460 results about "Geomatics" patented technology

The embodiment of the invention discloses a fire monitoring and early warning method and device based on big data analysis, and belongs to the technical field of data analysis. According to the embodiment of the invention, multi-source data such as satellite remote sensing heat source images, meteorological environment parameters, historical fire event records and geographic information raster data are integrated, a space-time associated dynamic monitoring system is constructed, and environmental anomaly symptoms before fire occurrence can be comprehensively captured. Wherein the space-time reference difference of different source data is eliminated through the space-time fusion processing, so that the heat source distribution, the meteorological change and the historical fire mode form multi-dimensional association under a unified geographic framework, the sensitivity and the reliability of early fire point identification are remarkably improved, and the subsequent accurate fire risk prediction is realized.

The invention discloses a forest fire risk assessment method based on a composite chain type disaster evolution mechanism, and relates to the field of forest fire risk assessment, and the method comprises the steps: carrying out the chain type feature field construction of multi-source heterogeneous data through the collection of the multi-source heterogeneous data, including remote sensing data, meteorological data, geographic information data and disaster loss data, obtaining a disaster chain core driving factor tensor, and carrying out the feature field construction of the multi-source heterogeneous data; performing composite disaster chain coupling analysis based on the disaster chain core driving factor tensor to obtain a disaster chain space-time cascade trajectory, performing dynamic risk assessment and grading based on the disaster chain space-time cascade trajectory to obtain a five-level risk zoning map, and performing intelligent early warning analysis based on the five-level risk zoning map to obtain a forest fire dynamic early warning map. According to the method, the chain type driving relation among drought, high temperature, vegetation dryness and fire danger is synthesized, the dynamic threshold value is adopted for risk grading, the dynamic influence of climatic variation and vegetation phenology on the fire danger can be captured, and the accuracy of forest fire risk assessment is improved.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly provides a photovoltaic power station inspection method and system based on an unmanned aerial vehicle and a GIS, and the method comprises the steps: obtaining initial geographic information data of a photovoltaic power station, enabling the unmanned aerial vehicle to execute initial surveying and mapping flight, collecting high-resolution image data, and constructing a three-dimensional model of the photovoltaic power station; fusing the high-resolution image data with the initial geographic information data to generate a multi-factor initial intelligent inspection path; the unmanned aerial vehicle flies along the initial intelligent inspection path, collects multi-modal data of the photovoltaic module, and transmits the multi-modal data to a GIS for data fusion and updating to obtain fused data; generating a fault distribution thermodynamic diagram and a maintenance priority map; the inspection path is dynamically adjusted, and the three-dimensional model of the photovoltaic power station is updated. According to the method and the system, comprehensive inspection can be carried out on the photovoltaic power station, the inspection accuracy and efficiency are improved, the inspection path can be dynamically adjusted, unnecessary repeated inspection is reduced, and the inspection cost is effectively reduced.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

The invention relates to the technical field of meteorological data processing and geographic information, in particular to a near-surface meteorological field downscaling method based on a topographic constraint Transform model, which comprises the following steps of: extracting multi-modal fusion features from reanalysis meteorological data and digital elevation model data; mapping to a target resolution by means of a topographic constraint Transform network to obtain initial downscaling meteorological data; latent variable modeling is carried out on the initial data through a variational auto-encoder to describe multi-scale uncertainty, physical constraints such as energy balance and mass conservation are incorporated in combination with an iterative updating mechanism based on meta-learning, and core downscaling meteorological data are generated; according to the invention, detail capture and dynamic adaptation of local meteorology are realized through self-supervised pre-training and online updating.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.

The embodiment of the invention provides a three-dimensional ray drawing method and device, and a curve control point is dynamically generated through precise conversion of a geographic coordinate system and a projection coordinate system in combination with an azimuth angle and an earth surface distance. A curvature correction mechanism based on a curved surface projection model is innovatively designed, the influence of the earth curvature on a ray trajectory is considered, and smooth space trajectory generation is realized through a Bezier curve parameter model. According to the system, the coordinate transformation compensation and spherical projection technology is adopted, accurate mapping of a geodetic coordinate system and a display coordinate system is established, accurate rendering of a three-dimensional space curve under different scales and view angles is ensured, and a high-precision technical scheme is provided for geographic information visualization. According to the method, the limitation of traditional straight line drawing is broken through, and the real trajectory expression considering the earth curvature is realized.

The invention relates to the technical field of water source pollution investigation, and particularly discloses a soil groundwater pollution fine investigation method which comprises the steps of S1, pollution source investigation, S2, sampling design, S3, laboratory analysis, S4, ecological risk assessment, S5, restoration suggestion and S6, dynamic monitoring. According to the invention, through machine learning and big data analysis, historical pollution data, field sampling data and spatial geographic information can be effectively integrated to form an accurate pollution source distribution map, which not only facilitates identification of pollution hot spot areas, but also can predict the pollutant diffusion trend through a data-driven model to provide dynamic prediction of future pollution evolution; according to the method, high-precision positioning of a complex pollution area is realized, blind sampling and invalid investigation are reduced, the resource utilization efficiency is improved, the area where pollutants are possibly diffused in the future can be predicted, prevention and control measures are taken in advance, and further environmental damage is avoided.

The invention relates to the technical field of Beidou positioning, and discloses a Beidou positioning inspection method and system. The method comprises the following steps: acquiring geographic information and equipment state parameters of an inspection area, and generating a structured task queue according to dimensions such as equipment types and fault levels; constructing a Beidou positioning dynamic model containing a position axis, a time axis and an environment factor axis; determining an initial inspection constraint condition according to the equipment type and the fault level, and performing path planning simulation to generate an initial inspection route; detecting a route anomaly, and adjusting an abnormal node by using a hybrid optimization algorithm to generate a stable route; model parameters are updated in combination with real-time environment data, and a final inspection instruction set containing information such as equipment moving time sequence is generated. The system comprises a data processing module, a model construction module, a constraint determination module, a path planning module, an anomaly detection module, an optimization adjustment module and the like. The inspection efficiency and precision are improved, the stability and reliability are enhanced, and the inspection requirements of complex environments can be effectively met.

The invention discloses an earth geology physical three-dimensional modeling method based on multi-data fusion, which comprises the following steps: A, data acquisition: acquiring geological data through geological exploration and measurement technologies, comprising geological profile, underground water level, lithology, structure, surface digital elevation model (DEM) data, remote sensing image data, surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data. According to the invention, data of geological profile, underground water level, lithology, structure, earth surface digital elevation model (DEM) data, remote sensing image data, earth surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data are collected. And the data can be effectively integrated.

The invention discloses a geographic information surveying and mapping data processing method based on image analysis, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting a multi-angle satellite remote sensing image, and dividing the multi-angle satellite remote sensing image into a plurality of image blocks according to a geographic coordinate system; extracting topographic feature parameters of the image blocks, and establishing feature vectors of the topographic feature parameters; according to the feature vectors, establishing a ground feature classification model to perform ground feature classification on the image blocks to obtain ground feature classification labels; constructing a three-dimensional geographic information model according to the ground feature classification labels and the topographic feature parameters, and marking ground feature boundary points; and generating surveying and mapping data based on the three-dimensional geographic information model and the surface feature boundary points. According to the method, the improved three-dimensional geographic information modeling algorithm is adopted, the ground feature boundary points are precisely marked, and the problems of high ground feature classification difficulty, fuzzy boundary recognition, poor model training stability and the like in the complex terrain are effectively solved.

The invention discloses an intelligent corn seed production land configuration method and system based on ecological environment factors, and relates to the technical field of agricultural information. The system comprises a data layer, a model layer and an application layer, geographic information data, meteorological data, soil data and corn variety data of a target area are collected and stored through the data layer, the weight of each index is determined by soliciting expert opinions through the model layer, and an ecological suitability evaluation model and a land suitability evaluation model are constructed. Evaluating the ecological suitability and the land suitability of the target area, and generating an optimal corn seed production land configuration scheme by using the intelligent configuration model according to set constraint conditions; and the application layer is used for visualization and providing scheme generation and scheme evaluation. The intelligent algorithm is utilized, ecological environment factors are comprehensively considered, the scientificity and rationality of configuration are improved, the configuration scheme is automatically generated, and the configuration efficiency is improved.

The invention discloses a sand source resistance and control dynamic configuration method for a hole-to-wind sand area, and relates to the technical field of wind and sand protection, and the method comprises the following steps: collecting meteorological data, wind and sand data and historical sand source change data of the hole-to-wind sand area, and obtaining geographic information and vegetation coverage conditions of the sand source area through a remote sensing technology and ground monitoring equipment; by monitoring key parameters such as wind speed, wind direction and dust concentration in real time and combining high-resolution ground image data acquired by an unmanned aerial vehicle and a satellite remote sensing technology, real-time sensing and accurate analysis of sand source changes are realized, the efficiency and accuracy of data collection are improved, and the detection accuracy is improved. In addition, the prevention and control measures can be dynamically and accurately arranged according to the specific sand source, and by dynamically adjusting the arrangement and density of prevention and control facilities and optimizing the type and area of vegetation coverage, sand storm invasion can be more effectively prevented, the occurrence frequency and intensity of sand storm are reduced, and the efficiency and accuracy of sand source prevention and control are remarkably improved.

The invention discloses a soil heavy metal content estimation method and device, and relates to the technical field of soil heavy metal estimation.The method comprises the steps of sample collection and data acquisition, specifically, based on geographic information of a research area, soil samples are collected through a layered and partitioned sampling method; according to the method, comprehensive and accurate collection and data acquisition of soil samples are realized through a layered and partitioned sampling method in combination with a high-precision positioning and measuring instrument, the sample representativeness and the data accuracy are ensured, and through an improved correlation analysis algorithm and a depth model based on feature fusion, the soil sampling accuracy is improved. Accurate analysis and efficient modeling of correlation between environmental magnetic parameters and heavy metal content are realized, prediction precision is improved, dynamic optimization of model parameters is realized by applying a self-adaptive step length strategy in model verification and optimization, model stability and generalization ability are guaranteed, and all modules of the device work cooperatively, so that the reliability of the device is improved. And deep analysis of heavy metal sources and migration and transformation rules is realized by using a geochemical analysis module and related models.

The invention relates to the technical field of ecological environment monitoring and geographic information, in particular to a coastal wetland soil sample point quality evaluation method and system based on multi-source environmental data. According to the method, remote sensing, climate, terrain, ocean and historical land coverage data are fused, and indexes are extracted and standardized; an environment similarity score (weighted Markov / neighbor weighted Euclidean), a historical consistency score (multi-temporal coverage + transfer penalty) and a source reliability score (metadata integrity + anomaly detection + NDVI-SOC residual) are constructed respectively; comprehensive scores are obtained through self-adaptive weight fusion, A / B / C grades are divided, and a report and a high-quality sample point set are output. The system comprises a data access module, a scoring engine module and a visualization module, and the precision and robustness of soil mapping and carbon storage evaluation can be remarkably improved.

The invention discloses an all-sky cloud imaging real-time simulation method of a three-dimensional structure, which is used for realizing real-time rapid simulation of a cloud image with set spatial resolution per hour, and comprises the following steps: obtaining basic geographic information data of a research area; carrying out background field data fusion; interpolating the ground meteorological data collected in real time to the grid points of the research area by adopting an interpolation method; obtaining surface meteorological analysis and background field data, and realizing three-dimensional temperature analysis according to a static relation; obtaining an initial guessing field of three-dimensional cloud analysis, and obtaining a three-dimensional cloud analysis field in combination with three-dimensional temperature analysis; an initial three-dimensional water vapor analysis field is obtained according to the background field data, and three-dimensional water vapor analysis is realized by performing optimization iteration solution on a target function; obtaining soil humidity analysis according to the surface meteorological analysis and the three-dimensional cloud analysis field; according to the surface meteorological analysis, the three-dimensional temperature analysis, the three-dimensional cloud analysis field and the three-dimensional water vapor analysis, derivative product analysis is achieved; and simulating to obtain all-sky imaging data.

The embodiment of the invention discloses a mineral resource exploration data analysis method and device based on three-dimensional space modeling. The method comprises the steps that back-flushing pushing-covering structure feature information is extracted through structure interface occurrence measurement, lithology combination analysis and structure movement trace recognition and converted into geologic structure feature parameters; stratigraphic age data and lithology distribution information are integrated, and a three-dimensional stratigraphic model is constructed with a stratigraphic superposition relation as a constraint; geophysical data are imported to extract geophysical response characteristics of stratums with different depths, and lithologic boundary correction is combined to obtain deep fracture characteristic parameters; sedimentary system division is carried out on lithofacies paleogeographic information, and deep fracture characteristic parameters are fused to establish a fracture system three-dimensional model; the multi-feature association relationship is identified, the ore body prediction model is established, the spatial data is input to obtain the three-dimensional visual prediction result of the hidden ore body spatial distribution, and the exploration efficiency and accuracy in the deep phosphorite exploration process are improved.

The invention discloses a line intelligent planning and fault early warning method suitable for electric power engineering design, and relates to the technical field of electric power engineering. The method comprises the following steps: quantifying geographic information, geological conditions and construction cost into a continuous decision curved surface through spatial semantic segmentation and a cost curved surface model, and identifying a large-area low-cost region as a primary feasible region in combination with a dynamic clustering algorithm; a multi-dimensional decision index is constructed based on spatial features, geological stability and facility association data, fault regions such as terrain abrupt change or facility conflict are identified through real-time association coefficients, and an exception handling mechanism is triggered; and selecting a seed region with an optimal construction condition as an anchor point, constructing a joint probability model of spatial features and cost, and generating an optimal path considering feasibility probability and cost effectiveness in combination with an expectation maximization algorithm and a graph optimization technology. According to the invention, scientificity and economical efficiency of cable planning under complex geological conditions are improved, and key technical support is provided for intelligentization of electric power engineering.

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

The invention relates to the technical field of water conservancy data processing, in particular to a GIS-based water conservancy project management system, which comprises a non-stationary data decomposition module, a nested autoregression modeling module, a space-time dynamic reconstruction module, a risk quantitative mapping module and a risk decision optimization module. According to the method, long-term changes and short-term fluctuations are quantified and synthesized term by term through hydrological time series data in trend fitting and fluctuation amplitude extraction, the precision and timeliness of time series prediction are improved, dynamic integration of data of multiple monitoring points is achieved in combination with geographic information matching and spatial difference value distribution analysis, and the accuracy and timeliness of time series prediction are improved. According to the method, the spatial distribution of water resources is accurately predicted, the correlation degree between regions is quantified, risk index quantification is combined with a spatial interpolation technology, so that risk region division is more detailed, the pertinence of emergency management is optimized, and the scientificity and execution efficiency of risk decision are further improved based on scheduling data analysis and influence factor quantification.

The invention provides an unmanned aerial vehicle intelligent inspection and digital management system for road and bridge facilities. The unmanned aerial vehicle intelligent inspection and digital management system comprises a cloud digital twin platform, distributed edge computing nodes and an unmanned aerial vehicle cluster capable of being dispatched and executed. The cloud digital twin platform is used for constructing and maintaining a high-precision three-dimensional semantic model corresponding to physical road and bridge facilities, and the model fuses geological and geographic information, historical operation and maintenance data and real-time sensor data. Through intelligent collaborative operation and edge real-time data processing of the unmanned aerial vehicle cluster, qualitative leap of inspection efficiency is achieved, labor cost and safety risks are greatly reduced, physical facilities are holographic digitalized, management decisions are made to be based on comprehensive, visual and dynamic visual data, and the inspection efficiency of the unmanned aerial vehicle cluster is improved. According to the method, the accuracy and scientificity of operation and maintenance management are remarkably improved, the evolution trend prediction of the health condition of the facility is realized through deep data mining and simulation analysis, a key basis is provided for accurately formulating a maintenance strategy, and the service life of the facility is effectively prolonged.

The invention provides an open pit coal mine safety detection and production evaluation method based on unmanned aerial vehicle surveying and mapping. The method comprises the steps of obtaining surveying and mapping data of a target mine field through unmanned aerial vehicle oblique photography surveying and mapping; obtaining geographic information data including a digital orthoimage, a digital elevation model, an oblique photography model and digital vector data based on the surveying and mapping data; constructing a three-dimensional digital twinborn scene of the target mine field based on the geographic information data; performing profile detection on the three-dimensional digital twinborn scene, constructing a vertical profile, and performing safety detection on the slope angle, the step height and the width based on the vertical profile; and performing filling or excavation measurement on the coal mining area, the coal piling area or the soil piling area of the target mining area, and calculating the volume to obtain a production evaluation result. The problems that in the prior art, a geographic surveying and mapping method is low in efficiency and disordered in data, and a safety detection and production evaluation method depends on detection equipment, so that safety detection and production evaluation of open pit coal mining are poor in accuracy and low in efficiency are solved.

The invention provides a geological disaster prediction method and device based on big data, equipment and a medium, and relates to the technical field of geological disaster prediction. Comprising the steps of performing model training based on obtained current remote sensing image data, meteorological rainfall data and geographic information data of a to-be-predicted area and historical remote sensing image data, historical meteorological rainfall data and historical geographic information data corresponding to sample points in the to-be-predicted area to obtain a preset geological disaster prediction model; according to the method, the predicted geological disaster risk point in the to-be-predicted area is obtained, and the geological disaster risk early warning prompt information is generated and output, so that geological disaster early warning prediction calculation of the whole monitoring area is realized, and the accuracy and timeliness of geological disaster monitoring early warning are greatly improved.

The invention relates to a train-ground communication quality optimization analysis system based on a heavy haul railway vehicle-mounted radio station, and the system comprises a system initialization module which is used for reading parameters of related interfaces; the information acquisition module is used for acquiring parameters of an access network in real time through a train-ground communication radio station interface; the GNSS monitoring module is connected with the GNSS module through a serial port and is used for receiving GNSS information in real time; the geographic information acquisition module is used for acquiring related geographic map information from a third-party API (Application Program Interface) through a public network; and the edge calculation module is used for collecting the information of the information acquisition module and the GNSS monitoring module and performing calculation processing. According to the invention, GNSS information and communication information are comprehensively considered; in combination with a geographic API, the influence of the geographic environment on the communication condition is intuitively known; through a visual interface, the communication coverage condition of a trackside base station is visually displayed, communication results of multiple trains are efficiently checked, and the positions of sensitive areas such as packet loss and switching are visually checked.

The invention discloses a surveying and mapping method based on remote sensing big data analysis, and relates to the technical field of remote sensing big data. During operation of the system, images and spectral data of a target water body area are collected through satellite remote sensing, unmanned aerial vehicle remote sensing or a ground sensor, the collected remote sensing data are preprocessed, and the target water body area is obtained through waveband combination or spectral line analysis; the method comprises the following steps: extracting characteristic parameters of a water body by utilizing spectral information in remote sensing data, carrying out multi-dimensional analysis on field data, calculating to obtain a water body turbidity coefficient Ct, an algal bloom coefficient Ca and an oil pollution coefficient Co, and analyzing the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body by combining the remote sensing data and geographic information. And performing spatial-temporal change analysis on the water body pollution condition, identifying the trend of pollutant expansion or degradation, and based on the results of pollution assessment and spatial-temporal analysis, comparing the comprehensive water quality index WQI with a preset threshold to generate water quality level early warning information.

The invention relates to the technical field of wind-solar power prediction, in particular to a wind-solar power prediction method based on an artificial intelligence large model, and the method comprises the steps: collecting the historical power data of a wind power plant and a photovoltaic power station, and synchronously collecting the related meteorological data and geographic information data; performing data cleaning on the collected data, and dividing the data into a training set, a verification set and a test set according to a time sequence; the method comprises the following steps: constructing a wind-solar power prediction model based on a Transform architecture, designing a data embedding module according to different types of data for coding and fusion, constructing parallel sub-network branches with the same number as the data types in the model, and learning and extracting features of different data; and training, optimizing and correcting the constructed wind-solar power prediction model, determining a model version, and performing actual wind-solar power prediction. According to the invention, multi-source data acquisition is combined with an artificial intelligence technology, the complexity and uncertainty of data are effectively processed, and the precision of wind and light power prediction is improved.

The invention relates to a regional pollutant concentration prediction method and device and a storage medium, and is applied to the technical field of pollution monitoring. The method specifically comprises the following steps: acquiring historical pollutant concentration monitoring data and meteorological data of each single site, and predicting future fine particulate matter concentration and pollutant chemical components of the single site based on an EnvMulti-KAN model fused by KAN and Transform architectures; according to the prediction result of each single station in the target area, particle diffusion and convergence are simulated by adopting a multi-station fine particle diffusion model in combination with geographic information and interaction among the single stations, the pollutant area concentration is constructed, the area fine particle concentration is accurately predicted, and the view limitation of the single station is broken; according to the method, the single-site model and the multi-site model are cooperated, the concentration of the regional fine particulate matter is accurately predicted, the main components of the pollutants are synchronously and accurately predicted, and a demonstration is provided for atmospheric chemistry theoretical research.

The invention discloses a geographic information data fusion system, and relates to the technical field of geographic information data processing, the system comprises the following components: a data acquisition and preprocessing unit, a deep learning semantic analysis model construction unit, a semantic level data fusion unit and a data application and visualization unit; according to the method, semantic relationship reasoning is carried out on geographic entities by using a Geo-SDNN model through the deep learning semantic analysis model building unit, the complex semantic relationship between the geographic entities can be accurately captured, and meanwhile, the semantic level data fusion unit adopts a fusion algorithm based on semantic hierarchy, so that the data fusion efficiency is improved. In addition, a multi-source data acquisition interface and a data quality evaluation module of the data acquisition and preprocessing unit effectively improve the quality of basic data, the accuracy of data fusion is further improved, the data processing flow is optimized through the overall design of the system, and the data fusion efficiency is improved. And the efficiency of geographic information data fusion is obviously improved.