555 results about "Weather station" patented 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 discloses a radar lifting control method and system based on meteorological monitoring, and relates to the technical field of radar lifting control, and the method comprises the steps: completing the switching of a power supply and communication after a radar is powered on, initializing a controller, collecting the data of a meteorological station, and generating a future fusion wind speed in real time through a Kalman filtering physical model and a residual neural network; future fused wind speed is converted into wind pressure for evaluation, the risk degree is judged according to the evaluation result, early warning is given out, and the controller is preheated to enter a lifting preparation state. The input stability is improved through meteorological data sliding window smoothing and feature extraction, wind speed dynamic prediction and uncertainty quantification are achieved through XGBoost prediction and residual variance estimation, the time sequence consistency and robustness are enhanced through remote API interpolation correction and adaptive extended Kalman filtering, residual correction is conducted through a neural network, the prediction precision is improved, and the prediction accuracy is improved. And a reliable decision basis is provided for radar lifting control.

Provided in the present invention are a power-distribution-network self-healing method and system taking photovoltaic output into consideration. The method comprises: acquiring historical operation data of a photovoltaic power station and irradiance observation data from a meteorological station; on the basis of the historical operation data of the photovoltaic power station and the irradiance observation data from the meteorological station, predicting the generated power of the photovoltaic power station by using a convolutional long-short-term memory recurrent neural network model that takes sparrow search into consideration; on the basis of the generated power of the photovoltaic power station, a segment-switch state of a power distribution network and a network topology of the power distribution network, constructing an objective function and a constraint condition for a power-distribution-network self-healing model, and obtaining the power-distribution-network self-healing model; solving the power-distribution-network self-healing model by using a propagation search algorithm, so as to obtain an optimal recovery strategy; and executing the optimal recovery strategy by means of segmented switches and node loads. The present invention can realize self-healing of a power distribution network while ensuring the minimum power generation cost of a distributed power source, the minimum network loss and the minimum node voltage deviation.

The invention relates to the technical field of meteorological remote sensing, in particular to a self-adaptive partition water vapor chromatography method and system based on multi-scale Bayesian prior, and the method comprises the steps: S1, obtaining and processing multi-source observation data of a to-be-analyzed region; s2, constructing a multi-scale grid of the tomographic inversion region; s3, multi-scale representation of the water vapor density is established, and a Bayesian prior constraint model is constructed; s4, constructing a three-dimensional chromatography inversion equation set; and S5, carrying out Bayesian inversion solution. According to the method, global navigation satellite system observation data, meteorological station data and sounding station data are fused, Bayesian prior constraints are constructed, and a multi-scale representation and self-adaptive partitioning method is adopted, so that the number of parameters is effectively reduced, and the stability, precision and calculation efficiency of tomography inversion are improved.

The invention discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention relates to the technical field of port operation state sensing and monitoring, and particularly discloses a port operation state sensing and monitoring system based on a data center, which monitors a wind speed value and a visibility value at each current time point in real time through a micro weather station array, and synchronously obtains tidal phase data of an observatory. Comprehensively generating multi-dimensional meteorological data in a set time period; dynamically calculating the berthing safety coefficient of the target ship through a multi-dimensional constraint condition in combination with the load tonnage of the target ship; according to the target ship berthing safety coefficient, a dynamic ship berthing strategy of the target ship is generated; through comprehensive application of multi-dimensional meteorological data acquisition, berthing safety coefficient calculation, dynamic ship berthing strategy generation and a feedback terminal, a port manager can monitor and optimize the ship berthing process in real time. Therefore, the operation safety and efficiency of the port are improved, a more scientific and flexible berthing scheme is provided for the ship, and the sustainability and economic benefits of port operation are ensured.

The embodiment of the invention provides a multi-element deep learning correction method and device for numerical mode forecasting. The method is applied to the technical field of data processing, and comprises the following steps: marking observation points, and performing loss function construction on the positions of the observation points to obtain a model optimization target; carrying out iterative updating on model parameters, and carrying out training process optimization by adopting a dynamic learning rate adjustment strategy to obtain a forecast field correction model; and applying the prediction field correction model to target numerical mode prediction data acquired in real time, performing prediction effect evaluation, and performing standardization processing and anti-standardization processing to obtain a correction prediction result. According to the method, the information extraction capability of a deep learning visual algorithm model is utilized, fine deviation correction of data of a numerical mode forecast space grid by meteorological station observation data is completed, meanwhile, multiple types of meteorological elements are fused, the deep learning model is utilized to learn mutual influences and restrictions between related elements, and the prediction precision of the numerical mode forecast space grid is improved. And finer deviation correction is completed.

The invention provides a low-cost and high-integration meteorological detection system for low-altitude meteorological service, and belongs to the technical field of meteorological detection. The low-cost and high-integration-level meteorological detection system for the low-altitude meteorological service comprises a plurality of small meteorological stations and MEMS sensors integrated with temperature, humidity, air pressure, wind speed, wind direction and the like. A plurality of small laser wind finding radars are based on an optical fiber laser and a VCSEL (Vertical Cavity Surface Emitting Laser) technology and adopt a modular design. And the data fusion module is used for fusing multi-source data of a ground station, a laser radar, a satellite and the like by adopting a four-dimensional variation assimilation algorithm to generate a 100-meter-resolution grid meteorological product. The technical bottleneck of traditional low-altitude meteorological detection is broken through, and a meteorological service solution is provided for the fields of urban fine management, navigation safety, intelligent agriculture and the like. The system has the characteristics of high cost performance, high flexibility and high resolution, and promotes low-altitude meteorological observation to be upgraded from sparse monitoring to global sensing.

The invention relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The invention relates to the technical field of snow melting disaster early warning, in particular to a snow surface rain disaster identification method based on a high and cold mountainous area and a related product, and the method comprises the steps: constructing a snow surface rain disaster single-point identification rule capable of being physically explained by using meteorological observation data and remote sensing inversion data of a small amount of weather stations in the high and cold mountainous area; the method is advantaged in that classification is carried out without depending on a black box model, precision and interpretability of single-point snow surface rain event discrimination are improved, regional popularization is carried out through dynamic downscaling simulation of a regional climate mode WRF coupling land surface process model Noah-MP, a point-surface fusion snow surface rain disaster discrimination and identification system is constructed, and the method is advantaged in that the method is simple and convenient to operate. The method is suitable for identifying snow rain disasters in complex terrain areas with high and cold areas, high altitude, large gradient, lack of data, even no data and the like, and solves the technical problems of insufficient snow rain disaster space identification capability and low precision caused by sparse observation data in the high and cold mountainous areas in the prior art.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and dynamic path planning method and system based on multi-modal perception and hybrid intelligent decision. Environmental parameters such as wind speed, illuminance, temperature and humidity and image quality indexes are collected in real time through an airborne weather station, an IMU and a visual sensor, a flight parameter-image quality coupling model is established, and multi-target optimization is achieved through support vector regression (SVR) and particle swarm optimization (PSO). And designing a dynamic strategy optimization module based on Q-learning, and designing a reward function in combination with image quality, obstacle avoidance safety and energy consumption. An obstacle three-dimensional model is constructed in real time through ORB-SLAM3, a dynamic danger coefficient is calculated, and an obstacle avoidance track is generated by adopting an improved APF-RRT * algorithm. And the online cooperative control module optimizes the control quantity by using a BFGS algorithm so as to ensure the flight stability and the task efficiency. The method realizes high-precision obstacle avoidance and path planning of the unmanned aerial vehicle in a complex environment, has the characteristics of high robustness and wide adaptability, and is suitable for practical application scenes such as routing inspection, surveying and mapping and the like.

The invention discloses a high-resolution peak accumulated snow depth inversion device and method based on a geographically weighted random forest, and the device comprises a data obtaining module which is used for obtaining the multi-source data of a research region, and the multi-source data comprise MODIS remote sensing accumulated snow data, Sentinel-1VV / VH backscattering data, topographic data, and meteorological station snow depth observation data; the high-resolution snow coverage data set construction module is used for processing the MODIS remote sensing snow data to obtain a high-resolution cloudless binary snow coverage data set; the feature construction module is used for constructing a comprehensive feature set containing a snow space-time process feature index, a back scattering feature index and a topographic feature index; and the model construction and analysis module is used for training a geographically weighted random forest model through the comprehensive feature set, and inverting the peak snow depth of the research area according to the optimal model. According to the invention, estimation of high-resolution peak snow depth can be realized.

The invention provides an energy coordination control system and scheduling method for multi-energy complementary new energy storage, and belongs to the technical field of new energy storage and energy technology management. The data acquisition and processing module is connected with a current and voltage sensor, a power transmitter, a meteorological station sensor and a battery management system sensor, and is used for acquiring voltage, current, power, state of charge and environmental data of photovoltaic power, wind power, energy storage and load through the sensors; and sliding window filtering and wavelet de-noising processing are carried out through the embedded processor. The method has the beneficial effects that the uncertainty of wind and light output is effectively processed by establishing a layered distributed optimization architecture and adopting a method of combining multi-objective optimization and robust optimization, and the consumption rate of renewable energy sources can be remarkably improved, the net load fluctuation can be reduced and the energy consumption can be reduced on the premise of meeting various system operation constraints. Therefore, dependence on conventional fossil energy is reduced, and the economic operation level and the environmental protection benefit of the whole energy system are improved.

The invention discloses a hydropower station operation state intelligent prediction method and system based on multi-source data fusion. The method comprises the steps that heterogeneous data including but not limited to water level, flow, generating capacity, equipment operation parameters, environmental meteorological data, hydrological data and the like are collected in real time from multiple data sources of a sensor network, an SCADA system, a meteorological station and a hydrological station of a hydropower station; the method comprises the following steps of: preprocessing collected original data, extracting time sequence features, fusing multi-source data, taking a fused multi-dimensional feature vector as input, and learning and training through an encoder-decoder architecture of a model so as to capture a complex dynamic mode of an operation state of a hydropower station; and inputting the data acquired and preprocessed in real time into the trained AI prediction model, and generating prediction results of key operation parameters, equipment states, residual life and potential risks of the hydropower station. The method can more accurately capture the complex nonlinear relation in the operation of the hydropower station, and significantly improves the prediction precision of key parameters and risks.

The invention provides a meteorological station power supply line lightning stroke fault positioning method, system and device and a medium. The method comprises the following steps: firstly, synchronously acquiring transient voltage and current traveling waves based on a preset multi-stage lightning protection area boundary to obtain multi-source transient data; performing cross-measuring-point fusion reconstruction on the acquired multi-source transient data, and marking waveform characteristics of the boundary of the protection area to obtain effective waveform data; then, calculating dynamic wave impedance, and determining a wave impedance break variable in combination with a boundary impedance calibration value; and through the reconstructed effective voltage waveform data or effective current waveform data, the traveling wave arrival time is calibrated by adopting a frequency-variable wave velocity model and wavelet transform. And finally, determining the positioning value of the lightning stroke fault at the access point of the meteorological station terminal equipment according to the frequency-varying wave velocity, the calibrated traveling wave arrival time and the wave impedance break variable, and outputting the position information of the lightning stroke fault in the multi-stage lightning protection area in combination with a preset multi-stage lightning protection area boundary position mapping table. According to the scheme, the lightning stroke fault can be accurately positioned in the multi-stage lightning protection area.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention belongs to the technical field of unmanned sailboat environment perception and path planning, and discloses an unmanned sailboat reinforcement learning path planning method based on dynamic wind field reconstruction. Comprising the following steps: step 1, setting a map and an initial setting; 2, wind field preparation and LES data simulation are carried out; step 3, performing reinforcement learning path planning; step 4, meteorological station wind field sampling; step 5, dynamic wind field reconstruction based on a Navier-Stokes equation is carried out; and step 6, path extraction and result calculation. According to the method, through combination of real-time wind field reconstruction and reinforcement learning, the accuracy and adaptability of path planning are remarkably improved, the sailing time and energy consumption are effectively reduced, and the sailing efficiency and safety of the unmanned sailboat are improved.

The invention relates to the technical field of wind field construction, and provides a low-altitude three-dimensional real-time wind field construction method and system based on unmanned aerial vehicle data inversion, and the method comprises the steps: collecting the multi-dimensional data of a flight attitude, a power system, navigation positioning and the like in real time through an unmanned aerial vehicle group in the process of executing a distribution task; and extracting key indexes such as attitude change features, control response deviation features, energy consumption features and the like from the data, inputting the features into a deep neural network with a physical model as a constraint to carry out wind field inversion training, and finally carrying out multi-scale spatio-temporal interpolation on discrete observation points through a Gaussian process regression algorithm fused with the physical constraint. And generating continuous three-dimensional wind field distribution meeting the law of conservation of mass. Dynamic wind field monitoring under urban complex terrains is realized by utilizing the characteristics of high-frequency and wide-coverage operation of the unmanned aerial vehicle, and the problem of insufficient observation data of a traditional weather station in a building dense area is effectively solved.

The invention discloses a heavy rainfall short and temporary trigger forecasting method based on cooperation of radar echo extrapolation and machine learning, and relates to the technical field of weather forecast, and the method comprises the steps: S1, multi-source data collection: collecting three types of basic data, namely conventional radar echo data, dual-polarization radar data and automatic weather station real-time observation data; according to the method, the dual-polarization radar data and the real-time observation data of the automatic weather station are fused, comprehensive capture of key features formed by heavy rainfall is achieved, the problems that in the prior art, the data dimension is single, and the forecasting basis is insufficient are solved, and the real-time forecasting of heavy rainfall is achieved through the steps of splitting data collection, preprocessing, fusion, extrapolation forecasting and collaborative forecasting. A radar echo extrapolation and machine learning cooperation mechanism is constructed, double improvement of precision and timeliness of heavy rainfall short and temporary trigger forecasting is realized, the problems of large forecasting deviation and poor timeliness caused by single use of a single algorithm or model are solved, and heavy rainfall disaster loss is effectively reduced.

The invention provides a micrometeorological prediction method and system, and belongs to the technical field of meteorological prediction. The method comprises the following steps: acquiring data of an unmanned aerial vehicle sensor, a ground meteorological station, satellite remote sensing and numerical weather forecast, and respectively constructing feature vectors of data sources; performing weighted average fusion on the multi-source feature vectors based on an attention mechanism at a target space-time position to obtain a fusion feature matrix; and training a target neural network by using the fusion feature matrix of the plurality of space-time positions, and taking the trained target neural network as a micro-meteorological prediction model to realize high-precision prediction of future micro-meteorological elements. According to the method, multi-source heterogeneous data and a deep learning technology are fused, the temporal-spatial resolution and accuracy of micrometeorological prediction are effectively improved, and the method is particularly suitable for unmanned aerial vehicle flight safety early warning and route dynamic optimization.

The invention discloses a micro-grid real-time optimization control method based on a micro-meteorological station and a deep learning model, and the method comprises the steps: collecting high-precision meteorological data, such as temperature, humidity, wind speed and solar radiation, in real time through the micro-meteorological station; inputting an LSTM deep learning model combined with a global attention mechanism to carry out load and renewable energy output prediction; and based on a prediction result, a micro-grid energy distribution scheme is optimized by adopting a genetic algorithm-adaptive weight particle swarm optimization algorithm, and dynamic adjustment is realized through model prediction control. The problems of insufficient utilization of meteorological data, low prediction precision, response lag and the like in traditional micro-grid management are solved, load prediction errors are reduced, the operation cost is reduced, the utilization rate of renewable energy sources is improved, and the stability, economy and sustainability of micro-grid operation are remarkably improved.

The invention belongs to the technical field of photovoltaic power generation dispatching, and discloses an AI-based distributed photovoltaic power grid intelligent dispatching method and system, and the method comprises the steps: employing a double-layer architecture of an edge node and a federation center, enabling the edge node to carry out the preprocessing and encryption of local data, enabling the federation center to cooperatively train a global model through parameter exchange, and achieving the intelligent dispatching of a distributed photovoltaic power grid. Privacy risks caused by original data transmission are avoided, and multi-node data features are fused; model training samples are more comprehensive, and the load prediction precision is remarkably improved; a'meteorological field sensing-power mapping 'dual-channel model is constructed, a meteorological field sensing channel captures the relevance between adjacent photovoltaic power stations and meteorological stations by using a graph convolutional neural network, a power mapping channel processes multi-scale features through time sequence decomposition transformer, meteorological radar echo data is introduced to deal with sudden weather, and the meteorological field sensing-power mapping dual-channel model is established. The model can accurately grasp the illumination change in a complex meteorological scene, greatly reduces the prediction error of a multi-cloud rapid movement scene and the like, and improves the accuracy of power generation power prediction.

The invention provides an ice and snow field snow melting early warning and intelligent snow removal scheduling method, which comprises the following steps: acquiring wet snow water content, snow layer density abnormity and field climate difference data through a multi-source sensor; the method comprises the following steps: performing primary processing on original data acquired by radar detection, infrared imaging and a micro weather station to obtain an initial distribution map of wet snow characteristics and local weather conditions; and according to the corrected wet snow characteristic distribution map, in combination with the initial distribution map and temperature influence factors and melting rate fluctuation characteristics obtained by data analysis of the micro meteorological station, aiming at equipment thrust loss and working surface friction loss, correcting acquisition deviation of multi-source sensor data, and obtaining a dynamic update map of the snow accumulation state in the risk area.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power station equipment, and discloses a photovoltaic power station equipment operation data analysis method and system based on the Internet of Things. The operation data analysis method is applied to data analysis equipment and specifically comprises the following steps that S101, a data analysis request is received, and the data analysis request collects parameter data of current, voltage, temperature, irradiance and environment humidity in real time through Internet of Things sensor nodes deployed on a photovoltaic module, a combiner box, an inverter and a meteorological station; according to the method, the problem of space-time misalignment of multi-source heterogeneous data is solved, the outlier recognition accuracy is improved, the data availability rate is greatly optimized, the limitation of traditional single-dimensional analysis is broken through by innovative four-dimensional feature engineering, the ripple spectrum entropy in the electrical features and the response delay time in the environment coupling features are effectively improved, and the reliability of the multi-source heterogeneous data is improved. And by combining a thermal-electric propagation model constructed by GCN, the detection rate of early faults such as microscopic subfissure is increased from 60% to 95%.

The invention discloses a solar power generation prediction method, device and equipment. The method comprises the following steps: acquiring current irradiance data of a meteorological station in a current irradiation period; the current irradiance data are input into an irradiance prediction model, next irradiance data of the next irradiation period output by the irradiance prediction model is obtained, and the irradiance prediction model is obtained through model optimization based on a land assimilation model and a numerical weather prediction model in combination with machine learning optimization adjustment logic; and according to the next irradiance data, the weather forecast data and the ambient air data, predicting the power generation output power of solar power generation. Through the method, the irradiance prediction model obtained by combining the land assimilation model and the numerical weather prediction model with the machine learning optimization adjustment logic optimization model is used for irradiance prediction, the accuracy of irradiance prediction is improved, and the power generation output power of solar power generation can be predicted more accurately.

The invention relates to the technical field of traffic management, and discloses a traffic decision-making system and method based on multi-source meteorological data fusion, and the system comprises a data collection module which obtains meteorological station observation data, satellite remote sensing data and meteorological information in a traffic camera image in real time; the preprocessing module is used for carrying out space-time alignment and standardization processing on the meteorological information to generate preprocessed meteorological data with a unified space-time reference; the multi-source fusion module is used for performing cross-modal feature extraction and correlation analysis on the preprocessed meteorological data based on a feature level fusion model of deep learning to generate a fused meteorological feature map; and the decision generation module inputs the fused meteorological characteristic spectrum into a pre-trained traffic decision neural network model, and synchronously outputs a traffic scheduling instruction and a safety management instruction. According to the invention, efficient fusion of multi-source meteorological data can be realized, and the accuracy and effect of traffic scheduling and safety management decisions are improved, so that the traffic scheduling and safety management decisions can better cope with complex and changeable traffic scenes.

The invention relates to the technical field of offshore wind plant area marine environment monitoring, and discloses an offshore wind plant wide-area environment panoramic sensing method and system, and the method comprises the steps: S1, laying seabed bottom type comprehensive observation equipment and low-altitude meteorological observation equipment in a target area of an offshore wind plant; the seabed bottom type comprehensive observation equipment comprises an acoustic Doppler flow velocity profiler, a tide gauge and a temperature-salinity-depth instrument; the low-altitude meteorological observation equipment comprises a meteorological station and a wind measuring radar; s2, collecting field data through the seabed bottom type comprehensive observation equipment and the low-altitude meteorological observation equipment, collecting remote sensing data through the meteorological remote sensing satellite, performing inversion on the remote sensing data to obtain remote sensing inversion data, and transmitting the field data and the remote sensing inversion data to a software processing platform and the like; according to the method and the system, collaborative acquisition, precise processing and fusion prediction of air-sky-sea multi-dimensional data can be realized, and the comprehensiveness and the accuracy of offshore wind plant environment perception are improved.

The invention discloses an artificial intelligence rainstorm prediction model construction method based on multi-source heterogeneous data fusion, and relates to the technical field of rainstorm prediction, and the method comprises the steps: collecting multi-source meteorological data of a to-be-predicted region, the meteorological data comprising satellite remote sensing data, weather radar data, ground meteorological station data and numerical prediction mode data; dividing the to-be-predicted region into different dominant type regions based on a preset dominant factor discrimination rule, and constructing a virtual cloud cluster entity based on the dominant type regions; a motion model of a virtual cloud cluster entity is established, a life development track of the virtual cloud cluster entity is generated, and then a cloud cluster motion map reflecting dynamic evolution of the virtual cloud cluster entity is constructed; the system analyzes and generates an intervention instruction based on the cloud cluster motion map, projects the intervention instruction into the cloud cluster motion map, and finally outputs a rainstorm prediction report including deterministic early warning and potential risk assessment; according to the invention, a virtual cloud cluster entity system with dynamic perception and active intervention capabilities is constructed, and accurate prediction of the rainstorm formation process is realized.

The invention belongs to the technical field of drought monitoring, and particularly discloses a drought monitoring method and system based on multi-source data and spatio-temporal context embedding, and the method comprises the following steps: obtaining drought index data, multi-source dynamic remote sensing factor data and static geographic data of a meteorological station in a research area, and carrying out the preprocessing, obtaining original remote sensing features; extracting attribute values from the static geographic data according to the longitude and latitude of a site, extracting a space context embedding vector and a time context embedding vector based on a multi-layer perceptron and a long-short-term memory network, generating a modulated space-time context embedding vector, and splicing the modulated space-time context embedding vector with the original remote sensing features to obtain a final feature vector; and inputting the information enhanced final feature vector and the monthly standardized rainfall evapotranspiration index SPEI-3 of each station into a GXGBDM model, and outputting a drought monitoring result. By adopting the technical scheme, the drought monitoring result can be efficiently and accurately generated by fusing the multi-source data and the spatio-temporal context embedding information.

The present invention discloses a smart grid fault warning and diagnosis system, which relates to the field of smart grid technology, including a multimodal data acquisition and fusion module, a deep reinforcement learning warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrating multi-task learning, an edge computing and cloud collaboration module, and a system testing and verification module. The system comprises the following steps: collecting multi-source data through power sensors, smart meters and weather stations, and using a deep autoencoder model to reduce the data dimension and fuse features; importing the collected data into the deep reinforcement learning warning module, and dynamically adjusting the warning strategy through interaction with the grid operation environment; analyzing the topological structure of the grid and identifying the fault location by using the fault diagnosis module based on the graph neural network; learning the model and processing the fault type; combining edge computing with the cloud for collaborative work; and testing, verifying and optimizing the model by the system testing and verification module.