106 results about "Meteorological models" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a plum rain season photovoltaic output prediction method and system based on small sample learning. The method comprises the following steps: constructing a multi-source feature system; clustering and dividing meta-learning tasks based on a meteorological mode; the method comprises the following steps: constructing a TCN-Transform hybrid neural network model, and embedding a learnable meteorological adapter; training a hybrid neural network model by adopting an MAML meta-learning framework, and realizing meta-learning task training of different meteorological models in combination with a physical constraint regularization loss function; model parameters are optimized in three stages through a course learning strategy, and a training set is enhanced by utilizing Mixup time sequence data; and deploying the trained model to carry out output prediction, and outputting a prediction result with a confidence interval. According to the method, high-precision photovoltaic output prediction in a small-sample and high-uncertainty plum rain season scene is realized, and reliable support is provided for power grid dispatching.

The invention discloses a power transaction supply and demand prediction method and system based on a regional meteorological large model, and the method comprises the steps: collecting and fusing multi-source historical meteorological data and geographic data of a target region, and forming a special data set; training the large base meteorological model by using the special data set, and outputting a preliminary meteorological forecast according to the large base meteorological model and the initial meteorological data of the target area; inputting the preliminary meteorological forecast into the super-resolution downscaling model, and outputting super-resolution meteorological element forecast by taking the super-resolution geographic data of the target area as a condition; and inputting the super-resolution meteorological element forecast, the historical power data of the target area and the market supply and demand data into the supply and demand prediction model, and outputting a supply and demand prediction curve according to a prediction task. According to the invention, super-resolution meteorological element forecast is generated through the large base meteorological model and the super-resolution downscaling model, so that an accurate supply and demand prediction curve can be obtained by using the supply and demand prediction model.

The invention discloses a water conservancy digital twin data fusion method, system and device based on multi-source heterogeneous data dynamic alignment and a medium, and belongs to the technical field of data fusion. Performing cleaning processing, format standardization processing and timestamp alignment processing based on a time window on the data to obtain preprocessed data; performing sliding window division processing on the preprocessed data, performing regional grid numbering processing according to a space division rule, and enabling each data point to correspond to a divided time window and a space grid to obtain time-space mapping data; and performing fusion processing on the data subjected to space-time mapping according to the data type. According to the method, the fusion strategy is designed by distinguishing the data types in the fusion stage, and it is guaranteed that the fusion result can still keep high reliability and stability when facing data of different qualities and different sources.

The invention discloses a typhoon path integrated prediction method based on marine meteorological big data, and the method comprises the steps: collecting multi-source marine meteorological data, carrying out the time and spatial resolution alignment and fusion of the multi-source marine meteorological data, and obtaining a marine meteorological model; extracting typhoon meteorological characteristics in the marine meteorological model, obtaining a historical typhoon data set, and predicting an initial action path of a typhoon according to the typhoon meteorological characteristics and the historical typhoon data set; detecting and acquiring an ocean temperature-salinity value on the initial action path, and correcting the initial action path according to the ocean temperature-salinity abnormal value to obtain a corrected action path; on the basis of a preset displacement prediction model, in combination with a typhoon passing path and the marine meteorological model, calculating a sudden change point and a turning probability of the typhoon; and according to the sudden change point and the steering probability thereof, correcting the corrected action path to obtain a final typhoon path, and displaying the final typhoon path on a geographic information platform.

The invention discloses a space-time matching comparison and uniformity evaluation method for a three-dimensional atmosphere live field fusion product. The method comprises the steps of constructing a multi-source data verification framework, implementing refined space-time matching, executing multi-level quality control, comprehensively inspecting and analyzing and outputting a product specification. By constructing a multi-source collaborative verification framework, integrating sounding, microwave radiometer, satellite and foundation observation data, and establishing a refined space-time matching model and a multi-level quality control system, the error condition of a live product can be quantitatively given. The method is suitable for space-time matching comparison and homogeneity inspection of air temperature, humidity and horizontal wind speed / wind direction elements, and supports meteorological model optimization and low-altitude economic application.

The invention relates to the cross technical field of weather forecast and energy prediction, and discloses a physical-data fused weather-wind and light power combined prediction method and system, and the method comprises the steps: constructing an initial combined prediction model, and enabling a weather forecast model and a wind and light power prediction model to be connected into a whole through the model; performing end-to-end training on the initial joint prediction model by adopting a historical meteorological data sample and a historical wind-solar power generation power data sample; and inputting the meteorological data of the target area into the trained joint prediction model, and outputting a wind-solar power generation power prediction value in a future time period. By adopting the end-to-end joint training method based on the differentiable calculation path, global collaborative optimization of the weather forecast model and the wind and light power prediction model is realized, so that the core problem of power prediction misalignment caused by unidirectional transmission and accumulative amplification of the weather model error in the traditional series architecture is solved; and the precision of final power prediction is ensured from the system level.

The invention relates to the technical field of meteorological model optimization and polar region ocean data processing, in particular to a method and system for optimizing a COARE model based on sea ice density, and the method comprises the steps: obtaining the historical actual measurement data of the polar region ocean, calculating the real roughness and the model roughness outputted by the COARE model, and determining the roughness difference value; dividing a wind speed interval, and fitting and establishing a correction function of a difference value between the sea ice concentration and the roughness; and introducing the sea ice concentration into the COARE model, judging the interval according to the wind speed, calling a corresponding function to correct the roughness of the model, and outputting the ocean-atmosphere flux conforming to the polar region sea ice condition to complete the model optimization. According to the method, the sea ice concentration is introduced, and the wind speed partition correction relation is established, so that the roughness of the polar region under the sea ice coverage condition is dynamically corrected by the COARE model, the limitation of the original model in the aspects of wind speed applicability and polar region adaptability is broken through, and the accuracy and practicability of polar region ocean-atmosphere flux calculation are remarkably improved.

The invention discloses a mountain land low temperature refined monitoring prediction method and system based on satellite-ground fusion and deep learning, and belongs to the technical field of ecological meteorological monitoring and agricultural disaster prevention and reduction. The invention aims to solve the problems of insufficient mountain low-temperature monitoring space coverage, neglect of local factors, rough prediction resolution and poor terrain adaptability in the prior art. The method comprises the steps of multi-source data acquisition, high-density meteorological station, multi-source satellite and meteorological mode data acquisition, spatial-temporal feature analysis, correlation analysis, key variable screening and weather type association, satellite-ground fusion air temperature reconstruction, generation of refined monitoring data based on a CNN-ANN deep learning framework, and short-term and temporary prediction modeling. And LSTM is combined with a terrain attention mechanism to construct a prediction model, and two-dimensional reliability verification is carried out. According to the method, the mountain low-temperature monitoring precision and the predicted terrain adaptability can be improved, accurate support is provided for low-temperature disaster prevention and reduction of mountain economic crops, and the method has remarkable practical value.

The invention provides a training method and device of a north pole sea fog model, a storage medium and electronic equipment. The electronic equipment obtains the sea fog visibility of the research area at a plurality of historical time points by using a pre-constructed PWRF model; for each historical time point, weighting the original meteorological data, the original sea surface temperature data and the original sea ice data of the plurality of meteorological models at the historical time point to obtain sample meteorological data, sample sea surface temperature data and sample sea ice data at the historical time point; and training the to-be-trained model through the synthesized data to obtain the north pole sea fog model. Therefore, the problem that sea fog visibility data with long time sequence, large range area and high spatial resolution cannot be obtained at present is solved, and input data formed by fusing multiple models has high confidence, so that the trained north pole sea fog model can effectively capture complex mechanisms such as humidity threshold effect, interaction of wind speed and sea ice and the like; and the sea fog prediction precision is improved.

The invention relates to the technical field of low-altitude atmosphere monitoring, and particularly discloses a low-altitude meteorological digital twinborn reconstruction method based on three-dimensional entity data modeling, seamless connection from a regional macroscopic wind field to an urban microscopic flow field is realized through combination of a mesoscale meteorological model and a computational fluid mechanics model, and the low-altitude meteorological digital twinborn reconstruction method based on three-dimensional data modeling of a real city is realized. The physical effect of surface features on airflow is directly considered, errors of a CFD grid model are continuously corrected through real-time observation data, the uncertainty of pure numerical simulation is made up, the capturing capacity of a space-time mutation phenomenon is improved, a hidden rule in the data is mined through an intelligent prediction model, and the real-time prediction model is established. According to the method, the precision of urban boundary layer airflow simulation can be remarkably improved, and a more accurate meteorological guarantee service is provided for the fields of atmospheric environment monitoring, urban planning, aviation safety and the like.

The invention discloses a weather forecast generation agent method and system introducing human verification nodes. The core is to construct a man-machine cooperation workflow. A manual intervention mechanism is embedded in three key decision nodes, namely model selection, site screening and report auditing, so that deep fusion of large model data processing capability and human expert experience is realized. A user can dynamically select a meteorological model, appoint an analysis site, and optimize defensive measure suggestions by means of a custom cue word template. The system improves the accuracy and scene adaptability of meteorological early warning through the collaborative operation of the knowledge enhancement retrieval node and the tool retrieval node, and is especially suitable for the fields of electric power meteorological services and the like needing personalized decision making.

The invention belongs to the technical field of ship tracking and positioning, and discloses a ship dynamic tracking method and system based on Beidou positioning, and the method comprises the steps: employing a ship-borne Beidou satellite receiver to determine the position in real time, judging whether to trigger a ship dangerous case alarm, marking a ship in distress, and transmitting the dangerous case alarm information to a Beidou comprehensive application service platform; positioning a ship in distress on an electronic chart, marking a dangerous case range, querying surrounding ships or a global marine meteorological model to generate a vector marine field of the dangerous case range, predicting a drifting high-probability area of the ship in distress, searching a prepared rescue ship for the ship in distress, generating a minimum-cost path, and calculating predicted arrival time; the method comprises the following steps: acquiring the position and state of a ship in real time, generating a pre-rescue ship list, sending a rescue request to the pre-rescue ship, marking an actual rescue ship according to a reply, and carrying out parallel obstruction detection and obstruction coordination on the actual rescue ship. And full-process tracking rescue from dangerous case alarm to rescue execution is realized.

A method, device, equipment and medium for independent modeling of troposphere anisotropy of dry and wet convection layers, the method comprising: constructing a troposphere anisotropy model, which adopts a tilted plane horizontal gradient model to respectively describe dry delay anisotropy and wet delay anisotropy; deriving oblique dry delay through a numerical meteorological model, calculating a dry horizontal gradient parameter through the oblique dry delay, and correcting the dry delay anisotropy by using the dry horizontal gradient parameter; taking a wet horizontal gradient parameter as an unknown parameter to be estimated into an observation equation and performing dynamic estimation, and correcting the wet delay anisotropy by using the estimated wet horizontal gradient parameter. The present application adopts an asymmetric processing strategy of dry delay anisotropy model correction and wet delay anisotropy parameter estimation, which not only avoids the ill-conditioned problem of observation equation caused by simultaneous estimation of dry and wet gradients, but also utilizes the high accuracy of the numerical meteorological model in describing the dry delay anisotropy, so that the positioning stability and accuracy are improved at the same time.