14545 results about "Atmospheric sciences" patented technology

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 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 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 discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The present invention relates to the technical field of very-short-term photovoltaic power forecasting, and in particular to a very-short-term photovoltaic power forecasting method and system for real-time control, which intend to improve precision and real-time performance in photovoltaic power forecasting. The method comprises the following steps: performing normalization processing on meteorological data, and performing a feature correlation analysis; using a BP neural network to perform short-term photovoltaic power forecasting, inputting the meteorological data and historical output data, and outputting a short-term forecasting value with a resolution of 15 minutes; and performing spline interpolation and outlier removal on an upper-layer result of the BP neural network, and using same as a long short-term memory recurrent neural network input, so as to improve a temporal resolution of forecast data and obtain very-short-term photovoltaic power forecast data with a resolution of 1 minute. The method comprehensively considers meteorological factors and uses advanced neural network models and data processing techniques to achieve photovoltaic power forecasting on a very short temporal scale while ensuring forecasting precision, making the method suitable for the real-time control and optimized operation of photovoltaic power stations.

The invention provides a landslide prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the following steps: obtaining multi-source geological data of a target region, constructing a three-dimensional geological model, and dividing slope units; calculating an initial safety coefficient of each slope unit by adopting a limit equilibrium method and Monte Carlo simulation; acquiring real-time monitoring data, establishing an inverse analysis optimization model, and inverting and calibrating physical and mechanical parameters; updating calibration parameters to the model, coupling rainfall infiltration and underground water seepage simulation, dynamically updating a pore water pressure field, and calculating a real-time dynamic safety coefficient; and according to a comparison result of the dynamic safety coefficient and a preset threshold value, determining the stable state of the slope and sending out corresponding early warning. According to the method, through multi-source data fusion, parameter dynamic inversion calibration and real-time hydrological and mechanical coupling simulation, accurate and dynamic evaluation and graded early warning of slope stability are realized, and the accuracy and timeliness of landslide prediction are remarkably improved.

An aircraft gas turbine engine includes a combustor, with a combustion chamber and fuel spray nozzles to inject fuel into the chamber. The nozzles include first and second subsets. The first subset of nozzles is supplied with fuel at a greater rate than each of the second subset. A ratio of nozzles in the first subset to the second subset is 1:3 to 1:6. A lean cruise nvPM emissions index ratio isEIcruise⁡(lean),SAFEIcruise⁡(lean),FF,where EIcruise(lean),SAF isEImaxTO,SAF+EIclimb,SAF2and EIcruise(lean),FF isEImaxTO,FF+EIclimb,FF2.EImaxTO,SAF is nvPM emissions index operating at 100% available thrust and EIclimb,SAF is the nvPM emissions index operating at 85% available thrust if fuel includes sustainable aviation fuel. EImaxTO,FF is the nvPM emissions index operating at 100% available thrust and EIclimb,FF is the nvPM emissions index at 85% available thrust if fuel is a fossil-based hydrocarbon. The lean cruise nvPM emissions index ratio is less than 1.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention discloses a multispectral pollution analysis method and system for a photovoltaic cleaning unmanned aerial vehicle, and belongs to the technical field of spectrum detection.The multispectral pollution analysis method comprises the steps that a multispectral image is obtained, dark current-radiation-atmosphere-shadow full-link preprocessing is carried out, multiband weighted threshold segmentation and morphological post-processing are constructed, and a pollution area is extracted; establishing a self-learning pollution spectral feature library, and identifying pollution types by using cosine similarity; generating a pollution degree distribution diagram in combination with the pollution type-efficiency attenuation mapping table; and when the overall efficiency loss exceeds a preset value, planning a three-dimensional track based on a cluster priority score and an ant colony-genetic fusion algorithm, and returning an image to perform closed-loop verification and complementary cleaning after the unmanned aerial vehicle performs cleaning. According to the invention, the pollution identification precision, the efficiency evaluation accuracy and the cleaning intelligence level are significantly improved, and the method can be widely applied to unattended operation and maintenance of the photovoltaic power station.

System and method embodiments are described for solar panel protection against severe environmental impacts. A tracker controller may be configured to rotate a solar tracker comprising a group of solar panels to a safe position to diminish hail impact during a hailstorm. Such a rotation may be implemented using one or more motors to drive arc gears coupled to a pair of supporting purlins that support the group of solar panels. The tracker may comprise a bumper rail placed on the top when the tracker is at the safe position for hail impact absorption. Implementing solar panel protection provides a practical solution to protect solar panels from damage. Thus, the need for costly repair or replacement may be avoided, minimized, or decreased. As a result, the economic efficiency of solar power plant operations may be improved significantly.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The present invention relates to the technical field of algae content monitoring. Disclosed is an algae content information monitoring method based on multi-source remote sensing data. The present invention comprises: acquiring multi-source remote sensing data comprising satellite remote sensing data, unmanned aerial vehicle remote sensing data and ground monitoring data, and preprocessing the data, involving radiation correction, geometric correction and noise elimination; and fusing the multi-source data into a trained algae monitoring model for prediction, and using Kalman filtering to assimilate observation data and model prediction data. The present invention fuses observation data and model prediction data by means of Kalman filtering technology, so as to dynamically adjust the model state, such that the model can more accurately reflect the actual observation situation. The Kalman filtering optimizes the real-time prediction capability of models by balancing the uncertainties between observation data and prediction data, thereby ensuring the accuracy and real-time performance of monitoring results, and also ensuring the reliability and adaptability of models under various environmental conditions.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention discloses a low-altitude wind field prediction method and system based on a space-time diagram convolutional network, and relates to the technical field of weather forecast and wind energy utilization, and the method comprises the steps: collecting wind field observation data and physical field data of all nodes of a target region, a dynamic space-time diagram is constructed based on a flow function-vorticity theory through a dynamic diagram construction module; extracting spatial information through a graph attention network to obtain a spatial feature tensor; the spatial feature tensor and the physical field data are processed by a PhysFusion-TransTCN encoder to obtain the deep spatial and temporal features of the wind field; performing hierarchical feature aggregation on the wind field deep spatial-temporal features through an output module to obtain a wind field prediction result of the target area; a wind field physical mechanism is deeply fused, multi-scale spatial-temporal feature fusion is realized, and prediction result precision and physical rationality are ensured.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

The invention discloses a prediction method for quantifying the development degree of undermining diseases of rammed earth cultural relics, and relates to the field of cultural relic protection. The method comprises the steps of obtaining three-dimensional laser point cloud data and close photography three-dimensional model data of the rammed earth cultural relics; fusion of the three-dimensional laser point cloud model and the close photography three-dimensional model is realized, and a rammed earth cultural relic undermining disease three-dimensional model is constructed; based on the rammed earth cultural relic undermining disease stereoscopic model, quantitative indexes of undermining disease development are extracted, and an undermining disease development prediction model is constructed and optimized; and generating and outputting a quantitative report of the development degree of the undermining disease of the rammed earth cultural relics. According to the method, three-dimensional laser scanning and close photogrammetry are combined, differentiated routes are designed for areas with different heights, alignment of cross section contours of different models is realized based on Fourier description, a Poisson curved surface reconstruction technology is combined with a hole filling algorithm to complement missing parts of the models, spatial distribution and morphological characteristics of undercut diseases are intuitively presented, and the method is suitable for large-scale popularization and application. The change rule of undercut diseases along with time is quantified, and protection work is promoted to be changed from passive repair to active prevention.

The invention discloses a landslide risk assessment method based on an extreme rainfall and geology coupling model, and relates to the technical field of geological disasters. Comprising the following steps: S1, constructing a three-dimensional probability density field of a fracture network and a non-Gaussian random field model of a permeability coefficient tensor; s2, setting a physical kernel layer according to the non-Gaussian random field model, setting a data driving layer through space-time Transform coding, and constructing a graph attention network model; s3, generating an adversarial network through physical information, constructing extreme rainfall coupling data, and updating the non-Gaussian permeability coefficient random field model according to the graph attention network model; and S4, acquiring an entropy generation rate according to the mechanical field data, the seepage field data and the temperature field data, and determining a risk level. Physical interpretability grading early warning of landslide risks is realized, and meanwhile, risk space distribution can be visually displayed through a sliding surface probability cloud picture, so that accurate decision support is provided for disaster prevention and control.

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The invention discloses a photovoltaic power generation power prediction method and system based on multi-source data fusion. The method comprises the steps of obtaining historical operation data of a target photovoltaic power station and historical meteorological data corresponding to the historical operation data; based on the historical operation data and the historical meteorological data, constructing and training a historical deviation mode correction downscaling model, and correcting future coarse resolution meteorological forecast data provided by a numerical weather forecast model to obtain refined meteorological forecast data of a target photovoltaic power station site scale; on the basis of the refined meteorological prediction data, utilizing a physical-statistical coupling prediction model to predict the future generation power of the target photovoltaic power station; according to the historical deviation mode correction downscaling model, a targeted correction function is established by analyzing a systematic deviation mode between a theoretical prediction value and an actual observation value in historical data, and conversion from coarse resolution prediction to site scale microscopic meteorology is realized, so that the photovoltaic power generation power prediction precision is improved.

The invention provides an in-tunnel inrush water pressure monitoring method and system based on dual-acquisition multi-modal data, and belongs to the technical field of inrush water pressure monitoring. Comprising the steps of collecting video data by adopting a high-definition camera; sequentially performing water flow area intelligent segmentation, optical flow calculation and ConvLSTM time sequence prediction on the video data to predict the water flow velocity; an infrared camera is adopted to collect temperature data; self-adaptive temperature gradient calculation and temperature anomaly detection are sequentially carried out on the temperature data to predict the water flow temperature; aligning and fusing the predicted data of the water flow velocity and the water flow temperature, and constructing a water pressure calculation model to obtain a water pressure predicted value; and determining an early warning level according to the water pressure predicted value so as to adjust the monitoring mode of the inrush water pressure in the tunnel. According to the method, the infrared technology and the vision field technology are combined, accurate prediction of the inrush water pressure in the tunnel can be achieved, and a corresponding early warning monitoring mode is provided according to the prediction result.

The invention discloses an atmospheric pollution remote sensing intelligent monitoring system and method based on multi-modal data, and belongs to the technical field of atmospheric pollution remote sensing monitoring. Comprising a multi-source heterogeneous data acquisition and preprocessing module, a dynamic adaptive modal fusion module, a sudden change scene adaptive module, a three-dimensional pollution inversion enhancement module, a space-time diagram convolution traceability module and a weak signal pollution source collaborative sensing module. According to the method, the concentration contribution values of a plurality of pollution sources are superposed, the three-dimensional distribution of pollutants in space and time is accurately simulated, a visual basis is provided for pollution source positioning and diffusion path analysis, the synchronization of an inversion result and a real-time environment state is ensured through dynamic parameter updating, and the spatial resolution and prediction precision of pollution monitoring are remarkably improved.

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention provides a snow eave growth monitoring system based on terrain coupling and a dynamic evolution model, and relates to the technical field of mountain snow disaster monitoring. A sensitive area identification module is used for obtaining environment data of ridge terrain and a wind field, constructing a wind and terrain micro-scale coupling tensor field according to the environment data, and sending the wind and terrain micro-scale coupling tensor field to a dynamic evolution module; identifying a potential formation sensitive area of the snow eave according to a wind and terrain micro-scale coupling tensor field; and the dynamic evolution module is used for establishing a dynamic evolution model fusing pneumatic jump accumulation and a microcosmic sintering mechanism according to the identified snow eave potential formation sensitive area, simulating a snow eave generation and growth process according to the dynamic evolution model, and obtaining a structure expansion result generated by the snow eave according to simulation. According to the invention, a sensitive area identification module fusing a wind field and a terrain micro-scale coupling tensor field is constructed, and a snow eave structure evolution model, a multi-dimensional sensing data fusion mechanism and a zoning risk output module are combined, so that the growth process of a snow eave under specific terrain and meteorological conditions is dynamically described.