88 results about "Storm surge" patented technology

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous marine data intelligent fusion and marine disaster prediction method and platform, and the method comprises the steps: carrying out the hierarchical deployment and network topology construction of marine environment monitoring equipment, communication relay equipment and data processing equipment, and obtaining a sea-air-shore integrated monitoring network; collecting marine environment data with a space-time identifier; carrying out ocean element modeling and dynamic feature extraction to obtain an ocean environment digital model; performing feature fusion and correlation analysis on the multi-source heterogeneous ocean data to obtain spatio-temporal feature data; and training and multi-model integration are carried out on the typhoon path prediction model, the storm surge prediction model and the red tide prediction model by adopting a transfer learning method to obtain a marine disaster prediction model set, so that the prediction precision and timeliness are improved, the allocation and scheduling of computing resources are optimized, and the real-time requirement of mass data processing is met.

The invention discloses a tidal river reach storm surge rapid forecasting method based on deep learning and an AI large model. The method comprises a data acquisition and preprocessing step, a meteorological element forecasting step, a typhoon path and flow forecasting step and a storm surge forecasting step. Multi-source data fusion is realized through a four-dimensional variational assimilation method and an adaptive weight fusion algorithm; a multi-model integrated system such as FourCatNet and the like is used for forecasting meteorological elements; an improved XLSTM-Transformer model is adopted to predict the flow and the typhoon path; and carrying out storm surge forecasting based on a three-layer nested model system and a multi-process coupling technology. According to the method, full-process automation from data acquisition to forecasting is realized, the forecasting timeliness is improved by 60%, the average absolute error is reduced by 45%, and a reliable basis is provided for disaster prevention and reduction decisions.

The invention discloses a seawall overtopping amount and overtopping risk calculation method considering a mangrove forest wave dissipation effect, and relates to the technical field of coast engineering. The method comprises the following steps: acquiring typhoon path data, inputting the typhoon path data into a Holland typhoon empirical model, and accurately generating a typhoon wind field; calculating coastal site tide level data in combination with a coastal storm surge numerical model; obtaining wave data during a typhoon by using a storm surge typhoon wave forecasting model based on the SWAN; historical typhoon data and relevant information of the seawall and the mangrove forest are collected, a water level-wave-vegetation-seawall coupling model is constructed, the wave dissipation effect of the mangrove forest is accurately simulated, and wave characteristic calculation is optimized; constructing a database, training the database through a CNN neural network model, and improving the prediction precision of seawall front wave height and wave force parameters; the wave overtopping amount is calculated based on the prediction parameters, a scientific basis is provided for wave overtopping risk assessment, it is ensured that real-time assessment of the wave overtopping risk is more accurate and efficient, and the disaster prevention and reduction capacity is effectively improved.

The invention relates to the technical field of marine disaster early warning, and discloses a digital bay hydrological condition early warning method and system, and the method comprises the steps: collecting historical meteorological data and real-time meteorological data of a to-be-predicted bay region; according to the historical meteorological data, establishing a wind and wave relation model by using a deep reinforcement learning and genetic algorithm, and generating wave prediction data based on the relation model and the real-time wind data of the to-be-predicted bay area; based on the theoretical model wind field and the background wind field, synthesizing a synthesized wind field of the to-be-predicted bay area; performing storm surge simulation by using a hydrodynamic mathematical model and a tidal current mathematical model according to the synthesized wind field and the real-time meteorological data; and on the basis of the wave prediction data and the storm surge simulation result, hydrological condition early warning information is generated in combination with an early warning index threshold value, the prediction precision and early warning timeliness of waves and storm surge in the bay area are improved, and accurate grading response of complex meteorological disasters is realized.

The invention relates to a tidal reach tide level forecasting method and system based on a combined model. The method comprises the following steps: mining the average propagation time of upstream and downstream tide levels through a derivative dynamic time warping method; feature decomposition is conducted on the tide level multi-influence-factor data through set and empirical mode decomposition, and high-frequency component information and low-frequency component information are extracted; a combined model is constructed by combining a deep residual shrinkage network, a bidirectional long-short term memory network and an attention mechanism, and spatial-temporal features are mined. The tide level propagation time between upstream and downstream tide level stations is mined by using a derivative dynamic time warping method, and a more accurate time reference is provided for tide level forecasting; a set and empirical mode decomposition method is adopted to perform characteristic decomposition on tide level data, spatial characteristics and time characteristics are extracted through a combined model, and an attention mechanism is utilized to perform fusion, so that the mining and learning ability of the model on multi-scale nonlinear characteristics and non-stationary characteristics is improved, and the tide level forecasting precision is further improved. And the forecasting error in the storm surge period is reduced.

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 belongs to the technical field of extreme ocean phenomenon forecasting, and discloses a typhoon and storm surge lightweight rapid forecasting system and method. The method comprises the following steps: preprocessing a multi-source marine environment data set and a typhoon moving path; constructing a typhoon and storm surge data set based on the preprocessed data; based on the constructed typhoon and storm surge data set, establishing a deep learning model VIT-TSS based on VIT; and comparing the output result of the deep learning model VIT-TSS with the open source data and the measured data to verify the forecast precision. The typhoon and storm surge intelligent forecasting model provided by the invention can realize typhoon path forecasting and regional water increase forecasting. According to the VIT-TSS model provided by the invention, the prediction time of the model is greatly shortened while the calculation precision is improved. The system provides key information for emergency management and navigation.

The invention provides a storm surge single-point conventional forecasting method, medium and system, and belongs to the technical field of ocean storm surge prediction.The method comprises the steps that historical water level data, historical flow speed data and historical meteorological data are collected at a forecasting target point to serve as training data; performing time sequence decomposition on the training data to obtain a fundamental component and a harmonic component, constructing a characteristic matrix, calculating a water level contribution coefficient, a flow velocity contribution coefficient and a meteorological contribution coefficient, and constructing a storm surge water level forecasting equation set comprising a storm surge dynamic equation and a storm surge continuous 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 set to obtain a storm surge forecasting result, and outputting an early warning signal when the storm surge forecasting result exceeds an early warning threshold value; the physical basis of forecasting is guaranteed, the calculation efficiency is improved, and the technical problem that in the prior art, forecasting precision and timeliness of the sudden strong storm surge are not enough is solved.

Provided is a technique for preventing repeated river, storm surge, and tsunami disasters. “Sea hollow (UTSURO)” is constructed at an estuary, violent tide is generated in an estuary basin water area of a river, ebb and flow energy is caused to exceed flow energy due to gravity in a downstream part of the estuary flow area conventionally dominated by the flow energy due to gravity, the flow energy in the river is thus redistributed to deeply dig the riverbed, enhance flood discharge ability, and prevent repeated river disasters, a levee body of the “sea hollow (UTSURO)” is shared, tsunami or storm surge is reflected in a coastal sea area and is prevented in the sea area, tsunami or storm surge invading the estuary, or estuary flood is caused to overflow into an upstream migration water path constituting the “sea hollow (UTSURO)”, and peak cut thereof is performed.

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 discloses a method for evaluating water level rise of a coast closed water area caused by overtopping in a typhoon process, and belongs to the technical field of coast engineering hydrodynamic force and overtopping. 2, establishing a physical model, carrying out a series of tests of wave overtopping of the structure in the typhoon process, and determining the total wave overtopping water body amount of the whole section of the structure; 3, establishing a terrain three-dimensional model of the closed water area between the coastal structures, and obtaining a relation curve of water increment and water level rise of the closed water area between the structures; 4, evaluating the water level rise of the coast closed water area caused by overtopping in the typhoon process. According to the method for evaluating the water level rise of the coast closed water area caused by the overtopping in the typhoon process, the water level rise of the coast closed water area caused by the overtopping in the typhoon process can be accurately evaluated.

The invention discloses a typhoon storm surge beach overtopping intelligent forecasting method based on a Vision Transform neural network. The method comprises the following steps: firstly, acquiring continuous storm surge water level data and wind field data at a forecasting moment; preprocessing the acquired continuous storm surge water level data and the wind field data at the forecast moment, performing extreme value normalization on the preprocessed data, scaling the data to 0-1, and dividing a training set and a test set; based on the training set, the normalized numerical value is input into a Vision transfer neural network for training, and a typhoon storm surge beach overtopping intelligent forecasting model is obtained; and inputting the test set into the typhoon storm surge beach overtopping intelligent forecasting model to adjust and optimize the typhoon storm surge beach overtopping to obtain a final typhoon storm surge beach overtopping intelligent forecasting model, and performing typhoon storm surge beach overtopping forecasting by using the typhoon storm surge beach overtopping intelligent forecasting model. According to the method, the two-dimensional distribution and the water increasing height of the typhoon storm surge overtopping beach can be accurately and quickly predicted.

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 provides an ocean storm surge emergency response scheme recommendation method and device, and the method comprises the steps: constructing a scene-response disaster disposal system fusing an event chain and an emergency plan, and a scene-response visual knowledge graph, through a constructed ocean storm surge scene-coping disaster disposal system, a scene-coping knowledge graph and a knowledge reasoning model, scene-coping matching of historical disaster events can be completed, pre-disaster rehearsal and post-disaster analysis are realized, and coping knowledge base text matching can be performed according to a disaster scene text generated in real time. And an emergency response scheme is generated and a map recommendation result is visually displayed, so that in-disaster response is realized, the whole-process disaster prevention and reduction chain of the ocean storm surge disaster event is perfected, the generalization ability of a knowledge reasoning method is improved, and important support is provided for further improving the intelligent disposal ability of the ocean storm surge disaster.

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.

The invention belongs to the field of artificial intelligence and big data, is applied to the field of smart cities, and relates to a storm surge early warning method, which comprises the following steps: receiving meteorological early warning data; performing storm surge identification processing on the meteorological early warning data through meteorological disaster codes to obtain storm surge early warning data; and analyzing the storm surge early warning data according to a preset data analysis rule by an array to obtain an early warning area and an early warning level of the storm surge, and performing early warning prompt on a target user in the early warning area of the storm surge according to the early warning level. The invention further provides a storm surge early warning device, computer equipment and a storage medium. According to the method and the device, the problems that early warning area recognition errors and early warning delay exist in an existing method, the early warning mode for the storm surge is relatively single, and most early warning messages are not differentially touched to clients according to the severity of the storm surge, so that the clients miss the optimal disaster prevention response time are solved.

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 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, and the method comprises the steps: obtaining LEO observation data and LEO broadcast ephemeris of a shore-based LEO station adjacent to a target water area; calculating angle information of the 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 a precision attenuation factor of the reflection height according to LEO observation data, angle information and LEO-IR; and calculating the water level height of the target water area based on the reflection height, the precision attenuation factor and the height of the antenna phase center. According to the embodiment of the invention, the method employs a larger number of LEO satellites with a higher moving speed to construct a time-consistent satellite arc-to-height rate error correction model and a precision attenuation factor model, solves a problem that the time resolution or spatial resolution or precision is lower in the related technology, and improves the accuracy of the satellite arc-to-height rate error correction model. The method has obvious application prospects and advantages in water level fluctuation monitoring in extreme weather such as earthquakes, tsunami and storm surge.

The utility model discloses a coast storm surge disaster early warning device, which belongs to the field of ocean safety early warning and comprises a base plate fixed on the ground, a hollow placing cylinder arranged at the top of the base plate, a fixing ring detachably connected with the base plate and arranged on the outer wall of the bottom of the placing cylinder, and first water inlets distributed at intervals and arranged on the outer ring wall of the placing cylinder. A vertical tide water barrel is arranged in the containing barrel, second water inlets distributed at intervals are formed in the barrel wall of the tide water barrel, a tide force sensor is arranged in the tide water barrel and located above the second water inlets, a signal emitter is arranged on the top of the tide force sensor, an annular scraper is arranged on the outer wall of the tide water barrel, and a power lifting assembly is arranged on the top of the scraper. The device for monitoring the storm surge can be automatically cleaned, the accuracy and reliability of data are improved, and meanwhile the requirement for manual cleaning is reduced.