51 results about "Weather warning" patented technology

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 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.

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.

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 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 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.

This invention relates to the field of image processing technology, and more particularly to a GPU-accelerated ultra-high-speed rendering and display method for early weather warning layers. The method analyzes GPU data to determine the aircraft's operational coefficients and analyzes power data to determine external fluctuation coefficients, thereby calculating data anomaly characterization values ​​and determining the acquisition tendency during the data collection process. For normal acquisition tendencies, the method analyzes the transmission quality coefficient of the data during transmission based on the transmitted data, obtains environmental data during data transmission to calculate the coupling influence coefficient, and combines the data anomaly characterization values ​​to calculate rendering anomaly feature values ​​to determine the data transmission status and whether the data can be used for rendering. For data that can be used for rendering, the method calculates the inter-layer similarity to determine the transmitted data and generates a warning rendering layer. This invention improves the accuracy and reliability of warnings by performing multi-source influence analysis on the data to determine the rendering data.

The invention relates to the technical field of radar signal processing, in particular to a weather radar echo nowcasting method fusing a mesoscale numerical mode. Comprising the following steps: S1, acquiring radar live echo signal intensity data and radar monitoring terrain data; carrying out binarization processing on the radar real-time echo signal intensity to obtain the radar real-time echo signal intensity after binarization processing; based on the radar monitoring terrain data, matching the radar live echo signal intensity after binarization processing with the radar monitoring terrain to obtain a matching result; and S2, on the basis of a result after matching, radar live echo data are obtained, and CMA-meso original data are collected. According to the method, the Fi-radar and Fusion models are provided, multi-source data are fused to eliminate the space-time difference, the extrapolation definition is optimized, and the disaster weather early warning precision is improved through adaptive adjustment of CSI, FAR, POD and MSE indexes.

The invention provides a meteorological early warning method, and the method comprises the steps: obtaining meteorological data and geographic data, carrying out the normalization processing of the meteorological data and the geographic data, and obtaining meteorological feature parameters and topographic feature parameters; inputting the topographic feature parameters into a first model to obtain a first alarm threshold value; inputting the historical alarm data into a second model to obtain a second alarm threshold value; inputting the first alarm threshold value and the second alarm threshold value into a third model for fusion to obtain a third alarm threshold value; and when the meteorological characteristic parameter exceeds the third alarm threshold, triggering a meteorological risk alarm of a corresponding level. According to the method, the adaptability and the accuracy of meteorological early warning are remarkably improved, the environmental difference and the time dimension change of different areas are effectively coped with, and the false alarm rate and the missing report rate are greatly reduced.

A method and system for identifying distribution network faults based on multi-source data is disclosed. This method involves acquiring weather warning level data, querying to obtain a matching data acquisition pattern, collecting feature data according to the matching pattern, normalizing the feature data, calculating feature impact data and dynamic weight coefficients, calculating line risk values ​​based on the feature impact data and dynamic weight coefficients, comparing the line risk values ​​with thresholds to obtain the distribution network risk status level, and finally sending distribution network warning information to the operation and maintenance monitoring platform based on the distribution network risk status level. This invention achieves distribution network fault identification by obtaining feature data through matching data acquisition patterns, calculating feature impact data, dynamic weight coefficients, and line risk values, and comparing line risk values ​​with thresholds. This invention can facilitate efficient scheduling of operation and maintenance resources and reduce distribution network fault losses.

1. The name of the design product: weather warning Tibetan language release graphical user interface of electronic equipment. 2. The use of the design product: for displaying graphical user interface. 3. The design points of the design product: the graphical user interface in the display screen panel. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: the interface is used to operate the weather warning Tibetan language release graphical user interface of electronic equipment according to the user. The front view is the interface after login and opening; click "weather service Chinese-Tibetan parallel corpus" in the front view to enter the weather service Chinese-Tibetan parallel corpus interface, as shown in the change state diagram.

The invention provides a severe convection weather early warning driven power transmission line inspection method, which comprises the steps of tracking a moving path of each severe convection monomer in a target power transmission line corridor, collecting sampling timestamps, and extracting a centroid latitude and longitude coordinate sequence of each severe convection monomer at the sampling timestamps one by one; acquiring the arrival time of the next severe convection monomer, and determining the time period from the starting time of the safe operation window to the arrival time of the next severe convection monomer as the effective time period of the safe operation window; counting the distance and the running speed of the inspection team from the gathering point to the target tower, determining the required travel duration, and subtracting the travel duration from the starting time of the safe operation window to determine the target departure time; and the inspection scheduling system receives the target departure time, overwrites a departure time field in the original fixed response plan, generates a scheduling work order and issues the scheduling work order to the inspection team terminal.

The invention relates to the technical field of radar detection, in particular to a visibility inversion method based on cooperation of radar reflectivity and laser extinction, which fully combines the advantages of high near-field extinction measurement precision of a laser ceilometer and long-distance wide-area coverage of a millimeter-wave radar, realizes full-range continuous monitoring from a near field to a far field, and improves the visibility inversion accuracy. And the monitoring blind area of the traditional scheme is eliminated. A drop spectrum state dynamic correction mechanism is established, and the industrial pain points of poor adaptability of a fixed coefficient and large inversion error are solved from the source. By adopting the three-dimensional Gaussian weighted space matching and Sigmoid easement curve smooth splicing technology, the time-space accurate alignment of the two types of data is realized, the boundary data jump is eliminated, and the continuity of the global extinction field is ensured. The method does not need complex scene prior calibration, is high in generalization capability, can stably adapt to various meteorological conditions, and can provide high-precision and high-reliability wide-area visibility data for highway meteorological early warning.

The application provides a photovoltaic power cross-substation prediction method based on a self-attention mechanism, and belongs to the technical field of photovoltaic power prediction. The application comprises the following steps: constructing a multi-source data set, generating a device health index and fusing the device health index into a static feature vector, constructing a space-time joint modeling model containing a time attention head, a space attention head and a gated residual network, training in combination with a cross-substation data enhancement mechanism, dynamically adjusting model parameters in real-time weather warning and adopting a quantile loss function for optimization, and finally outputting a cross-substation prediction result. The application realizes accurate prediction of photovoltaic power across sub-stations, effectively improves the generalization ability to new sub-stations, and through dynamic adjustment under real-time weather warning and quantile loss optimization, the prediction accuracy in extreme weather scenarios is enhanced, and the application is suitable for power prediction of various photovoltaic systems and can also be applied to energy output prediction in other solar energy utilization fields besides photovoltaic power generation.