39 results about "Meteorological disasters" patented technology

The application discloses a lightning target recognition model and method facing image input modal imbalance and high false alarm rate, and belongs to the technical field of image recognition and meteorological disaster monitoring. Firstly, Gaussian mixture density estimation and time-weighted fusion are performed on ground-based lightning location data to convert the image label with spatial probability distribution characteristics; then, Himawari-8 satellite multi-channel brightness temperature images and their derived feature maps and radar echo images are used as multi-source heterogeneous inputs. In view of the information imbalance between different observation modalities, an MDE-UNet deep learning model is constructed, a multi-scale feature fusion module, a MLP enhanced decoding unit based on a weighted sliding window and a radar image information enhancement module are innovatively designed, the key image features are enhanced and irrelevant noise is suppressed, and the feature imbalance problem in heterogeneous data fusion is solved. The model adopts an asymmetric weighted BCE-DICE loss function to strengthen the attention to lightning target pixels.

The application provides a characteristic agricultural multi-source sound-light alarm system, which is composed of a sound-light alarm module, an alarm display module and a power module connected with each other; the sound-light alarm module comprises a master control unit, a pulse trigger unit and a plurality of light modules; the master control unit is connected with the pulse trigger unit through a relay switch, the pulse trigger unit is connected with the plurality of light modules through a multi-way logic gate circuit, the master control unit is configured to receive an alarm signal of a remote server and control the plurality of light modules to cyclically and alternately turn on and off through the pulse trigger unit; the alarm display module is connected with the master control unit and the logic gate circuit respectively, and the alarm display module is configured to receive an alarm level signal sent by the master control unit and synchronously display the alarm level with the light modules. The application can meet disaster early warning in different application fields, realize dynamic multi-source sound-light alarm, significantly reduce the cost of agricultural meteorological disaster early warning, and significantly improve the efficiency of agricultural meteorological direct service.

Embodiments of the present disclosure disclose a distributed photovoltaic power station meteorological disaster risk dynamic assessment and real-time early warning method. A specific embodiment of the method comprises: preprocessing photovoltaic power station meteorological disaster related data; inputting the preprocessed photovoltaic power station meteorological disaster related data into a first disaster risk assessment model, a second disaster risk assessment model and a third disaster risk assessment model respectively; integrating the first disaster risk assessment result, the second disaster risk assessment result and the third disaster risk assessment result to generate a meteorological disaster risk value; setting a dynamic threshold according to the meteorological disaster risk value, dividing a risk level and generating an early warning signal; and linking a distributed photovoltaic power station monitoring system for real-time early warning. The embodiment realizes multi-platform data interconnection and intercommunication, is more suitable for the decentralized and multi-scenario operation and maintenance requirements of distributed photovoltaic power stations, and expands the technical application range.

The application relates to an installation structure of a meteorological disaster prevention and reduction early warning device, and relates to the technical field of outdoor meteorological equipment installation, and is used for solving the problem that the holes of a cement embedded seat are easily blocked outdoors. The installation structure of the meteorological disaster prevention and reduction early warning device is used for fixing and supporting the early warning device. The installation structure comprises metal ground foot columns, cross shaft universal joints, a stand column and connecting pieces. At least three metal ground foot columns are inserted into the ground at intervals. The bottom end of the metal ground foot column is formed with a tapered end. The top end of the metal ground foot column extends to the ground. The top of the metal ground foot column extends outward from the ring surface of the metal ground foot column and is formed with an end plate. At least three cross shaft universal joints correspond to the at least three metal ground foot columns one by one. The bottom mounting plate abuts against the end plate of the corresponding metal ground foot column. The stand column is arranged on the upper side of the cross shaft universal joint. The bottom of the stand column is formed with a fixing plate. The fixing plate is horizontally arranged. The at least three top mounting plates all abut against the bottom surface of the fixing plate. The early warning device is fixedly arranged at the top end of the stand column.

This invention discloses an IoT-based method for early warning and analysis of agricultural meteorological disasters, relating to the fields of IoT and agricultural disaster early warning technology. It involves collecting agricultural meteorological time-series data by deploying an IoT sensor network; learning causal dependencies between sensor nodes from the data using a spatiotemporal causal discovery algorithm to construct a directed acyclic causal graph; constructing a dual-mode encoder containing a causal encoder and an association encoder, extracting features along causal paths and spatial adjacency paths respectively, and adaptively fusing them through a gating mechanism; inputting the fused representation into a decoder to output disaster prediction values ​​and confidence intervals; performing counterfactual reasoning based on the causal graph to trace key causal paths and simulate intervention effects, generating interpretable early warning information including warning level, causal explanation, and intervention suggestions. This invention improves the interpretability and cross-regional generalization ability of the model, provides actionable decision support, and can be widely applied in the field of agricultural meteorological disaster monitoring and early warning.

This invention relates to a method and system for analyzing the meteorological suitability of tobacco cultivation, specifically a method for evaluating and spatially predicting the climate suitability of tobacco cultivation based on multi-source data fusion. It addresses the problems of insufficient spatial resolution of meteorological data, static evaluation models, inadequate multi-source data fusion, separation of disaster early warning and suitability evaluation, and lack of intelligent service capabilities in existing technologies. This invention collects and fuses multi-source data, filters key meteorological elements, constructs fuzzy membership functions and spatial distribution models, calculates grid meteorological element values, and calculates a comprehensive suitability index. Based on this, it classifies the data into levels and generates a climate suitability zoning map. This invention is applicable to fields such as climate suitability evaluation for tobacco cultivation, crop planting zoning, and agricultural meteorological disaster risk assessment.

This invention discloses a dynamic prioritization method for distributed photovoltaic (PV) site inspections based on meteorological factors. The method includes: numbering each PV site and extracting basic information for each site; calculating the lightning strike density at each site and at different distances from the site, and constructing lightning strike density evaluation indicators for each site; statistically analyzing the frequency of each meteorological factor within its corresponding range, and constructing meteorological disaster evaluation indicators for wind speed, temperature, and precipitation at each site; calculating the weights of the evaluation indicators, establishing a meteorological disaster risk assessment calculation model, and providing a dynamic prioritization of distributed PV site inspections. This invention can dynamically assess the meteorological disaster risk at each distributed PV site, providing a priority ranking decision for daily inspections. It overcomes the current situation where existing power plants conduct daily inspections of each site individually, lacking specificity and timeliness in identifying potential hazards at different sites. Simultaneously, it provides a basis for differentiated prevention measures against meteorological disasters at different sites.

The invention discloses a loess plateau power grid facility coupling meteorological disaster risk dynamic assessment method and system. The method comprises the steps of obtaining static parameters, gridding meteorological forecast data and geographical geological data of primary equipment of a power grid in a target area; based on the static parameters, the gridded weather forecast data and the geographic and geological data, combining a pre-constructed risk assessment index model to obtain various risk indexes of the target equipment in the prediction time period; and determining the comprehensive risk level of the equipment by adopting a weighted fusion or most dangerous principle based on each risk index, and generating a risk distribution diagram and early warning information. The adaptive power grid equipment risk assessment method is constructed according to the meteorological geology characteristics of the loess plateau region, the problem of water and soil disability of a general model is solved, main meteorological disasters are comprehensively covered through five indexes, and the assessment result is more comprehensive and accurate; on the basis of weather forecast data, risk rolling assessment of several hours to several days in the future can be realized, passive response is changed into active defense, and the operation and maintenance efficiency is improved.

The application discloses a meteorological disaster early warning and calling method and system. The calling method generates early warning information by acquiring and processing meteorological monitoring data, matches the corresponding calling objects of the risk area and generates a calling object list, determines and dynamically adjusts the total time limit of calling according to the disaster grade, and generates a calling instruction. The calling instruction is executed to send the calling information and record the sending state, receive and analyze the feedback information to obtain the feedback result containing the positive feedback state set and other states. Based on the sending state and the feedback result, the retry object is screened, the retry calling instruction is generated and executed in a cycle until the preset closed-loop condition is met, and the calling closed-loop record is generated. The application also discloses a corresponding system for implementing the above method, which can effectively improve the accuracy and response efficiency of meteorological disaster early warning and calling, realize the whole-process traceable closed-loop management and control, adapt to the dynamic change characteristics of extreme meteorological disasters, and maximize the reduction of disaster risk.

The application provides a highway slope meteorological disaster early warning model training method, an early warning method and a system, positive and negative samples are respectively constructed based on historical disaster record and non-disaster record; based on sample slope position and collection time, time series data of multiple meteorological elements before disaster is extracted from meteorological grid point data through spatial interpolation matching to generate meteorological statistical characteristics; initial training sample set is constructed by fusing slope static attributes and meteorological statistical characteristics, adding disaster type and risk level labels; the initial model is pre-trained based on the sample set, the feature importance is evaluated through multi-index, and the feature subset is formed by screening; the features outside the subset are removed from the initial training sample set to form the target training sample set, and multiple candidate models are trained based on this, and the target disaster type recognition model and the target risk level recognition model are determined according to the evaluation index. Through multi-source feature fusion, automatic feature screening and multi-model optimization, the accuracy and reliability of the early warning are improved.

This application provides a geological disaster safety risk assessment method based on rainfall, landslides, and transmission tower deformation, relating to the field of geological disaster prediction. The method includes: collecting multi-source data in the slope area where the transmission tower is located; constructing a rainfall-induced soil moisture content response model and dynamically correcting the soil shear strength parameters; calculating the landslide stability safety factor based on the corrected soil shear strength parameters; constructing a comprehensive deformation index for the transmission tower; obtaining a rainfall risk index; and combining the landslide stability safety factor and the comprehensive deformation index of the transmission tower to construct a comprehensive risk assessment model, outputting the risk level and early warning signal. This application constructs a dynamically coupled risk assessment model of the three elements of "rainfall-slope-tower," realizing a comprehensive analysis from meteorological disaster to structural response.

This invention provides a method for calculating the optimal time for rolling up greenhouse curtains. The method includes the following steps: calculating the solar altitude angle h for a given region, date, and time; obtaining the temperature equilibrium time point inside the greenhouse during winter; calculating the solar altitude angle h of the temperature equilibrium point inside the greenhouse on different dates during winter; and calculating the optimal curtain-rolling time T for different dates in the given region during winter. The method provided by this invention has the advantage of being able to calculate the optimal curtain-rolling time for greenhouses on different dates and in different geographical locations during winter without relying on complex sensors inside the greenhouse, by integrating geographical information, astronomical parameters, and historical observation data. This improves the utilization of climate resources and effectively mitigates meteorological disasters in facility agriculture.

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

The application discloses a machine learning driven electric wire icing prediction method considering the influence of high and low air meteorological factors and terrain conditions, a medium and a program product, and relates to the cross technical field of power meteorological disaster prediction and intelligent calculation. The method faces the power transmission line corridor, obtains icing event records and ground observation, multi-pressure layer reanalysis or numerical prediction and digital elevation model data; a time window is set at the event time and spatial neighborhood rules are used to complete space-time registration; a multi-dimensional feature vector containing low-altitude meteorological factors, high-altitude meteorological factors and pressure layer difference / gradient derived features and micro-terrain parameters is constructed; the icing thickness is divided into light and heavy icing, and the no-icing sample is completed under the same season or meteorological neighborhood constraint, and the prediction model is trained and adjusted in combination with the uneven processing; the icing occurrence probability and no / light / heavy grade results are output, which are used for line icing early warning, dispatching and operation and maintenance decision-making, and improve the early warning reliability and interpretability.

This invention discloses a method for determining sensitive factors of airport resilience under meteorological disasters. First, the average resilience and average resilience sub-characteristics of all airports within the analysis area affected by disasters throughout the year are calculated. Then, the significant influencing factors on the average resilience and average resilience sub-characteristics of affected airports within the analysis area under all disaster conditions within the analysis year are identified. Next, the significant influencing factors on the resilience and sub-characteristics of each affected airport within the selected analysis area under a specific disaster condition are determined through calculation. Finally, the sensitive influencing factors on the resilience and sub-characteristics of affected airports within the analysis area under a specific disaster condition are calculated. This method can quickly and accurately determine the sensitive influencing factors on airport resilience within the analysis area under different meteorological disasters, thereby improving the airport's resistance to disasters and its post-disaster recovery capabilities, ultimately improving the airport's operational efficiency and functional level.

The application discloses a power distribution network network-configuration type energy storage planning and deployment method and system, and the method comprises the following steps: obtaining historical meteorological disaster data, power grid data and energy storage parameter data of a target area; based on the historical meteorological disaster data, power grid data and energy storage parameter data, a three-layer coupled random optimization model for optimizing energy storage configuration, system operation under disasters and fault repair processes is constructed; a multi-agent deep reinforcement learning algorithm is used to solve the three-layer coupled random optimization model, and an optimal network-configuration type energy storage deployment scheme and a collaborative recovery strategy are output. The method realizes fine optimization of the whole-chain resilience of the power grid in extreme disasters, and significantly improves the economy and robustness of the planning scheme.

This utility model relates to the technical field of meteorological early warning devices, specifically an agricultural meteorological disaster early warning device for extreme weather conditions. It includes a main pole with a support plate fixedly mounted on it. A rotating rod is mounted on the upper surface of the support plate, and several wind vanes are fixedly mounted on the rotating rod. An impact ball is positioned on one side of each wind vane. A water tank is fixedly mounted on the support plate, and a float is installed inside the water tank. The float is connected to a sign via a vertical rod. When wind blows, it causes the wind vanes to rotate, striking the impact ball and producing a striking sound. The faster the wind speed, the faster the frequency of the sound produced by the impact ball, thus alerting the public. When the water level in the water tank rises, it causes the float to rise, raising the sign and clearly displaying changes in rainfall, reminding the public to take appropriate measures.

This invention belongs to the field of meteorological disaster forecasting and discloses a typhoon formation prediction method based on causal perception diffusion network. By introducing multimodal physical variables to constrain the diffusion generation process and combining causal discovery mechanism to screen key physical driving factors, it realizes physical consistency spatiotemporal modeling of the complex process of cloud evolution. At the same time, an adaptive knowledge distillation framework is designed to overcome the problem of missing ERA5 data in real-time prediction, thereby realizing accurate prediction of the entire typhoon formation process.

This invention provides a method and system for screening multiple severe faults in a power system under meteorological disasters. The method includes acquiring data and preprocessing it to obtain effective data; calculating the line fault probability and line importance score of transmission lines based on the effective data; calculating a comprehensive line risk index based on the line fault probability and line importance score; establishing a set of severely faulty lines based on set faulty line screening rules and the comprehensive line risk index; for combined scenarios where at least two lines in the severely faulty line set are simultaneously faulty, calculating a scenario comprehensive risk index based on the line fault probability and line importance score of all lines in the combined scenario; and outputting the severe fault set after sorting the scenario comprehensive risk index and the line comprehensive risk index in descending order. This method and system effectively achieve dynamic and accurate screening of faulty lines under meteorological disasters.

This invention discloses a machine learning-based method and system for predicting the stress level of rapeseed under low light conditions, belonging to the field of agricultural meteorological disaster early warning technology. It constructs a training sample set by acquiring meteorological time-series data and agronomic features; builds a cumulative effect function of photosynthetically active radiation, and generates physical adversarial negative samples through constraint optimization; constructs a time-series knowledge graph and uses a time-series graph convolutional network to obtain graph embedding vectors; constructs a dual-tower contrastive prediction network to extract meteorological feature vectors and agronomic feature vectors that fuse agronomic static features with graph embedding vectors; uses a contrastive loss function to narrow down the meteorological and agronomic features of the same sample; constructs a physical constraint loss function to impose physical law penalties; and jointly optimizes and trains to obtain a prediction model, outputting the stress level. This invention, through physical-guided contrastive learning and a time-series knowledge graph, solves the problems of low prediction accuracy and poor model interpretability for small sample events, and improves cross-regional generalization ability.

The present application belongs to the technical field of power system operation and extreme weather disaster response, and discloses a cold wave evolution driven power supply and demand phased risk assessment method and related device; wherein, the power supply and demand phased risk assessment method comprises: obtaining the stage identification and division result of the whole life cycle of the cold wave; based on the stage identification and division result, constructing a new energy output model and a load response model; based on the new energy output model and the load response model, the risk assessment index is calculated in stages and compared with the corresponding preset threshold value, forming the power supply and demand phased risk assessment result. The present application is a new energy generation and load supply and demand balance two-way risk assessment scheme starting from the stage characteristics before, during and after the cold wave, which can run through the whole life cycle of the cold wave, can realize the dynamic assessment of simultaneously identifying the early stage consumption pressure and the late stage power shortage risk, and improves the accuracy and foresight of the power system operation risk assessment under the cold wave weather.

The present application relates to the technical field of fault prediction, in particular to a kind of meteorological disaster fault analysis method and system of electric power equipment, comprising the following steps: obtaining the real-time wind speed, wind direction and air pressure data acting on transmission line and insulator string, calculating the tension vector and spatial displacement of transmission line under wind load, generating the real-time swing coordinates of the line, and substituting the X-axis, Y-axis and Z-axis components of the real-time swing coordinates of the line into the spatial position analytical matrix.In the present application, the air breakdown voltage threshold under the current environment is calculated by combining real-time air pressure data and applying the Bashen law ionization formula, the grid operating phase voltage is compared with the dynamic physical threshold by difference, the multi-physical field coupling diagnosis logic is constructed by fusing meteorology, mechanics, electromagnetism and insulation physics, the real-time and accuracy of transmission line windage discharge fault diagnosis under complex environment such as strong wind are improved, and the limitation that single electrical quantity monitoring is difficult to represent environment-induced fault is overcome.

The application relates to the technical field of weather forecasting, and provides a prediction method and system, equipment and a medium for an extreme precipitation event based on a space-time attention mechanism, the method comprising the following steps: S1: acquiring meteorological background features; S2: classifying extreme weather according to the meteorological background features; S3: obtaining joint attention by adopting a CNN-LSTM and a joint attention mechanism according to the meteorological background features and a classification result; and S4: obtaining a predicted precipitation condition according to the joint attention, physical constraint embedding and adaptive threshold learning. The scheme fuses multi-scale space-time features, embeds physical constraints and has adaptive attention to extreme events, fills the technical blank of fine classification and prediction of extreme weather, and improves the precision of extreme precipitation prediction and the reliability of disaster early warning. The innovative model with the space-time attention mechanism as the core can solve the above problems, realizes efficient and adaptive intelligent modeling of the extreme precipitation event, and promotes the practical application progress of the meteorological disaster intelligent early warning system.

The application provides a meteorology-based extreme climate risk real-time monitoring system and relates to the technical field of meteorology.The method comprises a calibration module used for constructing an analysis loop based on the time evolution relationship of representative feature points, analyzing the contribution of element space variation and transport process by calculating the movement speed of meteorological elements in the feature influence domain relative to the regional background field, quantifying the dynamic effect of movement on the evolution of extreme climate, calculating meteorological data calibration quantity, dynamically calibrating the standardized meteorological data sequence, obtaining the calibrated meteorological element sequence, and generating an extreme climate index.The application realizes dynamic identification, quantitative evaluation and multi-level early warning of extreme climate events, and improves the real-time perception ability and emergency response efficiency of meteorological disaster risks.

This invention provides an intelligent early warning method and device for urban extreme weather disasters based on data-physical fusion, relating to the fields of meteorological disaster simulation and artificial intelligence decision-making technology. The method includes: simulating windstorms and floods in a target city to obtain numerical simulation feature vectors; processing historical contingency plan texts to obtain contingency plan text vectors; constructing a vector database containing extreme disasters in the target city; designing a retrieval-enhanced generative agent, a disaster assessment agent, a report summarization agent, and a meteorological disaster dialogue agent, and constructing a multi-agent loss reduction decision-making big language model based on an agent scheduling framework; and generating customized loss reduction decision schemes for specific disaster scenarios by combining the vector database with the multi-agent loss reduction decision-making big language model. This invention solves the problem of transforming meteorological forecast information into accurate decision-making, realizing the transformation from single meteorological parameter forecasting to scenario-based loss reduction decision support, and significantly improving the intelligence level of urban disaster early warning.

This invention discloses a method and system for responding to meteorological risks in airline ticketing, relating to the field of artificial intelligence technology. The method includes: acquiring multi-source meteorological disaster early warning data and flight schedule data, performing dynamic spatiotemporal alignment, and identifying whether the flight's take-off and landing points or key points along the flight route fall within the spatiotemporal impact range of the meteorological disaster early warning data; if so, extracting a cross-modal composite feature set based on the spatiotemporal alignment results, inputting the composite feature set into a target disaster level prediction model, using the target disaster level prediction model to predict the impact of the meteorological disaster on the flight, outputting the disaster impact level of the flight, and, based on the disaster impact level, calling a preset parameterized refund and change rule template to generate a refund and change strategy corresponding to the disaster impact level. This invention solves the technical problems in related technologies where airlines' refund and change rule processes are rigid and inefficient, increasing service costs and reducing passenger experience when responding to meteorological disasters.

This invention discloses a method and system for assessing global population exposure based on tropical cyclone physical wind fields and population spatial resampling, belonging to the field of meteorological disaster risk assessment. It includes: establishing a unified standard latitude and longitude grid as a spatial calculation framework; traversing all years and cyclone events within the target time period, transforming annual population raster data to a unified grid through area-weighted resampling; reconstructing instantaneous wind speeds of the grid step-by-step using the Holland model based on optimal cyclone path data; calculating nonlinear exposure response coefficients using the Sigmoid function based on wind speeds; calculating instantaneous population exposure and incrementally accumulating it to a cumulative matrix; and finally synthesizing and outputting a multidimensional risk spatial distribution atlas. This invention solves the problems of coarse characterization of exposure relationships, distortion in spatial scale conversion, and low efficiency in long-term computation in existing technologies, achieving high-precision and high-efficiency global population exposure risk assessment.

This invention discloses a 24-hour forecasting method for heat waves triggered by approaching tropical cyclones, belonging to the field of meteorological disaster forecasting technology. The method first delineates the tropical cyclone approach area window and defines the approach sample time; then, it integrates tropical cyclone path, meteorological, and topographic data to construct a candidate factor pool for physical classification; next, it identifies regional heat wave events, generating 24-hour forecast labels and historical sample sets; subsequently, through grouped cross-validation and comprehensive score evaluation, it adaptively selects the optimal factor combination and trains the classification model; finally, it combines multi-objective optimization to determine the warning threshold and outputs a heat wave risk warning. This method solves the problems of weak scenario specificity and poor generalization performance of existing models, achieving accurate forecasting of heat waves under tropical cyclone approach scenarios, and providing reliable technical support for early prevention of disaster chains.