15066 results about "Meteorology" 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.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention provides a drainage basin digital twinning construction method and system based on multi-source data fusion. The method is applied to the technical field of drainage basin digital twinning construction, and comprises the following steps: acquiring water level monitoring data, surface flood routing path data, weather forecast rainfall data and terrain slope change data; rainfall and topographic slope data are fused to generate a runoff distribution relation, and a high-risk area is identified in combination with a water level sudden change amplitude. A digital twinning preliminary framework is established, and related characteristic parameters are generated by utilizing reverse feedback of a flood path and water level data. And inputting meteorological data, constructing a twinborn model based on the boundary condition of the catchment area, and simulating flood direction abnormity and flooding early warning signals. And according to the early warning space-time distribution and the terrain elevation data, generating emergency schemes such as flood dividing gate scheduling and flood storage and detention area starting, and realizing risk hierarchical management and control. According to the technical scheme provided by the invention, the flood inundation prediction precision and the emergency response efficiency are improved, and precise decision support is provided for flood control of a drainage basin.

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

The invention provides a terrain surveying method and device based on unmanned aerial vehicle cooperation and electronic equipment, and relates to the technical field of engineering surveying and complex terrain surveying and mapping. The method comprises the following steps: determining a first surveying flight route of an unmanned aerial vehicle in combination with ground elevation data of a to-be-surveyed area, controlling the unmanned aerial vehicle to collect initial area topographic data, identifying and classifying the initial area topographic data, and determining a weak texture false topographic area; re-planning a second survey flight route of the unmanned aerial vehicle according to the weak texture false terrain area, starting the millimeter wave radar, and scanning and collecting supplementary terrain survey data at different angles; fusing the supplementary topographic survey data at different angles to obtain local topographic survey data; and splicing and fusing the initial regional topographic data and the local topographic survey data to obtain real topographic survey data. According to the technical scheme, the measuring precision and the surveying efficiency of the topographic surveying data of the complex topographic region can be improved, and the reliability and the stability of the real topographic surveying data are improved.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

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 relates to the technical field of digital twinning, in particular to a high-density urban ponding evolution and early warning method based on digital twinning. The method comprises the following steps: real-time ponding depth, real-time river water level, real-time meteorological conditions and real-time drainage pipe network water level conditions corresponding to a city are collected in real time through ponding depth, river water level, meteorological monitoring and drainage pipe network sensors installed in the city, digital twinborn construction is carried out, and an urban inland inundation digital twinborn model is generated; obtaining drainage pipe network distribution and carrying out drainage grid division to generate an urban drainage grid model; acquiring a historical ponding event rule, and performing ponding area prediction analysis and ponding evolution coupling analysis to generate an urban ponding evolution distribution field; performing ponding space-time evolution analysis and ponding early warning processing on the urban ponding evolution distribution field so as to execute corresponding urban ponding emergency response work. According to the invention, accurate ponding evolution prediction and effective early warning can be provided.

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

The invention discloses a forest fire detection and disappearance method and system, and relates to the technical field of forest fire prevention and data processing, and the method comprises the steps: carrying out the fire inspection of a planned forest region through a plurality of inspection unmanned planes, and transmitting an image and airborne sensor data back to a command center in real time; identifying a fire behavior by using an artificial intelligence method, and performing fire behavior judgment and fire hazard grade division; the command center screens fire points according to the thermal images and updates the positions of the fire points in real time; the fire scene spreading trend is judged according to the returned data, and a basis is provided for the dispatching task of throwing the fire extinguishing bombs by the fire extinguishing unmanned aerial vehicle; using an MAPPO multi-agent reinforcement learning algorithm to train an optimal dynamic fire point collaborative disappearance regulation strategy; and after the open fire is extinguished, the inspection unmanned aerial vehicle continues to inspect to identify a re-ignition point or a smoke point, and secondary large-area fire is prevented. According to the method, efficient collaborative fire extinguishing, dynamic environment adaptation and resource optimization configuration can be achieved, and robustness is kept when part of intelligent agents fail.

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

The invention discloses a cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention relates to the technical field of electricity meter metering, and discloses an electricity meter adaptive error compensation method and system, and the method comprises the steps: calibrating an electricity meter, collecting baseline data under different environment conditions, and recording a measurement error; environment data and ammeter data are collected in real time through an ammeter, and key features are extracted; constructing an error model, and predicting the error of the current ammeter data; and performing real-time correction and parameter adjustment according to the prediction error, and outputting the corrected measurement data. By introducing a real-time error prediction and dynamic compensation mechanism based on environmental data and combining a particle swarm optimization algorithm to optimize regularization parameters in a ridge regression model, the measurement precision of the electric meter under a complex environmental condition is effectively improved. The method can adaptively adjust the measurement data of the electric meter, reduces errors caused by environmental factors, improves the accuracy and stability of electric power measurement, and provides more reliable data support for an electric power management and monitoring system.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

A 100-meter gridded spatialization method for carbon emissions of different land use types based on multi-source heterogeneous data is provided, including the following steps: S1, fitting Luojia-1A Satellite nighttime light data year by year with defense meteorological satellite program-operational linescan system (DMSP-OLS) and national polar-orbiting partnership-visible infrared imaging radiometer (NPP-VIIRS) fused 1-kilometer gridded nighttime light data, performing additive fusion on previous light data and current light data, introducing a time inertia weight factor to improve particle swarm optimization-back propagation (BP) neural network algorithm, and forming 100-meter nighttime light data correctable on a long time series; and S2, simulating a complex nonlinear dynamic changing relationship between multilevel 100-meter gridded data of different land use types in different industries and energy carbon emissions in different industries based on the 100-meter nighttime light data, and establishing a 100-meter spatialization inversion model for energy carbon emissions of different land use types in different industries.

The invention relates to the technical field of hydrological data analysis, in particular to a dynamic hydrological coupling data analysis method and system for a complex watershed. The method comprises the following steps: acquiring historical meteorological data, historical hydrological data and historical remote sensing data, and performing hydrological cycle simulation analysis to generate a rainfall-evaporation-permeation-runoff hydrological cycle simulation model; performing drainage basin gridding processing on the rainfall-evaporation-permeation-runoff hydrological cycle simulation model to generate a hydrological cycle gridding model; obtaining actually measured meteorological data and actually measured hydrological data, and carrying out space-time simulation division and hydrological space-time correlation analysis to obtain a hydrological change space-time correlation correlation relationship among the sub-partitions of each drainage basin; and performing drainage basin space-time coupling early warning processing on the hydrological cycle gridding model to generate a hydrological change space-time coupling early warning response result corresponding to the complex drainage basin. The method can accurately analyze and predict the hydrological change and the mutual coupling relation thereof in the complex watershed.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention discloses a tunnel fan energy-saving control method and system, a storage medium and a program product, and relates to the field of general control or regulation systems.The method comprises the steps that historical train flow data are obtained, and the predicted train flow of the next time period is obtained through calculation according to the historical train flow data; collecting real-time gas concentration data and wind speed and direction data; calculating a theoretical gas concentration increment based on an aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data; calculating according to the theoretical gas concentration increment and a pollutant diffusion model to obtain a pollutant diffusion trend; determining a target section according to the pollutant diffusion trend and a preset gas concentration threshold value; according to the position information of the target section and the pollutant diffusion trend, the minimum starting number of tunnel fans and the corresponding minimum rotating speed are obtained through calculation; and generating a fan control instruction according to the minimum number of started fans and the minimum rotating speed. According to the method, the effect of reducing the energy consumption of the tunnel fan while the ventilation effect of the tunnel is maintained is achieved.

Disclosed in the present invention is a method for whole-time space-air-ground integrated hyperspectral stereoscopic remote sensing, tracing and prediction of greenhouse / pollution gas, comprising: performing remote sensing of multi-source heterogeneous data: utilizing a hyperspectral stereoscopic remote sensing device, a hyperspectral imaging device, a hyperspectral unmanned aerial vehicle remote sensing device, a hyperspectral greenhouse gas remote sensing device based on grating light splitting and a night hyperspectral stereoscopic remote sensing device to perform the remote sensing of the multi-source heterogeneous greenhouse / pollution gas data; performing tracing and early warning of greenhouse / pollution gas components: progressively realizing tracing of greenhouse / pollution gas components at different locations based on the multi-source heterogeneous greenhouse / pollution gas data, and performing emission early warning according to a traced result; and performing stereo fusion and prediction of the multi-source heterogeneous data: performing data feature fusion based on the multi-source heterogeneous greenhouse / pollution gas data, and performing prediction of the greenhouse / pollution gas components at a future moment based on a fusion result. The system can complement the deficiencies of existing greenhouse / pollution gas monitoring and tracing technologies.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention provides a river region disaster monitoring and pre-warning method and system based on multi-source monitoring data analysis, and the method comprises the steps: obtaining the multi-source monitoring data of a river region, carrying out the data preprocessing of the multi-source monitoring data, eliminating the noise interference in real-time position data, and carrying out the time synchronization alignment of channel image data and environment parameter data, thereby achieving the early warning of the river region disaster. The method comprises the steps of generating a standardized monitoring data set, extracting water flow dynamic characteristics, meteorological anomaly characteristics and channel obstacle distribution characteristics of a river region, generating a multi-dimensional disaster associated characteristic set, inputting the multi-dimensional disaster associated characteristic set into a preset disaster early warning model for dynamic analysis, generating a disaster early warning signal, and determining a disaster type and an influence range. And triggering an autonomous separation mechanism of the dragging airship and a quick start instruction of the unmanned aerial vehicle, broadcasting early warning information to ships in a channel through the unmanned aerial vehicle, and synchronously transmitting the disaster type and the influence range to a command center. According to the invention, the timeliness of disaster early warning and the space adaptation precision of treatment measures can be improved.

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention relates to the technical field of water conservancy project risk early warning, in particular to a multi-modal time-space fusion water conservancy risk real-time early warning system, which comprises a multi-modal time-space data fusion module for performing time-space alignment and feature extraction calculation based on reservoir group sensor data flow, remote sensing images and weather forecast to obtain a fusion time-space situation value. According to the invention, by fusing the reservoir group sensor data flow, the remote sensing image and the weather forecast data, executing the space-time alignment and feature extraction calculation, and constructing the fused space-time situation value, the collaborative analysis precision and situation awareness of the multi-source heterogeneous data are improved, and the data utilization rate and the risk analysis accuracy are improved; when a potential scheduling operation set is established, gate opening degrees and flood discharge flow adjustment options under different time windows are defined, and the flexibility and the strain capacity of a scheduling scheme are enhanced, so that the scheduling operation better fits actual hydrological condition changes.

Disclosed are a rapid inversion method and apparatus for icing thickness of a power transmission line, and a related device. The method comprises: acquiring meteorological data of the place where the power transmission line is located, wherein target meteorological factors in the meteorological data are determined in advance by means of association analysis, and the meteorological data includes meteorological monitoring data and / or meteorological forecast data; and inputting the meteorological data into a trained rapid inversion model to obtain an icing thickness inversion value, wherein the model is obtained by training by using the meteorological monitoring data as a training sample and icing thickness data as a sample label, and the icing thickness data comprises icing thickness monitoring data and icing thickness simulation data obtained by means of numerical simulation calculation. Abundant icing thickness simulation data is obtained by means of numerical simulation calculation, solving the problem of insufficient icing thickness data, meeting the high-precision training requirements of deep learning, and improving the generalization ability of the model; and using the trained rapid inversion model to implement rapid inversion of the icing thickness of the power transmission line achieves relatively high accuracy.

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The invention belongs to the technical field of environmental science, and relates to a multi-source satellite remote sensing cooperative atmospheric pollution monitoring and dynamic tracing method and system. The method comprises the following steps: collecting, processing and screening multi-source data; constructing a pollutant diffusion model, and generating pollutant concentration distribution data; dynamically adjusting the diffusion coefficient and attenuation rate of the pollutant diffusion model, and generating a pollutant concentration distribution thermodynamic diagram; performing space matching, and iteratively updating the diffusion coefficient and the attenuation rate of the pollutant diffusion model until matching parameters meet set conditions; and verifying pollutant concentration distribution data in the pollutant concentration distribution thermodynamic diagram. According to the invention, parameters of the pollutant diffusion model can be dynamically adjusted and optimized based on real-time meteorological data, the accuracy of pollutant concentration distribution data generated by the pollutant diffusion model is ensured, and real-time tracking and accurate positioning of a pollution source are realized; the monitoring precision is obviously improved; the cooperative monitoring efficiency is improved, and comprehensive monitoring of atmospheric pollutants is realized.

The invention provides a satellite navigation and atmosphere fusion positioning method and system based on an inertial navigation system, and relates to the field of navigation. The method comprises the following steps: firstly, acquiring multi-source navigation original data, including inertial navigation data, satellite navigation data and atmosphere information data; performing feature extraction through deep learning to obtain multi-modal feature data; and performing quality evaluation on the satellite navigation data, and judging whether the satellite navigation signal is effective or not. When the satellite navigation signal is effective, carrying out combined navigation calculation by adopting a federated filtering algorithm, and establishing an atmospheric information error model; and when the satellite navigation signal is invalid, the error model is utilized to compensate the atmosphere information, and is fused with inertial navigation data, so that high-precision navigation is realized, and the flight safety of the unmanned aerial vehicle in a complex environment is guaranteed.

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention discloses a forest fire monitoring system based on satellite remote sensing, particularly relates to the field of forest fire monitoring, and is used for solving the problems of incomplete data coverage, inaccurate boundary recognition, difficulty in sheltered area restoration and the like in a forest fire monitoring process, and accurately capturing comprehensive coverage of a fire area through fusion of multi-source remote sensing data. And full space-time monitoring under complex weather and topographic conditions is realized. Dynamic changes of spectrums and vegetation before and after a fire disaster are continuously tracked, and continuous basic data are provided for evaluating the influence of the fire disaster. And in combination with the analysis of spectrum range and thermal signal propagation, the fine identification capability of a fire high-temperature area and an edge area is enhanced, and particularly, the boundary identification of a low-disturbance area is effectively optimized. According to the method, the space-time reconstruction and interpolation algorithm is adopted, data missing caused by smog and cloud shielding is comprehensively restored, the integrity and continuity of the image are ensured, and the accuracy of disaster assessment and decision support is improved.