91 results about "Weather system" patented technology

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

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 weather system identification method based on multi-source meteorological information. The method comprises the following steps: S1, acquiring historical multi-source meteorological data of a target area; s2, cleaning and complementing the original meteorological data; s3, marking the type and position of a weather system, and constructing a training set; s4, training a single-source meteorological feature extraction model; s5, fusing the single-source features, and training an integrated recognition model; s6, deploying the trained model to a reasoning environment; and S7, inputting real-time meteorological data, and outputting an identification result. According to the invention, efficient fusion and accurate identification of multi-source weather information are realized, and the real-time performance and accuracy of weather system identification are significantly improved.

The invention discloses a tropical region offshore marine meteorological fusion analysis method. The method comprises the following steps: step 1, data collection and preprocessing: collecting offshore multi-platform collaborative networking observation data of a tropical region; 2, data quality control: checking the data, and analyzing error characteristics of marine meteorological observation data; 3, data fusion processing: establishing a tropical region offshore multi-source data fusion analysis system, performing intelligent fusion on the multi-source observation data subjected to quality control, and constructing a marine meteorological fusion analysis data set; 4, constructing a coupling analysis model: performing coupling analysis on marine meteorological elements by combining a physical model and a numerical simulation technology, and discovering an evolution rule and influence factors of a marine meteorological system by simulating and predicting an interaction process between the ocean and the atmosphere; and 5, dynamic application and evaluation: through real-time analysis of key meteorological elements, potential marine meteorological disaster risk affairs are discovered and early warned.

The invention provides a sea fog forecasting method, medium and system based on ocean station observation data, and belongs to the technical field of electrical digital data processing, and the method comprises the steps: firstly, obtaining years of continuous hydro meteorological observation data of an observation station, including air temperature, dew point temperature, seawater temperature, wind direction and wind speed and other data; performing multi-scale decomposition through wavelet transform to obtain variable components of different time scales; then, forecasting factors including temperature difference, stability index and the like are calculated, and layered Fisher discriminant analysis is carried out according to seasons and weather system types. A polynomial discrimination equation set is established by calculating a weight coefficient, and a discrimination critical value is determined by combining a probability density function, so that whether the weather is foggy or not is judged. And finally, verifying by adopting a three-level inspection system, and constructing a complete forecast product release system including the strength grade of fog, the space-time evolution and the like, thereby realizing the accurate forecast of the sea fog. The technical problem that the accuracy of a traditional forecasting method fluctuates greatly under different conditions is solved.

The invention discloses an X-band weather radar reflectivity data quality control method based on deep learning, and belongs to the technical field of meteorological data processing. The deep learning technology is introduced, the convolutional neural network is used for extracting the space and time characteristics of the original reflectivity data, high-precision data quality evaluation and classification are achieved, noise, low-quality and high-quality data can be efficiently recognized under the complex meteorological condition, and through real-time monitoring and a periodic optimization mechanism, the real-time performance of the system is improved. Model parameters are dynamically adjusted to adapt to data input changing in real time, the accuracy and reliability of data processing are guaranteed, through modular integration of a deep learning model, the deep learning model can be embedded into an existing meteorological system, automatic and intelligent application of data quality control is achieved, the data processing efficiency and quality control precision are remarkably improved, and the data processing efficiency is improved. And technical support is provided for the fields of weather prediction, climate research, disaster early warning and the like.

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 provides a traffic flow intelligent sensing scheduling method and system under a special meteorological condition, and relates to the technical field of traffic management, and the method comprises the steps: carrying out the conjoint analysis of a historical traffic data set and a historical special meteorological data set, and constructing a meteorological-traffic correlation model; connecting a meteorological system to obtain real-time special meteorological data; inputting the real-time special meteorological data into the meteorological-traffic association model to obtain predicted traffic data of the next time period, and generating an initial traffic flow scheduling scheme; constructing a traffic accident thermodynamic diagram through traffic simulation; and optimizing the initial traffic flow scheduling scheme according to the traffic accident thermodynamic diagram to obtain a traffic flow scheduling optimization scheme. Through the traffic flow scheduling method and device, the technical problem of poor traffic flow scheduling efficiency caused by incomplete risk identification under the special meteorological condition in the prior art can be solved, the traffic risk under the special meteorological condition is comprehensively identified through joint analysis of the traffic flow data and the special meteorological data, and the traffic flow scheduling efficiency is improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for emulating the evolution of meteorological phenomena within a weather system. In one aspect, a system comprises receiving observation data characterizing an initial state of a weather system at a first time step, encoding the initial state as an observation representation, updating the observation representation for each of a sequence of time steps, the updating comprising: calculating one or more dynamical tendencies for the weather system using a numerical solver, processing an input comprising the observation representation using a physical tendency neural network to generate one or more physical tendencies for the weather system, combining the observation representation with the one or more dynamical and physical tendencies to update the observation, and decoding the observation representation to generate a predicted observation of a future weather state at the final time step of the sequence of time steps.

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 relates to the technical field of weather three-dimensional simulation, in particular to a three-dimensional dynamic simulation method and system based on multi-modal weather data. The method comprises the following steps: acquiring multi-modal meteorological observation data including ground observation station information, radar observation information and satellite observation information, and calculating a time difference value between observation time of each data source; taking the ratio of the time difference value to the spatial distance between the key observation points as a disturbance propagation speed; establishing a space-time correlation window based on the disturbance propagation speed, calculating a deviation correlation coefficient between the observation sources in the space-time correlation window, and judging an atmospheric state type through the deviation correlation coefficient; and mapping the atmospheric state type into a multi-source coordination parameter. According to the method, a proper range can be set according to propagation characteristics of a weather system through the space-time correlation window, observation data intensive with physical consistency is realized, and the problem of dislocation of different observation source data on timestamps or coverage ranges is effectively avoided.

The present invention provides a weather forecasting method and device for multi-scale numerical weather prediction model fusion. The weather forecasting method includes: obtaining target forecasting factors for multi-scale numerical weather prediction model fusion based on general circulation forecast data and convective weather forecast data; and inputting the target forecasting factors into a pre-trained deep learning forecasting model to obtain a weather forecasting result. In the present invention, by fusing the forecasting factors of numerical weather prediction models at different scales and inputting them into a pre-trained deep learning forecasting model, the weather forecast is obtained, which solves the defect in the prior art that the forecasting effects of weather systems at different scales cannot be taken into account simultaneously, realizes the fusion of multiple-scale numerical weather prediction models, and effectively improves the forecasting effects of weather systems at different scales.

The invention discloses a radar echo data processing method and system based on multi-elevation subspace coding, and relates to the technical field of meteorological data processing, and the method comprises the steps: carrying out the preprocessing of multi-elevation radar echo data; independently coding the radar echo reflectivity of each elevation angle into the corresponding subspace to obtain the subspace feature representation of each elevation angle; the subspace feature representations corresponding to the elevation angles are integrated to form a complete implicit vector space; integrating the implicit vector space with the spatial position embedded information and the time period embedded information, inputting the information into a pre-trained time sequence model, capturing the dependency and evolution rule of a convective weather system in time and space, and forecasting the multi-elevation radar echo reflectivity in future 2 hours; the time sequence model is constructed based on a deep learning network with a self-attention mechanism. According to the method, the radar echo reflectivity of different elevation angles is fully utilized, the complex evolution process of a meteorological system is captured, and the accuracy of meteorological forecast is remarkably improved.

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 a cooperative scanning scheduling method based on a severe convection moving direction and radar layout, and belongs to the field of radar detection, and the method comprises the steps: collecting radar data, forming puzzle data, and obtaining a severe convection center moving direction; the distances between all the schedulable radars and the severe convection target are calculated, the orientations of all the schedulable radars relative to the severe convection target are calculated, and the included angles between all the schedulable radars and the operation orientations of the severe convection target are calculated; selecting the optimal vertical analysis radar, calculating the sending direction of the severe convection target relative to the final vertical analysis radar, scheduling two roundabout scheduling of the radars to execute the vertical analysis task according to all the selected optimal vertical analysis radars if the same radars are selected, otherwise, scheduling the radars to execute the vertical analysis task all the time, and the next radar scheduling period is finished. According to the invention, a plurality of severe convection targets can be observed and tracked continuously in the severe convection weather process, and the internal structure and strength change of the severe convection weather system can be obtained.

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 provides an in-game real-time weather system physical interaction verification method and terminal, and the method comprises the steps: dynamically generating weather parameters through a meteorological parameter generator, and the weather parameters comprise wind speed, humidity and temperature; the weather parameters are mapped into a calculation rule of a physical engine through a physical engine coupler, and a weather-physical coupling model is constructed to simulate the change of the weather parameters along with time; a linkage event trigger is adopted to trigger a corresponding linkage event according to the meteorological-physical coupling model, and a game engine is called to carry out physical interaction parameter measurement on the linkage event to obtain an actual value; and comparing the actual value with the theoretical value of the theoretical model through a synchronism detector, and verifying the interaction consistency of the weather event and the physical engine. According to the method, automatic verification of interaction between a real-time weather system and a physical engine in a game is realized by dynamically generating weather parameters, constructing a meteorological-physical coupling model, triggering a chain event, measuring an actual value and performing consistency verification on the actual value and a theoretical value.

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.