23662 results about "Atmospheric sciences" patented technology

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 field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

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 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 present invention relates to a method and system for estimating forest carbon storage that combines artificial intelligence algorithms and multimodal remote sensing data. This approach comprehensively utilizes laser radar satellites, multi- / hyperspectral satellites, radar satellites, high-resolution optical imagery, etc. A hybrid technical system is employed for different forest coverage areas, resulting in high-precision forest carbon storage mapping with a resolution of 10 meters and area coverage. This provides technical support and assurance for assessing global forest carbon storage and supporting forestry carbon sequestration transactions.

The invention discloses a multi-sensing and data physical fusion frozen soil hot water force response test system and method, and the system integrates an ultra-weak fiber grating, an active heating fiber, a miniature dielectric constant sensor and an optical frequency domain reflection technology, and constructs a freezing process-oriented temperature, moisture, ice content and strain synchronous monitoring network; the method comprises the steps of constructing a multi-field fusion data set based on thermal disturbance lag correction, temperature-strain decoupling and a moisture-strain residual term compensation mechanism, and extracting key criterion characteristics of an ice lens growth rate, frost heaving force evolution and a shear deformation change rate; and in combination with a physical information neural network (PINN) embedded into a hot water force control equation, risk identification and grade judgment of the freezing abnormal behavior are realized. The method supports indoor heating and water pressure linkage adjustment, realizes a complete closed loop of multi-source sensing-risk identification-response verification under model driving, and breaks through the bottlenecks of low multi-physics field coupling identification precision, large parameter cross interference and insufficient risk identification real-time performance of the existing monitoring technology.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention relates to the technical field of data processing, and discloses a geological disaster meteorological risk early warning method and system based on machine learning. The method comprises the following steps: acquiring rainfall intensity, soil saturation, underground water level change and slope runoff coefficient by a multi-source sensor, and constructing a geological disaster meteorological data set; performing sensitivity weight distribution on the meteorological factors according to geological conditions to obtain a weight matrix; carrying out weighted fusion on the weight matrix and meteorological time series data, and extracting features through a geological constraint long-short-term memory network to obtain a risk probability vector; dynamically adjusting an early warning threshold value based on the safety coefficient change rate; and carrying out Bayesian fusion on the risk probability vector and an adaptive early warning threshold to obtain a graded early warning result. The technical problem that an existing geological disaster early warning technology lacks a multivariate meteorological factor intelligent weight distribution and geological condition adaptive threshold adjustment mechanism is solved.

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

The invention relates to a wind field sensing and wind resistance control device and method for a cross-domain unmanned aerial vehicle, belongs to the technical field of air-ground unmanned aerial vehicles in the field of air-ground coordination, and solves the problem that in the prior art, an unmanned aerial vehicle is poor in wind resistance flight capacity under the conditions that the wind environment is complex and the wind speed changes drastically. Original data are collected through a multi-mode sensor group and processed to obtain a standardized data set; s2, constructing a PINN model, and taking the standardized data set as input processing to obtain a wind field prediction result; s3, the PINN model is trained, and a trained PINN model is obtained; s4, inputting a standardized data set acquired and processed in real time into the trained PINN model to obtain a wind field prediction result output in real time; s5, establishing an active-disturbance-rejection controller, and outputting a final control quantity based on a wind field prediction result output in real time; and S6, processing the final control quantity to generate a motor distribution instruction, and providing the motor distribution instruction to a motor of the unmanned aerial vehicle.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.

The invention relates to the technical field of new energy power generation prediction, in particular to a new energy power prediction method fusing typhoon meteorological information and a micrometeorological prediction result, which comprises the following steps: collecting multi-source observations such as a satellite scatterometer, a radar wind profile and laser wind measurement, and unifying coordinates; constructing a mesoscale wind field by adopting four-dimensional variational assimilation, inferring a micrometeorological field by utilizing a spectrum embedding diffusion network, and generating a multi-scale meteorological field by frequency domain phase consistency fusion; probabilistic wind speed is sampled in the countercurrent model meeting the condition of mass and momentum conservation, an energy conservation graph neural network is input, the wake effect is coupled, and a unit power quantile value is obtained; the power probability is sent to a risk weighting model, the weight is adjusted in real time according to the peak load, the reserve capacity and the climbing rate risk, a dispatching power curve and uncertainty are output, and grid-connected power errors are used for periodically updating the weight and the parameters of the last layer of the reversible model. According to the invention, the safety acceptance margin of the power grid to new energy is obviously improved.

The invention provides a marine reanalysis wind field data correction method based on buoy observation data, and belongs to the technical field of meteorologies.The marine reanalysis wind field data correction method comprises the steps that firstly, abnormal value elimination and quality control are conducted on the buoy observation data, and space-time matching is conducted through a bilinear interpolation method; constructing a dynamic time window, and adaptively adjusting the time precision according to the meteorological event type; calculating wind speed and wind direction errors, and extracting spatio-temporal characteristic parameters; the method comprises the following core steps of: optimizing Gaussian process regression by using an ocean physical constraint neural network model, wherein the model comprises a surface layer physical process coding layer, a boundary layer dynamic embedding layer and a multi-head sparse attention layer; constructing a space-time covariance model to generate an error field; correcting the reanalyzed wind field data and outputting uncertainty evaluation; the technical problem that a significant space-time error exists between reanalysis wind field data and an actual observation value in a complex marine environment is solved.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention discloses a multi-mode cooperative local intelligent thunder and lightning grading early warning method and system and a storage medium, and relates to the technical field of meteorological disaster early warning, the multi-mode cooperative local intelligent thunder and lightning grading early warning method comprises the following steps: preprocessing three types of heterogeneous data sources of lightning positioning data, radar cloud picture data and atmospheric electric field data; a unified input reference of space-time alignment is constructed, and then the lightning movement state, the thundercloud movement direction and the lightning occurrence probability are obtained through a lightning movement track prediction module, a thundercloud movement direction obtaining module and a lightning occurrence probability calculation module; fusing the lightning moving state, the thundercloud moving direction and the lightning occurrence probability through a multi-mode dynamic fusion module to obtain a lightning comprehensive risk probability and lightning predicted arrival time, and analyzing the lightning comprehensive risk probability Prisk and the lightning predicted arrival time Tarrival through a grading early warning module to obtain a lightning grading early warning result. The system overcomes the limitation of a single data source, is high in adaptability, and achieves the precise protection and early warning of local thunder and lightning.

The invention discloses an industrial area atmospheric environment monitoring management system and method, and relates to the technical field of industrial Internet of Things environment monitoring, and the method comprises the steps: triggering polarization imaging to scan a target region based on the time-space information of an abnormal event signal, obtaining the polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure abnormal indexes and the spatial distribution characteristic parameters into an industrial equipment leakage causal model, calculating a pollution source confidence coefficient, and when the pollution source confidence coefficient reaches a process safety dynamic threshold value, obtaining an industrial equipment traceability result and a leakage level; according to the traceability result of the industrial equipment and the leakage level, a hierarchical control instruction is obtained, meanwhile, the actually measured attenuation rate of the industrial pollutants is calculated, and the dynamic response effect is verified; according to the method, cross-modal fusion modeling is carried out on acoustic abnormal information and pollutant spatial distribution characteristics, so that the technical limitation that pollution source positioning is carried out by depending on a static empirical formula traditionally is broken through.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention discloses a bad weather image restoration method based on a multi-modal state space model, and belongs to the technical field of computer vision. In order to solve the problem that an existing unified bad weather image restoration method has limitations in the aspects of global receptive field and computational efficiency, the unified restoration of various bad weather images is realized by designing a parallel multi-mode encoder and an adapter to generate comprehensive prompts containing degeneration semantics. Through parallel connection of a state space module and a local context sensing module of a double-attention mechanism, simultaneous capture of global long-range dependence and local detailed features is realized. Experiments show that the method is high in generalization ability, high in processing speed and low in calculation complexity, and can show good adaptive capacity on a plurality of bad weather image removal tasks.

The invention discloses a forest carbon sink function monitoring method based on multi-source data. The method comprises the following steps: collecting data such as vegetation, soil, weather, landform and interference factors; the collected data are preprocessed; the preprocessed data are input into a Biome-BGC model for processing, and ecological system carbon flux parameters are obtained; determining a vegetation carbon sink amount and a soil carbon sink amount based on an ecological system carbon flux parameter, and obtaining a comprehensive carbon sink amount by combining artificial and natural interference carbon emission amounts; determining the forest carbon sink potential through a constructed forest carbon sink potential calculation formula based on the calculated comprehensive carbon sink amount; based on the calculated comprehensive carbon sink amount, time stability, space stability and anti-interference stability are determined, weighted summation is carried out on the time stability, the space stability and the anti-interference stability, and the carbon sink stability is determined; according to the method, the forest carbon sink function is monitored through multi-source data, the monitoring accuracy is improved, and the forest carbon sink potential, the carbon sink stability and the investment value are accurately quantified.

The invention discloses a dam seepage stability analysis method based on flood forecasting, and relates to the technical field of hydraulic engineering safety monitoring, comprising the following steps: S1, integrating a flood forecasting system and a seepage monitoring system into a data acquisition layer, and acquiring flood forecasting data and seepage monitoring data by a sensor of the data acquisition layer; s2, performing space-time alignment on the flood forecast data and the seepage monitoring data through a space-time coupling interface of the dynamic fusion layer, and generating a dynamic driving variable of a seepage field boundary condition based on a flood routing model and a geographic information system spatial interpolation technology; through the space-time coupling interface, space-time alignment and dynamic driving variable generation of flood forecast data and seepage monitoring data are achieved, the problem that the real-time flood routing process cannot be reflected due to the fact that the boundary condition is static or lagged in traditional analysis is solved, the seepage field calculation boundary condition can be dynamically updated along with flood forecast, and the real-time flood routing process cannot be reflected. And the timeliness and accuracy of model calculation are improved.

The invention provides a high-precision motion capture real-time calibration method based on multi-sensor fusion, and the method comprises the steps: obtaining motion data and environmental parameters collected by multiple sensors, the multiple sensors comprising an inertial measurement unit and an optical sensor; constructing a dynamic calibration model based on the action data and the environmental parameters, and performing real-time error compensation and fusion processing on the sensor data; and establishing a time-varying error dynamic model, and performing real-time compensation on zero offset drift, vibration noise and electromagnetic interference of the inertial measurement unit based on the environmental parameters. According to the invention, the IMU and the optical sensor are fused, respective advantages are exerted, and motion data are comprehensively collected. And meanwhile, the time-varying error dynamic model compensates errors generated by environmental interference in real time, so that real actions can be accurately restored through action capture.

The invention relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method, and the method comprises the steps: multi-dimensional data collection: synchronously obtaining data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring region; data space-time calibration processing: performing space-time reference unification processing on the acquired data to generate a multi-source displacement data set after space-time calibration; cold door parameter extraction: generating an environment coupling parameter matrix based on ground temperature gradient field data and underground water flow velocity field data in multi-dimensional data acquisition; modeling a three-dimensional deformation field: generating an initial three-dimensional deformation field model; dynamic model correction: performing iterative correction on the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; and visual early warning output: generating an early warning signal with a risk level identifier. According to the method, a multi-source heterogeneous data fusion system is constructed, and a cold geological parameter dynamic compensation mechanism is combined, so that high-precision three-dimensional deformation field modeling and early warning are realized.

The invention relates to the technical field of meteorological observation, and provides a ground hail identification method and system based on a hydrogel classification result, and the method comprises the steps: carrying out the time-space correlation of multi-source hail data through a time-space matching algorithm, and obtaining a hail event data set of time-space matching; through a dynamic membership function optimization algorithm, self-adaptive phase state identification is carried out on the dual-polarization radar data, and multi-elevation hail phase state characteristic parameters containing rain-ice mixture categories are obtained; based on the multi-elevation hail phase state characteristic parameters, performing integrated preprocessing on the multi-source meteorological data to obtain standardized multi-dimensional meteorological characteristic data fused with phase state characteristics; performing unsupervised pre-training and supervised fine-tuning training on the DCNN-DBN hybrid neural network through the standardized multi-dimensional meteorological feature data to obtain a ground hail recognition model; and outputting a hail falling area identification result through the ground hail identification model. According to the invention, the distinguishing capability of easily-confused phase states is improved, and the false alarm rate and the missing report rate of hail identification are reduced.

The invention provides an indoor environment monitoring method and system integrating temperature and humidity, and is applied to the field of sensing data processing. According to the method, the reading difference of the temperature sensor is collected, whether local deviation exists or not is judged, the arrangement type of the sensor is combined, a low temperature value generated by the wall-leaning sensor due to the cold bridge effect is dynamically compensated, misjudgment of an environment system is avoided, and the human body temperature is introduced as a sensing reference by constructing a temperature difference deviation function. According to the method, comprehensive temperature indexes closer to real experience can be generated, when abnormal fluctuation is detected, the space structure and heat source information are further combined, boundary conditions are constructed, dynamic heat flow and temperature and humidity distribution diagrams are simulated, temperature sensing deviation is effectively and reversely corrected, the precision, stability and energy-saving performance of an environment adjusting system are remarkably improved, and the comfort degree of a human body is improved.