54 results about "Weather system" patented technology

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention relates to a weather situation intelligent analysis method based on artificial intelligence multi-modal causal reasoning, and the method comprises the following steps: obtaining multi-modal meteorological data, and carrying out the preprocessing of the multi-modal meteorological data, and obtaining the processed data; processing the processed data through a pre-constructed multi-modal causal reasoning neural network model to obtain a weather situation intelligent interpretation result; performing feedback correction on the weather situation intelligent interpretation result through an automatic feedback correction mechanism based on a large model so as to update the weather situation intelligent interpretation result; and generating a weather situation analysis result based on the updated weather situation intelligent interpretation result, and visually displaying the weather situation analysis result. Through the cooperative work, the weather system can be automatically identified, the causal relationship between the systems can be analyzed, a professional weather situation analysis report can be generated, and multi-mode forecast comparative analysis is supported. According to the invention, the efficiency and accuracy of weather situation analysis can be improved, and reliable technical support is provided for weather forecast service.

The invention discloses a precipitation forecasting method and system based on multi-scale double attention and electronic equipment, and relates to the technical field of artificial intelligence application, and the method comprises the steps: constructing a deep learning model comprising a multi-scale encoder, a terrain perception space-time fusion module and a decoder, and training the deep learning model to obtain a precipitation forecasting model; and based on the historical rainfall data of the preset area, carrying out rainfall forecasting by using the rainfall forecasting model to obtain a rainfall forecasting result of the preset area. The method has excellent performance in the aspects of capturing local extreme rainfall, responding to a small and medium-scale weather system and considering a terrain coupling relation, local sudden change and long-distance dependence modeling are balanced, and the accuracy and applicability of rainfall forecasting are improved.

The invention provides a multi-source heterogeneous data fusion method and device. Equipment state data of a computer monitoring system, equipment ledger and historical operation and maintenance data of a production management system and environmental parameter data of a meteorological system are accessed in real time through a distributed data bus; performing standardization processing on the three types of heterogeneous data based on a unified spatio-temporal index model, and establishing a dynamic association model including a time dimension, a space dimension, an equipment dimension and a working condition dimension; performing causal relationship analysis on the cross-system data by using the dynamic association model to generate a multi-dimensional data association result related to the current accident; and according to a data association result, dynamically generating an emergency disposal scheme and pushing the emergency disposal scheme to a remote centralized control center so as to support real-time decision and disposal operation. According to the method, second-level fusion and intelligent correlation analysis of cross-system heterogeneous data can be realized, the diagnosis efficiency and emergency disposal accuracy of hydroelectric accidents are remarkably improved, the response time is effectively shortened, and the risk of repeated accidents is reduced.

The invention is suitable for the technical field of rainfall forecast correction, and provides a regional short-term rainfall quantitative forecast machine learning correction method integrated with weather system information, and the method comprises the steps: determining an initial forecast factor based on meteorological elements in an ECMWF mode, the meteorological elements comprise a precipitation field, ground transformation, convection effective potential energy, UV wind fields of 500hPa, 850hPa and 925hPa, relative humidity, temperature and vertical speed; taking the dichotomy labels corresponding to the 500hPa slot line and the tangent line between 850hPa and 925hPa as newly added forecast factors, and constructing a four-dimensional training set according to the initial forecast factors and the newly added forecast factors; converting the four-dimensional training set into a three-dimensional training set; training a random forest regression model through the three-dimensional training set, and outputting a rainfall forecast field within a preset time; according to the CMASH3 mode rainfall forecast data and a preset judgment logic, dynamically correcting the rainfall forecast field within a preset time, and generating a target rainfall forecast product; and the accuracy of heavy rainfall forecasting is effectively improved.

The invention discloses a mountainous area meteorological rainfall supplementation observation station site selection evaluation method based on a high-influence weather influence path in the technical field of meteorological observation, and the method comprises the steps: obtaining meteorological grid point data in an observation region, screening the meteorological grid point data to obtain a rainstorm process, generating a path sequence based on the rainstorm data in the rainstorm process, and carrying out the site selection evaluation of a mountainous area meteorological rainfall supplementation observation station. A dynamic time warping (DTW) method is adopted to generate an influence area with a widened rainstorm path, and grid point rainfall forecast error data is generated based on multi-source forecast data and station meteorological data. According to the invention, by extracting the evolution path of the rainstorm process in time and space and combining the multi-source mode forecast error, the regional rainfall distribution, the existing station network density, the topographic feature and the settlement distance element, the evaluation method can be dynamically quantified and the monitoring demand and cost constraint can be balanced; the problems of data missing in the initial stage of a rainstorm system, insufficient upstream observation of a weather system path, insufficient station network coverage rate and weak data representativeness are solved.

The invention discloses a convective gale recognition and analysis method based on machine learning, and belongs to the technical field of weather forecast and artificial intelligence technology crossing, and the method comprises the following steps: multi-source data acquisition and feature fusion: obtaining multi-source meteorological data of a target region in a preset time window, the multi-source meteorological data at least comprises numerical mode forecast data, satellite remote sensing data and ground observation data. According to the invention, by introducing the space-time deep learning model and combining the three-dimensional convolution and recurrent neural network, the evolution rule of the weather system in the space-time dimension is effectively captured, and the perspectiveness and continuity of convective gale recognition are improved. According to the invention, by integrating an interpretability technology and a credibility evaluation mechanism, the transparency and reliability of the model in business application are improved. According to the method, through a self-supervised pre-training and lightweight fusion strategy, the adaptability and the calculation efficiency of the model are improved while the performance is ensured.

The invention discloses a meteorological early warning information fusion method and system based on a road network, and the method comprises the steps: obtaining the topology and meteorological characteristics of the road network, carrying out the clustering to form an aggregation unit, and setting a sharing threshold value; obtaining multi-source early warning data according to units and standardizing the data; a comprehensive result is obtained through duplicate removal, sorting and conflict resolution of a scheduling mechanism; a weather system movement parameter is extracted to calculate a differentiated early warning advance; determining a risk level and a confidence interval by combining real-time traffic state fuzzy reasoning; inquiring emergency resources and linking the traffic control system to push early warning; historical data closed-loop feedback optimization model parameters are collected; and applying the optimization model to realize road network meteorological early warning. According to the invention, the space-time accuracy and response efficiency of meteorological early warning are significantly improved, and the road network traffic risk in severe weather is reduced.

The invention relates to a short temporary rainfall forecasting method, device and equipment based on multi-source data and a memory. The method is based on a short and temporary rainfall forecasting network fusing radar data and numerical mode prior data, the correlation and complementarity between the radar data and the numerical mode are fully mined by effectively combining the advantages of the radar data and the numerical mode prior data, and higher precision and timeliness are achieved in short and temporary rainfall forecasting. Through deep learning training of the multi-source data, the trained network can provide a high-resolution and accurate rainfall forecast result, and the performance of short-term and temporary rainfall forecast is significantly improved. According to the short and temporary rainfall forecasting method based on the fusion radar data and the numerical mode prior data, the complex dynamic state of the rainfall process can be more accurately captured, the wide influence of a weather system is fully reflected, and the precision of short and temporary rainfall forecasting is effectively improved; the extrapolation timeliness can be further improved by adjusting the network structure and increasing the number of prediction sequence output data frames.

The invention discloses an ice accumulation forecasting method and system, a readable storage medium and a program product, and the method comprises the steps: S1, selecting a specific region, obtaining meteorological data, and processing the meteorological data into a unified format, S2, constructing an initial ice accumulation potential algorithm of the specific region based on the meteorological data, and S3, constructing an initial ice accumulation potential algorithm of the specific region based on the meteorological data. S4, identifying a specific weather system and a key meteorological factor of the specific area, and establishing a membership function of the key meteorological factor under the specific weather system, and optimizing the initial icing potential algorithm in combination with the membership function to obtain a localized icing potential algorithm. And S5, outputting the icing potential based on the localized icing potential algorithm. Therefore, a localized icing potential algorithm can be established for a specific region, so that the icing risk can be predicted more accurately.

The invention discloses a wind field quality control method based on a single moment image structure, which comprises the following steps of: converting original wind direction and wind speed data into U and V wind components, and interpolating the U and V wind components and wind direction site observation data into regular grid data; adopting a curvelet decomposition method to decompose the grid-processed wind field data into two parts, namely a main term and a remainder term taking random change as a main term; for U and V wind speed and wind direction data, calculating a spatial gradient of a target grid point by using the main item data after curvelet analysis, and judging that the grid point with the gradient greater than a preset gradient threshold value is abnormal; performing progressive quality control for multiple times to identify discontinuous abnormal values in the main item; after main term curvelet analysis is completed, curvelet decomposition is carried out to obtain remainder term data, an abnormal threshold value is set, and a wind field area is divided into a weak rotating wind area and a strong rotating wind area; the quality control method is suitable for high-intensity weather system influence.

The application discloses a kind of meteorological warning information fusion method and system based on road network, comprising: obtaining road network topology and meteorological characteristics, clustering forms aggregation unit and sets shared threshold;According to unit, obtain multi-source warning data and standardization;By scheduling mechanism, deduplicate, sequencing and conflict resolution get comprehensive result;Extract weather system movement parameter and calculate differentiated early warning advance;Determine risk level and confidence interval in combination with real-time traffic state fuzzy reasoning;Query emergency resource linkage traffic control system and push warning;Collect historical data closed-loop feedback optimization model parameters;Application optimization model realizes road network meteorological warning.The application significantly improves the spatiotemporal accuracy and response efficiency of meteorological warning, reduces the road network traffic risk under adverse weather.

This invention discloses a fully automated, three-dimensional active defense system and method, belonging to the field of weather modification technology. The system monitors thunderstorm weather systems via airborne remote sensing satellites, triggering an early warning signal when the system moves to the outermost layer of the defense area. It combines this with a network-based ground-based radar monitoring system to continuously track and monitor thunderstorm cloud weather systems in real time, simultaneously sending various parameters to a lightning monitoring and early warning module. This provides criteria for detecting strong lightning electromagnetic fields, inverts the spatiotemporal parameters of lightning occurrence, and provides early warning and forecasting of the development trend of thunderstorm weather systems. A central control system then determines lightning dissipation and initiates operational commands. Finally, a mobile artificial lightning induction system is used to artificially intervene in the charge structure of thunderstorm clouds. This invention can mitigate the direct and indirect electromagnetic damage effects of strong lightning caused by the spatiotemporal unpredictability of severe convective weather and the instantaneous nature of strong thunderstorm discharge processes, protecting the safety of key locations and important sensitive targets.

The embodiment of the invention provides a water heater control method and related equipment, and belongs to the technical field of water heaters. The method comprises the following steps: acquiring environment temperature and sunlight data; performing season judgment according to the environment temperature and the sunlight data, and determining a current season; if the current season is the preset season, determining a weather condition through a weather system; according to weather conditions, a solar module and an air energy module of the water heater are controlled according to different strategies so as to heat a water source. According to the embodiment, the energy utilization rate and the heat supply stability can be improved.

The invention relates to the technical field of weather radar synthesis, in particular to a weather radar reflectivity synthesis method and system based on a synchronous stationary satellite. The method comprises the following steps: acquiring satellite multi-channel observation data and weather radar data; performing data preprocessing based on the acquired satellite multi-channel observation data and weather radar data; the method comprises the following steps of: constructing a weather radar synthesis model based on an improved Swin-Transform and a U-Net; performing model training on the constructed weather radar synthesis model; and performing forward reasoning by using the trained model and obtaining weather radar reflectivity synthesis. According to the method, high-quality weather observation data of the areas are provided, the problem of data vacancy of the weather radar is solved, the observation range of the weather radar is remarkably expanded, and particularly, the monitoring capability of a large-scale weather system and severe convective weather is improved.

The utility model discloses a meteorological acquisition controller and a meteorological system with the same, and relates to the technical field of meteorological monitoring. The meteorological acquisition controller comprises a power supply input module, a data acquisition module and a power supply output module, wherein the power supply input module is configured to convert alternating current into direct current; the power output module is configured to supply power to the meteorological sensor; and the data acquisition module is configured to acquire a conversion signal transmitted by the meteorological sensor, cut off the connection between the power supply output module and the meteorological sensor when the communication has no response or the conversion signal is abnormal, and establish the connection between the power supply output module and the meteorological sensor after a preset duration. By adopting the meteorological acquisition controller, the meteorological sensor can be powered on or powered off, so that the abnormity recovery capability is improved.

Systems and methods are provided for landing site selection and flight path planning for an aircraft using soaring weather conditions. The methods may include, with one or more processors of a controller onboard the aircraft: receiving data indicative of terrain, airports, airspace, aerodynamics of the aircraft, real-time weather, and real-time status of the aircraft, determining a gliding range of the aircraft based at least in part on soaring weather conditions that include environmental regions of thermal draft capable of producing lift sufficient to extend the gliding range of the aircraft, determining a landing site for the aircraft based on the gliding range of the aircraft, and determining a flight path of the aircraft that uses the soaring weather conditions to extend the gliding range of the aircraft and land at the landing site.

The invention relates to a method for realizing cloud shape automatic identification based on a ground-based millimeter wave cloud radar, and the method comprises the following steps: S10, carrying out the quality control of collected cloud radar data, and removing abnormal echo data; s20, performing cloud boundary identification based on the data after quality control to obtain data sets of different cloud blocks; s30, calculating a cloud feature quantity by using the data sets of different cloud blocks; and S40, cloud identification is carried out based on the characteristic quantity. According to the method, the cloud radar is used for detecting data, through observation data analysis, quality control, cloud boundary identification and cloud characteristic quantity calculation, the cloud characteristic quantity is used as a basis, a threshold value method and a K-nearest neighbor machine learning method are used, automatic cloud identification is achieved, automatic cloud observation of a meteorological system is improved, and the method is suitable for automatic cloud observation of the meteorological major.

The embodiment of the invention provides an icing prediction method and device, equipment and a storage medium, and the method comprises the steps: obtaining secondary season icing related data, carrying out the convolution operation of the secondary season icing related data, determining the space structure information of a weather system, building a reference mapping model based on the space structure information, and carrying out the calculation of the reference mapping model. Taking a model parameter in the reference mapping model as an initial value, adjusting the initial value by using historical forecast data, obtaining a correction model according to an adjustment result, inputting forecast data to be predicted into the correction model to obtain air temperature forecast and rainfall forecast, and calculating the content of supercooled liquid water according to the air temperature forecast and the rainfall forecast. And inputting the content of the supercooled liquid water into a preset icing growth physical model to obtain an icing thickness growth rate, and predicting icing by using the icing thickness growth rate. The accuracy of sub-season scale icing prediction is improved, the systematic deviation of a power mode is corrected, the reliability of a power grid disaster prevention plan is enhanced, and the operation mode is optimized.

The application relates to a three-dimensional radar echo extrapolation method based on a deformation convolution dynamic field constraint, which comprises the following steps: S1, continuously collecting radar reflectivity and radial velocity data within 2 hours; S2, converting the radar reflectivity data into three-dimensional data in a Cartesian rectangular coordinate system, mapping the radial velocity data into a radial rectangular coordinate system and constructing a sample set; S3, acquiring middle-layer radial convergence feature information in a radial velocity graph; S4, building a radar echo extrapolation model; S5, introducing deformation convolution to automatically extract the dynamic field information of a convection system; S6, dividing the sample set into a training set, a verification set and a test set; and S7, obtaining a reflectivity image in the next 1 hour. The application fully considers the three-dimensional structural features and dynamic characteristics of a weather system, simultaneously combines multi-source physical constraint information, and realizes accurate simulation and prediction of a complex evolution process of the weather system.

The application discloses a radar echo space-time extrapolation method based on asymmetric space modeling, belongs to the cross field of meteorological radar data processing and artificial intelligence technology, and first constructs an asymmetric input space sample containing a target region and a surrounding extended region by preprocessing original meteorological radar observation data, builds an asymmetric space modeling prediction network based on a 3D U-Net architecture, adopts a regional mask mean square error loss function to complete model training, and finally realizes accurate space-time extrapolation of the target region radar echo. The application solves the technical problems of information fragmentation and significant long-time prediction error accumulation of the traditional symmetric modeling method, improves the precision and stability of radar echo extrapolation under complex weather systems, and is suitable for 0-1 hour nowcasting and meteorological disaster accurate early warning scenes.

The application provides a target area burst weather automatic early warning method, and belongs to the technical field of weather early warning.The application adopts adaptive filtering processing and frequency domain signal separation technology to establish a first-level dense matrix, uses a marine weather identification model with a spiral progressive network structure to generate a second-level dense matrix, uses a dynamic statistical equation to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, adopts a maximum flow minimum cut algorithm to optimize the weather system coupling relationship network to calculate a coupling degree matrix, establishes a dynamic threshold adjustment mechanism according to the coupling strength parameter to adjust the early warning detection frequency, and establishes a hierarchical early warning system based on the comparison result of the maximum eigenvalue of the coupling degree matrix and a preset risk threshold to output corresponding early warning signals to control the emergency response system of the offshore oil and gas platform, thereby solving the technical problem that the coupling relationship of the multi-time scale weather process cannot be effectively processed.

The invention discloses an extreme weather early warning monitoring method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and solves the problems that the prior art is often based on a physical model or a simple statistical threshold value and is poor in adaptability to a nonlinear and complex weather system, so that false alarm and missing alarm are caused, and the working efficiency is high. And the accuracy and the efficiency of the extreme weather early warning monitoring method are low. The method comprises the following steps: determining a space-time meteorological feature vector based on multi-modal data; generating an extreme weather result based on the space-time meteorological feature vector; according to the method, differential early warning data is generated based on an extreme weather result, a subtle precursor signal of extreme weather occurrence is learned from a high-dimensional and complex space-time meteorological feature vector by using strong nonlinear mapping and mode recognition capabilities of artificial intelligence, and the extreme weather result is more accurately and efficiently generated; and the accuracy and efficiency of the extreme weather early warning monitoring method are improved.

The application discloses a weak weather forcing heavy precipitation event objective identification method based on visual information, and comprises the following steps: obtaining data and visual information thereof, analyzing and utilizing the information to exclude the influence area of strong weather systems such as tropical cyclones, vortices, shear lines and fronts; further determining the area and time affected by the weak weather forcing in the research area, and identifying the corresponding heavy precipitation event; the application effectively continues the traditional prediction experience, and provides support for the mechanism research and prediction technology development of the weak weather forcing heavy precipitation event.

This invention discloses a simulation method and system for three-dimensional cloud precipitation detection based on a spaceborne conical scanning radar. The method simulates a three-dimensional cloud field and acquires water condensate information by inputting typical cloud precipitation scene data into a numerical model; it establishes a water condensate particle spectrum and microphysical model, and calculates its scattering and attenuation characteristics using a T-matrix; it simulates a wide-swath three-dimensional scanning trajectory based on satellite orbital parameters and conical scanning geometry; it matches the trajectory with the cloud field using the nearest neighbor method and calculates the radar reflectivity factor before and after attenuation; finally, it reconstructs the three-dimensional gridded radar echo data using a range-weighted method. This invention overcomes the shortcomings of existing simulation technologies in completely reconstructing wide-swath three-dimensional cloud and rain structure and wind field information, and can simulate three-dimensional radar observation scenarios of weather systems such as typhoons with high fidelity, providing an effective simulation verification platform for the system design and parameter optimization of next-generation spaceborne conical scanning radar.

The application discloses a weather system classification method, device and equipment and a storage medium, and is applied to the meteorological analysis field. Time-space weather data containing multiple meteorological elements are acquired, four-dimensional meteorological data containing time, meteorological elements, longitude and latitude are constructed based on the time-space weather data, a weather system classification model is constructed based on a time-space feature fusion module, the time-space feature fusion module is connected with an encoding layer and a decoding layer of the weather system classification model, the weather system classification model is trained based on the four-dimensional meteorological data to obtain a trained weather system classification model, and four-dimensional to-be-detected meteorological data is input into the trained weather system classification model to obtain an output weather system classification result. The encoding layer and the decoding layer of the weather system classification model are connected with the time-space feature fusion module for extracting the time-space correlation, so that the model can effectively learn the time-space correlation of the meteorological data, and the accuracy of weather system classification is improved.

The invention relates to the technical field of computer-aided scientific research, and discloses an all-weather system review automatic generation and updating method and system.The method comprises the steps that academic literature data pre-input into local storage are automatically scanned in an all-weather mode, system review lacking or needing to be updated is recognized, and corresponding research subject description is generated; expanding and generating a multi-database retrieval strategy set; retrieving the plurality of academic databases to obtain multi-source data of the candidate literatures, and performing standardized cleaning and duplicate removal processing on the obtained multi-source data to form a unified candidate literature set; inputting literature titles and abstracts into a trained screening model, and automatically outputting judgment results and reasons of the literatures; and a set formed by the included documents is input into the artificial intelligence writing model, a systematic review first draft is generated in a structured mode, statements in the first draft are all associated to corresponding document sources, and full-process automation of systematic review from intelligent retrieval and automatic screening to structured draft is achieved.

The invention relates to the field of airport construction monitoring, in particular to an airport runway FOD recognition device and method.The airport runway FOD recognition device comprises a plurality of airport monitoring radars, satellite radars, infrared thermal imagers, wind speed sensors and wind direction sensors, and the airport monitoring radars, the satellite radars, the infrared thermal imagers, the wind speed sensors and the wind direction sensors are in signal connection with a monitoring system. The monitoring system is in signal connection with the meteorological system to obtain meteorological information, and the monitoring system is also in signal connection with an airport scheduling system. The method can predict the moving trajectory of the objects in the air, timely performs early warning in combination with the airport operation condition, realizes non-stop construction, and brings great help to the expansion of the airport.

The invention belongs to the technical field of electric power weather forecast, and discloses a power grid facility-oriented cold wave disaster process risk assessment method and system, and the method comprises the steps: collecting field data outputted by a numerical weather forecast model, and recognizing and analyzing the key weather system and evolution characteristics of cold waves; the method comprises the following steps: constructing a GIS spatial analysis framework comprising a meteorological element graph layer and a power grid facility graph layer on the basis of a key weather system and evolution characteristics of cold waves, and performing overlay analysis on the GIS spatial analysis framework to obtain meteorological element distribution of an area where power grid facilities are located; and based on the meteorological element distribution of the area where the power grid facility is located, combining preset vulnerability parameters of the power grid facility and disaster characteristics of meteorological elements, constructing a risk assessment model, and outputting a risk assessment result. Accurate analysis, evaluation and early warning of the influence of the cold wave disaster on the power grid facilities are realized.

The invention discloses a gale event classification method based on iterative clustering analysis, and the method comprises the following steps: collecting the observation data, reanalysis data and rainfall data of an automatic weather station in a target region, screening and extracting gale event samples, and calculating a plurality of environmental physical quantities corresponding to each gale event; based on the environmental physical quantity, performing first clustering analysis on all the gale event samples by using an unsupervised clustering algorithm to obtain a preliminary gale type division result; based on the primary classification result, evaluating the importance of each environmental physical quantity to different strong wind types by using a random forest algorithm, and screening out key feature variables; and performing secondary clustering analysis on the gale events which are misjudged or have fuzzy boundaries in the primary classification result by using the key feature variables, and re-dividing the types of the gale events to obtain a final gale weather system classification result.