257 results about "Storm" patented technology

The invention discloses a power distribution network operation safety risk assessment method considering typhoon and rainstorm composite disasters, and the method comprises the following steps: generating different typhoon paths as main scenes based on the typhoon and rainstorm disaster forecast data, and endowing each main scene with a weight according to the occurrence probability; the influence of microtopography and rainfall intensity on the wind speed V (r) in the main scene is considered, the corrected wind speed is calculated, the total failure probability of the distribution line between the node i and the node j is calculated according to the fault probability of the distribution line between the node i and the node j and the failure probability caused by foreign matter hanging, and the power distribution equipment failure probability is calculated according to the water accumulation amount of the power distribution network area; simulating and generating N fault scenes s through a Monte Carlo method to form a fault scene set S, and endowing each fault scene with a weight; and taking the minimum expected total load loss amount in all fault scenes as an objective function, constructing a risk assessment model, and solving the risk assessment model in combination with constraint conditions.

The invention discloses a typhoon-rainstorm flood composite disaster monitoring and evaluating method and system, and belongs to the field of intelligent disaster monitoring, and the method comprises the following steps: building a disaster recognition model through a machine learning algorithm based on multi-factor data, and obtaining a composite disaster judgment result; based on the composite disaster judgment result, constructing a disaster evaluation model for the area forming the disaster; dynamically updating parameters of the disaster assessment model based on real-time monitoring data to obtain a real-time composite disaster assessment result; based on the real-time composite disaster assessment result, dynamically adjusting loss probability distribution to obtain a comprehensive disaster damage assessment result; and carrying out disaster situation space-time evolution based on the comprehensive disaster damage assessment result to obtain a disaster situation development trend.

The invention discloses a typhoon wave height prediction method based on a deep learning MOE-Transform model, and the method comprises the following specific steps: collecting historical typhoon data of a research region, and constructing a virtual typhoon data set in combination with a central pressure difference formula; processing the historical and virtual typhoon data sets through a Holland typhoon empirical model and a storm growth relation to obtain a wind field, an air pressure field and a significant wave height field; correcting the field data by using an error model on the basis of the ERA5 data set to form a typhoon space-time fusion database containing meteorological data, the significant wave height field and typhoon data; and training and testing the MOE-Transform model to obtain a typhoon wave significant wave height prediction model for typhoon wave height prediction. According to the method, the multi-task adaptability and generalization ability are improved, and the precision and timeliness of typhoon wave height prediction are improved.

The invention relates to a ship operation energy consumption prediction method considering an actual storm condition. The method comprises the following steps: fusion and data processing of multi-source navigation environment data; the still water resistance and the wave increase resistance are calculated through a program, and the navigational speed-power relation of the ship under the actual wind wave condition is calculated; predicting the power-oil consumption relation of the ship according to the route segments: based on the navigational speed-power relation of the ship under the actual storm condition, establishing a prediction agent model of the power-oil consumption relation of the ship according to the historical oil consumption data of the ship; and ship operation energy consumption prediction: combining the annual route segment information of the ship, predicting the agent model based on the branch route segment ship power-oil consumption relation, thereby obtaining the oil consumption of each route segment of the ship in the whole year, and calculating the annual operation energy consumption of the ship. According to the method, the prediction precision is remarkably improved, the generalization ability and the real-time response speed of the model are enhanced, and powerful support is provided for energy conservation and emission reduction of ships.

The invention discloses a radar echo extrapolation method based on a space-time attention mechanism, and the method comprises the steps: firstly obtaining the radar echo data of a target region, carrying out the preprocessing of the obtained radar echo data, carrying out the sliding grouping of the preprocessed radar echo data, carrying out the data augmentation, and obtaining a radar echo sequence data set, dividing the data into a training set and a test set; a radar echo prediction network model based on a SimVP architecture is constructed, training and testing are carried out, and a multi-target loss function is adopted to supervise model training; and a future radar echo image is obtained through real-time prediction. Through the method, the dynamic evolution of complex weather phenomena such as storm can be more accurately captured, the precision and interpretability of short-time approaching weather forecast can be remarkably improved, more reliable technical support is provided for timely and accurate early warning of meteorological disasters, and the method has important practical application value.

The invention discloses a hail growth trajectory simulation method. The method comprises the following steps: calculating a motion trajectory of hail; according to the environment field and the cloud micro physical field, the processes of collision and supercooling of raindrops, desublimation, sublimation, melting and the like of each hail and the process that unfrozen supercooled water on the surface of the hail immerses into hail gaps and falls off from the surface of the hail are calculated in detail, so that parameter changes of the particle size, the mass and the density of the hail are forecasted; according to the hail growth simulation device, the influence of different environments and different parent storms on the hail diameter can be reflected, the influence is kept consistent with actual observation data, and the accuracy of hail growth simulation is reflected.

The invention provides a substation micrometeorological monitoring system and an early warning method. A monitoring device is provided with a six-element meteorological sensor, a lightning sensor, a data acquisition preprocessing module, an edge calculation module, a communication module, a power supply module and an anti-surge module. Local micro-meteorological data of a transformer substation are comprehensively acquired through a six-element meteorological sensor and a lightning sensor, and the meteorological data of the transformer substation are analyzed, processed and protected through a data acquisition preprocessing module, an edge calculation module, a communication module, a power supply module and an anti-surge module; the early warning system realizes comprehensive meteorological monitoring and lightning early warning of the transformer substation through five steps of data acquisition, data preprocessing, feature extraction, data fusion and early warning triggering. According to the substation micro-meteorological monitoring device and the early warning system, traditional six-element meteorological monitoring is expanded, extreme weather such as storm, rainstorm and thunder is warned in real time, the disaster prevention capability of the substation is improved, real-time linkage management is achieved, and the safe operation of the substation is improved.

The invention discloses a wave height short-term prediction system and method based on variational mode decomposition and wind and wave time correlation, and belongs to the technical field of ocean engineering.The method comprises the steps that firstly, original storm time series data are decomposed into IMF components with different center frequencies through variational mode decomposition; secondly, performing data cleaning on the decomposed IMF component based on a Pearson's correlation coefficient; then combining two wind speed IMF components with the strongest correlation with the wave height IMF component based on correlation analysis to form a wind speed wave height component combination, and determining an input time length through an autocorrelation coefficient ACF of wave height data and a cross correlation coefficient CCF of the wind speed IMF components and the wave height IMF components; and finally, according to the formed wind speed wave height component combination and the determined input time length, carrying out short-term prediction on the wave height based on a long short-term memory neural network. According to the system and the method provided by the invention, the influence of noise on prediction is reduced, and the prediction precision is improved while the prediction efficiency is improved.

The invention relates to a storm water filtration system comprising: (i) a storm water drain pit (12); (ii) a filtering well (1) for filtering storm water; (iii) a storm water collection system (7) for storing filtered storm water: wherein the filtering well comprises: an inlet (2) for storm water to enter the filtering well; wherein the inlet (2) is in fluid communication with the storm water drain pit; an outlet (3) for filtered storm water to exit the filtering well; wherein the outlet (3) is in fluid communication with the storm water collection system (7); a filter (4) comprising man-made vitreous fibres (MMVF) for removing particles from the storm water, wherein the filter (4) divides the filtering well (1) into an inlet chamber (5) and an outlet chamber (6) such that storm water enters via the inlet (2) into the inlet chamber (5), passes through the filter (4) into the outlet chamber (6) and exits via the outlet (3). The invention also relates to a method of filtering and storing storm water using a storm water filtration system as described herein and a method of installing a storm water filtration system as described herein.

The invention discloses a future maximum wind speed prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring actual measurement data of at least one preset anemometer tower in a first historical period and related data of meteorological reanalysis data in a second historical period; according to a data rule of related data of meteorological reanalysis data in a second historical period and actually measured data of the first historical period, a plurality of speculated maximum wind speeds of the at least one preset anemometer tower in the second historical period are speculated through a matrix time sequence method; and analyzing the predicted maximum wind speed in a third time period in the future through any one or more of a national standard method, an independent storm method and an EWTSII method. According to the technical scheme, the data sample of the future maximum wind speed of at least one anemometer tower is obtained based on the correlation between the long-term ERA5 meteorological reanalysis data and the short-term measured data, and the future predicted maximum wind speed is speculated.

The present invention discloses a method for analyzing the spatio-temporal variation of the earthquake geomagnetic field for short-term and impending earthquake monitoring, which is as follows. First, the geomagnetic stations within a given range of longitude and latitude are divided into multiple rectangular grids according to longitude and latitude. The multi-component data of each station within the range of longitude and latitude on the detection day and the previous 60 days are preprocessed into multiple segments of standard time series. Then, the daily absolute mean error of each component prediction sequence is taken, and the difference sequence is subjected to anomaly detection through a sliding interquartile range detection algorithm to obtain the daily anomaly value of each component. Then, according to the daily geomagnetic disturbance index, the anomalies caused by magnetic storms are removed. Finally, the daily anomaly index matrix of each station is generated according to the grid size, and the sum of the anomaly index matrices of all stations on the detection day and the previous 29 days is obtained to get the final regional daily anomaly index matrix, and the range of anomalous longitude and latitude is obtained. This method monitors the anomalous changes of the geomagnetic field within the region and provides a reference range for the geographical locations where earthquakes may occur.

The invention discloses a radar and ground rainfall fused hydrodynamic urban flooding risk early warning method. The method comprises the steps of integrating rainfall station monitoring data, collecting radar echo map data and integrating rainfall forecasting grids; rainfall fusion interpolation is carried out based on actually measured rainfall of a rainfall station and a radar echo map; extracting a real-time rainfall grid sequence; extracting a forecast rainfall grid sequence; calculating surface net rain after rainfall deduction evaporation and soil absorption before coupling rainfall input of the two-dimensional rainfall boundary; constructing a two-dimensional rainfall boundary data file and a one-dimensional river channel upstream boundary data file of the HEC-RAS model; according to HEC-RAS model input and control parameters, the model is driven to calculate the maximum water depth and flooding duration; performing two-dimensional flooding risk assessment according to the flooding risk grade index; performing riverway flood risk assessment according to the riverway design flood and the flood control characteristic information; the problems of simulation and early warning application of the urban'rainstorm-waterlogging 'risk are solved.

The invention discloses a medium-low latitude ionosphere space-time prediction method and system during a strong magnetic storm, and the method comprises the steps: fusing ground-based GNSS and air-based COSMIC occultation ionosphere observation data, employing a multi-mode space-time sequence ionosphere model, integrating the advantages of a time convolution neural network and a Transform model through a cross attention mechanism, constructing a dual-space-time-flow TransTCN-XA prediction model, and carrying out the prediction of the medium-low latitude ionosphere through a time convolution neural network. And the spatial-temporal change of the medium-low latitude ionosphere during the strong magnetic storm period can be effectively captured. By adopting the technical scheme of the invention, higher resolution and more accurate ionosphere forecasting can be provided for the fields of space weather forecasting and navigation positioning, so that the accuracy and reliability of forecasting are remarkably improved.

The invention relates to a control method of a multi-parameter oblique single-axis photovoltaic tracking device and the oblique single-axis photovoltaic tracking device. The control method comprises the steps that S1, the current illumination intensity, the current environment temperature, the current environment wind speed and the current photovoltaic output power fluctuation rate are acquired; and S2, according to the current illumination intensity, the current environment temperature, the current environment wind speed and / or the current photovoltaic output power fluctuation ratio, a target control scheme of the inclined single-axis photovoltaic tracking device is determined, and the target control scheme comprises a conventional weather control scheme and an extreme weather control scheme. According to the invention, the inclination angle of the inclined single-axis photovoltaic tracking device is cooperatively regulated and controlled through multiple parameters, the inclination states of the photovoltaic panel in different weathers are dynamically switched, the adaptability to extreme environments such as high temperature, strong wind, rainstorm and snow is enhanced, the damage of the extreme weather to the photovoltaic panel is effectively resisted, and the service life of the photovoltaic panel is prolonged. Therefore, the problem that the photovoltaic panel is possibly damaged in extreme environments such as high temperature, strong wind, rainstorm and snow is avoided.

The invention discloses a civil aviation medium or heavy rainfall intensity intelligent identification method based on a phased array radar, and relates to the technical field of civil aviation meteorological monitoring and alarm, and the method comprises the following steps: S100, obtaining historical data of a phased array radar storm identification characteristic quantity, and corresponding automatic station rainfall historical data; s200, analyzing the distribution characteristics and the characteristic distinction degree of the storm identification characteristic quantity under different precipitation amounts, and taking the distribution characteristics and the characteristic distinction degree as the characteristic basis of a fuzzy logic algorithm; s300, analyzing the relationship between the precipitation of the automatic station and the storm identification characteristic quantity, and establishing a fuzzy logic model through a fuzzy logic algorithm; and S400, identifying the precipitation amount in real time by using a fuzzy logic model, and calculating the precipitation intensity. The method has the advantages that intelligent recognition of heavy rain storms, moderate rain storms and light rain storms is achieved by introducing the storm recognition characteristic quantity, the recognition effect of the algorithm is comprehensively evaluated through a quantitative test method, and a reliable basis is provided for follow-up optimization of the algorithm.

The invention discloses an ionosphere TEC forecasting method, system, medium and equipment fused with a geomagnetic field, and belongs to the field of ionosphere TEC forecasting, the method comprises the following steps: obtaining current ionosphere TEC grid data, solar activity index data and global geomagnetic field observation data, and constructing to obtain multi-channel fusion data; based on a preset ConvLSTM model, processing the multi-channel fusion data, and outputting an ionized layer TEC forecast result; wherein the ConvLSTM model is obtained by training an input sequence in historical multi-channel fusion data and a corresponding target sequence; the input sequence is composed of multi-channel fusion data of a plurality of consecutive time steps, and the target sequence is composed of ionized layer TEC grid data immediately after the input sequence. By implementing the method, the forecasting precision of the ionized layer TEC under complex space weather conditions such as magnetic storm can be improved.

The invention provides a hail identification and early warning method based on a storm space-time channel, and the method comprises the steps: constructing a complete system from the tracking of a storm to the early warning of a disaster-bearing body, storing the information of a storm carrier in all directions through the space-time channel, and precisely identifying hail through multi-feature fusion and an SVM algorithm. According to the method, radar data inversion and a semi-Lagrange extrapolation method are used for realizing hail extrapolation forecasting, the hail forecasting and disaster-bearing body data are deeply fused, accurate early warning is provided for different disaster-bearing bodies, and the loss caused by hail disasters is reduced to the greatest extent. According to the invention, the historical motion trail and the coverage range of the storm carrier can be stored in all directions, the states of the storm at different moments can be known by analyzing data in the space-time channel, and the evolution trend of the storm on the time axis can be clearly seen; through combination of multi-source data, the accuracy and reliability of hail early warning are remarkably improved, hail falling area forecast and disaster-bearing body data covering population, building and agricultural distribution are subjected to overlay analysis, and the risk is accurately evaluated.

The invention discloses a wind power generation output uncertainty modeling method based on a typhoon and rainstorm coupling mechanism. The method comprises the following steps: S1, constructing a first model and a second model; the first model comprises a typhoon wind speed model based on dynamic feedback iteration; the second model comprises a rainstorm rainfall rate model considering typhoon center air pressure change; s2, constructing a third model according to the first model and the second model in combination with an equivalent theory; the third model comprises a wind-driven rain equivalent wind speed model based on an equivalent theory; s3, constructing a fourth model according to the third model in combination with the microtopographic factors; the fourth model comprises a wind speed correction model; s4, constructing a wind power generation output uncertainty model according to the fourth model in combination with the first curve; the first curve comprises a wind speed and power characteristic curve of the wind power generation equipment. According to the method, the influence of extreme weather on the wind power equipment can be accurately predicted and dealt with, and a solid foundation is laid for safe and stable operation of a power distribution system under typhoon rainstorm.

The invention discloses a rainstorm flood risk early warning method and system based on machine learning and multi-source data fusion, and belongs to the technical field of flood prediction.The method comprises the steps that multi-source sample data is constructed based on flood disaster historical data; the multi-source sample data type comprises rainfall characteristic data, hydrological characteristic data, landform characteristic data, earth surface attribute characteristic data and social economic characteristic data; an XGBoost ensemble learning algorithm is adopted to construct a risk prediction model, and training and evaluation are carried out based on multi-source sample data; historical rainstorm flood event records are taken as labels in the training process; performing quantitative and application verification on the risk prediction model, and calibrating a risk level probability output by the risk prediction model based on a risk level distribution probability of historical disaster situation data; performing real-time estimation based on the optimized risk estimation model; and generating a spatial refined rainstorm flood risk grade early warning map in a future preset time period according to an estimation result in a rolling manner.

The invention discloses a ship route planning method and system based on deep learning large-scale extreme wind speed forecast, and relates to the technical field of artificial intelligence and maritime navigation. An incremental principal component analysis method is introduced, a large-scale sea surface extreme wind speed forecasting model based on depth separable convolution is constructed, and potential physical sudden change disturbance information is decoded and reserved in a variable high-dimensional mapping space by the model through integration of an attention mechanism module and a residual learning algorithm; the attention mechanism module maps high-dimensional feature information in an extreme wind field time sequence to a multi-dimensional feature matrix of a coder-decoder. The method can perform refined dynamic forecasting on the large-scale sea surface extreme wind speed, accurately detect the extreme wind speed sea area and the boundary thereof, forecast and master the intermittent sudden change rule of the sea extreme wind speed in advance, help a ship driver to dynamically adjust the ship route in real time and avoid severe storm weather, and further ensure the navigation safety.

The invention provides a simulation system and method for a typhoon rainstorm induced combined flow-slip disaster, and the system comprises a side slope simulation box which is used for simulating a side slope covered by vegetation; the wind load control system is used for simulating a wind load applied to the slope simulation box and controlling a wind load time history according to a set typhoon level; the rainfall control system comprises a variable-frequency water pump and is used for determining the rainfall intensity according to the set typhoon level, determining the target power of the variable-frequency water pump according to the rainfall intensity, simulating rainfall and controlling the rainfall time history; and the spatial three-dimensional visual data acquisition system is used for acquiring multi-section visual three-dimensional data of the three-mechanism coupling catastrophe simulation process of the slope model in real time under the action of the wind load, rainfall and the plant model. Through the application, the three-mechanism coupling catastrophe process of the slope model under the action of wind load, rainfall and the plant model is accurately simulated, the three-mechanism coupling catastrophe mechanism in the soil body is disclosed, and dynamic three-dimensional analysis of engineering is realized.

The invention belongs to the technical field of hydrological forecasting, and particularly provides a method for forecasting future session flood based on peak rainstorm distribution characteristics, and the method comprises the steps: collecting and reorganizing data, and processing the data into a time sequence with equal time intervals; obtaining space-time distribution of the flood peak formation rainstorm amount of each session of the research basin; calculating the rainfall process of each sub-basin of the flood to be forecasted, and analyzing the spatial and temporal distribution characteristics of peak rainstorm; performing similarity analysis on the spatial and temporal distribution characteristics of the peak storm of the to-be-forecasted flood and the historical session flood to obtain a session flood which is highly similar to the spatial and temporal distribution characteristics of the peak storm of the to-be-forecasted flood and is close to the peak storm amount, and taking the session flood as a to-be-calibrated session flood; establishing a multi-objective function; optimizing the objective function to obtain an optimal parameter of the hydrological model; and performing hydrological forecasting on the to-be-forecasted flood. According to the method, the parameters of the hydrological model for forecasting the flood in the future session with high accuracy can be obtained, so that a high-accuracy flood forecasting result is obtained.

The invention relates to the technical field of rainstorm and flood disaster assessment, and particularly provides a rainstorm and flood disaster assessment method and system based on multi-scale rainfall data, and the method comprises the steps: obtaining a digital elevation model of a target region, calculating a terrain curvature distribution field and an elevation mutation characteristic field, a terrain vertical gradient zone is obtained, and multiple layers of ground precipitation observation station point groups are arranged; generating a vertical weight factor according to observation station altitude distribution, and combining a radar beam direction and a terrain slope to generate a slope direction shielding coefficient; fusing ground-based radar, satellite remote sensing and ground station data, and generating a terrain optimization precipitation field through a vertical weight factor and a slope shielding coefficient; and inputting the precipitation field into the terrain coupling flood model, and deducing disaster space distribution in combination with a water flow path network and a soil infiltration correction mechanism. According to the method, the problem of observation distortion caused by terrain shielding and rainfall vertical differentiation in alpine and valley areas is solved, and the precision and reliability of rainstorm flood disaster assessment are remarkably improved.

The invention discloses an extreme rainfall prediction method based on deep learning and numerical calculation, and the method comprises the following steps: employing a random forest classification method to process a rainfall data sequence and multi-element physical quantity data, employing a long-short-term memory network to optimize the processed data, and obtaining an optimized multi-element association sequence; performing feature recognition on the optimized multi-element association sequence, and performing dynamic association modeling and iterative parameter updating on multi-element expression to obtain prediction model input of comprehensive expression; performing regression and deviation correction on the spatial-temporal distribution data of the extreme rainfall event based on prediction model input of comprehensive expression, and performing multi-element dynamic association integration on the residual error part of the multi-scale characteristics by adopting a feedback cycle mechanism to obtain a final rainstorm event judgment basis; and based on a final rainstorm event judgment basis, carrying out evolution process simulation on real-time environmental factor updating data, and determining an accurate output sequence of extreme rainfall prediction.

The invention relates to the technical field of hydrological analysis, and provides a mangrove forest water level reduction evaluation method and system based on hydrodynamic force and factorial design, and the method comprises the following steps: constructing a hydrodynamic model, and extracting the tide level and typhoon meteorological driving data of a target area; mangrove forest parameters are designed, mangrove forest data and tide level and typhoon weather driving data are input into the hydrodynamic model to drive the model, and the output water level is obtained; taking the output water level as a dependent variable to carry out two-factor factorial design test and analysis to obtain a factorial analysis result; and analyzing the factorial analysis result to obtain a storm water level reduction evaluation result under the mangrove forest influence. According to the method, a hydrodynamic numerical simulation method and a factorial test design method are combined, the main effect and the interaction effect of independent variables can be quantified, and the influence effect of different mangrove forest parameters in the storm period can be analyzed more comprehensively and perfectly.

The invention relates to the field of multi-modal data fusion, and discloses a response progress collaborative management system based on multi-modal data fusion, which is used for solving the problems of inaccurate rainstorm sediment interference identification, response lag and insufficient multi-system collaboration caused by single-source data limitation in traditional water affair monitoring. Comprising a multi-source data acquisition module, a multi-modal feature learning and interference identification module, a space-time consistency verification module, an intelligent scheduling and cooperative command module and a repair work order acquisition module. A multi-modal feature vector is constructed, a cosine similarity algorithm is adopted to compare with historical disaster data, accurate recognition of storm sediment interference is achieved, multi-dimensional cross validation is conducted on abnormal data through a space-time consistency verification module, the misjudgment rate is remarkably reduced, a repair work order is generated in combination with intelligent scheduling, and accurate dredging treatment is achieved.

The invention provides an adjustable floating wind power foundation based on reinforcement learning and a modular construction method, and belongs to the technical field of offshore wind power. Comprising a fan, and the bottom of the fan is fixed to a tower drum; the bottom of the tower drum is fixedly connected with the floating base; the floating base comprises a first buoy, a second buoy and a third buoy which are arranged in a triangular shape; wherein the bottom of the tower drum is fixedly connected with the first buoy; the first buoy, the second buoy and the third buoy are fixedly connected through supporting steel pipes; the tower drum is fixedly connected with the second buoy and the third buoy through inclined supports; the buoy is further connected with an anchor chain, and the other end of the anchor chain is connected with the suction barrel and anchored to the seabed through the suction barrel. By adopting a coupling connection mode of the inclined support, the tower drum and the floating drum, the storm resistance of the whole structure is improved. A steel-concrete composite structure is adopted, the steel consumption is reduced, and the material cost is reduced. A modular construction method is adopted, the offshore construction time is greatly shortened, and the influence of weather and sea conditions on engineering is reduced.

The invention discloses a large hail identification method and system based on dual-polarization radar three-body scattering characteristics, and relates to the technical field of meteorological identification, and the method comprises the steps: obtaining dual-polarization radar observation data of a historical hail event, and extracting a characteristic threshold value of a hail region dual-polarization radar parameter; acquiring dual-polarization radar base data in real time, retrieving the dual-polarization radar base data according to the characteristic threshold value, and judging whether a hail area exists or not; if the hail region exists, TBSS dual-polarization radar feature recognition is carried out on the hail region of the dual-polarization radar base data, and whether TBSS dual-polarization radar features exist at the hail region is judged; and when TBSS dual-polarization radar characteristics exist, determining that large hail exists at the position. According to the invention, quantitative analysis is carried out on the large hail based on the observation data of the dual-polarization weather radar, and then the large hail is identified, so that the vacancy of dual-polarization radar products is solved, and a powerful means is provided for monitoring and early warning of severe convective storms.

The invention relates to the field of global satellite navigation system service quality evaluation, and particularly discloses a method for evaluating influence of geomagnetic storm on aviation GNSS service precision based on ADS-B data, which comprises the following steps: S1, acquiring two different content messages in ADS-B, namely, messages of state vectors SV and mode states MS of all ADS-B in a to-be-analyzed range, acquiring flight positions of all aircrafts in the concern area range and navigation precision categories NACp and vertical geometric precision GVA of the aircrafts at the positions through a fusion algorithm; s2, carrying out fusion processing on the two messages in the S1, and carrying out data alignment according to timestamps in the SV message and the MS message; and calculating the position information in the MS message according to the time information and the position information in the SV message through interpolation. According to the method for evaluating the influence of the geomagnetic storm on the precision of the aviation GNSS service based on the ADS-B data, the flight position and navigation precision parameters of the aircraft are adopted, and then the precision change of the GNSS service under different geomagnetic conditions is evaluated.

The invention discloses a comprehensive assessment method for weak links of a power system involving multi-dimensional indexes in extreme weather. The method comprises the following steps: firstly, establishing a multi-dimensional index system containing safety and optimized operation, wherein the system covers the line outage probability, the load loss probability, the electric energy insufficiency expectation, the line heavy load rate and the network loss rate; secondly, constructing a line fault model and a cascading fault model under extreme storm weather, describing fault probability change in combination with a Gaussian function and Weibull distribution, and simulating line segment failure by adopting Poisson distribution; a system state after a fault is calculated through a direct-current optimal power flow model, and a large amount of simulation data is obtained in combination with a non-sequential Monte Carlo method. Furthermore, a fuzzy analytic hierarchy process and a CRITIC method are adopted to calculate subjective and objective weights respectively, subjective and objective comprehensive evaluation is realized through weight fusion, and weak links of the system are identified. The method gives consideration to evaluation scientificity and calculation efficiency, and is suitable for real-time risk evaluation and weak link identification of a large-scale power system in extreme weather.