330 results about "Weather station" patented technology

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 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 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 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 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 invention discloses an emergency method for extreme weather prediction of a power system. The method comprises the following steps: comprehensively analyzing data from a meteorological satellite, a ground meteorological station and a radar detection system by using a multi-source data fusion algorithm; developing a precise weather prediction model by integrating numerical weather prediction, machine learning optimization and data visualization technologies; carrying out vulnerability analysis on the key components of the power system to establish a risk assessment model, and assessing the operation risk of the power system under the extreme weather event; based on a risk assessment result, a real-time monitoring system and an automatic early warning mechanism are established, and an early warning signal is sent to an electric power system operator in time; strategy measures are formulated and implemented according to different weather situations, so that reliable power supply of the key area is ensured; and according to geographical and climate conditions of different regions, configuration and function optimization are carried out on the power management software. Through accurate meteorological data processing and model prediction, the weather prediction accuracy is improved, and early warning information is sent out in time.

The invention discloses a mountain land low temperature refined monitoring prediction method and system based on satellite-ground fusion and deep learning, and belongs to the technical field of ecological meteorological monitoring and agricultural disaster prevention and reduction. The invention aims to solve the problems of insufficient mountain low-temperature monitoring space coverage, neglect of local factors, rough prediction resolution and poor terrain adaptability in the prior art. The method comprises the steps of multi-source data acquisition, high-density meteorological station, multi-source satellite and meteorological mode data acquisition, spatial-temporal feature analysis, correlation analysis, key variable screening and weather type association, satellite-ground fusion air temperature reconstruction, generation of refined monitoring data based on a CNN-ANN deep learning framework, and short-term and temporary prediction modeling. And LSTM is combined with a terrain attention mechanism to construct a prediction model, and two-dimensional reliability verification is carried out. According to the method, the mountain low-temperature monitoring precision and the predicted terrain adaptability can be improved, accurate support is provided for low-temperature disaster prevention and reduction of mountain economic crops, and the method has remarkable practical value.

The invention relates to the field of power prediction, and discloses a multi-modal photovoltaic power generation power prediction system based on a logistics unmanned aerial vehicle cluster, and the system comprises the steps: obtaining multi-modal data through employing the unmanned aerial vehicle cluster; processing and evaluating the multi-modal data, and evaluating the accuracy, integrity and consistency of each modal data; constructing a respective prediction model for each model, and performing iterative training by using the obtained data to obtain a feature extraction value of each modal; and carrying out multi-modal fusion on the feature extraction values obtained by each modal based on Transform, inputting the feature extraction values into an integrated prediction model, and predicting to obtain the power generation power of each optical storage and charging cluster. Through multi-modal data complementarity enhancement, the unmanned aerial vehicle collects local cloud layer images, high-resolution meteorological sensor data and historical power generation time sequence data, and the space-time blind area of traditional single meteorological station data is effectively made up. A complementary prediction mechanism is formed by adopting the dynamic feature extraction of the CNN on the cloud layer image and the time sequence modeling of the LSTM on the historical power generation data in combination with the physical rule regression analysis of the meteorological data.

The invention provides an intelligent construction safety monitoring system and method for a U-shaped stacked box rigid frame aqueduct, and the system is suitable for the U-shaped stacked box rigid frame aqueduct for hanging basket construction, and comprises a sensing layer which is used for collecting the stress state of a hanging basket; the environment monitoring unit is a micro weather station mounted on an upper cross beam of the hanging basket; the posture monitoring unit is used for monitoring the lateral and longitudinal inclination angles of the hanging basket in real time; the execution layer comprises a catenary anti-overturning device arranged behind the rear anchor point of the hanging basket, the catenary anti-overturning device comprises a hinged steel chain, and the hinged steel chain and the foundation anchoring end form a catenary structure; the control layer comprises an edge computing terminal and a threshold library in which early warning thresholds are stored, and the edge computing terminal is used for receiving the data of the sensing layer in real time, performing multi-source data fusion analysis and judging whether an early warning or emergency mechanism is triggered or not according to an analysis result; and the interaction layer comprises a visual platform, and the visual platform is used for carrying out fusion display on the monitoring data and the BIM model and presenting the risk level in a color coding mode.

The invention discloses an odor concentration prediction method and system based on graph neural network and Monte Carlo coupling, and the method comprises the steps: obtaining the original data of the historical wind direction and wind speed of a meteorological station, carrying out the data preprocessing through a self-adaptive Monte Carlo algorithm based on Sobol global sensitivity, and obtaining a working condition directory data set; performing wind direction grouping on the working condition directory data set to obtain standardized meteorological data; based on the standardized meteorological data, constructing a graph data basic structure, obtaining two-dimensional node-level features, and determining species concentration; constructing an odor concentration prediction neural network model; and based on the odor concentration prediction neural network model, predicting the concentration of the species, and outputting an odor concentration prediction result. By considering the physical conservation of the vertical flux and the concentration, high-precision and real-time prediction of peculiar smell diffusion is realized. The peculiar smell concentration prediction method and system based on the graph neural network and Monte Carlo coupling can be widely applied to the technical field of peculiar smell concentration prediction.

The invention discloses a marine environment disaster early warning method based on big data, and relates to the technical field of marine environment disaster early warning, and the method comprises the following steps: building a multi-source marine environment data collection mechanism, collecting original environment data from a buoy, satellite remote sensing, a radar and a marine weather station, and carrying out the unified coding according to a timestamp; according to the invention, by introducing an abnormal silence detection mechanism, the reliability of marine environment data and the stability of an early warning system are effectively improved; in combination with Mahalanobis distance analysis and a multi-model fusion strategy, the recognition capability and prediction accuracy of a disaster prediction model to nonlinear changes are enhanced; meanwhile, a unified data processing and encryption pushing system is constructed, multi-source data fusion and efficient early warning response are achieved, it is ensured that early warning information can be accurately transmitted to related departments and users in the first time, the intelligent level and emergency response efficiency of marine disaster monitoring are greatly improved, and the method has remarkable practical value and popularization prospects.

The invention provides a fire danger dynamic early warning system and method based on an unmanned aerial vehicle group and satellite remote sensing. The system is composed of satellite remote sensing, an unmanned aerial vehicle group, meteorological data and an ST-CNN model, cooperatively collects multi-source data and carries out space-time fusion, and outputs a pixel-level fire danger level probability graph and a diffusion trend. Unmanned aerial vehicle cluster adaptive formation and task optimization are carried out to realize rapid and fine inspection of a suspicious area; the satellite realizes large-range scanning; and the meteorological station provides real-time environmental parameters. The method comprises the steps of data acquisition, pre-processing registration, time sequence data set construction, model evaluation and early warning pushing, and formation of a monitoring-early warning-verification closed loop. Compared with a traditional scheme, the method is faster in response and wider in coverage, the false alarm rate is reduced by more than 40%, the precision is improved by 35%, 30-minute fire behavior prediction can be supported, and the method is suitable for complex forest areas.

The invention relates to the field of visual display devices, such as a monitor or a television receiver, and is intended for integration with additional functional modules (1). These may include, for example, an RGB light module (6), a weather station (7), a light projector, a smoke and gas sensor (5), etc. The invention consists of a device for visual display of information, comprising a cover (3), a power supply, and a control board located inside the cover (3), and means for connecting additional functional modules (1) placed on or inside the cover (3). The means for connecting additional functional modules (1) are linked to the power supply and allow the modules (1) to operate independently of the main device. The additional functional modules (1) include means for wireless transmission of electromagnetic signals and may be compatible with Smart Home systems. The technical effect of the invention is the possibility of expanding the functionality of the visual display device, ensuring autonomous power supply to additional functional modules (1), improving integration with smart environments, enabling control of lighting and climate, and detecting harmful substances in the room.

The invention discloses an intelligent water bloom prediction and early warning system and method based on an AI large model. The system is mainly composed of a data acquisition module, a data preprocessing module, an AI large model module, an early warning module and a visualization module. The data acquisition module is responsible for widely collecting multi-source data of a target water area. A water quality sensor, an underwater robot and other equipment go deep into a water body to accurately collect physical and chemical parameters of the water body, a meteorological station is utilized to obtain comprehensive meteorological data in real time, hydrological data is obtained from a water conservancy department database or on-site hydrological monitoring equipment, and satellite remote sensing data is obtained by means of a satellite receiving station or a third-party remote sensing data platform. Macroscopic information such as water color, chlorophyll a concentration distribution and water area change is covered. The invention belongs to the field of water environment monitoring and early warning, and particularly relates to an intelligent water bloom predicting and early warning system and method based on an AI large model.

The application provides a kind of method and device for high-cold arid coal mine area vegetation damage repair, it is related to mine vegetation technical field, the present application is divided into sub-region to coal mine area, the water content monitoring is carried out to each sub-region, the meteorological data of meteorological station is combined, to analyze the evapotranspiration of each sub-region, combined with evapotranspiration, rainfall, vegetation water absorption, to analyze whether the water content of sub-region meets the requirement of plant growth, and output irrigation index reflecting the current demand irrigation degree of sub-region, while also image shooting and multispectral data acquisition are carried out to sub-region, generate greening evaluation index for reflecting the growth condition of sub-region vegetation, combined with greening evaluation index and irrigation index analysis, obtain sub-region repair index, finally output sub-region repair priority area, priority is carried out targeted vegetation damage repair to repair priority area, greatly improve repair efficiency.

A system for emergency planning and response to an incident related to the oil and gas industry comprising one or more databases that store data of oil spills and trajectories models, real-time locations of emergency vehicles, H2S gas sensors, LEL gas sensors, and corrosion pumps real-time readings, real-time weather stations, gas wells cross sections drawings and locations of daily drilling rigs. The system further comprises ERP, satellite imagery or aerial photography, and AVL; one or more servers; an ERP system; an electronic display device; and a GIS. The GIS comprises a processing system, and a GIS map displayed by the electronic display device. The GIS map is configured to display a common operating visual of the incident to incident personnel as it unfolds and to locate the emergency vehicles near to the incident.

The invention relates to the technical field of solar simulation adjustment, and discloses an intelligent controllable method and system for simulating dynamic change of solar radiation. Long-term irradiation data are obtained from a database and a local weather station and fused, the discontinuity of the daily boundary is overcome by applying a circumferential mapping and clustering method, and the maximum daily radiation intensity, the typical sunrise moment and the sunset moment of each month are obtained; on the basis, constructing an attenuation model taking a monthly typical value as a benchmark, introducing random disturbance and physical constraints conforming to meteorological statistical characteristics, generating intra-month daily simulation parameters, further utilizing a trigonometric function improvement model to dynamically represent the daily simulation parameters of the current month as target irradiation intensities at all moments, standardizing the target irradiation intensities and mapping the target irradiation intensities into command control quantities, and calculating the target irradiation intensities. Precise reproduction of the natural solar radiation curve by the illumination intensity is realized; then actual light intensity is detected through an irradiation sensor, execution errors are calculated, control action quantity is generated in real time through a PID control law, and finally execution quality is evaluated through a control action quantity sequence.

This invention relates to the field of AI weather forecasting technology and discloses a weather forecasting method based on an AI weather forecasting model and a WRF model. The method includes: inputting ERA5 global atmospheric reanalysis gridded data into the AI ​​weather forecasting model to obtain an assimilated background field; constructing an effective multi-source observation vector based on observation data from ground automatic weather stations, radiosonde observation data, geostationary meteorological satellite radiance data, and Doppler radar VAD wind profile data; solving for the optimal atmospheric analysis field; extracting atmospheric state variables from the optimal atmospheric analysis field and writing them into the WRF model's side boundary driving file, driving the WRF model to complete the integration operation, and obtaining a gridded forecast field of meteorological elements. This method enables the WRF boundary condition update frequency to be higher than the traditional 6-hour static scheme, effectively characterizing the rapid evolution of the atmospheric state in the boundary region at the sub-hourly scale, thereby improving the WRF model's forecasting capability for high-impact weather events such as severe convection.

The utility model discloses an agricultural meteorological disaster early warning device, relates to the technical field of agricultural meteorological monitoring and early warning, and is used for solving the problems related to crop harvesting caused by insufficient agricultural meteorological guidance. The agricultural meteorological disaster early warning device comprises meteorological monitoring equipment, meteorological early warning equipment and an upper computer. A plurality of meteorological monitoring devices are arranged, the plurality of meteorological monitoring devices are distributed in a preset area at intervals, and wireless communicators are arranged in the meteorological monitoring devices; the meteorological early warning equipment is arranged among a plurality of meteorological stations, the meteorological early warning equipment comprises a wireless communication module, a control module, a display screen and a loudspeaker, the wireless communication module is electrically connected with the control module, the control module is electrically connected with the display screen, and the wireless communication module is in signal connection with the wireless communicator; the upper computer wireless communicator is electrically connected with the upper computer, and the wireless communication module is electrically connected with the upper computer. According to the invention, the meteorological information of the small-range region is displayed by arranging the small-range meteorological early warning equipment.

This invention discloses a short-term intelligent numerical weather prediction method and system based on environmental perception, belonging to the field of meteorological management technology. The system includes a data acquisition and fusion module, an intelligent zoning and feature mining module, a short-term forecasting and risk assessment module, and a visualization and focusing module. The data acquisition and fusion module collects and fuses environmental data from meteorological radar and weather stations, as well as GIS maps. The intelligent zoning and feature mining module divides the data into grids based on the GIS map, analyzes the rainfall synergy between grids based on historical environmental data to construct analysis groups, and selects and constructs an impact set for each analysis group. The short-term forecasting and risk assessment module fits near-term precipitation intensity relationships and short-term precipitation intensity relationships based on the impact sets of each analysis group. The visualization and focusing module calculates and outputs the near-term and short-term predicted precipitation intensities for each analysis group based on the relationships, identifies key areas of concern using topographic and hydrological models, and enhances visualization.

The utility model discloses a meteorological station with a compact structure. The meteorological station comprises a supporting seat, a wind speed detection device and a wind direction detection device, the wind speed detection device comprises a first shaft, a wind rotating body, a wind speed sensor and a wind speed circuit board, the first shaft is provided with a vertically-through hollow area, the first shaft is arranged on the supporting seat, and the wind rotating body is arranged on the outer side of the first shaft in a sleeving mode; the wind direction detection device comprises a second shaft, a wind indicator, a wind direction sensor and a wind direction circuit board, the second shaft penetrates through the upper side and the lower side of the first shaft through the hollow area and can rotate relative to the first shaft, and the wind indicator is located above the wind rotating body and arranged at the upper end of the first shaft. The wind indicator can be stably supported above the wind rotating body, so that the measured wind speed and wind direction data are ensured to be very close to the same position, and the consistency and accuracy of the data are improved. The layout is more compact, and space is saved.

The invention discloses a sea surface meteorology and hydrology and temperature and salt flow profile detection device, system and method. The device comprises a sea surface unmanned ship and an underwater detector. The sea surface unmanned ship comprises an unmanned ship, a transmission cable is communicated between the bottom of the unmanned ship and the underwater detector, a solar power generation and supply assembly is fixedly embedded in the upper side of the unmanned ship, and a sea surface weather station, a satellite navigator, a waterproof camera, a remote communicator and an automatic ship recognizer are fixedly connected to the upper side of the unmanned ship. And the right side of the unmanned ship is fixedly connected with a water surface propeller. According to the invention, unmanned automatic operation can be realized, so that the use cost can be reduced, the size is small, the detection depth can be adjusted, the application range is effectively improved, the device can be accurately positioned through a satellite navigator and a ship automatic identifier after detection is completed, and the unmanned ship can always float on the water surface, so that the unmanned ship is convenient to use. And therefore, the device can be conveniently recycled and reused, and resource waste and environmental pollution are avoided.

The present invention relates to a device (1) for monitoring the cleanliness of a surface (2), comprising an electrical power supply (3) configured to be connected to the surface (2) to energize the surface (2) at several points, a voltage and current measurement system (4), a weather station (6) configured to acquire environmental data of the surface (2), and the monitoring device (1) comprising a control unit (5) configured to: - control the power supply (3) according to a power profile, - determine, from the measured current and voltage, an insulation impedance induced by the surface (2), - determine a level of soiling of the surface (2) relative to an initial "clean" state of the surface (2) from the impedance measurement and environmental data from a weather station (6). Figure for the abstract: Fig. 1

The application discloses a lattice rainstorm prediction method, and the method adopts circulation background field, U\V prediction field, rainfall prediction field, model adjustment field and data of a ground meteorological station, and constructs a training sample set; an XGBoost method is used to establish a prediction model based on a mapping relationship of a circulation background field of an EC model and prediction products and future 0-24h precipitation; the training sample set is trained through the prediction model, so that the prediction model can perform medium and short-term precipitation prediction. The prediction ability (rainstorm TS) of the method for precipitation above rainstorm is improved by more than 17% compared with the EC model prediction, the prediction precision of medium and short-term precipitation can be effectively improved, and the method has a good application prospect and provides more accurate prediction services for disaster prevention and reduction.