56 results about "Storm surge" patented technology

The invention discloses a storm surge water increase prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seawater monitoring, and the method comprises the steps: obtaining multi-source observation data of a target sea area, the multi-source observation data comprising satellite remote sensing data, near-shore monitoring data, meteorological mode data and drainage basin data; performing space-time alignment and exception processing on the multi-source observation data to generate standardized space-time grid data; performing hybrid prediction modeling on the standardized space-time grid data through a physical data dual-drive modeling layer to obtain a target probability water increasing field; and dynamically correcting the target probability water increasing field by using real-time observation data to obtain a storm surge water increasing predicted value. A physical mechanism model and a probability generation model are combined, limitation of a single model is overcome, and the nonlinear evolution process of the storm surge is effectively captured; the time-space dual-drive architecture is adapted to complex coast terrains and changeable meteorological conditions, and the risk of missing report or false report is reduced.

The invention relates to the technical field of marine disaster monitoring and early warning, in particular to an AI-based marine disaster prevention monitoring and early warning system. Comprising a multi-source data acquisition module for acquiring multi-dimensional data through a sensor network, remote sensing data access and a meteorological data interface; the data preprocessing and fusion module is used for carrying out cleaning, space-time calibration and feature fusion on the collected data; the artificial intelligence analysis module is used for realizing accurate analysis and prediction of different levels of storm surge by using a multi-scale prediction model cluster unit; the risk assessment and early warning generation module is used for calculating risk indexes according to a related index system and dividing early warning grades; and the interaction and release module is used for realizing visual display and multi-channel release of the early warning information. According to the invention, advantages of multi-source data and artificial intelligence technology are integrated, and real-time and accurate monitoring and early warning can be carried out on storm surge and coast submerging conditions.

The invention provides an intelligent storm surge prediction method and device based on deep learning and a medium, and relates to the field of storm surge disaster risk assessment, and the method comprises the steps: obtaining multi-source heterogeneous data of a typhoon storm surge disaster, and carrying out the preprocessing; the multi-source heterogeneous data comprises meteorological and marine observation data and high-precision geographic information data; constructing a typhoon and storm surge prediction network; the typhoon and storm surge prediction network comprises a multi-scale spatial-temporal feature module, a physical mechanism enhancement module and a spatial-temporal sequence prediction model based on a fusion attention mechanism and a graph convolutional network; training a typhoon and storm surge prediction network through multi-source heterogeneous data; acquiring meteorological and tide level live data; and inputting the meteorological and tide level actual data into the trained typhoon and storm surge prediction network to obtain a typhoon and storm surge state prediction result. A physical mechanism is introduced into the neural network to predict the states of the typhoon and the storm surge, and the prediction precision and efficiency are remarkably improved.

The embodiment of the invention provides an estuary region storm surge disaster assessment method and device. The assessment method comprises the steps of obtaining basic geographic data, meteorological data, tide and wave data and disaster-bearing body data of a preset region of an estuary region; constructing a discrete grid according to the basic geographic data; according to the tide and wave data, constructing a storm surge and wave numerical simulation model in a discrete grid; simulating different typhoon scenes in a numerical simulation model of the storm surge and the waves according to the meteorological data to obtain simulation results; evaluating according to the simulation result to obtain the danger level of each area; evaluating according to the disaster-bearing body data to obtain the vulnerability level of each region; and determining the comprehensive risk level of each area according to the danger level and the vulnerability level. According to the embodiment of the invention, the evolution process of storm surge disasters can be simulated more accurately, and then the comprehensive risk level of each area of the estuary region is determined.

The invention discloses a near-shore wave-flow field-sediment coupling simulation method considering a wave breaking nonlinear effect, and belongs to the field of near-shore hydrodynamics, and the method comprises the steps: inputting WRF high-resolution wind field, tide boundary conditions and water depth information, and initializing a wave module, a flow field module and a sediment module; calculating local wave breaking dissipated energy by adopting an improved wave breaking energy dissipation formula to obtain a wave energy density spectrum and a breaking probability; a near-shore flow field is calculated under radiation stress nonlinear correction, and hydrodynamic boundary conditions are updated; calculating sediment suspension and deposition by using a coupling diffusion coefficient, and updating sediment concentration distribution; waves, flow fields and sediment are mutually coupled through loop iteration, and coastal wave-flow field-sediment simulation is achieved under extreme conditions such as typhoons, storm surge and strong storm waves. The method can improve the prediction precision of the wave height, the flow velocity and the sediment concentration under the condition of offshore strong storm waves, and is used for typhoon storm surge prediction, harbor basin deposition evaluation and offshore ecological environment management.

The invention discloses a storm surge nearshore disaster identification method, device and equipment fused with a typhoon dynamic influence domain, a medium and a product, and relates to the technical field of meteorological disaster monitoring. The method comprises the following steps: acquiring a meteorological ocean reanalysis data set in a historical set age limit of a target area, and preprocessing multi-source data; then background field elimination processing is carried out; determining a dynamic influence radius based on the typhoon intensity grade and the storm surge abnormal data; determining the center position of the typhoon according to the dynamic influence radius, and generating a typhoon dynamic influence domain space mask by adopting a spherical distance calculation method; a near-shore buffer area is determined according to the typhoon dynamic influence domain space mask, and a storm surge disaster area is determined based on a near-shore buffer area constraint and water increase threshold value judgment method; and carrying out storm surge near-shore disaster identification on the basis of the storm surge disaster area by adopting a geographic space visualization method to obtain an identification result. According to the invention, accurate storm surge disaster discrimination can be realized.

The invention belongs to the technical field of storm surge disaster early warning, and relates to a coastal town storm surge disaster intelligent prevention and control system and method.The system comprises an internet-of-things sensing layer, a data resource platform and an application supporting platform, the application supporting platform comprises a forecasting module, and the forecasting module is based on a multi-layer nested storm surge model; through nesting calculation of an open sea grid and a near-shore grid, the dam overtopping water flow and the water level and range of a submerged area are predicted; the early warning module is used for dynamically analyzing hydrology and water quality data in combination with a risk threshold value, generating an early warning signal and visually issuing the early warning signal through a G IS engine; the rehearsal module is used for carrying out real-time spatial model rendering on a prediction result of the prediction module by adopting an OpenGLES underlying graphic API and a GPU rendering pipeline optimization technology so as to realize restoration of dynamic change of a physical world; according to the system, the precision and the real-time performance are considered, a forecasting, early warning, rehearsing and pre-planning full-linkage module is constructed, and the decision response speed is improved.

The invention provides an urban storm surge forecasting method based on multi-source information coupling, and the method comprises the steps: taking multi-source typhoon driving element data as the input information of a preset typhoon structure reconstruction model, and outputting typhoon key physical parameters through the typhoon structure reconstruction model; based on a Kriging interpolation and radial basis function mixing method, carrying out fusion processing on the multi-source topographic data, and constructing topographic grid information; extracting tide harmonic constants of a specified number of partial tides from the multi-source tide factor data; based on the typhoon key physical parameters, the terrain grid information and the tide harmonic constants, determining input information of a predetermined urban storm surge forecasting model; and outputting a storm surge level abnormal value and a horizontal flow velocity component corresponding to the multi-source prediction associated data through the urban storm surge prediction model. According to the scheme, the precision of urban storm surge forecasting is improved.

The invention relates to a tropical cyclone composite disaster multi-source data intelligent fusion early warning system, and the system comprises a data collection module which is used for collecting a multi-source data set; the preprocessing module is used for preprocessing the multi-source data set to obtain a standardized multi-source data set; the feature mining module is used for performing spatial feature mining and time feature mining on the standardized multi-source data set to obtain a spatial feature vector matrix and a time feature vector; the fusion module is used for fusing the spatial feature vector matrix and the time feature vector by adopting an attention mechanism to obtain a multi-dimensional fusion feature; the composite disaster prediction module is used for performing trajectory calculation and intensity evolution analysis on the multi-dimensional fusion features, and acquiring occurrence probabilities of rainstorm, flood and storm surge according to trajectory calculation data and intensity evolution data; and the early warning module is used for obtaining a comprehensive early warning result according to the occurrence probability of the rainstorm, the flood and the storm surge. According to the invention, accurate early warning of tropical cyclones and composite disasters caused by the tropical cyclones can be realized.

The invention provides a Bohai sea temperature prediction model based on a fusion model, the previous research mainly carries out prediction through single models, and the single models have corresponding limitations more or less. In order to solve the problem, a Bohai sea temperature prediction model based on a fusion model is provided. According to the method, through fusion of an Informer model and an LSTM model, and through feature optimization and XGBoost correction, the model obtains better prediction performance. According to the model, the prediction precision of the sea surface temperature of the semi-closed sea area is improved, and high-reliability data support is provided for ocean decisions such as storm surge early warning, fishing ground scheduling and ecological management.

The invention discloses a buoy attitude self-adaptive wind wave calibration method for a typhoon landing path sea area, and relates to the field of ocean monitoring, and the method comprises the steps: obtaining attitude information, original wave height information and wind speed and direction information of a target buoy through a six-degree-of-freedom IMU, a micropressure array wave height meter and a three-axis ultrasonic wind speed sensor carried by the target buoy; compensating the original wave height information based on the attitude information and the wind speed and direction information to obtain compensated wave height information; based on remote sensing inversion data, a storm surge numerical forecasting model or historical tide level data, obtaining a storm surge lifting height of the current area, and taking the storm surge lifting height as a zero drift error of the micropressure array wave height meter; and outputting target wave height data based on the zero drift error and the compensated wave height information. According to the calibration mechanism of fusion compensation and multi-source correction provided by the method, the adaptability and practicability of a traditional buoy system under extreme meteorological conditions are remarkably improved.

The invention discloses a storm surge coupling numerical simulation method and system based on climate change, and relates to the technical field of numerical simulation, and the method comprises the steps: determining the size of a regional grid according to a pre-obtained regional feature, and carrying out the data reconstruction processing of the obtained heterogeneous spatial data based on the size of the regional grid, and obtaining a comprehensive heterogeneous data set; constructing a comprehensive heterogeneous model, performing model coupling to obtain a comprehensive coupling model, and performing friction coefficient adjustment to obtain a storm surge coupling numerical simulation model; performing storm surge simulation on the comprehensive observation data acquired in real time, and evaluating and optimizing the storm surge coupling numerical simulation model to obtain a storm surge simulation model for storm surge coupling numerical simulation under climate change. Through the comprehensive coupling model, the limitation of traditional single water adding simulation is broken through, and the simulation capacity of extreme water level, offshore water adding and composite disaster processes is remarkably improved.

The invention discloses a near-shore disaster-bearing body disaster-causing path tracing method based on a digital-analog experiment, and relates to the technical field of ocean dynamic disaster simulation, the near-shore disaster-bearing body disaster-causing path tracing method comprises the following steps: determining ocean dynamic disaster characteristics of a near-shore research area, integrating multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructing a time-space association database; and based on a three-dimensional ocean and third-generation ocean wave mode, an offshore storm surge-ocean wave coupling numerical mode is established and verified. According to the method, a storm surge-sea wave coupling numerical mode is constructed, multi-disaster-type nonlinear interaction is comprehensively considered, an energy conservation equation is introduced to quantify generation, transmission and dissipation processes of disaster energy, limitation of a traditional single-disaster-type linear model is solved, real-time feedback of a disaster-bearing body three-dimensional dynamic model is combined, and the disaster-bearing body three-dimensional dynamic model is optimized. A closed energy-damage association mechanism is formed, the tracing accuracy of a disaster chain type propagation path is remarkably improved, and it is ensured that recognition of key nodes and dominant paths better conforms to physical reality.

The invention discloses a coast vegetation community zoning planting optimization design method, which comprises the following steps of: selecting suitable high trees and low trees according to the design water level and topographic features of a coast region in combination with coast vegetation suitable conditions; according to the shoal width, the zoning width of a coast vegetation community is determined, wherein the zoning width comprises the width of a high tree planting area and the width of a low tree planting area; according to the coast vegetation community planting width and a wave dissipation formula, the wave height at the embankment foot is calculated, and the width of the grass planting belt is determined through a wave climbing calculation formula. The problem of water immersion of extreme waves in the storm surge period to a city behind the dike is solved.

The invention discloses a storm surge forecasting method combining empirical orthogonal decomposition and deep learning, and belongs to the field of ocean storm surge forecasting. The method comprises the following steps: a, determining a wind field area according to the position of a forecast ocean station and the like; b, collecting and arranging a 10-meter wind distance flat data matrix from the reanalysis data set, and collecting observed storm surge data from the ocean station; c, performing empirical orthogonal decomposition on the data matrix to obtain a main modal matrix and a main component matrix; d, taking a principal component in the principal component matrix as a forecast factor, taking the storm water increase data as a forecast quantity, and constructing a sample library; carrying out model training to obtain a long-short term memory neural network forecasting model; and e, collecting a 10-meter wind distance flat data matrix for future forecasting, calculating a principal component value, and substituting the principal component value into the forecasting model to obtain future storm surge data. According to the method, the precision of storm water increase forecasting can be improved, and compared with the actual observation value of storm water increase, the calculated root mean square error is low.

This invention belongs to the field of coastal engineering and ecological restoration technology. It discloses a method for optimizing the design of beach ecological restoration trenches based on hydrodynamic simulation. The method includes: collecting beach hydrological datasets, topographic datasets, and ecological datasets, and preprocessing them. The hydrological dataset includes wave data, tidal current data, storm surge data, and sediment transport data. The topographic dataset includes shoreline slope, beach elevation, underwater topography data, and sediment source storage data. The ecological dataset includes tidal flat vegetation distribution, benthic organism density, ecologically sensitive area range, and water environment quality data. This invention, through the coupled processing of hydrological, topographic, and ecological multi-source datasets, obtains hydraulic reference values ​​and topographic adaptation values ​​for the trench, directly reflecting the regional dynamic conditions and the degree of topographic adaptation, significantly reducing the workload of manual calculation and trial and error.

The invention discloses a wave impact resistance structure optimization method for a near-shore high-piled wharf of a port. The method comprises the following steps: firstly, based on historical observation and reanalysis data, carrying out conditional sampling according to a significant wave height, a peak period, an incidence direction, storm surge, a long-period energy ratio and a water depth, and obtaining a sea condition sample set covering a design quantile; secondly, building a high-pile wharf parameterized geometry and working condition model; and finally, performing high-fidelity full-sample regression verification on the candidate solution to form a design result document including design variables, sea condition coverage domains, robustness indexes and key response statistics. According to the method, high reliability and high efficiency are unified, and the method is suitable for wave impact resistance design and engineering implementation of the near-shore long-piled wharf.

A storm surge intelligent forecasting method integrating causal structure and graph neural network, relating to the fields of artificial intelligence and marine storm surge forecasting technology, includes: Step 1. Multi-source data fusion; Step 2. Data preprocessing and standardization; Step 3. Using UNet-LSTM to achieve fusion modeling of storm surge meteorological spatiotemporal characteristics and water level temporal characteristics; Step 4. Causal structure inference and graph structure learning; Step 5. Using graph convolutional neural network to obtain a UNet-LSTM-DGCN combined model for final storm surge water level forecasting; Step 6. Combined model training and validation; Step 7. Visualization of prediction results; Step 8. Interpretability analysis. This invention constructs an intelligent method combining dynamic graph neural networks for storm surge forecasting, effectively capturing the dynamic causal relationships in water level changes at multiple stations under the influence of typhoons. While improving forecast accuracy, it reveals the decision-making basis within the model through visualized graph structures.

The invention discloses a comprehensive evaluation method and system for offshore sea conditions, and relates to the technical field of marine hydrology and marine wind energy resource utilization. The method comprises the steps of obtaining an initial data set of a wind field, a shoreline, water depth and a tide level of a sea area to be researched, and generating a prediction data set through a hydrodynamic force and wave bidirectional coupling numerical model; a hierarchical hyper-parameter optimization strategy is adopted to carry out iterative adjustment on model parameters, and the relative error is controlled within 5% in combination with measured data; and fusing a typhoon field and a background wind field to generate extreme working condition data, carrying out comprehensive analysis on characteristic parameters of tides, waves, tidal currents and storm surge on prediction data output by the model, and generating a sea condition comprehensive evaluation report. According to the method, the model parameter calibration efficiency and simulation precision are improved, the survey cost of offshore sea condition evaluation is reduced, and the method can be widely applied to site selection and feasibility analysis of offshore engineering such as offshore wind power and port construction.

The invention discloses a disaster risk assessment method, system and device suitable for an offshore wind plant, and relates to the technical field of engineering, and the method comprises the steps: collecting long-term offshore disaster data, extracting typhoon, wave, storm surge, salt mist and other core parameters, and calculating a correlation coefficient between disasters to quantify a synergistic effect; typhoon wave collaborative loss, storm surge loss and salt spray corrosion loss models are established, and refined quantification of multi-disaster influences is achieved; various risk indexes are fused in a weighting mode to generate an offshore comprehensive risk index, and differentiated protection measures are matched according to risk levels. According to the method, the strong relevance and environmental particularity of the offshore disaster are fully considered, the accuracy of risk assessment and the scientificity of decision making are improved, and the safety and economy of the offshore wind power system are enhanced.

This invention provides a method, apparatus, and equipment for predicting storm surge hazard level parameters for typhoons, relating to the field of storm surge data processing. The method includes: acquiring target characteristic parameters of the current typhoon in the target area and a preset storm surge intensity level index comparison table for the target area; inputting the target characteristic parameters into a preset storm surge prediction model, and outputting prediction results through the preset storm surge prediction model; wherein the preset storm surge prediction model is determined using multiple sets of historical typhoon data for the current target area; and determining the hazard level parameters of the current storm surge in the current target area based on the prediction results and the preset storm surge intensity level index comparison table for the target area. The solution of this invention achieves real-time prediction of storm surge hazard level parameters for the current area under extreme environments, and has the advantages of fast prediction speed and easy interpretation of prediction results.

To provide an estimation and control method for frequency errors of extreme events.SOLUTION: The present invention proposes a frequency shift functional and a frequency shift functional estimation formula based on probability estimation, and constructs a method for reversely solving a confidence frequency shift value epsilon according to the frequency shift functional to improve a frequency distribution function. The present invention is suitable for estimating and controlling errors of occurrence frequency prediction of extreme events in various frequency statistical models by using hydrological observation data under probability statistical significance, so that the prediction accuracy and reliability of hydrological extreme events such as storm surge, rainstorm flood, tsunami, and the like are improved, risks caused by extreme natural disasters are reduced, and loss of life and property that may be caused is reduced.SELECTED DRAWING: Figure 1

The present invention provides a single-point conventional storm surge forecasting method, medium and system, belonging to the technical field of marine storm surge prediction. The method comprises the following steps: collecting historical water level data, historical flow velocity data and historical meteorological data at a forecast target point as training data; performing time series decomposition on the training data to obtain fundamental wave components and harmonic components and constructing a characteristic matrix; calculating a water level contribution coefficient, a flow velocity contribution coefficient and a meteorological contribution coefficient; constructing a storm surge water level forecasting equation group including a storm surge dynamic equation and a storm surge continuity equation according to the contribution coefficients; inputting real-time water level data, real-time flow velocity data and real-time meteorological data into the storm surge water level forecasting equation group to obtain a storm surge forecast result; and outputting a warning signal when the storm surge forecast result exceeds a warning threshold. The method ensures the physical basis of the forecast and improves the calculation efficiency, thereby solving the technical problem of insufficient forecast accuracy and timeliness of sudden strong storm surges in the prior art.

The utility model discloses a marine ranching coral reef typhoon -resistant reinforcing structure and ecological barrier system relates to marine ranching technical field, including coral reef pedestal and reinforcing component, the coral reef pedestal is integrated into one piece structure, and the surface of coral reef pedestal is provided with the cylindrical containing groove, the reinforcing component sets up at the surface of coral reef pedestal, and reinforcing component includes oyster shell composite layer, reinforcing threaded drill rod, bottom fixed drill, fixed bottom plate and spiral reinforcing drill. The utility model discloses a marine ranching coral reef typhoon -resistant reinforcing structure and ecological barrier system, through reinforcing component makes the device enhanced the stability of coral reef, and the typhoon -resistant performance improves, can enhance the stability of ecological barrier, provides the lasting habitat for fish and resists the sea wave erosion, through the degradable polymer grid, the connecting plate, the fixed drill rod and the barrier component make the device can provide the biological habitat for marine ranching, purify water quality and resist the storm surge impact, so as to be convenient for maintaining ranching ecological balance and productivity.

The invention relates to the field of numerical simulation optimization, in particular to a marine ranch-oriented storm surge disaster numerical simulation optimization method. The method comprises the steps that the boundary and the initial still water level of a marine ranch are obtained, the geographic space of the marine ranch is discretized into a rectangular grid layout, meteorological grid point data of the marine ranch are obtained, the total water level increment is calculated through a wind pressure wind field physical driving water level increment calculation algorithm, and the updated water level is obtained; and based on the updated water level, introducing a similarity weight mechanism of storm path projection, performing local enhancement correction on a water level field in storm surge simulation to obtain a water level after weight correction, and outputting a water level spatial distribution map. The problem that most of traditional storm surge disaster numerical simulation optimization methods neglect the dynamic adjustment effect of wind speed changes on sea surface roughness and wind resistance is solved; the effect of'inverted barometer 'is neglected, and air pressure change cannot be dynamically responded; and the regulation and control effect of geographic space factors on the simulated water level field cannot be accurately reflected.

This invention discloses a marine disaster early warning method and system based on an AI model specifically designed for surveying. The invention embeds the KdV equation for internal solitary waves as a constraint into the model using a physical information neural network, giving it physical interpretability and enabling accurate reconstruction of the flow field based solely on sparse observations. The method transforms water acoustic echo data and shipborne inertial measurement unit attitude data into effective inputs, achieving perception of internal wave structure and sea surface wave spectrum without increasing hardware costs. The system utilizes Transformer-based multimodal fusion technology to correct satellite cloud image features using meteorological forecast data, significantly improving the reliability of typhoon and storm surge predictions. Ultimately, high-precision environmental perception is translated into customized operational decisions, filling the gap in general meteorological navigation for engineering protection and effectively ensuring the safety of surveying equipment and the project schedule.

The application relates to the technical field of navigation satellite reflection remote sensing, in particular to a water level measurement method and device based on LEO-IR, wherein the method comprises the following steps: acquiring LEO observation data and LEO broadcast ephemeris of a target water area adjacent to a shore-based LEO station; calculating angle information of a LEO satellite relative to the shore-based LEO station based on the LEO broadcast ephemeris and the actual position of the shore-based LEO station; calculating a reflection height and an accuracy attenuation factor of the reflection height according to the LEO observation data, the angle information and the LEO-IR; and calculating the water level height of the target water area based on the reflection height, the accuracy attenuation factor and the height of an antenna phase center. According to the application, more LEO satellites with higher moving speed are utilized to construct a time-consistent satellite arc-to-height rate error correction model and an accuracy attenuation factor model, so that the problem of low time resolution, low spatial resolution or low accuracy in the related art is solved, and the application has a significant application prospect and advantage in monitoring of water level fluctuation in extreme weather such as earthquake tsunamis and storm surges.

The invention provides a storm surge intensity influence area automatic identification method and system and a medium, and relates to the technical field of disaster emergency response, and the method comprises the steps: collecting storm surge correlation influence data, carrying out intensity influence analysis, and constructing a storm surge intensity influence factor set; performing classification training fusion on the historical storm surge event samples to generate a storm surge intensity influence grade discriminator; carrying out twinborn fusion to obtain a storm surge intensity prediction agent; performing water level prediction simulation to generate a multi-dimensional storm surge intensity field; and carrying out clustering analysis and geographical matching identification, outputting a spatial distribution diagram of the intensity influence region, and carrying out storm surge early warning and forecasting. The technical problem that the reliability and accuracy of an identification result are insufficient due to the fact that storm surge intensity influence area identification in the prior art often depends on a simple numerical simulation model or only uses a single storm surge influence factor and cannot flexibly adapt to complex marine environment changes is solved.

The invention discloses a storm surge space-time prediction method based on a deep learning neural network, and relates to the technical field of data processing, and the method comprises the steps: constructing a space-time variable data set according to typhoon features and typhoon space-time features; the typhoon characteristics comprise typhoon paths, typhoon wind speeds, typhoon air pressures and storm surges corresponding to the typhoon paths; determining storm surge, typhoon wind speed and typhoon pressure of each time step as input parameters according to the time-space variable data set; inputting the input parameters into a pre-constructed deep learning neural network for training, so that the deep learning neural network predicts the storm surge of the next time step of each time step as an output parameter; and inputting a to-be-predicted input parameter into the trained deep learning neural network to obtain a predicted storm surge. According to the method, the typhoon features and the spatial-temporal features of the typhoon are fully integrated, the trained deep learning neural network is utilized to obtain the accurate predicted storm surge, and the capability and accuracy of predicting the spatial-temporal distribution of the storm surge are improved.

The invention discloses a storm surge probability forecasting method, device, equipment, medium and product, and relates to the field of disaster early warning, and the method comprises the steps: obtaining typhoon metadata and marine environment parameters; performing space-time alignment and screening on the typhoon metadata and the marine environment parameters to obtain a feature matrix and a typhoon influence area; predicting by utilizing a probability prediction model according to the feature matrix to obtain a storm surge prediction result; constructing a typhoon intensity fitting mask according to the typhoon metadata and the typhoon influence area; constructing a near-shore mask according to the typhoon influence area; determining a final mask according to the near-shore mask and the typhoon intensity trial mask; and determining a final prediction result according to the storm surge prediction result and the final mask. The accuracy of storm surge prediction can be improved.