50 results about "Geographical distance" patented technology

The invention discloses a short-term wind speed prediction method for multiple offshore wind power plants, relates to the technical field of power system intellectualization, and constructs a dynamic graph structure fusing the correlation between geographic distance and wind speed according to the correlation between the geographic position of a target area and the wind speed, namely a multi-wind-plant connected graph, and represents the spatial topological relation of a wind power plant group. A wind power plant group is mapped into a node network by constructing a dynamic graph structure fusing geographic distance and wind speed correlation, spatial dependence intensity between nodes is quantized by using a weighted adjacent matrix, spectral domain convolution operation is carried out by a graph convolution network based on a normalized Laplacian matrix, and the spatial dependence intensity between nodes is quantized by using a normalized Laplacian matrix. Efficient neighborhood feature aggregation is achieved through Chebyshev polynomial approximation, complex spatial association caused by geographic position difference and meteorological condition interaction can be accurately captured, the defect of non-Euclidean spatial relationship modeling in a traditional method is overcome, and the representation capacity of the spatial dependency relationship in the multi-wind-power-plant environment is remarkably improved.

The invention discloses a multi-source environment monitoring data fusion analysis and abnormity early warning method based on deep learning. According to the method, the spatial attention weight is calculated by fusing the feature similarity and the geographic distance, and the modeling precision of the complex spatial dependency relationship between the environmental monitoring nodes is remarkably improved. Specifically, the model firstly divides a monitoring area into fine grid units, each unit serves as an independent node to participate in calculation, the direction consistency of node feature vectors is quantified through cosine similarity, and meanwhile, a geographic distance attenuation factor is introduced to dynamically adjust weight distribution. The double consideration enables nodes with similar environmental characteristics in adjacent areas to obtain higher attention, and effectively avoids the problem of spatial correlation misjudgment easily occurring in a complex terrain in a traditional fixed adjacent matrix. When local pollution diffusion occurs in a monitoring area, the model can quickly position a propagation path of a core pollution source and surrounding affected nodes, and the space correlation identification efficiency is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, and the method comprises the steps: wind power data collection and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, construction of a short-term power prediction model, and short-term power prediction. According to the method, the historical window length and the decomposition scale can be autonomously adjusted according to the inherent fluctuation characteristics of the data, and multi-scale accurate characterization of the non-stationary power sequence is realized; according to the method, a dynamic space-time diagram fusing geographic distance and instantaneous power correlation is constructed, and a diagram attention network combined with trend similarity gating is designed, so that dynamic refined modeling of a space incidence relation is realized; according to fluctuation intensity self-adaptive loss function dynamic balance point prediction precision and interval prediction reliability, synchronously outputting deterministic and probabilistic prediction results; the rated power limit and the ramp rate constraint are embedded into the model in a soft mode, and it is ensured that the prediction result conforms to the actual operation rule of the wind turbine generator.

The invention provides a GIS buffer generation method and system based on fuzzy semantics and geographic constraints, and belongs to the technical field of geographic information systems.The method comprises the steps that fuzzy descriptors, membership values and geographic entity types are input into a pre-trained language-space mapping model, and basic distance parameters are obtained; querying a spatial database based on the geographic entity keywords, obtaining geometric features associated with the geographic entities, and calculating feature lengths corresponding to the geometric features; correcting the basic distance parameter based on the intention direction, the membership value, the geographic entity type and the feature length expressed by the fuzzy description word to obtain a geographic distance threshold; generating an initial two-dimensional buffer polygon based on the geographic distance threshold and the spatial geometric features of the geographic entity; and querying a digital elevation model to obtain topographic data based on the initial two-dimensional buffer region polygon, and generating a three-dimensional buffer region curved surface through a gradient sensitive interpolation algorithm. According to the invention, the accuracy of generating the GIS buffer area is improved.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and graph neural network combined earth surface deformation space-time prediction method, which is suitable for space-time prediction of earth surface deformation. Based on DS-InSAR, obtaining radar sight line-to-time sequence earth surface deformation of the high-coherence measuring points; calculating the geographic distance between the measuring points and the mutual information between the deformation sequences corresponding to the points; the method comprises the following steps: judging connectivity between high-coherence measuring points according to a geographic distance between the measuring points and mutual information to generate an adjacent matrix, and organizing an original InSAR deformation monitoring result into graph structure data; and inputting the graph structure data into the LSTM-GCN model to predict and obtain the surface deformation of all the high-coherence measuring points in the whole area. The method is high in prediction precision and wide in application range, and can be effectively applied to the fields of space-time prediction of deformation of earth surfaces and buildings (structures) caused by mine closure, underground resource development, natural disasters and the like.

The invention discloses a distributed photovoltaic ultra-short-term solar irradiance prediction method based on multi-scale spatial-temporal feature fusion, and the method comprises the steps: constructing a multi-source spatial adjacency matrix through fusing a geographic distance adjacency matrix, a historical correlation matrix and a dynamic adjacency matrix generated by a graph self-attention network; therefore, the inter-site spatial dependency relationship is dynamically captured. Meanwhile, multi-scale time features are extracted in combination with a convolutional long-short-term memory network, dynamic weighted integration of spatial-temporal features is finally realized through a gating fusion unit, and high-precision prediction of future ultra-short-term irradiance is completed. A verification result on a certain eight distributed photovoltaic station data set shows that the model is remarkably superior to a traditional neural network model under different weather conditions, and particularly, the model shows higher adaptability and prediction precision in sudden change weather and an irradiance peak value interval; and reliable technical support can be provided for distributed photovoltaic power station power prediction and power grid dispatching.

The invention discloses a coastal wind turbine group generation power multi-scale space-time prediction method, which mainly comprises the following steps: carrying out space interpolation and error correction on a target wind turbine position by adopting a numerical weather forecast statistical downscaling technology, generating a high-resolution wind speed prediction sequence covering a short term and a long term, aligning and splicing the predicted wind speed, the field actually-measured wind speed, the environment and the unit operation variables into node dynamic input characteristics; the method comprises the following steps: encoding longitude and latitude and time sequence monitoring data of N fans of a coastal fan group into graph nodes, determining an edge weight according to geographic distance and wake flow coupling, and forming a fan graph network containing static and dynamic characteristics; and inputting the static and dynamic feature sequences into a graph neural network comprising a space attention layer, a time recursion layer and a physical constraint regular term, completing model training, and outputting the generated power of each fan in a plurality of time steps in the future and the total power predicted value of the fan group. The method can provide powerful support for wind power plant operation scheduling, power grid-connected management and new energy consumption.

The invention relates to the technical field of data processing, in particular to a calculation method for simulating urban road network traffic efficiency by spatial partitions, which comprises the following steps: acquiring population raster data and road network data; converting the population raster data into a two-dimensional matrix and standardizing the two-dimensional matrix; spatial clustering is carried out by adopting a comprehensive distance index fusing geographic distance and population difference, a partition set is generated, a shortest path between point pairs is searched in a road network, the ratio of the length of a spatial linear path to the length of the shortest path is calculated as a length efficiency index, and a comprehensive traffic efficiency index is output. According to the method, population distribution characteristics are fused through a space partitioning technology, diversified travel scenes are covered by Monte Carlo simulation, and the accuracy of urban road network traffic efficiency evaluation is remarkably improved by combining a path length and shape two-dimensional evaluation mechanism. The comprehensive traffic efficiency index directly represents the overall efficiency level of the road network, and a quantitative basis is provided for road network structure optimization.

The invention relates to the technical field of supply chain management, in particular to a CBAM-based product full-life-cycle dynamic supply chain optimization method and management system, and the method comprises the steps: obtaining materials and numbers required by an outlet, building the association between raw materials and transportation links, collecting production carbon release details, and screening compliance paths to form an operation list. And judging the position overlap ratio to generate a resource path atlas, and outputting a supply chain scheduling scheme according to time consumption and span sorting. The method comprises the following steps: quantifying a geographic distance and a transportation mode in a raw material path, establishing mapping between transportation and a carbon source, identifying a carbon release path by combining an equipment operation section sequence, rejecting non-compliant paths according to target market limitation, associating a screening result with a carrying node space position, and constructing a connection combination according to a position overlap ratio. The path is optimized through time consumption and span sorting, and collaborative optimization of path screening and job scheduling is achieved.

The invention provides a credit evaluation model construction method and a credit evaluation method, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring financial index data, geographical location information and time period data of each merchant; calculating the geographic distance between the merchants based on the geographic position information, and determining the period similarity in combination with the fluctuation correlation of the financial indexes in the same time period; the geographic distance and the period similarity are fused to generate comprehensive association strength, and the closer the geographic distance is, the higher the period similarity is and the higher the association strength is; constructing a spatio-temporal data graph according to the comprehensive association strength, and taking the merchant as a node and the association strength as an edge weight; and establishing a dynamic credit evaluation model based on the spatio-temporal data graph. According to the scheme, dynamic influences of regional economic differences and seasonal fluctuations on credit risks are captured in real time, so that the real-time performance and accuracy of credit assessment are remarkably improved, and the problem that a static assessment strategy and a dynamic environment are disjointed in the prior art is effectively solved.

The invention relates to a rainfall prediction map convolution method based on graph reconstruction and spatial self-attention, and belongs to the technical field of crossing of artificial intelligence and urban meteorological early warning technologies. The method and the device are used for solving the problems of excessive smoothness and insufficient spatial dependency capture caused by dependence on a geographic adjacency relation in precipitation prediction of a traditional graph convolutional network. A sparse graph structure reflecting real meteorological association is constructed by calculating a Pearson's correlation coefficient of Beidou multi-base-station meteorological time series data, and a traditional geographic adjacency matrix is replaced; designing a dynamic space self-attention mechanism, fusing the static association graph and a dynamic attention weight, and realizing adaptive aggregation of long-range dependence between nodes; and in combination with multilevel feature propagation of graph convolution and an attention matrix, the modeling capability of a heterogeneous meteorological spatial mode is improved. According to the method, the accuracy of short temporary rainfall prediction is remarkably improved, the method is particularly suitable for association mining of base stations which are far in geographic distance but similar in meteorological feature, and a high-robustness spatial feature extraction scheme is provided for a meteorological early warning system.

The application provides a GPS-based visual positioning method, system, computer and storage medium, which comprises the following steps: acquiring the GPS coordinates of a query image, and screening reference images with close geographical positions from a database based on the GPS coordinates; screening reference images with a geographical distance less than or equal to a preset radius as a candidate set; determining several similar images based on k-nearest neighbor search; clustering according to the co-visibility relationship of each similar image to generate a plurality of locations corresponding to the candidate image, each location containing commonly observed 3D points; performing 2D-3D matching on each location, and estimating and outputting a six-degree-of-freedom camera pose through a perspective n-point algorithm and a random sample consensus algorithm to realize visual positioning. The GPS coordinates are used to dynamically screen reference images, the search time is reduced, the quick response requirement of real-time scene application is met, and the cross-environment high robustness is guaranteed.

This invention proposes a hydrological trend prediction method based on big data analysis. It constructs an initial graph structure with monitoring stations as nodes and geographical distance and water system connectivity as edge weights. A message passing mechanism is used to iteratively aggregate node features, capturing the spatial interaction of hydrological variables between stations. Based on rainfall changes, the water system connectivity parameters and edge weights are dynamically adjusted to generate an adaptive graph structure. Combining graph neural networks, dynamic Bayesian networks, and spatiotemporal co-Kriging interpolation algorithms, hourly-level hydrological dynamic transmission features are extracted to form a spatiotemporally coupled prediction model. A prediction deviation threshold triggers model parameter optimization and message passing mechanism adjustments, achieving an adaptive prediction process of "dynamic modeling - feature fusion - closed-loop optimization." This method can accurately characterize the spatiotemporal correlation of hydrological elements, improve the analytical accuracy of complex hydrological processes, and enhance adaptability to sudden hydrological events and changes in the watershed environment.

The invention relates to the technical field of crowd flow prediction, discloses a crowd OD flow prediction method and related equipment, and effectively overcomes the defects in fine-grained city internal OD flow prediction in the prior art by constructing an urban crowd flow prediction model fusing multi-dimensional key factors. Spatial correlation among geographic units is captured by means of a spatial adjacency encoder, regional function complementarity and geographic distance characteristics are mined through a land utilization complementation encoder, traffic reachability information is integrated by means of a traffic connection encoder, and a characteristic pattern cooperatively output by a multi-dimensional encoder provides comprehensive support for prediction. According to the design, the comprehensive influence of spatial positions, land utilization and traffic conditions on urban travel is fully considered, the defects that a traditional model ignores spatial dependence and an existing machine learning and deep learning model is insufficient in multi-factor integration are overcome, and the prediction accuracy and the fitting degree are remarkably improved.

The present application relates to electric vehicle charging scheduling technical field, especially to distributed charging pile group dynamic load balancing and power optimization allocation system and method, including: cloud coordinator obtains the space correlation data between piles containing geographical distance and traffic similarity and fuses with historical and weather data, inputs into the spatiotemporal graph neural network prediction model, outputs probability distribution prediction value and prediction confidence interval and issues, edge control node constructs multi-objective optimization model based on prediction uncertainty, generates dynamic allocation table and backup scheme under capacity constraint to minimize charging time and maximize renewable energy consumption as the target, issues allocation table to execute power allocation and obtains actual power feedback, when the actual power exceeds the confidence interval range, switch backup scheme to preferentially reduce flexibility load. The present application effectively improves the deficiency that single-point prediction lacks defense, realizes reliable global dynamic load balancing under the premise of guaranteeing the safe operation of power grid transformer.

The invention discloses a global no-data area visibility forecasting method considering geographical distance, and relates to the technical field of visibility forecasting, and the global no-data area visibility forecasting method considering geographical distance mainly comprises the steps: carrying out the preprocessing of meteorological grid data and visibility monitoring data, and obtaining a forecasting data set; the constructed artificial intelligence model comprises an encoder and a decoder; and training and evaluating the artificial intelligence model by using the forecast data set to obtain a basic artificial intelligence model, and predicting the visibility of the global data-free area to obtain a prediction result. By implementing the global data-free area visibility forecasting method considering the geographic distance, the seasonal forecasting accuracy of the visibility of the global station-free area can be improved.

The application discloses a next interest point recommendation system based on space-time power law attention, and relates to the field of recommendation system. t The application comprises a short-term preference module: a recurrent neural network is used to capture the short-term preference h t of a user check-in sequence; a long-term preference module: a time interval power law distribution, a geographical distance power law distribution and space-time power law attention are calculated; the short-term preference h t is combined with the long-term preference and user representation p u to predict a next interest point through a neural network and make a recommendation. The system uses the power law attenuation properties of the time interval and geographical distance between each check-in to model the long-term preference of a user through space-time power law attention, and considers the space-time relationship between non-continuous check-ins in modeling, thereby improving the effect of next interest point recommendation.

A training apparatus performs: acquiring an anchor sample, a positive sample, and a plurality of negative samples, extracting first hard-negative samples from the plurality of the first negative samples, selecting, as a selected first negative sample, one of the first hard-negative samples based on a geographical distance between the place represented by the anchor sample and the place represented by each of the first hard-negative samples; and updating a first feature extracting model and a second feature extracting model based on the feature value of the anchor sample, a feature value of the first positive sample, and a feature value of the selected first negative sample. The feature value of the data of the first type and the feature value of the data of the second type is used to determine whether the data of the first type matches the data of the second type.

This invention discloses an image stitching method, apparatus, device, and storage medium based on a line scan camera. The method includes: obtaining the starting and ending geographic coordinates corresponding to an initial image sequence, and determining the geographic distance corresponding to the initial image sequence; stitching together first-type images in the initial image sequence to obtain a second-type image corresponding to the initial image sequence; obtaining the image length of the second-type image, and adjusting the second-type image according to the geographic distance and the image length to obtain a target image corresponding to the initial image sequence. This invention determines the geographic distance corresponding to the initial image sequence based on the starting and ending geographic coordinates in the initial image sequence, and adjusts the second-type image obtained by stitching together each first-type image in the initial image sequence according to the geographic distance, so that images of the same resolution represent the same length information, thereby enabling the stitching of images of the same resolution.

The application belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, which comprises wind power data acquisition and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, short-term power prediction model construction and short-term power prediction. The method can autonomously adjust the historical window length and decomposition scale according to the inherent fluctuation characteristics of the data, realize the multi-scale accurate representation of the non-stationary power sequence, construct a dynamic space-time graph by fusing the geographical distance and the correlation of instantaneous power, design a graph attention network combined with the trend similarity gate, realize the dynamic fine modeling of the spatial correlation, dynamically balance the prediction accuracy and interval prediction reliability through the fluctuation intensity adaptive loss function, synchronously output the deterministic and probabilistic prediction results, and embed the rated power limit and the climbing rate constraint in the model in a soft manner, so that the prediction results conform to the actual operation law of the wind turbine.

This invention discloses a graph convolutional neural network (GCN) method for water level prediction based on geographic optimal similarity. The method includes: acquiring hydrological monitoring data from multiple stations and related explanatory variables, and preprocessing the data; calculating the geographic optimal similarity (GOS) between stations based on the historical statistical characteristics of the explanatory variables of each station, and constructing a dynamic adjacency matrix reflecting nonlinear hydrological associations; using this adjacency matrix as the graph structure of a graph convolutional neural network (GCN) to construct a GOS-GCN model for water level prediction; training and testing the GOS-GCN model using real hydrological data, and evaluating the prediction results. This invention effectively solves the limitations of relying solely on geographic distance for mapping, and significantly improves the prediction accuracy and robustness of the model under complex spatiotemporal conditions.

This invention discloses an air quality prediction method based on a deep learning model using potential source contribution analysis. Applied to the field of air quality prediction technology, it addresses the problem that existing technologies only consider the geographical distance of the research object, lacking a deeper exploration of its spatial diffusion impact. This invention utilizes potential source contribution analysis based on airflow trajectories to construct a spatial impact map to represent the spatial correlation between cities. Then, by using a graph convolution algorithm, it extracts effective spatial features from the spatial impact map in conjunction with the pollution status of each city. Furthermore, it employs a long short-term memory model to learn the temporal characteristics of past air pollutant concentration data, generating prediction results. First, the results of potential source contribution analysis are applied to a graph convolutional network, and then combined with the long short-term memory model to predict urban air pollution data. The method of this invention can accurately predict urban air pollutants on both spatiotemporal scales.

The application discloses a kind of short-term wind speed prediction methods for offshore multi-wind farm, it is related to electric power system intelligentization technical field, according to the geographical location of target area and wind speed correlation, construct the dynamic graph structure of fusion geographical distance and wind speed correlation, i.e. The application maps the wind farm group into a node network by constructing the dynamic graph structure of fusion geographical distance and wind speed correlation, quantifies the spatial dependence intensity between nodes using a weighted adjacency matrix, performs spectral domain convolution operation based on a normalized Laplacian matrix using a graph convolution network, and realizes efficient neighborhood feature aggregation through Chebyshev polynomial approximation. This can accurately capture complex spatial correlations caused by geographical location differences and meteorological condition interactions, overcome the shortcomings of traditional methods in modeling non-Euclidean spatial relationships, and significantly improve the representation ability of spatial dependence relationships in a multi-wind farm environment.

This invention discloses a method and system for spatiotemporal prediction of charging station load based on multi-source matrix fusion. The method includes: collecting historical load data and geographical coordinates of charging stations, performing standardization and sample partitioning; constructing in parallel a prior adjacency matrix based on geographical distance, a temporal similarity adjacency matrix based on dynamic time warping distance, and a dynamic adaptive adjacency matrix based on learnable node embedding; adaptively weighting and fusing the three matrices through trainable scalar parameters to generate a fused adjacency matrix; and constructing and utilizing a spatiotemporal graph convolutional network for load prediction based on the fused adjacency matrix as the spatial relationship basis. This invention comprehensively improves the accuracy and reliability of spatiotemporal prediction of charging station group load by fusing complementary spatial relationships from multiple sources, enhancing the robustness of temporal similarity measurement through dynamic time warping, and combining data-driven adaptive learning.

This invention relates to the field of power generation prediction technology, and discloses a method, apparatus, equipment, and storage medium for joint prediction of power generation in a hydropower station cluster. The method includes: collecting historical power generation data and multiple hydrological data related to power generation from each hydropower station in the cluster; collecting geographical distance data between hydropower stations; constructing a spatial impact prediction term for power generation based on the power generation matrix time series and geographical distance data; constructing a temporal impact prediction term for power generation based on the historical moment power generation matrix in the power generation matrix time series; constructing a temporal impact prediction term for hydrological data based on the historical moment hydrological matrix in the hydrological matrix time series; combining these with the construction of a matrix-valued time series spatial autoregressive model; training the matrix-valued time series spatial autoregressive model to obtain a joint prediction model for power generation in the hydropower station cluster; and predicting the power generation of the hydropower station cluster. This invention can improve the accuracy of power generation prediction.

This disclosure provides a method, apparatus, device, medium, and product for matching geographic sites. The method involves dividing the geographic site based on its location information to obtain a grid index; determining a candidate grid range around the site to be matched based on the grid index; the candidate grid range includes candidate matching sites corresponding to the site to be matched; determining the geographic location difference between each candidate matching site and the site to be matched, and determining candidate matching sites that meet geographic distance conditions based on the geographic location difference to obtain a target matching site; determining the spherical distance between the target matching site and the site to be matched to obtain a target geographic distance; and identifying the target matching site corresponding to the target geographic distance that is less than a preset spherical distance threshold as a successfully matched site of the site to be matched.

The application provides a fire point positioning method based on a thermal imaging camera, comprising: establishing an elevation data model with the camera position as the center, for querying the latitude and longitude coordinates and the elevation value according to the geographic distance and the angle; collecting a plurality of sets of data pairs of pixel coordinates and actual geographic distances under different angles; establishing a set of mapping functions from pixel coordinates to geographic distances based on the set of data pairs; for a detected fire point, the corresponding mapping function is selected to calculate the geographic distance from the fire point to the camera according to the pixel coordinates and the angle of the camera relative to the reference direction; and based on the elevation data model, the latitude and longitude coordinates and the elevation value of the fire point are queried using the geographic distance and the angle. Through the combination of the elevation data model and the set of mapping functions, the application realizes the rapid and accurate conversion of the fire point from the pixel coordinates to the geographic coordinates, effectively improves the positioning accuracy and efficiency, and provides reliable technical support for fire monitoring in complex terrains.

The invention discloses an IP cross-region detection method and system based on network traffic. The method comprises the following steps: constructing a geographically associated fusion network traffic feature set; the fusion network traffic feature set comprises a plurality of sample pairs containing'traffic feature pair-geographic distance-classification label '; the traffic feature pairs in the sample pairs have the same IP but different time windows; training a traffic encoder by using the sample pair to realize that a traffic embedding space output by the traffic encoder is consistent with a physical geographic space; extracting the traffic characteristics of the current data stream of the target client, and inputting the traffic characteristics into the traffic encoder to obtain the current traffic embedding space of the target client; and according to the current traffic embedding space of the target client and the historical latest traffic embedding space of the target client, judging whether the target client is subjected to cross-regional migration or not. According to the method, the accuracy, the efficiency and the robustness of an IP cross-region detection technology are improved.

The present application relates to the technical field of supply chain management, in particular to a product whole life cycle dynamic supply chain optimization method and management system based on CBAM, which comprises the following steps: obtaining materials and numbers required for export, establishing correlation between raw materials and transportation links, collecting production carbon release details, screening compliance paths to form operation lists, judging position coincidence degree to generate resource path atlas, and outputting supply chain scheduling scheme according to time consumption and span sorting. The present application quantifies the geographical distance and transportation mode in the raw material path, establishes the mapping between transportation and carbon source, identifies the carbon release path in combination with the equipment operation section sequence, eliminates non-compliant paths according to the target market restrictions, associates the screening results with the spatial position of the carrying node, constructs the connection combination according to the position coincidence degree, optimizes the path through time consumption and span sorting, and realizes the collaborative optimization of path screening and operation scheduling.

The invention relates to the field of agricultural meteorology early warning methods, in particular to an agricultural meteorology early warning method based on regional microclimate data interpolation and correction. An adversarial network model is constructed by introducing a topographic physical constraint loss function and an uncertainty quantization mechanism to generate a high-resolution, physically credible and static meteorological background field, so that the generated meteorological background field is ensured to be rich in space details and strictly follow basic physical rules such as a temperature vertical declining rate and the like; and meanwhile, the uncertainty of each lattice point can be self-evaluated and is used as prior information of a Bayesian layering dynamic correction model, a spatial covariance function fusing a geographic distance and a terrain distance is constructed in the Bayesian layering model, and the purpose that the spatial covariance function of the geographic distance and the terrain distance is fused in a complex terrain area with sparse meteorological stations is achieved. And meanwhile, a meteorological analysis field with high spatial resolution and high timeliness is obtained, so that the early warning accuracy and timeliness of sudden agricultural meteorological disasters in local areas such as frost, dry and hot air and the like are remarkably improved.