67 results about "Weather warning" patented technology

The invention relates to the technical field of electric power energy storage system planning analysis, and discloses an electric power system planning operation optimization method, system and device and a storage medium. The method comprises the following steps: collecting electrical and equipment states and environmental parameters for feature extraction, and evaluating the health state of equipment; grouping the distributed energy sources based on health state evaluation and weather early warning information to form a virtual power plant resource pool; performing task decomposition according to the characteristics of the resource pool, and formulating a collaborative scheduling strategy; arranging a maintenance plan according to the equipment state and the scheduling strategy; and optimizing system operation parameters based on the scheduling strategy and the maintenance plan to obtain a balance control scheme. According to the invention, under the condition of considering the health state of the equipment and the influence of extreme weather, the dynamic balance of the minimization of the operation cost of the power distribution network and the maximization of renewable energy consumption is realized through the intelligent arrangement technology of the virtual power plant group.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention discloses an electric power dispatching method and device fusing multi-modal data and dual verification, electronic equipment and a storage medium, and belongs to the field of intelligent dispatching of an electric power system.The method comprises the steps that multi-modal dynamic data, including equipment images, load time sequences, logs, dispatching instructions, meteorological early warning and electricity price data, of an electric power system are obtained; visual, time sequence and semantic features are extracted, cross-modal alignment is carried out, and a joint representation vector is generated; based on the vector and the real-time data, an initial scheduling strategy is generated through a reinforcement learning model; and through historical scheme matching and physical constraint verification, determining a target scheduling strategy and executing scheduling. Therefore, by implementing the method and the device, the problems of incomplete power grid state perception caused by insufficient multi-modal data fusion and low reliability caused by lack of automatic credibility verification of the AI scheduling strategy in the prior art can be solved.

The invention relates to the technical field of meteorology, in particular to a dangerous weather intelligent early warning system based on data analysis, which comprises a multi-source weather data acquisition module for acquiring dangerous weather data such as meteorology, geology and hydrology; the multi-source weather data transmission module transmits data to the cloud through a communication interruption coping mechanism and sets access authority; the multi-source weather data processing module is used for cleaning and proofreading the data and extracting key weather characteristics; the avalanche judgment model building module is used for building an avalanche judgment model based on an avalanche occurrence probability and a confidence interval by using GAN enhanced feature data; the early warning generation module simulates an avalanche path and an influence area through an avalanche physics model and a shallow water model, evaluates a danger level and issues an early warning; and the model updating module dynamically optimizes the avalanche judgment model based on the latest data, so that the accuracy and timeliness of the early warning capability are improved.

The invention provides a multi-feature factor matching and interrupt processing method for storm tracking. The method comprises the following steps: calculating storm features required by tracking; carrying out discretization processing and coding on the storm features, and constructing a sparse matrix; constructing an auto-encoder model, performing data reconstruction on the sparse matrix based on the auto-encoder model, and calculating main features of the single storm; on the basis of the main features, calculating the feature vector distance between the previous and later time secondary storms, and constructing a feature space distance matrix; a bipartite graph algorithm is adopted, the incidence relation of the previous and later time-order storms is obtained based on the feature space distance matrix, and a storm matching result is constructed; identifying an interrupted storm based on a storm matching result and carrying out secondary matching; and constructing a storm trajectory based on a secondary matching result and removing a repeated storm trajectory. According to the method, the movement track of the storm can be accurately tracked from formation to extinction of the storm, reliable data support is provided for meteorological monitoring and forecasting, and the timeliness and accuracy of severe convective weather early warning are improved.

The application provides a new energy photovoltaic dynamic inspection method and system based on artificial intelligence, and relates to the technical field of intelligent inspection of photovoltaic power stations.The application triggers inspection according to weather warnings, performance alarms or timing tasks, carries out initial path planning in combination with terrain, weather and historical data, and updates the path dynamically through an RRT* algorithm, collects multi-modal data including visible light images, infrared thermal imaging, EL detection data and positioning data during unmanned aerial vehicle inspection, integrates unmanned aerial vehicle data and ground sensor data to generate a unified fault feature matrix, uses a deep neural network to locate a defect area in real time, judge a defect type and divide a fault grade, and finally scores a photovoltaic system health degree according to the fault grade and analyzes a safe operation trend.The application realizes real-time fusion of multi-modal data and dynamic path planning, improves fault recognition accuracy and inspection efficiency, reduces artificial inspection cost and risk, and provides strong support for intelligent operation and maintenance of photovoltaic systems.

The invention discloses a risk early warning method and system based on a power service sensitive event, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining a customer service voice text, work order data and weather early warning information, and constructing a heterogeneous space-time diagram containing power distribution equipment and power consumers; inputting the heterogeneous space-time diagram into a graph neural network model to obtain event propagation path probability distribution and potential source nodes; generating an early warning work order based on the event propagation path probability distribution and a preset sensitive word library; triggering a supervision process according to the early warning work order, generating a supervision work order and configuring an elink message sending rule; and sending the elink message to a specified person in charge, feeding back a supervision processing result and updating model parameters. According to the method, leap-type upgrading from passive disposal to active prevention and control, from experience judgment to data intelligence and from single-point analysis to system governance is realized, and the method has remarkable technical advancement, management practicability and engineering popularization value.

The utility model discloses a severe convection weather early warning detection device, which comprises a detection assembly, a base, a vertical rod, a power distribution piece, a detection piece and a solar piece, the vertical rod is arranged on the outer side of the base, and the power distribution piece is arranged on the outer side of the base. The utility model has the beneficial effects that the worm drives the worm wheel to rotate, the worm wheel drives the gear to rotate, the gear is meshed with the toothed plate, the toothed plate is driven to move in the connecting shell, the toothed plate drives the frame to move, the frame is adjusted to a horizontal position, and the wind area is reduced, so that the wind resistance is reduced; according to the utility model, the air flow can flow away from the air guide plate and the frame through the air guide holes and the through holes, the impact force of wind power on the frame is reduced, rain and snow on the solar panel can be quickly melted and dried through the electric heating metal tubes, melted snow water can be prevented from permeating into the solar panel, and internal elements can be protected.

This invention relates to the field of intelligent management technology and discloses an early warning and control method based on time-series learning of big data on tourist flow in scenic areas. The method includes: analyzing the time-series characteristics of meteorological forecast data during the period of sudden weather warning for the target scenic area to obtain meteorological event characteristic parameters; combining the meteorological event characteristic parameters, real-time tourist flow, and historical tourist flow database to extrapolate the influx of tourists into indoor refuge areas to obtain the estimated total tourist flow; coupling road network topology and real-time traffic status data to construct a traffic impedance diagram, assessing the congestion of potentially stressed road sections and entrance nodes to obtain an initial congestion risk index; when the risk index exceeds a safety threshold, planning the source diversion and allocation of refuge routes to obtain differentiated guidance routes; encapsulating the guidance routes and pushing them to tourists' mobile terminals, comparing the location data with the path vector trajectory to obtain a flow convergence index; when the convergence index exceeds a preset level, adjusting the timing and frequency of the push according to the excess range, and encoding evacuation guidance instructions.

The invention discloses a floating character presentation method of real-time information on a touch screen terminal and a user interface. According to the method, a floating window with the transparency lower than 10% is created, and real-time information symbols are displayed on the floating window, so that information is presented in a floating symbol form. The method specifically comprises the following steps: acquiring real-time data from a server; creating a transparent floating window; the data are converted into numbers, characters or graphic symbols and displayed in a high-contrast mode; and a user is supported to perform touch interaction and drag-and-drop operation through the floating window area. The user interface is characterized in that the background of the floating window is almost transparent, information symbols are clearly visible and can be dynamically updated, and the position of the floating window can be freely adjusted. The problem that real-time information display interferes with user operation in the prior art is solved, and the method is suitable for scenes needing non-intrusive information display such as financial quotation and weather early warning.

This application belongs to the field of heating station heat load prediction technology, and particularly relates to a method, system, equipment, and medium for dynamic compensation prediction of heating stations under extreme weather conditions. The method includes: acquiring current multi-source real-time meteorological data; analyzing the multi-source real-time meteorological data based on an early warning algorithm; determining whether an extreme weather warning has been triggered based on the analysis results; if the determination result indicates that an extreme weather warning has been triggered, calculating the building thermal inertia correction factor for the target heating area; obtaining historical compensation strategies based on a historical similar scenario matching mechanism; calculating a dynamic compensation coefficient by combining the weather warning level, the building thermal inertia correction factor, and the historical compensation strategy; and correcting the basic heat load prediction value based on the dynamic compensation coefficient to generate a final heating load prediction result; and sending the final heating load prediction result to the heating control system to adjust the heating output. This application improves heating quality.

The invention discloses a navigation method and system based on vehicle and road cloud and a digital twinning technology, and a medium. Relates to the technical field of navigation. The method is improved on the basis of a traditional navigation technology, weather early warning data around a main vehicle and information of a lane where the main vehicle is located are obtained based on the vehicle-road cloud technology, traffic early warning information and dynamic scenes around the main vehicle are constructed through a third-party service platform, twin presentation and reminding are conducted on a navigation platform through a digital twin technology, and the navigation efficiency is improved. Early warning information, a road lane and a lane where a main vehicle is located can be displayed, and navigation data of a navigation platform can be displayed at the same time, so that a user can know the scene condition around the main vehicle and the early warning information more visually; and through multi-platform collaborative optimization division and cooperation, the calculation efficiency and accuracy are improved.

1. The name of the design product: climbing stick (intelligent). 2. The use of the design product: climbing stick for GPS positioning, distress, intercom, weather warning, lighting, and beast driving. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective drawing.

Method for predicting and activating AWD on a route, where a process - Receives weather data (401) and uses this data to determine whether AWD and / or 4WD should be automatically activated or deactivated, and at the - any areas on the route corresponding to gravel, dirt or otherwise unpaved roads are considered first (403), with a flag being set (405) for each of these areas, and - wherein a predetermined distance or time before reaching these areas the process activates AWD, wherein the process also checks whether any weather warnings indicating possible upcoming weather over regions of the route exist for the time the vehicle is predicted to travel (407), wherein the process sets a flag for AWD or 4WD for these areas (409) if there are any weather warnings for any regions of the route, and wherein if and when the vehicle encounters an area with a flag set (411), the process checks whether a timestamp is associated with the area (413), and if there is no timestamp, activates AWD (417) shortly before the vehicle enters the area with a flag set, or wherein alternatively, a point on the route is set at which AWD is to be activated and another point at which AWD is to be deactivated.

1. The name of the design product: city flood emergency graphical user interface for electronic devices. 2. The use of the design product: for displaying graphical user interface. 3. The design points of the design product: the graphical user interface content displayed on the screen panel. 4. The picture or photo that best indicates the design points: front view. 5. No design points, omit rear view, left view, right view, top view, bottom view. 6. The use of the graphical user interface: for city flood emergency basic information collection. 7. The human-computer interaction mode of the graphical user interface: the front view is the initial interface of the city flood emergency management platform, the bottom of the front view is the menu options, which are divided into three modules: weather warning, real-time monitoring, emergency rescue. The selected button becomes highlighted after clicking the module. After clicking the "weather warning" module button of the front view, enter the interface change state diagram.

This application provides a method, apparatus, device, and storage medium for meteorological risk assessment in multiple operational scenarios. The method includes acquiring forecast data for at least one meteorological indicator during a target time period and multiple historical values ​​during a predefined historical time period for a target area requiring multi-operational scenario meteorological risk assessment. For any given meteorological indicator, based on its corresponding multiple historical values, at least one first parameter capable of characterizing the statistical characteristics of the multiple historical values ​​is determined. Based on the forecast data corresponding to the same meteorological indicator and at least one first parameter, a second parameter corresponding to that meteorological indicator (capable of characterizing the degree of deviation of the forecast data corresponding to that meteorological indicator relative to historical values) is determined. Then, based on the second parameter and weight value corresponding to each meteorological indicator, the travel risk of transportation vehicles is assessed. This enables timely and accurate weather warnings.

The invention discloses a severe convection weather intelligent early warning method based on phased array weather radar collaborative networking, and belongs to the field of weather early warning. Comprising the following steps: S1, adopting multiple phased array (dual-polarization) weather radars to carry out collaborative networking observation, and obtaining observation data with high temporal-spatial resolution; s2, carrying out quality control on the obtained dual-polarization radar observation base data, wherein the quality control comprises ground clutter suppression, electromagnetic wave interference suppression, radial velocity defuzzification and missing measurement data filling; and S3, projecting observation data of the plurality of phased array weather radars into three-dimensional grid point data under a Cartesian coordinate system, and performing data fusion processing. The method has the beneficial effects that the intelligent severe convection weather early warning method based on phased array weather radar collaborative networking is provided, the dual-polarization detection data and the characteristic quantity thereof are increased, and the recognition precision and the suppression rate of ground clutters are remarkably improved.

The invention relates to a multi-energy interconnection system in the technical field of new energy distribution. The multi-energy interconnection system comprises a central control module, a weather early warning module, a real-time monitoring module, a new energy acquisition module, an energy storage module and an energy conversion module used for converting electric power of the new energy acquisition module and the energy storage module to be connected with a power grid system. The new energy acquisition module comprises a wind driven generator, a hydroelectric generator and a solar panel; the weather early warning module and the real-time monitoring module are respectively used for acquiring prediction information and real-time information of wind power level, rainfall capacity and sunlight intensity; the central control module is used for adjusting the power transmission and distribution condition of the new energy acquisition module and the charging and discharging condition of the energy storage module according to the prediction information and the real-time information; the multi-energy interconnection system can better dispatch power grid resources through multi-energy interconnection and accurate prediction.

The invention discloses a low-altitude weather early warning method and early warning system based on binary Logistic regression, and the method comprises the steps: obtaining training group data and test group data through the processing of meteorological data in previous years, obtaining a regression coefficient through building a training model, employing the training group data, employing a likelihood function estimation algorithm and a Newton iteration method in sequence, and removing a constant term. Obtaining a weather prediction preliminary model formula; and detecting the weather prediction preliminary model formula through test group data, judging whether the prediction success rate reaches a set standard or not, and outputting a weather prediction model formula after the prediction success rate reaches the set standard. The early training group data and the detection group data are a large amount of data, and the prediction accuracy is further improved through the likelihood function estimation algorithm and the Newton iteration method, so that the accuracy of the result obtained by the weather prediction model formula is high.

The invention relates to a disaster recovery control method and system for energy storage equipment. The method comprises the steps of obtaining power failure factor data of an area where energy storage equipment is located, and judging whether the energy storage equipment meets a plurality of preset disaster recovery mechanism starting conditions or not based on planned power failure data, weather early warning data and power grid load data in the power failure factor data and the obtained position where the energy storage equipment is located, when it is determined that the energy storage device meets at least one of the preset disaster recovery mechanism starting conditions, a power failure early warning event is generated, the power failure early warning event comprises estimated power failure time, and an early warning message carrying the estimated power failure time is sent to the energy storage device so as to indicate the energy storage device to enter a disaster recovery mechanism. And charging is carried out after a charging strategy is determined according to the estimated power failure time. By adopting the scheme, the occurrence probability of the power failure event can be accurately predicted, whether the energy storage equipment needs to enter a disaster recovery mechanism or not is judged, the response capability of the energy storage equipment to the power failure condition is effectively improved, and the power demand is met.

The present application relates to a commodity order prediction method, apparatus, equipment and medium based on a large model, including determining target sales feature information from the sales guidance document of the commodity based on a pre-trained industry dictionary of the industry in which the commodity is located; obtaining special time description information, promotion time information of the ordering store, store address information and promotion terms information, and determining spatiotemporal correlation information based on the special time description information, promotion time information, store address information and promotion terms information; an acquisition module obtains weather warning information and historical store sales information; according to the target sales feature information, spatiotemporal correlation information, weather warning information and historical store sales information, dynamically adjusts weights through an attention weight allocation mechanism to determine the predicted commodity order information of the store; and visually displays the target display information through the risk warning module of the large model when the predicted commodity order information meets the preset conditions.

This application relates to a method and system for early warning of sudden weather in scenic areas based on meteorological data linkage. The method includes: acquiring real-time meteorological monitoring data of the scenic area; performing correlation analysis on the real-time meteorological monitoring data based on a pre-set historical meteorological disaster database to obtain a target case set; the historical meteorological disaster database includes comprehensive monitoring data of historical cases in the scenic area; the target case set includes at least one of the historical cases; analyzing the disaster evolution process of each historical case in the target case set based on the comprehensive monitoring data to obtain an evolution chain template vector; and analyzing the real-time meteorological monitoring data based on the evolution chain template vector to obtain comprehensive data on current risk evolution; and comprehensively analyzing the comprehensive data on current risk evolution based on the historical meteorological disaster database to obtain a scenic area disaster early warning report. This method can improve the accuracy and foresight of weather warnings for scenic areas.

The application discloses a short-term and short-time integrated hail weather warning method for a FAST area, relates to the technical field of hail weather warning, and comprises the following steps: selecting sounding data of the nearest sounding time before hail occurs to perform warning index statistics, and obtaining a statistical result; constructing a warning index threshold value according to the statistical result; screening an initial sample set of a hail weather circulation situation feature according to the warning index threshold value, and obtaining a screened sample set; constructing an initial short-term prediction model of hail weather; testing an optimized short-term prediction model according to historical hail weather radar data and the screened sample set, and outputting a best hail weather prediction quantity threshold value; and issuing a hail short-time warning and a hail short-term warning according to the best hail weather and preset strong convective cell identification conditions. The application significantly improves the hail warning capability in a complex terrain area.

The invention provides a severe convective weather multi-scale intelligent fusion seamless forecasting method and system, and relates to the technical field of weather forecasting, and the method comprises the steps: obtaining multi-source meteorological data which comprises observation data, mesoscale numerical mode data and weather scale numerical mode data; performing cascade scale decomposition on the multi-source meteorological data to obtain a small-scale feature component, a medium-scale feature component and a weather scale feature component; cross-scale features are extracted; the method comprises the following steps: acquiring cross-scale features, coding forecast timeliness and geographic area information into embedded vectors, performing adaptive dynamic fusion on the embedded vectors and the cross-scale features, and outputting a severe convection weather forecast result according to a fusion result, so that the recognition accuracy of a small-scale convection system is improved, cooperative processing and space-time perception of the multi-scale features are realized, and the recognition accuracy of the severe convection weather forecast is improved. The prediction precision of the full life cycle of severe convective weather inception, development and attenuation is improved, full-time seamless prediction is achieved through the dynamic fusion strategy, and reliable support is provided for severe convective weather early warning.

The application discloses a kind of ocean hydrographic weather multi-source data fusion analysis early warning method and system, it is related to ocean hydrographic weather data analysis technical field, including: through the multi-source heterogeneous data intelligent processing analysis platform collection ocean current velocity, baric field distribution and so on multidimensional original data, after classified screening extraction calibration data subset, input hydrographic weather coupling collaborative analysis model completes coupling correlation operation, again through time series data dynamic correction fusion algorithm carries out data correction and fusion, will the data after processing input ocean environment multidimensional coupling early warning model and carry out risk grade quantization analysis, finally generate and push multidimensional early warning information.Coupling collaborative analysis, dynamic correction fusion and multidimensional early warning model form core technical support, the application realizes the efficient integration and depth mining of multi-source heterogeneous data, improves the systematicness and accuracy of ocean hydrographic weather warning, applicable to ocean environment safety guarantee related scene.

The utility model discloses a kind of reflective cone windproof fixing devices, belong to airport ground support equipment technical field.It include trolley body, multiple reflective cone body and fixed component, multiple the reflective cone body is stacked and placed on the trolley body, the fixed component is installed on the trolley body.A kind of reflective cone windproof fixing device of the application, by the combination design of iron ring, iron chain and hook, reflective cone can be firmly fixed on trolley body, effectively prevent under adverse weather such as gale, typhoon, reflective cone is blown displacement, to avoid the security risk that reflective cone intrudes taxiway or runway etc., the device adopts trolley form, it is convenient for ground crew to move and deploy reflective cone quickly, compared with traditional manual recovery mode, greatly save manpower and time, improve operating efficiency.Under emergency weather warning condition, the recovery or deployment work of reflective cone can be quickly completed, meet the demand of airport rapid response.

The application discloses a kind of passive commuting weather warning pendants and methods based on bag action trigger, belong to intelligent wearable device technical field, solve the technical problem that current weather warning scheme operation friction is big, rely on mobile phone, privacy protection is insufficient.The application determines to pick up backpack action and wakes up master control chip by the combination output of accelerometer and gyroscope;Comprehensive stay length, current time and the history of travel law that has been learned, travel event is identified from multiple bag actions;Only for travel event, infer matching commuting mode, query weather along the way node with time in anonymized rough coordinate, and determine whether to warn, warning is output by output unit.Output unit is output.The personalized commuting weather warning of user less operation, device independent operation, privacy localization protection is realized.

The application discloses a kind of civil aviation weather short-time prediction methods based on weather radar echo extrapolation, by combining the optical flow method of pyramid algorithm to construct the motion vector field of radar echo map of radar wave, on the basis of motion vector field, extrapolation is obtained to predict the radar echo map of future time, and fusion ADS-B terminal system is displayed and early warning.The method realizes 2 hours extrapolation prediction of weather radar echo, helps weather, control and flight personnel to grasp the reference of weather;Realize a weather, control and flight organic integration of diversified platform, conducive to weather, control, flight personnel to establish consistent scene consciousness, guarantee flight safety.Control and flight personnel can effectively, timely obtain the latest meteorological information and dangerous weather warning information, and combine with ADS-B technology, according to aircraft track and meteorological warning information, guide aircraft to avoid dangerous weather, so as to improve airspace utilization.

The present invention discloses a low-altitude weather warning method and system based on the Internet of Things, which relates to the technical field of low-altitude flight traffic control. The low-altitude weather warning method collects meteorological data in real time, including temperature, humidity, wind speed, air pressure, etc., by deploying a variety of Internet of Things sensor devices in the low-altitude flight area; uploads the collected meteorological data to the cloud server by using a data transmission network; combines a meteorological prediction model and historical big data to conduct real-time analysis and prediction of the low-altitude weather conditions; and when detecting abnormal or potentially dangerous meteorological conditions, sends warning messages to relevant users through a variety of communication methods. The present invention improves the accuracy and timeliness of low-altitude weather warning and effectively guarantees the safety of low-altitude flight air traffic.

The application belongs to the technical field of electric power warehousing, and discloses a multi-source material demand prediction and replenishment decision method for electric power warehousing, wherein a cross-system dynamic federal query system is constructed, an OPC UA protocol and a RESTful API fusion gateway are adopted, seamless docking of multiple systems such as electric power warehousing, power grid monitoring and weather warning is realized, data is standardized into a four-dimensional structure of materials, time, scene and attributes, and then implicit space-time correlation is completed through a space-time enhanced knowledge graph to form a standardized fusion data set; a hybrid prediction model combining migration learning is constructed, small sample scenes are adapted through similar scene data migration, and differentiated sub-models are designed for regular consumption materials and emergency materials, combined with time series trend decomposition and attention mechanism to strengthen the influence of key scenes; meanwhile, an adaptive data cleaning module and an automatic feature extraction mechanism are adopted, abnormal data is removed through double verification, missing values are intelligently completed, and core features are dynamically screened.