154 results about "Storm" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a coastal vegetation damage and recovery evaluation method after an extreme storm and related equipment, and relates to the technical field of data processing, and the method comprises the steps: constructing a synchronous grid stack; four states of storm, moisture, scattering and damage are mapped into a node set, and a pixel-level community lag causal graph is constructed according to each node; solving according to the pixel-level community lag causal graph to obtain a water film scattering expected value, and determining a net damage sequence by using the water film scattering expected value; dividing to obtain ecological partitions of different environments, and calculating a scattering value in each ecological partition so as to obtain a natural fluctuation envelope; and packaging the net damage sequence, the natural fluctuation envelope, the edge weight of pixel-level moisture and scattering in the pixel-level community lag causal diagram and the confidence factor into match input and inputting the match input into a parameter player, further outputting various parameters of vegetation damage and recovery, and then determining the peak damage rate, the half-recovery duration and the complete recovery duration of the vegetation. According to the invention, the evaluation accuracy of vegetation damage and recovery can be improved.

The invention relates to the technical field of thunder and lightning mode recognition, and discloses a thunder and lightning intelligent target early warning method and system for a local key target, and the method comprises the steps: collecting target feature description, estimating storm motion information based on lightning positioning data and radar echo images at continuous moments, and carrying out the recognition of a thunder and lightning mode. And generating a risk corridor in the upstream direction of each target area, and forming a target area around the target area in combination with the risk corridor. Unified spatio-temporal data is obtained, spatio-temporal feature extraction is carried out to obtain storm features, cross-modal correlation selection and weighting are carried out on the storm features with target feature description as a drive, and target features are constructed. And generating an early warning result in a predetermined time window according to the target feature. And when the thunder risk probability reaches an early warning threshold and the spatial risk distribution intersects with the buffer range of the target area, generating targeted early warning information. According to the invention, thunder and lightning targeted early warning is realized, the hit rate is improved, and false alarms are reduced.

A Surface-affixed, Storm-Resistant Containment Unit involves a shelter structure, with internally-mounted photovoltaic solar panels, and movable doors which may be ‘closed’ to protect the photovoltaic solar panels from storm-related damage, e.g. hail, extreme wind and airborne debris. When weather conditions improve, the movable doors may be ‘opened’, allowing sunlight to strike the undamaged photovoltaic solar panels, and renewable energy production to resume. These Storm-Resistant Containment Units may be installed, and electrically connected in multiples to comprise a storm-resistant, commercially-sized, renewable power generating station (or ‘Micro-Grid’) for use in geographic areas prone to severe storms and hurricanes. Such storm-resistant, commercially-sized, power generation station may be advantageously applied at existing ‘Brown-Field’ industrial sites that have existing, unused electrical-grid infrastructure.

The utility model discloses a courtyard umbrella with umbrella cloth convenient to replace, which belongs to the technical field of courtyard umbrellas and comprises an umbrella stand, adjusting mechanisms are arranged on the upper portion and the lower portion of the umbrella stand respectively, and a supporting mechanism is arranged below the adjusting mechanisms. The adjusting mechanism is of a double-end-adjustable folding structure, the erected courtyard umbrella can be quickly and integrally placed on the ground, the front face of the supporting framework faces upwards, a user can conveniently trim or replace the umbrella cloth body, in addition, due to the adjustability of the adjusting mechanism, the whole courtyard umbrella can cope with more severe storm weather, and the service life of the courtyard umbrella is prolonged. The wind force borne by the umbrella cloth body is greatly reduced, and the umbrella cloth body is effectively protected; the supporting mechanism can be installed through bolts or embedded underground and can be installed and adjusted according to actual needs, and in addition, a balancing weight, a limiting inserting groove, an inserting ring and a limiting inserting rod in the supporting mechanism are used in cooperation with the adjusting mechanism to assist in folding or erecting the courtyard umbrella.

The utility model relates to the technical field of box-type transformer substations, in particular to a dustproof box-type transformer substation shell which comprises a shell body, ventilation grooves are formed in the two sides of the shell body, a plurality of rotating shafts are rotationally installed in the ventilation grooves, one end of each rotating shaft and one end of a louver board are fixedly installed, a vertical groove is formed in the shell body, and the louver board is fixedly installed in the vertical groove. And the other end of the ventilation groove penetrates through the vertical groove and is provided with a worm gear, a worm meshed with the worm gear is rotationally installed in the vertical groove, and a protection assembly for protecting the worm gear and the worm is arranged in the vertical groove. According to the utility model, the worm is rotated to enable the worm gear to drive the rotating shafts to rotate, so that the louver boards can be driven to rotate, in case of heavy rain or sand storm, the louver boards rotate downwards to relatively seal the ventilation slots, so that rainwater or sand dust entering the box transformer substation can be reduced, and the plurality of rotating shafts rotate simultaneously and the louver boards rotate synchronously through the meshing of the worm and the worm gear, so that the box transformer substation is prevented from being damaged. And one-by-one adjustment is not needed.

The present application relates to a kind of underpass peak-shifting regulation and storage anti-flooding system and regulation and storage method, belong to urban drainage field, especially to a kind of urban road overpass underpass anti-flooding system.By regulation and storage unit, pump station, control unit is formed, regulation and storage unit is set in underpass approach, does not occupy land additionally, several series regulation and storage pools are arranged in ladder shape, and the road surface rainwater of underpass is collected and discharged into regulation and storage pool temporary storage.In the whole rainfall process, control unit according to rainfall amount dynamically controls the flow of regulation and storage pool into pump station, so that rainwater in storm peak time period is stored by peak-shifting regulation, reduces the flow of regulation and storage pool into pump station in rainfall peak, effectively deal with extreme weather rainfall, prevent pump station from exceeding discharge capacity and not discharging to cause underpass waterlogging.

The invention discloses a processing method and system for a ship intelligent navigation scene, and relates to the technical field of ship intelligent navigation, and the method comprises the following steps: carrying out the continuous waveform analysis of an energy return path of a laser ranging link based on the sudden increase of the density of seawater suspended particles caused by the frontier of a storm, constructing a particle disturbance recognition zone, and carrying out the recognition of the particle disturbance; generating a disturbance identifier carrying an energy rebound mode characteristic according to an analysis result; and expanding the ranging pulse to a time domain by using the disturbance identifier, performing pulse form reconstruction on an energy abnormal section of the ranging pulse, so that the ranging pulse still keeps a stable main peak structure under the influence of energy rebound, and outputting a stable main peak waveform after pulse form reconstruction. According to the invention, through constructing a disturbance identification and form reconstruction mechanism, continuous restoration and smooth output of the distance measurement signals are realized, and the stability and anti-interference performance of distance sensing are improved; and through approaching artifact weakening and gradual change operation control, excessive operation is prevented, and stable course and avoidance safety of the ship in a storm environment are ensured.

This application discloses a method and related equipment for assessing coastal vegetation damage and recovery after extreme storms, relating to the field of data processing technology. The method includes: constructing a synchronous grid stack; mapping four states—storm, moisture, scattering, and damage—to a set of nodes, and constructing a pixel-level community lag causality graph based on each node; solving for the expected value of water film scattering from the pixel-level community lag causality graph, and using the expected value of water film scattering to determine the net damage sequence; dividing the area into ecological zones of different environments, calculating scattering values ​​in each ecological zone to obtain the natural fluctuation envelope; encapsulating the net damage sequence, the natural fluctuation envelope, and the edge weights and confidence factors of pixel-level moisture and scattering in the pixel-level community lag causality graph as game inputs and inputting them to the parameter player, thereby outputting multiple parameters of vegetation damage and recovery, and determining the peak damage rate, half-recovery time, and full recovery time of the vegetation. This application can improve the accuracy of vegetation damage and recovery assessment.

This invention provides a peak-scale response analysis method based on nested sub-basin division. By constructing a nested sub-basin structure controlled by area thresholds, and based on a unified design storm conditions and hydrological model parameter system, the method calculates peak discharge under different scale conditions by only changing the sub-basin division scale. It also establishes a logarithmic decay function model between peak discharge and the number of sub-basin units, achieving a quantitative expression of peak-scale response. Furthermore, by comparing and analyzing the scale decay coefficients under different design frequencies, the method quantifies the sensitivity of peak discharge to changes in the division scale. This method forms a complete technical process from data acquisition, structure construction, control variable setting, model running to function modeling and sensitivity analysis. It features a clear logical closed loop, strong repeatability, and good generalization, and does not depend on specific hydrological model software or specific watershed conditions. Under the premise of meeting the unified storm input and nested division principles, it can be widely applied.

The application discloses a kind of disaster-causing rainstorm space-time characteristic quantitative description method, comprising: obtaining the time series rainfall grid data and basic geographic environment data of target area;Based on time series rainfall data, space-time clustering analysis is carried out, and the storm object and its physical properties are extracted every time;Establish the correlation of adjacent time storm object, construct storm evolution track, and calculate space-time evolution characteristics;Based on storm object and storm evolution track, combined with basic geographic environment data, calculate storm-environment coupling disaster-causing characteristics;Output disaster-causing rainstorm space-time characteristic vector containing physical properties, space-time evolution characteristics and storm-environment coupling disaster-causing characteristics.The application realizes the leap from static statistical description to dynamic disaster-causing mechanism coupling by constructing space-time evolution vector field and storm-river topological parameters, effectively improves the accuracy of disaster warning.

This invention relates to a method for predicting sandbar response under the combined effects of water level and waves, considering hysteresis, during storms. The method includes: starting with initial sandbar morphology data of a target shoreline during a specific storm event, using a sandbar evolution model considering hysteresis to predict sandbar response, and outputting a predicted curve of sandbar position change throughout the storm process. The sandbar evolution model calculates storm dynamic forcing based on historical storm hydrodynamic data, establishes a model relating relaxation timescales to storm dynamic forcing, obtains a relaxation timescale characterizing the hysteresis characteristics of the sandbar response, and establishes a model relating the sandbar equilibrium position to hydrodynamic forcing based on historical hydrodynamic forcing data and sandbar position changes, obtaining... t The sandbar equilibrium position at any given moment. This invention can more accurately simulate sandbar behavior throughout the entire storm process, especially during the attenuation phase, significantly improving the practicality and reliability of sandbar response prediction throughout the entire storm process (including the aftereffect period).

The application discloses a wind power generation site selection method based on ancient storm deposition background and relates to the field of power generation site selection, which comprises the following steps: setting a unified space reference, forming an input data set, performing wind source basin assimilation fusion, generating modern wind field three types of grids, and configuring a modern wind field deposition response calibration reference domain. Long-term wind direction and wind speed prior are established by using storm deposition records and direction evidence, annual uncertainty caused by only relying on short-term wind measurement is reduced, hard constraint filtering is formed by using soft sediment deformation risk coding and boundary stability marking, a penalty term is introduced into scoring, ground investigation and construction stage risks are reduced, candidate area output synchronously gives evidence chain index and uncertainty level, short-term wind measurement review, ground investigation review and deviation backwriting self-calibration iteration are performed, and finally, site selection achievement package containing superimposed graph and evidence chain list is solidified, so that site selection decision transparency and landing efficiency are improved.

This invention discloses a hail identification and tracking method and system for the entire life cycle of hail embryos, relating to the field of meteorological detection. The method includes: S1 acquiring dual-polarization radar volume scan data; S2 identifying hail embryo particles; S3 determining whether the hail embryo particles are new particles, and if so, creating a life profile; S4 tracking target particles in spatiotemporal correlation and updating the life profile; S5 determining whether the target particles have transformed into hail; S6 acquiring the current life profile of the hail particles; S7 determining whether hail precipitation conditions have been triggered, and if so, proceeding to S8, otherwise returning to S1; S8 analyzing the location of the precipitation area; S9 spatially clustering the precipitation area location and generating a probability map of the hail precipitation area and early warning information. By establishing a "life profile" for each hail embryo particle that includes its trajectory, phase evolution, and environmental thermodynamic conditions, the complete process of hail embryo formation from initial formation to final hail precipitation is tracked. Based on the statistical analysis of the life profile, key transformation characteristics of different hail storm types are adaptively identified, thereby achieving early hail warning.

The invention discloses a method and equipment for determining an urban rainstorm amount formula considering both the residence and building drainage standard and the water conservancy waterlogging control standard, and the method comprises the following steps: (1), calculating the theoretical design rainstorm amounts of different durations and different return periods through frequency analysis by using an actually measured rainfall database; (2) solving a rainstorm amount formula parameter, and (3) compiling a rainstorm amount formula. According to the method, in view of non-uniform design standards of urban drainage, waterlogging control and flood control projects, a rainstorm amount formula which is simultaneously suitable for the drainage standard of the residence and construction department and the waterlogging control standard of the water conservancy department is established, and the designed rainstorm amount under any length and short duration and any high and low recurrence period can be directly calculated; and the recurrence period level of any historical rainstorm event can be estimated, and multi-department cooperative treatment of urban rainfall flood can be promoted.

The application discloses a storm forecast method, device and system based on multi-mode integration, and belongs to the technical field of weather forecast. The method comprises the following steps: acquiring and preprocessing multi-mode precipitation forecast data; calculating the dynamic weight of each mode through the Spearman correlation coefficient based on the historical data of the dynamic sliding window, and generating a precipitation spatial distribution field through weighted average; at the same time, screening an optimal forecast subset based on the symmetric average absolute percentage error, and generating a precipitation intensity field through the interval median of the subset forecast value sorting; finally, replacing the forecast value of the spatial distribution field with the same rank value in the intensity field according to the rank matching, and obtaining the final storm forecast product. The application realizes the double optimization of storm spatial falling area and intensity, significantly improves the forecast accuracy, and reduces the false alarm rate and the missing alarm rate.

The invention relates to the technical field of ocean storm information analysis, in particular to a typhoon storm prediction method fusing numerical simulation and deep learning. The method comprises the following steps: establishing a heterogeneous wind field mapping model of the marine typhoon based on marine typhoon multi-source observation data, and generating the heterogeneous wind field mapping model of the marine typhoon; establishing a wind field space-sea wave spectrum net effect relation model through the multi-order grid wave space change data of the marine typhoon heterogeneous wind field mapping model; performing model optimization reconstruction processing of fusion wind field trend simulation on the marine typhoon heterogeneous wind field mapping model by using a preset long-short-term memory recursive network model to generate a fusion wind field trend simulation optimization model; and carrying out typhoon wave trend simulation numerical model analysis processing on the fused wind field trend simulation optimization model by utilizing a wind field space-sea wave spectrum net effect relationship model to generate a typhoon wave trend simulation model. According to the invention, accurate typhoon storm prediction is realized.

The present application relates to the technical field of storm flood disaster assessment, and specifically provides a storm flood disaster assessment method and system based on multi-scale precipitation data, which comprises the following steps: obtaining a digital elevation model of a target region, calculating a terrain curvature distribution field and an elevation mutation characteristic field, obtaining a terrain vertical gradient zone and arranging a multi-layer ground precipitation observation site group; generating a vertical weight factor according to the altitude distribution of the observation site, combining the radar beam direction with the terrain slope to generate a slope-shielding coefficient; fusing ground-based radar, satellite remote sensing and ground site data, generating a terrain-optimized precipitation field through the vertical weight factor and the slope-shielding coefficient; inputting the precipitation field into a terrain-coupled flood model, combining a water flow path network and a soil infiltration correction mechanism to deduce the spatial distribution of disasters. The present application solves the problem of observation distortion caused by terrain shielding and precipitation vertical differentiation in high mountain and canyon areas, and significantly improves the accuracy and reliability of storm flood disaster assessment.

The invention provides a mooring load automatic prediction method and system based on wind wave big data, and the method comprises the steps: firstly collecting the wind wave action data of a target sea area to construct a wind wave action chain, and forming a wind wave action sequence set; then, a load conduction network is constructed according to the structural component distribution of the mooring equipment, and a load conduction model is formed; inputting the wind wave action chain into a load conduction network, and establishing a dynamic response mapping model in combination with historical data; and acquiring real-time wind wave action data, converting the data into a real-time wind wave action chain, inputting the real-time wind wave action chain into the dynamic response mapping model to obtain a real-time load conduction distribution result, and determining a mooring load prediction value. And finally, a load regulation and control strategy is generated according to the mooring load prediction value and is sent to the control module to realize dynamic load adjustment, so that the accuracy and the real-time performance of mooring load prediction are improved, and the ocean engineering safety is guaranteed.