2065 results about "Weather data" patented technology

The invention belongs to the technical field of intelligent health monitoring, and discloses a retired old people home safety monitoring system based on Internet of Things, which comprises the following steps: deploying multiple types of sensors for the home environment of old people to carry out all-weather data acquisition and analysis to obtain behavior data with activity feature tags; performing correlation analysis to obtain a personalized security risk assessment model; performing real-time monitoring and mode recognition to obtain a behavior track analysis report; performing security level division and intelligent configuration to obtain a virtual security guard area and a self-adaptive reminding rule; performing grade classification processing and early warning signal generation to obtain graded early warning information and a response authority strategy; performing integration analysis to obtain a dynamic intervention strategy, and updating the personalized security risk assessment model according to the dynamic intervention strategy to obtain a target security monitoring model; blind spots and defects of traditional manual monitoring are effectively overcome, and a safe home environment is provided for the old.

The embodiment of the invention discloses a method for determining wind power in different weathers. The method comprises the following steps: collecting historical weather data and carrying out clustering classification on the historical weather data to obtain wind profile index data, and carrying out data enhancement on the wind profile index data to obtain a first data set; determining a plurality of wind profile indexes according to the first data set, and determining a corresponding wind profile index under an extreme weather condition according to a nonlinear relation model of the plurality of wind profile indexes and meteorological parameters; determining a dynamic prediction model of the wind profile index according to the corresponding wind profile index under the extreme weather condition by using a bidirectional long-short-term memory network; collecting ground wind speed data in real time, and determining the wind speed of the hub height of the wind turbine generator according to the dynamic prediction model of the wind profile index; determining power output curves at different wind speeds according to the corresponding relation between the historical ground wind speed data and the historical wind turbine generator power output data; and determining the wind power corresponding to the wind speed of the hub height of the wind turbine generator according to the power output curve.

The invention discloses a distributed new energy storage optimal configuration method and system for a power distribution network, and the method comprises the following steps: building a load prediction model through employing a probability discretization method according to historical power utilization data and real-time weather data; according to load demand data output by the load prediction model, a multi-target optimization model is constructed by taking maximization of distributed new energy power generation utilization rate, minimization of distributed new energy storage energy loss and minimization of operation cost of energy storage and scheduling as optimization targets; according to the multi-objective optimization model, adjusting weight distribution of the three optimization objectives, gradually optimizing power distribution between new energy power generation and energy storage, and generating an optimal new energy complementation strategy; charging and discharging are adjusted according to the optimal new energy complementation strategy, the health state, the battery temperature, the number of charging and discharging cycles, the environment temperature and the load demand change of energy storage equipment are collected in real time, and new energy power generation and energy storage configuration is optimized by dynamically comparing multiple indexes under different energy storage configurations in combination with power grid frequency fluctuation.

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention discloses a multi-source multi-temporal-spatial feature fusion short and temporary rainfall prediction method (M4Cester), which realizes layered temporal-spatial feature extraction through a multi-space multi-time aggregator (MMA), and comprises a multi-scale patch embedding (MSPE) module, a cross-scale perception optimization (CPR) module and a multi-time self-attention (MTS) module. The first and second spatial features are respectively used for capturing local-global spatial features, dynamically balancing cross-scale information and mining a time sequence dependency relationship; meanwhile, a bidirectional bridging fusion module (MSFM) is designed, bidirectional alignment and enhancement of radar and satellite features are realized by using a cross attention mechanism, and modal difference is relieved through residual connection. Experiments on a weather data set in the Yangtze River Delta region show that the key success index (CSI) reaches 0.267 and the HSS reaches 0.372 in heavy rainfall (greater than or equal to 50dBZ) prediction by the method, which are obviously improved compared with the existing advanced model, particularly, the limitation of single-source data is effectively overcome in the prediction of a convection initiation (CI) event, and a high-precision solution is provided for short and temporary rainfall prediction.

The invention discloses a power load prediction method and device based on multi-factor influence, and particularly relates to the technical field of power load prediction. Historical weather data and power load data are collected, preprocessing and feature extraction are carried out, temperature change deviation features and power load lag features are extracted and input into a prediction model for training, the accuracy of a prediction result is evaluated, and when the prediction accuracy is low, the prediction result is subjected to prediction. The abnormal degree is further analyzed and predicted, and the power dispatching strategy is adjusted in time, so that the violent fluctuation of the load demand is dealt with, the stable operation of the power grid is ensured, the accuracy of power load prediction is effectively improved, and the risk of overload or collapse of the power grid is reduced especially in the case of extreme weather.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses a photovoltaic power ultra-short-term prediction method and system based on itransfomer, and relates to the technical field of photovoltaic power generation. A traditional prediction method is relatively limited in precision under the conditions of rapid cloud layer change and abnormal local weather. The method comprises the steps of weather data collection and processing, feature construction based on weather data, iTransform model construction and training, and photovoltaic power ultra-short-term prediction. According to the technical scheme, the multi-dimensional features are constructed by processing the weather data, and efficient feature extraction and photovoltaic power prediction are realized in combination with the multi-head attention mechanism and nonlinear mapping of the iTransform model. Model training is combined with actual measurement data and weather forecast correction, accuracy and reliability of input data are ensured, prediction precision is improved, high instantaneity and adaptability are achieved, high accuracy, instantaneity and reliability are achieved under the complex weather processing condition, powerful support is provided for power grid dispatching, and especially in the aspect of ultra-short-term photovoltaic power prediction, the method has a good application prospect. And the safety and reliability of a power system can be effectively improved.

The invention belongs to the technical field of artificial intelligence and big data analysis, and particularly relates to a scenic spot passenger flow trend prediction analysis method based on an AI big language model. According to the method, big data and artificial intelligence technologies are utilized, LBS data, scenic spot data, holiday and festival information, third-party maps, weather data and other multi-source data are comprehensively applied, a comprehensive multi-dimensional knowledge base is constructed, and meanwhile, the knowledge search and generation capability is improved by utilizing an RAG technology; through model pre-training and fine tuning, the universal model capability is developed, and a multi-agent system is further constructed; based on the natural language processing capability of the AI large language model, the LLM can intelligently analyze the text input of the scenic region administrator, drive the cooperative work among multiple agents, and achieve the intelligent prediction and analysis of the scenic region passenger flow trend. The method can more accurately predict the passenger flow trend of the scenic area.

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention provides a deep learning traffic flow prediction method based on meteorological information fusion, and the method comprises the following steps: 1, carrying out the preprocessing of traffic data and weather data; step 2, establishing a time alignment module to solve the time dislocation problem of traffic data and weather data; step 3, carrying out embedded coding on the traffic data and the weather data; step 4, establishing an enhanced space-time convolutional network, extracting space-time features from the traffic data through the enhanced space-time convolutional network, and extracting time sequence features from the meteorological data through the enhanced space-time convolutional network; 5, establishing a comparative learning module, and performing feature fusion and comparative learning on the spatio-temporal features and the time sequence features; and step 6, generating a predicted value of the traffic flow. According to the method, the defects of an existing traffic flow prediction method in the aspects of multi-source data integration, long-time dependence capture, feature fusion and the like are effectively overcome, and a new technical approach is provided for improving the precision of traffic flow prediction and the universality of practical application.

The invention discloses a weather detection method and system based on a weather rainfall radar and a storage medium. The system comprises the weather rainfall radar, a weather data centralized control management center and a radar time sequence chart neural network detection algorithm arranged in the weather data centralized control management center. According to the method, different cloud layer characteristic weathers are collected through a meteorological rain detection radar, and obtained electromagnetic wave scattering parameters of the different cloud layer characteristic weathers are input into a radar time sequence chart neural network detection algorithm for analysis and calculation, so that polarization phase differences corresponding to the different cloud layer characteristic weathers are obtained to detect the height and vertical distribution of cloud layers; and when the height and the vertical distribution of the cloud layer exceed boundary threshold values, sending out risk early warning information of different weather types, and automatically detecting spectral widths of different cloud layer characteristic weather and corresponding polarization phase differences. And the weather detection precision and efficiency are improved.

The invention provides a smart energy comprehensive management method and system for dual-carbon supervision, and belongs to the technical field of energy management. Energy consumption abnormal coefficients and energy consumption abnormal time periods of the air conditioning system in different time periods are determined, the number of time periods matched with different time period groups in a preset time period is determined on the basis of the prediction result of the weather data, and the distribution data of the energy consumption abnormal time periods in the different time period groups are combined; when it is determined that the energy consumption abnormal probability of the air conditioner system meets the requirement, a matching time period group is determined through the number of matching time periods, and the change situation of energy consumption data of the air conditioner system under different control parameters in the matching time period group is determined; and the dual-carbon supervision strategy of the air conditioning system is determined by utilizing the change condition of the energy consumption data under different control parameters and the energy consumption abnormal probability, so that the reliability of supervision processing of the energy consumption data is improved.

The invention discloses an extreme climate self-adaptive flight control system for an unmanned aerial vehicle in a complex terrain, and relates to the technical field of unmanned aerial vehicle self-adaptive flight control. The extreme climate adaptive flight control system for the unmanned aerial vehicle in the complex terrain comprises a flight data acquisition and preprocessing module, a climate prediction and risk assessment module, an optimal path and task sequence adjustment module, a real-time flight control and path optimization module and a safety emergency response and recording module. Wherein the flight data acquisition and preprocessing module is used for acquiring flight data; the climate prediction and risk assessment module is used for constructing a neural network model to predict climate; the optimal path and task sequence adjusting module is used for performing path and task security evaluation; the real-time flight control and path optimization module is used for converting a path and a task sequence into an aircraft instruction; and the safety emergency response and recording module is used for executing and recording the emergency strategy. The problem that the unmanned aerial vehicle is difficult to adjust flight in real time due to continuous change of climate states in the prior art is solved.

The invention discloses a risk level prediction method and device for a power distribution network, and electronic equipment, and relates to the technical field of power distribution network operation and maintenance or other related fields, the method comprises the following steps: obtaining current operation data of a target power distribution network, the current operation data being acquired by a sensor network in real time, comprising line equipment operation data of the target power distribution network, geographical environment data of the position where the target power distribution network is located and disaster weather data; all the current operation data are input to a target model, the risk prediction level and early warning information are output, the target model is a decision tree model pre-established based on a random forest algorithm and is used for extracting key features from the line equipment operation data, the geographical environment data and the disaster weather data, and therefore risk level study and judgment are carried out; and early warning information is generated according to different risk prediction levels. According to the invention, the technical problem of low power distribution network risk prediction accuracy in extreme weather in the prior art is solved.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention provides an expressway variable speed limit control method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: collecting traffic flow data, road condition data and weather data in real time; preprocessing the collected data, and predicting a traffic jam trend and a safety risk index through a machine learning algorithm; determining weight factors of traffic flow, safety risk and congestion prediction through an analytic hierarchy process; dividing a road section speed range by using a clustering algorithm, and calculating a road section unit speed; constructing a target function which aims at maximizing the unit speed of the road section and minimizing the safety risk and the congestion time; and based on the target function, solving an optimal speed limit value by adopting a particle swarm optimization algorithm, and dynamically issuing speed limit information in different lanes. The highway traffic big data is analyzed in real time, the speed limiting strategy is intelligently formulated, the speed limiting strategy is published to the driver in real time through multiple channels, and the passing efficiency and safety of the highway are effectively improved.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention provides a method for reducing power transmission and distribution loss of a power grid, which belongs to the technical field of power transmission and distribution and comprises the steps of collecting and analyzing historical power consumption data in a region, extracting power time characteristics, acquiring historical weather data related to power consumption requirements and extracting power weather characteristics. Establishing a load prediction model based on the electric quantity time characteristics and the electric quantity weather characteristics; identifying distributed energy resources in the area, classifying and positioning the distributed energy resources, generating a resource distribution heat map, establishing an energy database, and developing a virtual power plant; the virtual power plant outputs a power grid electric energy transmission and distribution strategy according to a predicted load curve output by the load prediction model, performs defect analysis on the power grid electric energy transmission and distribution strategy, deploys an energy storage system and formulates an energy storage scheduling strategy; a final scheduling strategy is obtained by combining an energy storage scheduling strategy and a power grid electric energy transmission and distribution strategy, so that the stability and flexibility of power supply are improved, the utilization rate of renewable energy sources is improved, resource allocation is optimized, and diversified requirements are met.

A laser-based system and method for cleaning solar panels that have accumulate dirt or grime. The laser-based system employs methods for identifying optimum timing when the laser-based cleaning is to be performed. Otherwise, a selective cleaning will be performed on the solar panel. The method receives a combination of weather data, power output data, and solar image data to pin-point dust contours from the solar panel and removes the dust hotspots through pointed laser cleaning on accounting the tradeoff between the power consumed the by the laser and power gained from the cleaned solar panel. To achieve pin-pointed laser beam cleaning, the system adjusts the focal length of the laser beam. The method pin-points the removal of dust contours responsible for reduction in power output by analyzing weather, power output and time series images and optimally determines the region of interest to remove the dust hotspots through pointed laser cleaning.

The invention provides a flexible preparation system and method for sustainable aviation fuel, and the system comprises a green power supply module which is used for outputting fluctuating green electric energy; the process execution device is connected with the green power supply module and comprises five process modules, namely an air carbon capture module, a co-electrolysis module, a Fischer-Tropsch synthesis module, a hydrogen production module and a hydrogenation refining module; the data acquisition module is used for acquiring the temperature, the pressure, the liquid level, the raw material input flow and / or the product output flow of each process module in the process execution device; and the decision control module is used for predicting the future power supply condition of the green electricity based on the historical weather data of the green electricity location, adjusting the distribution of the power in each process module according to the future power supply condition, and adjusting the temperature, pressure and / or raw material input flow in each process module. According to the invention, the cost, the reliability, the operation and maintenance performance, the product quality and the green power utilization rate can be substantially improved at the same time, and the flexible and stable operation of the system under the green power condition is realized.

The invention discloses an acid mine wasteland ecological restoration plant dynamic optimization configuration method, and relates to the field of ecological restoration, and the method comprises the following steps: 1, collecting soil attribute data in a predetermined range of an acid mine wasteland in a dotted manner, and synchronously obtaining growth attribute data of to-be-selected plants and periodic weather data of a target area; step 2, extracting a deterioration feature set having a deterioration effect on plant growth based on the soil attribute data, and analyzing a bioremediation feature set capable of inhibiting the deterioration feature in the plant attribute data to generate a candidate plant set; 3, constructing a dynamic environment simulation model based on a deep learning algorithm, wherein input dimensions comprise weather data time sequence features, soil deterioration feature vectors and a plant bioremediation feature matrix; the method can accurately predict the restoration effects of plants under different climate and soil conditions, simulate the effects of different intervention measures in real time, output the optimal intervention scheme, and ensure the balance between the resource utilization efficiency and the restoration effects.

The invention provides an unmanned aerial vehicle group path planning method and system based on environment analysis, and relates to the technical field of unmanned aerial vehicle path planning. Unmanned aerial vehicle operation data are collected, unmanned aerial vehicle operation abnormity is analyzed based on the unmanned aerial vehicle operation data, an unmanned aerial vehicle group flight path is collected, and the flight distance in the unmanned aerial vehicle flight process is obtained; the method comprises the steps of screening out a flight risk interval based on a flight interval in a flight process of an unmanned aerial vehicle, collecting weather data, analyzing flight abnormity generated by the flight risk interval under the weather influence based on the weather data, and obtaining unmanned aerial vehicle group density in a set range when the unmanned aerial vehicle generates the flight abnormity based on an unmanned aerial vehicle group flight path. The flight influence of flight abnormity on the unmanned aerial vehicle group is analyzed based on the unmanned aerial vehicle group density, the optimal flight path of the unmanned aerial vehicle group is screened out based on the flight influence of the flight abnormity on the unmanned aerial vehicle group, potential flight risks such as out-of-control, course deviation and even crash of the unmanned aerial vehicles can be predicted, and therefore the flight safety is improved.

The invention belongs to the technical field of power system control, and discloses an intelligent adjusting system and method for a transformer. The method comprises the following steps: inputting transformer operation data and weather data collected in real time into a pre-trained load prediction model to obtain predicted load data; inputting the acquired physical parameters and operation data and preset calibration parameters into a pre-constructed digital twinborn model to obtain a cooling capacity index; calculating the total heat source power of the copper loss and the iron loss according to the predicted load data and the electrical parameters of the transformer collected in real time, and dynamically quantifying the balance state of the total heat source power and the cooling capacity index through a matching degree evaluation function in combination with the cooling capacity index to obtain a matching degree evaluation index; and the problem of dynamic unbalance between the heat dissipation capability of the transformer and the load demand in an extreme environment is systematically solved.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention discloses a short-term power load prediction method in combination with weather parameters. The method comprises the following steps: acquiring power load data and weather parameters of corresponding time; preprocessing the data; using an EEMD method to decompose the power load data into a high-frequency nonlinear component and a low-frequency linear component; in combination with weather parameters, training a CNN-LSTM model which introduces an attention mechanism and is based on multi-scale feature extraction, and outputting a high-frequency nonlinear component predicted value; for the low-frequency linear component, a time sequence model SARIMA is adopted, and a low-frequency linear component predicted value is output; and adding the high-frequency nonlinear component predicted value and the low-frequency linear component predicted value to obtain a complete load prediction result, carrying out error analysis, and carrying out iterative optimization on the model. According to the method, weather data are introduced, high-frequency nonlinear components are extracted through sequence decomposition, weather parameters are used as input to construct a deep learning prediction model, and the prediction capability of complex load fluctuation and the precision and reliability of short-term load prediction are improved.

The invention discloses a typhoon long-distance rainstorm rapid identification method based on an AI meteorological model, a storage medium and a computer program product, and the method comprises the steps: obtaining historical weather data and / or forecast weather data, selecting an AI weather forecast model which is based on a deep learning technology and carries out the training through a large amount of historical weather data, representative and typical rainfall examples are screened, a control experiment and a sensitivity experiment are designed, and typhoon core vortexes are removed from an initial field. By comparing the difference between the results of the control experiment and the sensitivity experiment, the influence of the typhoon on the long-distance rainfall is quantified based on the whole-layer water vapor divergence change and rainfall change analysis, and the long-distance rainstorm influence range and intensity of the typhoon are identified and determined. According to the method, the precision of medium-term weather forecast and extreme weather event recognition is remarkably improved, efficient calculation and wide practical application value are achieved, a scientific basis is provided for disaster early warning and disaster reduction management, and a new method is provided for meteorological scientific research and teaching.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.