilizing computational fluid mechanics and generating a multi-working-condition sample data set; training a neural network model by using the sample data, and constructing a rapid concentration prediction agent model; in combination with measured data of a monitoring area, an ensemble Kalman filtering algorithm is adopted, the agent model serves as a forward prediction operator, and collaborative inversion of parameters such as the soil type, the leakage direction and the flow is achieved through iterative updating; and finally, outputting complete underground gas concentration distribution in the monitoring area based on the leakage parameters obtained through identification. According to the method, rapid identification of multiple leakage parameters and accurate prediction of concentration distribution are realized under the condition of limited measuring points, and the method has remarkable disaster early warning and engineering application values.

The invention relates to the technical field of computer data processing and artificial intelligence, in particular to a data processing method and device for aircraft atmospheric turbulence early warning and a medium, and the method comprises the steps: carrying out the offline training of a first-scale meteorological risk assessment model and a second-scale turbulence disturbance model; in the flight process of the aircraft, time sequence flight state data and corresponding first-scale meteorological data are obtained in real time, and first-scale meteorological features and second-scale turbulence features are extracted; based on an ensemble Kalman filtering algorithm, taking the output of the first-scale meteorological risk assessment model as a background trend term, generating a nonlinear correction term from real-time data based on a key indication factor determined by the second-scale turbulence disturbance model, and fusing the background trend term and the nonlinear correction term to generate a forecast result of atmospheric turbulence intensity; and generating a turbulence early warning signal according to the forecast result. According to the invention, through multi-scale information fusion, accurate and early warning of atmospheric turbulence is realized, and the flight safety is effectively improved.

The invention relates to the technical field of agricultural non-point source pollution load evaluation, and provides an agricultural non-point source pollution real-time simulation method, device and equipment for an event rainfall. The method comprises the following steps: acquiring preset basic data of a target area, corresponding forecast rainfall of each hydrological response unit under a time scale, and initial conditions of each hydrological response unit before a rainfall event occurs; according to the forecast rainfall capacity, preset basic data and initial conditions of the model, prediction is carried out in combination with an agricultural non-point source pollution model of the rainfall event, and the flow and nitrogen and phosphorus load simulation values at the hydrological monitoring node at the current time point are obtained; and real-time correction simulation of the session event process is carried out based on an ensemble Kalman filtering algorithm and real-time monitored and transmitted flow and nitrogen and phosphorus load observation values. According to the method, the precision of the initial condition of the agricultural non-point source pollution model of the session rainfall event can be effectively improved, and real-time simulation of the process of the agricultural non-point source pollution model of the session rainfall event is realized.

The application relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraints, and specifically comprises the following steps: step 1: synchronously collecting data such as spectrum information, DO, COD, temperature and pH at key monitoring sites, constructing a water dynamics-water quality coupling equation, and simulating the space-time dynamic distribution of water quality parameters; step 2: outputting a water quality sensitive area through a water dynamics-water quality model, screening sensor layout points by combining information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution map by adopting a spatial interpolation method; step 3: analyzing main pollution sources according to the synchronous collection of spectrum information at key monitoring sites, and adopting principal component analysis and an attention mechanism neural network; step 4: based on real-time optical characteristic value-DO data, combining a spatial topological network, a water quality gradient and a cross-region covariance to capture the spatial correlation of water quality parameters between different points, and constructing an optical characteristic value-DO-COD dynamic prediction model; combining pollution tracing and spectrum characteristic correction to correct the COD prediction value, adopting ensemble Kalman filtering to assimilate multi-source data, and improving the model accuracy.

The invention discloses an online set Kalman filtering paleoclimate assimilation method based on a deep learning substitution model. The method comprises the following steps: giving a trained deep learning substitution model and observation needing assimilation; after deep learning is carried out to replace model integration, a mixed prior cycle set is constructed; constructing standardized cyclic set disturbance based on the mixed prior cyclic set and estimating a flow dependent background error covariance; constructing standardized climate state set disturbance based on the climate state set and estimating a static background error covariance; constructing augmented set disturbance through an augmentation mode, estimating a mixed background error covariance, and updating set average and set disturbance; and adding the updated first N set disturbances back to the set average to obtain N posterior set members, and taking the N posterior set members as initial conditions of the deep learning substitution model to carry out next assimilation cycle. The method is less affected by sampling errors. And meanwhile, the mixed analogy set enhances'flow dependency 'and avoids filtering divergence in the assimilation process.

The application relates to a river flood discharge capacity dynamic evaluation and hydrological early warning method. The method comprises the following steps: based on a parameter sensitive observation subset, sequentially updating an initial hidden variable in a low-dimensional hidden space through an unscented particle filter algorithm to obtain an optimized hidden variable, and inputting the optimized hidden variable into a river dynamic resistance field generator based on an initial dynamic roughness field to perform decoding processing to obtain a dynamic roughness field of a current period; based on the dynamic roughness field and a state sensitive observation subset, performing analysis and update processing on a state variable field of a hydrodynamic model through a deterministic ensemble Kalman filter algorithm to obtain an assimilated water level field and a flow velocity field; according to the water level field, the flow velocity field and the dynamic roughness field, the maximum flood discharge capacity of a section is calculated in combination with dike top elevation data, and a flood discharge capacity dynamic index is calculated based on the maximum flood discharge capacity of the section and a current actual flow. The method can realize real-time dynamic quantification of river flood discharge capacity and predictive evaluation of flood risk.

The present invention discloses an electromagnetic inversion uncertainty assessment method based on an ensemble Kalman filter, belonging to the field of geophysical electromagnetic inversion technology. The method comprises: performing time recursion on an initial sample set through the prediction step of the ensemble Kalman filter to obtain predicted state variables; calculating observed variables of the predicted state variables through the update step of the ensemble Kalman filter; constructing a cross-covariance matrix based on the predicted state variables and the observed variables, and performing a localization operation on the cross-covariance matrix using a localization weight matrix to correct its estimation bias; constructing a Kalman gain matrix based on the corrected cross-covariance matrix, and updating the predicted state variables in combination with actual observation data; and performing electromagnetic inversion uncertainty assessment based on the updated state variables. The method can effectively improve the accuracy and stability of electromagnetic inversion and provide uncertainty assessment for two-dimensional magnetotelluric inversion results.

A set Kalman filtering turbulence model assimilation method based on full field similarity criterion is disclosed, which comprises the following steps: sensitivity analysis is performed on the coefficients of the candidate turbulence model to obtain sensitive coefficients and their sampling range for Latin hypercube sampling, i.e., sampling interval; model constant sample combinations are extracted from the sampling interval by using a directional Latin hypercube sampling method, and target flow field results are calculated; a data assimilation algorithm based on the set Kalman filtering algorithm is used for the target flow field results and the model constant sample combinations, and after obtaining the optimized model constant combination, the model constant combination is updated and verified in cycles to obtain the optimal model parameter combination; finally, the improved turbulence model is used to simulate the internal flow field of the fuel assembly in the core of the pressurized water reactor with high similarity. The present application can provide an objective evaluation standard for the full physical field similarity of the data assimilation method, significantly improve the convergence speed of the algorithm, strengthen the robustness of the algorithm, and reduce the calculation cost of the algorithm.

The invention relates to a gamma radiation field reconstruction method based on Lagrange particle model parameter assimilation, and the method comprises the steps: determining the spatial positions of a plurality of radioactive aerosol particles based on a Lagrange particle model; constructing state parameters based on the plurality of spatial positions and the source intensity of each aerosol particle; forming a background state vector by using the state parameters, and dynamically updating the background state vector by adopting an ensemble Kalman filtering algorithm to obtain a corrected assimilation state; determining an optimized transportation track based on the spatial position of the radioactive aerosol particles in the corrected assimilation state; constructing a local concentration field corresponding to the radioactive aerosol particles by adopting a Gaussian kernel function; and determining a gamma radiation field corresponding to the radioactive aerosol particles. The three-dimensional radiation field reconstruction is carried out based on the background state vector and the real-time monitoring data by taking the parameters of the radioactive aerosol particles as the core, and the purposes of correcting the background state variable, obtaining the corrected assimilation variable and improving the accuracy of the three-dimensional radiation field reconstruction are achieved.

The invention discloses a data and model fusion driving-based space stack operation parameter correction and inversion method, which comprises the following steps of: 1, analyzing the influence degree of empirical constants in a space stack simulation model on an operation parameter prediction result, and determining empirical constants needing to be corrected and operation parameters; 2, predicting N groups of operation conditions containing empirical constant error disturbance by using the simulation model, obtaining N groups of simulation model estimation vectors, and forming an initial prediction matrix; 3, summarizing on-orbit actual operation data or ground test actual measurement data acquired by a space stack sensor, and providing an actual measurement data matrix for correcting space stack operation parameters; 4, fusing the characteristics of the prediction data and the actual measurement data of the simulation model by using an ensemble Kalman filtering algorithm to obtain a corrected empirical constant; 5, updating the empirical constants in the space stack simulation model into the corrected empirical constants to realize the correction of the simulation model; and 6, using the corrected simulation model to carry out operation condition analysis, and realizing operation parameter inversion calculation. The method is high in prediction efficiency and accurate in result.

The invention discloses a low-altitude three-dimensional wind field nowcasting method and device, and the method comprises the steps: firstly collecting and preprocessing wind field data through an API, constructing a spatial-temporal feature project, and dividing a data set according to a time sequence; thirdly, a LightGBM framework is adopted, and a static optimal forecasting model is trained by means of Bayesian optimization; the method is characterized in that the static optimal forecasting model and an ensemble Kalman filtering framework are deeply fused, a dynamic assimilation forecasting system is constructed, forecasting output is continuously corrected and optimized, and error accumulation is effectively restrained. And finally, after performance verification, the system is operated in a business mode, and a final wind field forecasting result containing the NaN identifier is output.

The application relates to a cloud platform-based water resource management method, device, equipment and medium. The method integrates multi-source water data through a cloud platform, constructs a hierarchical Bayesian model after data cleaning, uniformly quantizes model parameters, input and posterior distribution of structural errors, drives ensemble Kalman filtering dynamic assimilation of real-time observation data based on the same, generates a prediction distribution fusing multi-source uncertainty, further analyzes the contribution source of prediction variance in real time through a Sobol' index, identifies dominant uncertainty factors, constructs a coupled error model according to the same, feeds the coupled error model back to the assimilation cycle as a state variable for online correction, and finally forms an improved prediction model capable of dynamically tracking and quantifying the uncertainty coupling propagation mechanism, thereby significantly improving the accuracy and reliability of hydrological prediction, and providing risk quantification basis and optimization scheme for flood control scheduling, water resource allocation and urban drainage management decision-making.

The application provides a multi-scheme cooperative yield prediction method based on data assimilation and model parameter optimization, and belongs to the field of agricultural information technology.The method comprises the following steps: obtaining historical and real-time data of a target region and a target crop growing season; constructing multiple combination simulation schemes of a WOFOST model; using an ensemble Kalman filter (EnKF) method combined with a Gaussian disturbance strategy to perform data assimilation on a leaf area index and soil humidity; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination, and running the model; improving a water stress function; constructing a rolling update yield prediction framework, dynamically optimizing yield prediction results; and dynamically selecting an optimal simulation strategy to perform yield simulation and prediction.The application significantly improves the yield simulation accuracy and prediction stability of the crop model under different water year types, and provides a reference for developing a new framework of crop model meteorological year type self-adaptive dynamic simulation.

This invention discloses a sound velocity profile assimilation and inversion method and system based on dual physical data driving. The method includes: performing adaptive observation and sparse characterization of the sound propagation path to obtain the sparse characterization results; performing long-term noise cross-correlation calculation and passive time delay self-calibration to obtain the virtual sound source arrival time series after absolute time delay reference correction; performing constraint verification through a physical-data dual-driven, dual-stream coupled generative inversion network; acquiring a broadband electromagnetic response time series and performing cross-medium electromagnetic-acoustic joint inversion constraints to obtain a strongly constrained calibrated full-depth sound velocity profile; and performing ensemble Kalman filtering assimilation and uncertainty propagation based on POD order reduction. This invention can quantify the uncertainty of the inversion results. As a sound velocity profile assimilation and inversion method and system based on dual physical data driving, this invention can be widely applied in the field of underwater acoustic engineering technology.

This invention discloses a method for collaborative control of mold temperature field based on digital twins, belonging to the field of mold temperature control technology. The method includes: constructing a digital twin model of the mold temperature field covering macroscopic, mesoscopic, and microscopic scales; deploying distributed fiber optic temperature sensors to achieve continuous temperature field sensing; establishing a physical-digital real-time synchronous mapping through ensemble Kalman filtering; implementing model predictive control based on the digital twin; using augmented reality technology to achieve three-dimensional visualization of the temperature field and multimodal human-computer interaction; and using reinforcement learning to enable the digital twin to have autonomous optimization capabilities. This invention, through the deep integration of physical and digital spaces, achieves accurate sensing, real-time mapping, and intelligent control of the mold temperature field, significantly improving temperature control accuracy and system adaptability.

The application provides a multiscale geological model cross-scale nesting and fusion modeling method, belonging to the geological field, including four steps of multiscale data preprocessing, data fusion, cross-scale nesting and fusion and dynamic coupling. This method can integrate multi-source heterogeneous data, realize data fusion through joint probability space, variational assimilation framework, fuzzy logic conflict factor and alternating direction multiplier method, utilize deep learning auxiliary fusion technology such as multi-scale convolutional adversarial network to improve the stability and precision of the model. At the same time, the model parameters are determined through volume average upsampling algorithm and random field conditional simulation, and the consistency of the parameters between the models is realized through dynamic adjustment of ensemble Kalman filter and localized ensemble transform Kalman filter, bidirectional feedback and iterative optimization. Finally, through the double grid strategy and the restrictive interpolation, the multiscale convergence is realized, and the convergence and dynamic balance of the model on the scale of kilometers to microns are ensured.