175 results about "Meteorological models" patented technology

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses a three-dimensional wind field spatial-temporal feature reconstruction and efficient prediction method, and relates to the field of spatial-temporal feature reconstruction and efficient prediction. Constructing a three-dimensional terrain computational domain based on the digital elevation model of the target mountain region, performing multi-scene numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, and constructing and training a wind field spatial feature mapping model; training a wind speed and wind direction short-time prediction model based on the actually measured data set; inputting the monitoring data obtained in real time into the wind speed and wind direction short-time prediction model to obtain a future wind speed and wind direction prediction value of each monitoring station; and inputting the wind speed and direction predicted values into the wind field spatial feature mapping model to obtain the mountain overall wind field distribution of the target mountain region at the future moment. By constructing an'actual measurement-simulation-modeling-prediction-reconstruction 'integrated technical framework, high-temporal-spatial-resolution short-time prediction from observation of local wind speed and wind direction to the overall three-dimensional wind field of the mountainous region is realized.

The invention discloses a storm surge water increase prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seawater monitoring, and the method comprises the steps: obtaining multi-source observation data of a target sea area, the multi-source observation data comprising satellite remote sensing data, near-shore monitoring data, meteorological mode data and drainage basin data; performing space-time alignment and exception processing on the multi-source observation data to generate standardized space-time grid data; performing hybrid prediction modeling on the standardized space-time grid data through a physical data dual-drive modeling layer to obtain a target probability water increasing field; and dynamically correcting the target probability water increasing field by using real-time observation data to obtain a storm surge water increasing predicted value. A physical mechanism model and a probability generation model are combined, limitation of a single model is overcome, and the nonlinear evolution process of the storm surge is effectively captured; the time-space dual-drive architecture is adapted to complex coast terrains and changeable meteorological conditions, and the risk of missing report or false report is reduced.

The invention relates to the technical field of weather forecasting, in particular to a weather clock forecasting system based on multi-source data fusion, which comprises an equipment terminal and a cloud end, the cloud end comprises a data acquisition module, a data processing module and a data transmission module, the data acquisition module is used for acquiring position information of the equipment terminal and multi-source data at the position, and the data processing module is used for processing the position information; the data processing module performs grid division on the target area range based on the position information of the equipment terminal to obtain grid units, and the data processing module is further used for mapping the position information of the equipment terminal into the grid units to obtain a target unit grid of the position where the equipment terminal is located. According to the method, the local feature data is introduced into the fine meteorological data of the target grid unit for correction, so that the finally obtained meteorological correction data has a finer space description capability, and the defect that prediction is inaccurate due to the fact that a traditional meteorological model ignores the local feature data under complex terrains or urban microclimate is overcome.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses a grain yield estimation method based on a land-gas coupling model and a machine learning algorithm, and relates to the technical field of agricultural meteorological prediction and intelligent decision making, and the method comprises the following steps: S1, carrying out the kernel-level algorithm fusion of a dynamic corn growth model and a weather research and forecast model, constructing a WRF-Crop coupling system, and carrying out the kernel-level algorithm fusion of the WRF-Crop coupling system; and the WRF-Crop coupling system realizes real-time interaction of transpiration and albedo change of the corn canopy and temperature, humidity and radiation data through a bidirectional data channel. According to the grain yield estimation method based on the land-gas coupling model and the machine learning algorithm, the limitation of isolated operation of a traditional dynamic corn growth model and a meteorological model is broken through by constructing a WRF-Crop bidirectional coupling model, dynamic mutual feedback simulation of vegetation growth and local climate is realized, and a crop stress response mechanism under an extreme climate event is effectively captured.

The invention discloses a typhoon long-distance rainstorm rapid identification method based on an AI meteorological model, a storage medium and a computer program product, and the method comprises the steps: obtaining historical weather data and / or forecast weather data, selecting an AI weather forecast model which is based on a deep learning technology and carries out the training through a large amount of historical weather data, representative and typical rainfall examples are screened, a control experiment and a sensitivity experiment are designed, and typhoon core vortexes are removed from an initial field. By comparing the difference between the results of the control experiment and the sensitivity experiment, the influence of the typhoon on the long-distance rainfall is quantified based on the whole-layer water vapor divergence change and rainfall change analysis, and the long-distance rainstorm influence range and intensity of the typhoon are identified and determined. According to the method, the precision of medium-term weather forecast and extreme weather event recognition is remarkably improved, efficient calculation and wide practical application value are achieved, a scientific basis is provided for disaster early warning and disaster reduction management, and a new method is provided for meteorological scientific research and teaching.

The embodiment of the invention provides an intelligent optimization system and method for a photovoltaic power station design scheme, and the system comprises a data input module which receives and analyzes input data which specifically comprises topographic data, meteorological data and equipment parameters, and transmits the input data to a data processing module; the data processing module is used for carrying out standardization processing and cleaning processing on the input data, complementing missing values and obtaining a constructed high-precision terrain and meteorological model; and the optimization algorithm module adopts a multi-objective optimization algorithm, takes the power generation efficiency, the economic cost and the land utilization rate as optimization objectives, dynamically adjusts the layout, the inclination angle and the line spacing of the photovoltaic modules based on the input data, and obtains an intelligent optimization result and a related report of the design scheme of the photovoltaic power station. The generating capacity and the cost of the intelligent optimization result of the photovoltaic power station design scheme are predicted through a high-precision terrain and meteorological model; and the result output module is used for outputting an intelligent optimization result and a related report of the photovoltaic power station design scheme.

The invention relates to the technical field of typhoon path prediction, and discloses a typhoon path prediction method, system, terminal and storage medium based on multi-modal data of sounding equipment and a sky big model, and the method comprises the steps: obtaining multi-source heterogeneous data collected by detection equipment related to typhoon prediction, carrying out fusion and adaptive enhancement on the multi-source heterogeneous data on the basis of a big Pantou meteorological model, and generating a high-resolution three-dimensional field; constructing a cascaded AI downscaling network, introducing a physical constraint layer, carrying out local refined modeling on a typhoon eye region according to the high-resolution three-dimensional field, and outputting a high-resolution three-dimensional meteorological field; and designing a typhoon-environment field interaction model based on a graph neural network, performing typhoon prediction according to the high-resolution three-dimensional meteorological field, and outputting a typhoon path ensemble forecast containing a confidence interval and a typhoon thermodynamic structure analysis report. The typhoon path prediction speed and precision are improved, the prediction time is shortened, the prediction error is reduced, and calculation resources are saved.

The invention discloses a regional numerical model forecasting system utilizing observation guidance of an AI meteorological big model, and relates to the field of data processing systems.An AI forecasting module in the system inputs global weather forecasting mode initial field data into the AI meteorological big model and outputs AI weather forecasting mode data; and then the observation data fusion module performs grid fusion on the global weather forecast mode initial field data and the AI weather forecast mode data by using an observation guidance technology to obtain fused observation data, and then the fused observation data is used for driving a regional numerical forecast model to obtain a regional numerical mode forecast result. Under the limitation that an output result of an AI meteorological large model cannot be directly utilized to drive a regional numerical forecasting model, effective information in the AI meteorological large model is introduced through an observation guiding technology, so that better initial field and boundary conditions of the regional numerical forecasting model can be obtained, and a regional numerical mode forecasting result with higher forecasting quality can be obtained.

The present application discloses a typhoon forecasting method based on domestic satellite data, which includes: a data fusion process: used to fuse GNSS-R sea surface wind data and IFS forecast data from the European center to generate a two-dimensional gridded sea surface vector wind field; a meteorological forecasting process combined with artificial intelligence, including a large meteorological model forecasting step and a numerical model forecasting step; a multi-typhoon path discrimination and typhoon intensity forecasting process, which is used to extract key meteorological element variables from regional forecast data files at different times, and judge the typhoon center based on the key meteorological element variables. The present application combines domestic satellite data and artificial intelligence technology, and through the deep combination of data fusion and numerical forecasting technology, improves the accuracy and refinement of typhoon path and intensity forecasts. In addition, through the application of large meteorological models, nonlinear and massive data can be processed more effectively, improving the forecast accuracy.

The invention discloses a flight space directional training auxiliary system and method, and the system comprises a flight task simulation module; the meteorological environment simulation module is used for simulating a meteorological scene corresponding to a flight task, and simulating the meteorological scene by establishing a multi-dimensional meteorological model and combining meteorological parameters; the meteorological environment parameter control module comprises a time step length control module which adopts a preset time step length to input meteorological parameter observation data collected in real time to the meteorological parameter setting module; the specific meteorological event control module is used for transmitting meteorological parameters of a specific meteorological event area to the meteorological parameter setting module when the airplane triggers a specific meteorological event; and the training scheme generation module is used for generating a proper training scheme according to the historical training information of the flight personnel. According to the system and the method, a real and personalized simulation training environment can be provided for the flight personnel, and the professional skills and the capability of coping with various flight conditions of the flight personnel are effectively improved.

The invention discloses a tower disaster situation sensing and early warning system based on wireless communication, and relates to the technical field of tower disaster monitoring and early warning. The tower top monitoring module is integrated with a multi-dimensional sensor, adopts a wind-solar complementary power supply and self-calibration mechanism, and intelligently adjusts data acquisition; the timed inspection module optimizes an inspection path and identifies 10 types of defects by using an unmanned aerial vehicle carried laser radar and an AI model; the meteorological early warning module combines a micrometeorological model and a random forest algorithm to dynamically correct a threshold value, and associates multi-source data to trigger early warning; the self-protection module integrates lightning protection and block chain technologies to guarantee data security; according to the invention, the monitoring precision and the communication reliability are obviously improved, the early warning response time is shortened to be within 30 seconds, and the power grid disaster prevention capability is enhanced.

The invention belongs to the technical field of safety early warning, and discloses a forest fire suppression remote command system based on an artificial intelligence meteorological big model and a digital twinborn technology. The laser radar monitoring module collects fire scene basic information and transmits the fire scene basic information to the artificial intelligence meteorological large model prediction module for trend prediction. A prediction result and monitoring data are fused through the digital twinborn situation display module and then fire scene situation information is displayed. A commander makes a decision based on the three-dimensional visual fire scene environment, the command decision and emergency response module generates a hierarchical response and combat scheme, and the personnel, material and air-ground collaborative scheduling module executes specific tasks according to the hierarchical response and combat scheme. The method has the advantages that the fire scene information can be acquired in real time and completely; the prediction capability is obviously improved and dynamically updated; multi-source data deep fusion and digital twinning dynamic display are realized; a complete hierarchical response and command closed loop is formed; and cross-region and cross-department resource scheduling is more efficient.

The invention discloses an automatic driving evaluation system based on artificial intelligence, and relates to the technical field of automatic driving, and the system comprises the following components: a meteorological model coupling module, a sensor parameter adjustment module, an automatic driving simulation module and an evaluation analysis module. According to the invention, data connection is established between the meteorological model coupling module and an external meteorological model, comprehensive meteorological data including temperature, humidity, wind speed, rainfall, snowfall, visibility and the like are acquired in real time, and a simulated extreme weather scene is constructed based on the data. The sensor parameter adjusting module automatically adjusts performance parameters of a vehicle sensor according to different weather types to ensure the accuracy of sensor data under extreme weather conditions, and the automatic driving simulation module operates an automatic driving system model in a simulated extreme weather scene to simulate the vehicle driving process.

The invention discloses an energy internet operation state evaluation method and system, and relates to the technical field of energy internet, and the method comprises the following specific steps: building a micrometeorological model: combing the geographic position information of various energy facilities distributed in the energy internet, according to the method, a regional differentiation comprehensive evaluation mechanism considering ecological benefits and energy utilization efficiency is established by fusing dynamically quantified ecological environment related indexes and energy operation state indexes obtained based on micrometeorological differences, so that real-time influences of the energy internet on the ecological environment in different regions are comprehensively and accurately mastered; according to the method and the system, comprehensive evaluation results such as the interference degree of a wind power plant on a bird migration path, the ecological interference degree of a solar power station on land vegetation and the influence range and intensity of electromagnetic radiation on surrounding ecology are evaluated, and energy internet optimization operation strategies and ecological protection measures which are more practical and more targeted are formulated based on the comprehensive evaluation results.

The invention discloses a distribution line icing disposal environment modeling and application method, and relates to the technical field of disaster prevention and reduction and intelligent decision cross of a power system, and the method comprises the steps: obtaining the original data of a multi-source sensor, generating a standardized feature vector, constructing an environment model of a reinforcement learning agent, and designing an action space and a reward function. Integrating a physical model, and constructing a multi-dimensional feature, a line physical model, a meteorological model, a line and equipment state model and a historical and auxiliary information model so as to obtain a lead icing environment model; designing an icing increment formula fusing collision coefficients, and designing a comprehensive network model; carrying out reinforcement learning training on the integrated network model in a simulation environment; integrating the trained comprehensive network model to a power system monitoring platform, and carrying out real-time monitoring and automatic disposal; the method can effectively improve the icing handling capacity of the distribution line, reduces the load current fluctuation, and guarantees the safety and stability of the line.

The invention belongs to the field of electric power, and discloses a downscaling method, device and equipment for a rainfall forecast field, and a storage medium, and the method comprises the steps: obtaining meteorological data, inputting the meteorological data into a fortune meteorological model, and enabling the fortune meteorological model to generate an initial forecast field based on the meteorological data; cutting the initial forecasting field to obtain an initial rainfall forecasting field of the target area, and inputting the initial rainfall forecasting field into a pre-trained downscaling model which comprises a shallow feature extraction module, a deep feature extraction module and a reconstruction module, a shallow feature extraction module extracts initial features from the initial rainfall forecast field based on a convolutional layer; the deep feature extraction module captures the spatial dependency relationship of the precipitation field from the shallow feature map through the residual block; the reconstruction module converts the deep feature map into a high-resolution downscaling precipitation field based on a pixel rearrangement operation. The spatial resolution and accuracy of rainfall forecast are improved through a downscaling model based on SwinIR.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention provides a power dispatching method, system and equipment under disasters, and the method comprises the steps: constructing a meteorological model of a target meteorological disaster, and obtaining meteorological characteristic parameters of the target meteorological disaster in an area where a power grid is located based on the meteorological model of the target meteorological disaster; acquiring line fault parameters of a target structure influenced by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, and acquiring output parameters of target power generation equipment influenced by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters; and determining a target power flow parameter of the power grid based on the line fault parameter and the output parameter, determining a target Pareto solution of each target function in a preset function set based on the target power flow parameter and a preset normal constraint algorithm, and performing power dispatching on the power grid according to the target Pareto solution of each target function. Based on the scheme of the invention, the distribution and utilization of power resources in the power grid can be optimized, and the toughness and disaster resistance of the power grid under meteorological disasters are improved.

The invention provides a new energy wind-solar power prediction method and device based on a meteorological large model, and the method comprises the steps: making a meteorological large model ensemble forecast data set ENS0, carrying out the ensemble assimilation, and carrying out the forecasting, and obtaining a deterministic forecast data set F0; constructing a fine tuning localization matrix, and performing traversal optimization based on the fine tuning localization matrix and the meteorological large model ensemble forecast data set ENS0 to obtain a deterministic grid forecast data set F100; performing spatial scale separation on the deterministic grid forecast data set F100, and performing traversal optimization to obtain a deterministic grid forecast data set D100 of the meteorological large model; and calculating the weight by using the historical gridding data of the deterministic grid forecast data set D100 of the meteorological large model, and carrying out weighted average according to the weight to obtain a fused power forecast time sequence. According to the method, the influence of the localization matrix is comprehensively considered, the influence under different spatial scales is distinguished, and the stability and accuracy of new energy power prediction can be remarkably improved.

The invention relates to a tropical cyclone strength prediction method and device based on a large meteorological model, and the method comprises the following stages: 1, mapping tropical cyclone strength information to a discrete vector implicit space through an encoder under the conditions of a satellite image and reanalysis of atmospheric field data, a mapping result is compared with all vectors of a learnable discrete codebook, the closest vector is taken and restored to original tropical cyclone strength information through a decoder, and iterative training is carried out on the encoder, the discrete codebook and the decoder; and a second stage: inputting historical meteorological data into the trained encoder and discrete codebook, converting the historical meteorological data into discrete hidden variables, carrying out autoregression iteration to obtain forecast hidden variables, and restoring tropical cyclone strength information through the decoder by taking wind-wind forecast field data as physical constraints to obtain a tropical cyclone strength prediction result. Compared with the prior art, the method has the advantages that long-term forecasting can be achieved, good performance is preserved, and the calculation cost is reduced.

The invention belongs to the technical field of power prediction. The invention provides a regional ultra-short-term new energy power prediction method based on multi-modal information fusion, and the method comprises the steps: obtaining satellite remote sensing data, station observation data, meteorological mode data and station attribute data of a target region, and carrying out the time-space alignment and abnormal value processing; performing feature extraction from the data; performing multi-level fusion on the extracted features by using a feature fusion network to obtain fused features; and inputting the fusion features into a prediction model to obtain the predicted ultra-short-term new energy power of the target area. According to the method, multi-modal data features are integrated by using a multi-level fusion algorithm, more comprehensive and targeted data support is provided for regional ultra-short-term new energy power prediction, the performance and generalization ability of a prediction model can be significantly improved, a power dispatching department can make a decision more accurately, and the prediction efficiency is improved. And efficient consumption of new energy and stable operation of a power grid are realized.

The invention discloses an observation station data assimilation method based on deep learning, and belongs to the technical field of meteorological data assimilation, and the method comprises the steps: obtaining forecast background field data of a Fuhu meteorological big model, obtaining observation station data, the observation data comprises the actual measurement data of a ground meteorological observation station, a radar, a sounding station and a wind profile observation station, and the actual measurement data of the ground meteorological observation station, the radar, the sounding station and the wind profile observation station; performing spatial interpolation, time alignment and standardization on the background field and the observation data, and introducing position codes and time codes; constructing a deep learning assimilation model based on a sliding window Transform, inputting a background field and observation data, and outputting an assimilation analysis field result; and the effectiveness of the method is verified through comparison and evaluation with fifth-generation global atmosphere reanalysis data of the European mid-term weather forecast center. According to the method, the fineness and the physical consistency of local meteorological elements can be remarkably improved while the integrity of large-scale forecasting is kept, and high-precision assimilation of meteorological forecasting data is realized.

The invention discloses an agricultural meteorological data processing system and method, and the method comprises the steps: carrying out the preprocessing of historical growth data and historical meteorological data, and obtaining a historical growth sample data set and a historical meteorological data sample set which affect different growth cycles of a to-be-detected crop; training the historical growth sample data set and the historical meteorological data sample set to obtain a crop meteorological model influencing the growth of the to-be-detected crop; acquiring real-time growth data of crops to be detected and real-time meteorological data of the planting area of the crops to be detected, and inputting the real-time growth data and the real-time meteorological data into the crop meteorological model for analysis to obtain meteorological characteristic data influencing crop growth; preprocessing the meteorological characteristic data to form meteorological time sequence data of the crops to be measured in different growth periods; and predicting the growth risk situation of the to-be-detected crop according to the meteorological time sequence data. According to the invention, the meteorological data of each growth stage of crops can be predicted from details, and the precision and reliability of the growth data in the time period are improved.

The invention relates to the technical field of aerospace meteorology, and particularly discloses a domestic numerical weather forecast mode-based evaporation waveguide height determination method, which comprises the following steps of: obtaining meteorological coupling data output by a domestic numerical weather forecast mode CMA-MESO; determining parameters according to the meteorological coupling data and a turbulent flux algorithm COARE; constructing a temperature vertical profile and a specific humidity vertical profile according to the parameters, the Monin-Obchhoff similarity theory and an NPS model; constructing a modified refractive index profile according to the temperature vertical profile and the specific humidity vertical profile; and determining the height of the evaporation waveguide according to the height corresponding to the lowest point of the corrected refractive index profile. The method is suitable for determining the evaporation waveguide height in a domestic business meteorological mode.

The invention provides a wind power prediction method and device based on a meteorological large model, a server and a medium. The method comprises the following steps: constructing a multi-scale prediction model; acquiring historical actual power of the wind power station, and acquiring multi-scale historical meteorological elements through the large meteorological model; training the multi-scale prediction model, and performing hyper-parameter optimization on the multi-scale prediction model through an improved arithmetic optimization algorithm; and obtaining multi-scale instant meteorological elements through the meteorological large model, inputting the multi-scale instant meteorological elements into the multi-scale prediction model after hyper-parameter optimization, and generating a wind power prediction result based on the meteorological large model. According to the wind power prediction method and device based on the meteorological large model, the server and the medium, the limitation of a traditional wind power prediction method can be overcome, and the accuracy and the stability of a wind power prediction result are improved.

The invention relates to the technical field of fire spreading prediction, and discloses a fire spreading prediction method and system based on local real-time high-precision weather prediction, a terminal and a storage medium, and the method comprises the steps: obtaining fire observation data collected by a plurality of pieces of information collection equipment for a target region, preprocessing the fire observation data through an edge calculation layer to obtain target fire observation data; a lightweight 3D-Transform architecture is designed based on a big archaeological meteorological model, and the target fire observation data and historical fire scene case data are fused to generate a meteorological prediction result; and constructing a fire spread prediction network model, jointly inputting the meteorological prediction result, the terrain elevation and the fuel humidity, and outputting a fire spread probability graph and a spread velocity field in a future preset time. According to the method, the fire spreading prediction precision and speed are improved, and a high-precision and high-response full-chain solution is provided for forest fire emergency management.

The invention belongs to the technical field of navigation positioning, and discloses an offshore positioning method based on fusion of a numerical meteorological model and a Beidou short message, so as to realize offshore second-level continuous high-precision positioning by using single Beidou short message equipment. According to the offshore positioning method based on fusion of the numerical meteorological model and the Beidou short message, a CNES real-time satellite orbit and clock error data are adopted, NWP model meteorological data are utilized to invert zenith troposphere delay, and a time baseline method considering NWP improvement is utilized to weaken time accumulation errors and improve time baseline positioning performance; and then, based on the improved time baseline offshore high-precision positioning method, the PPP and Beidou short message communication technologies are combined, so that the BDS / GNSS offshore high-precision positioning navigation target can be realized by utilizing a single Beidou short message device, and the high-precision positioning cost is saved.

The invention discloses a regional meteorological model error control method based on a neural network, and particularly relates to the field of regional meteorological model error control, and the method comprises the steps: obtaining reanalysis data of a first data source and station observation data of a second data source, and forming an input matrix containing space coordinates, time indexes and meteorological element values; preprocessing operation is executed on the input matrix, and preprocessing comprises the steps that interpolation correction is conducted on missing values, normalization processing is conducted on data of different dimensions, sequence alignment is executed based on a unified time index, and a first input matrix and a second input matrix which are consistent in structure are formed. By constructing a double-residual channel and executing residual reciprocal correction in a training process, dynamic balance fusion of reanalysis data and station observation data is realized on a spatial-temporal scale, so that the problem of cross-source data fusion imbalance in the prior art is solved.