Method, device, equipment, medium and product for recognizing storm surge near-shore disaster fused with typhoon dynamic influence domain

Abstract

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.

Description

Technical Field

[0001] This application relates to the field of meteorological disaster monitoring technology, and in particular to a method, device, equipment, medium and product for identifying storm surge nearshore disasters by integrating the dynamic influence domain of typhoons. Background Technology

[0002] Storm surge refers to the abnormal rise in sea level exceeding the normal astronomical tide level under the influence of weather systems such as strong winds and low pressure systems. During a storm, strong winds push seawater towards the shore, causing sea level to rise; simultaneously, the decrease in air pressure in low-pressure areas also contributes to sea level rise, although its impact is relatively small. When this abnormal sea level rise reaches a certain level, it can have a serious impact on coastal areas. Based on the maximum rise during a storm surge (i.e., the peak value of the storm surge), storm surges can be classified into different levels: Typical storm surge: maximum rise in water level ≥ 1.0 meter.

[0003] Moderate storm surge: Maximum rise in water level ≥ 1.5 meters.

[0004] Major storm surge: Maximum rise in water level ≥ 2.0 meters.

[0005] Storm surge: Maximum rise in water level ≥ 2.5.

[0006] Extreme storm surge: Maximum water level rise ≥ 3.0 meters.

[0007] During a storm, a storm surge event is defined as one where the maximum water rise reaches or exceeds 1.0 meter. Traditional storm surge identification methods have the following drawbacks: (1) Static thresholds are often used, without considering the dynamic changes in the radius of influence of typhoon intensity, resulting in a high rate of missed reports of near-shore water increase during weak typhoons.

[0008] (2) Background noise (such as astronomical tides and seasonal sea level fluctuations) interferes with the calculation of water level rise and affects accuracy.

[0009] (3) The lack of a disaster spatial screening mechanism for nearshore areas makes it easy for offshore noise to lead to misjudgment.

[0010] (4) The location of the typhoon center does not match the temporal and spatial data of the marine response.

[0011] Therefore, accurate storm surge disaster assessment is of paramount importance. Summary of the Invention

[0012] The purpose of this application is to provide a method, device, equipment, medium and product for identifying storm surge nearshore disasters by integrating the dynamic influence domain of typhoons, which can achieve accurate storm surge disaster identification.

[0013] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for identifying storm surge nearshore hazards by integrating the dynamic impact domain of typhoons, including: Obtain meteorological and oceanographic reanalysis datasets for a target region within a specified historical timeframe; Preprocessing is performed on the multi-source data in the meteorological and oceanographic reanalysis dataset; the multi-source data includes: sea surface height data and CMA data; Based on the preprocessed multi-source data, background field removal was performed to identify storm surge anomaly data; Based on the typhoon intensity level and the aforementioned storm surge anomaly data, the dynamic impact radius is determined; The typhoon center location is determined based on the dynamic influence radius, and a spherical distance calculation method is used to generate a typhoon dynamic influence domain spatial mask based on the typhoon center location. The nearshore buffer zone is determined based on the spatial mask of the typhoon's dynamic impact domain, and the storm surge disaster area is determined based on the nearshore buffer zone constraint and the method for determining the water level rise threshold. Using geospatial visualization methods, nearshore storm surge hazards are identified based on the storm surge hazard areas, and the identification results are obtained. The identification results are used to generate risk evolution sequences and determine risk assessments.

[0014] In one embodiment, preprocessing of multi-source data in the meteorological-oceanographic reanalysis dataset specifically includes: The spatiotemporal alignment method and bilinear interpolation algorithm are used to process the multi-source data to obtain preprocessed multi-source data.

[0015] In one embodiment, background field removal is performed based on the preprocessed multi-source data to identify storm surge anomaly data, specifically including: Determine the typhoon impact period; the typhoon impact period is determined by the typhoon's start date to end date in the meteorological and oceanographic reanalysis data. The background period is determined based on the typhoon impact period; the background period is determined based on a preset time period from the start date of the typhoon to the end date of the typhoon impact period. The background field is calculated for sea surface height data at all time points within the background period to obtain the spatial average value; Storm surge anomaly values ​​are calculated based on the actual sea surface height during the typhoon's impact period and the aforementioned spatial average value, thus obtaining storm surge anomaly data.

[0016] In one embodiment, the formula for calculating the spatial average value is: ; in, This is the spatial average. For a point in time; For the background period time set; Total data at any given time point; For time points The corresponding sea surface height data.

[0017] In one embodiment, the formula for calculating the storm surge anomaly is: ; in, This represents an anomaly in storm surge. This represents the spatial average sea surface height during typhoon season; This is the spatial average.

[0018] In one embodiment, the mathematical expression corresponding to the spherical distance calculation method is: ; in, The distance is the spherical distance. The radius of the Earth; The latitude corresponding to the grid point; This refers to the latitude corresponding to the center of the typhoon. Longitude; This is the longitude increment.

[0019] Secondly, this application provides a storm surge nearshore disaster identification device that integrates the dynamic influence domain of typhoons, including: The data acquisition module is used to acquire meteorological and oceanographic reanalysis datasets for the target area over a set historical period. The preprocessing module is used to preprocess the multi-source data in the meteorological and oceanographic reanalysis dataset; the multi-source data includes: sea surface height data and CMA data; The elimination module is used to perform background field elimination processing based on preprocessed multi-source data to identify storm surge anomaly data; The dynamic impact radius determination module is used to determine the dynamic impact radius based on the typhoon intensity level and the storm surge anomaly data. The typhoon dynamic impact domain spatial mask generation module is used to determine the typhoon center position based on the dynamic impact radius, and generate a typhoon dynamic impact domain spatial mask based on the typhoon center position using a spherical distance calculation method. The storm surge disaster area determination module is used to determine the nearshore buffer zone based on the spatial mask of the typhoon dynamic impact domain, and to determine the storm surge disaster area based on the nearshore buffer zone constraint and the water level rise threshold determination method. The identification module is used to identify nearshore storm surge hazards based on the storm surge hazard area using geospatial visualization methods, and obtain identification results; the identification results are used to generate risk evolution sequences and determine risk assessments.

[0020] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for identifying storm surge nearshore disasters by integrating the dynamic influence domain of typhoons.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for identifying storm surge nearshore disasters by integrating the dynamic influence domain of typhoons.

[0022] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for identifying storm surge nearshore disasters by integrating the dynamic influence domain of typhoons.

[0023] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, apparatus, equipment, medium, and product for identifying storm surge nearshore disasters by integrating the dynamic impact domain of typhoons. It acquires a meteorological and oceanographic reanalysis dataset of the target area over a set historical period and preprocesses multi-source data; then, it performs background field removal; based on typhoon intensity level and storm surge anomaly data, it determines the dynamic impact radius; based on the dynamic impact radius, it determines the typhoon center location and generates a spatial mask of the typhoon dynamic impact domain using a spherical distance calculation method; based on the typhoon dynamic impact domain spatial mask, it determines the nearshore buffer zone, and based on nearshore buffer zone constraints and storm surge threshold determination methods, it identifies the storm surge disaster area. This application dynamically matches typhoon intensity and impact radius to improve the coverage accuracy of strong typhoons and the identification sensitivity of weak typhoons; a dual spatial constraint mechanism (nearshore buffer zone constraint and storm surge threshold determination) integrates the typhoon impact domain and the nearshore buffer zone, eliminating offshore interference and focusing on disaster-sensitive areas. Using geospatial visualization methods, it identifies storm surge nearshore disasters based on the storm surge disaster area, obtaining the identification results. Therefore, this application can achieve accurate storm surge disaster identification. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart for identifying nearshore storm surge hazards by integrating the dynamic impact domain of typhoons; Figure 2 Structural diagram of a storm surge nearshore disaster identification device that integrates the dynamic impact domain of typhoons; Figure 3 Design structure diagram of a storm surge nearshore disaster identification device that integrates the dynamic impact domain of typhoons; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] In one exemplary embodiment, such as Figure 1 As shown, a method for identifying nearshore storm surge hazards by integrating the dynamic impact domain of typhoons is provided, including: Step 100: Obtain the meteorological and oceanographic reanalysis dataset for the target area within the set historical time frame.

[0029] Step 200: Preprocess the multi-source data in the meteorological-oceanographic reanalysis dataset. The multi-source data includes sea surface height data and CMA data.

[0030] In one embodiment, preprocessing of multi-source data in a meteorological-oceanographic reanalysis dataset specifically includes: The spatiotemporal alignment method and bilinear interpolation algorithm are used to process the multi-source data to obtain preprocessed multi-source data.

[0031] Step 300: Based on the preprocessed multi-source data, perform background field removal processing to identify storm surge anomaly data.

[0032] In one embodiment, background field removal is performed based on preprocessed multi-source data to identify storm surge anomaly data, specifically including: Determine the typhoon impact period; the typhoon impact period is determined by the typhoon's start date to end date in the meteorological and oceanographic reanalysis data.

[0033] The background period is determined based on the typhoon's impact period; the background period is determined based on a preset time period from the start date of the typhoon to the end date of the typhoon's impact period.

[0034] The background field was calculated from the sea surface height data at all time points within the background period to obtain the spatial average value.

[0035] The formula for calculating the spatial average is: .

[0036] in, This is the spatial average. For a point in time; For the background period time set; Total data at any given time point; For time points The corresponding sea surface height data.

[0037] Storm surge anomaly values ​​are calculated based on the actual sea surface height and spatial average value during the typhoon impact period, and storm surge anomaly data are obtained.

[0038] The formula for calculating storm surge anomalies is: .

[0039] in, This represents an anomaly in storm surge. This represents the spatial average sea surface height during typhoon season; This is the spatial average.

[0040] Step 400: Determine the dynamic impact radius based on typhoon intensity level and storm surge anomaly data.

[0041] Step 500: Determine the typhoon center location based on the dynamic influence radius, and generate a typhoon dynamic influence domain spatial mask based on the typhoon center location using the spherical distance calculation method.

[0042] In one embodiment, the mathematical expression corresponding to the spherical distance calculation method is: .

[0043] in, The distance is the spherical distance. The radius of the Earth; The latitude corresponding to the grid point; This refers to the latitude corresponding to the center of the typhoon. Longitude; This is the longitude increment.

[0044] Step 600: Determine the nearshore buffer zone based on the spatial mask of the typhoon's dynamic impact domain, and determine the storm surge disaster area based on the nearshore buffer zone constraint and the method for determining the water level rise threshold.

[0045] Step 700: Using geospatial visualization methods, identify nearshore storm surge hazards based on storm surge hazard areas, and obtain the identification results. The identification results are used to generate risk evolution sequences and determine risk assessments.

[0046] This application acquires marine reanalysis data and typhoon track information and completes spatiotemporal alignment of the 0.1° grid; calculates the sea level background field based on the 10-day impact-free period before and after the typhoon, and extracts the storm surge anomaly value ΔH; classifies typhoons into six levels from TD to SuperTY based on wind speed and assigns dynamic radii of 150km-400km, and generates a spatial mask using the spherical distance formula; combines a 100km coastal buffer zone and a storm surge threshold of ≥1.0m, determines the disaster area through spatial intersection, and then generates a dynamic risk map, rasterized disaster products, and statistical reports.

[0047] This application innovatively breaks through the limitations of traditional static thresholds: dynamic matching of typhoon intensity and impact radius improves the coverage accuracy of strong typhoons and the sensitivity of weak typhoon identification; a dual spatial constraint mechanism integrates the typhoon impact domain and near-shore buffer zone, eliminating interference from the open sea and focusing on disaster-sensitive areas; and a 20-day background period design effectively separates seasonal and long-term trend noise.

[0048] Specifically, we acquire sea surface height and other data from the historical 20-year meteorological and oceanographic reanalysis dataset of the target area, and extract typhoon track, central pressure, and wind speed information from the CMA tropical cyclone optimal track dataset.

[0049] Typhoon path data is matched to the marine data spatial grid (0.1°×0.1° resolution) using a bilinear interpolation algorithm; the storage structure is organized according to typhoon event ID, and each event contains daily sea surface height data during the typhoon and corresponding typhoon metadata.

[0050] Determining the typhoon's impact period: The impact period is defined as the time from the start to the end of the typhoon; Constructing the background period: The background period is defined as 10 days before the start of the typhoon and 10 days after its end; Calculating the background field: The spatial average of sea level height data at all time points within the background period is calculated. .

[0051] Calculate storm surge anomalies: .

[0052] Typhoon intensity classification: Typhoons are classified into 6 levels based on their maximum sustained winds near the center: Tropical Depression (TD): Wind speed <17.2m / s; Tropical Storm (TS): 17.2m / s ≤ Wind speed <24.5m / s; Severe Tropical Storm (STS): 24.5m / s ≤ Wind speed <32.7m / s; Typhoon (TY): 32.7m / s ≤ Wind speed <41.5m / s; Severe Typhoon (STY): 41.5m / s ≤ Wind speed <51.0m / s; Super Typhoon (SuperTY): Wind speed ≥51.0m / s.

[0053] Determination of dynamic impact radius: A fixed impact radius is assigned to each typhoon level, as shown in Table 1.

[0054] Table 1 Typhoon Classification and Impact Radius

[0055] Spatial mask generation: Calculating the distance between grid points and the typhoon center based on the spherical distance formula. .

[0056] in, It is the Earth's radius (6371 km).

[0057] Nearshore buffer zone generation: based on high-precision global coastline data (10m resolution); calculate the spherical distance from each grid point to the nearest coastline. .

[0058] Construct a 100-kilometer near-shore buffer zone : .

[0059] Joint spatial constraints: The dynamic impact area of ​​the typhoon and nearshore buffer zone Find the intersection: .

[0060] disaster area determination: In the effective area Grid points with storm surge anomalies ≥ 1.0 meter: .

[0061] Dynamic risk visualization: Draw a spatial distribution map of storm surge anomalies, using a red-blue gradient color scheme to represent the intensity of the surge; overlay the typhoon's movement path and display the typhoon's position evolution in a time series; mark the boundary of the 100-kilometer buffer zone near the coast.

[0062] Risk product generation: Output storm surge anomaly field data (NetCDF format); generate disaster spatial distribution map (raster image format); generate daily risk evolution sequence map during typhoon.

[0063] Disaster characteristic statistics: Calculate the maximum water increase and its location; calculate the total area of ​​the disaster area; output a risk level assessment report.

[0064] In one exemplary embodiment, such as Figure 2 As shown, a storm surge nearshore disaster identification device integrating the dynamic influence domain of typhoons is provided, including: The data acquisition module is used to acquire meteorological and oceanographic reanalysis datasets for the target area over a set historical period.

[0065] The preprocessing module is used to preprocess multi-source data in the meteorological and oceanographic reanalysis dataset; the multi-source data includes sea surface height data and CMA data.

[0066] The elimination module is used to perform background field elimination processing based on preprocessed multi-source data to identify storm surge anomaly data.

[0067] The dynamic influence radius determination module is used to determine the dynamic influence radius based on the typhoon intensity level and the storm surge anomaly data.

[0068] The typhoon dynamic impact domain spatial mask generation module is used to determine the typhoon center position based on the dynamic impact radius, and to generate a typhoon dynamic impact domain spatial mask based on the typhoon center position using a spherical distance calculation method.

[0069] The storm surge disaster area determination module is used to determine the nearshore buffer zone based on the spatial mask of the typhoon's dynamic impact domain, and to determine the storm surge disaster area based on the nearshore buffer zone constraint and the water level rise threshold determination method.

[0070] The identification module is used to identify nearshore storm surge hazards based on storm surge disaster areas using geospatial visualization methods, and obtain identification results. The identification results are used to generate risk evolution sequences and determine risk assessments.

[0071] like Figure 3 As shown, in practical applications, the storm surge nearshore disaster identification device that integrates the dynamic impact domain of typhoons mentioned in this application includes: a data acquisition and processing module, a background field removal module, a typhoon dynamic impact domain modeling module, a nearshore disaster identification module, and a disaster risk output module.

[0072] The data acquisition and processing module obtains data such as sea surface height (SPH) and CMA typhoon path by using a multi-source data spatiotemporal alignment method.

[0073] The background field removal module obtains storm surge anomaly data by calculating the average value of the period before and after the typhoon's impact-free period; the typhoon dynamic impact domain modeling module generates a spatial mask of the typhoon dynamic impact domain by calculating the typhoon intensity classification and spherical distance; the nearshore disaster identification module obtains storm surge disaster areas by using nearshore buffer zone constraints and storm surge threshold determination methods; and the disaster risk output module obtains storm surge risk distribution maps and early warning products by using geospatial visualization methods.

[0074] The data acquisition and processing module is responsible for the acquisition, preprocessing, and spatiotemporal alignment of multi-source data. First, it acquires a 20-year historical meteorological and oceanographic reanalysis dataset (CORA v2.0) of the target area, extracting key elements such as sea surface height. Simultaneously, it obtains typhoon track, central pressure, and wind speed information from the CMA tropical cyclone optimal track dataset. Using a bilinear interpolation algorithm, the typhoon track data is matched to a 0.1°×0.1° resolution oceanographic data spatial grid, resolving the spatial scale differences between different data sources. Finally, a structured storage system is constructed based on typhoon event IDs. Each event contains daily sea surface height data during the typhoon's impact period and corresponding typhoon metadata (track, intensity, time, etc.), providing a standardized data foundation for subsequent analysis.

[0075] Background field elimination module: This module determines the typhoon impact period (from start to end) and constructs a background period (10 days before the start date to 10 days after the end date). By calculating the spatial average of sea level height data at all time points within the background period, a sea level background field representing the state without typhoon interference is obtained. The key calculation process is: subtracting the background field value from the actual sea level height data during the typhoon impact period to obtain the storm surge anomaly value ΔH. This method effectively separates anomalous sea level changes caused by typhoons, eliminates interference from seasonal and long-term trends, and provides accurate water level anomaly data for disaster identification.

[0076] The typhoon dynamic impact domain modeling module includes the following steps: A spatial dynamic model of typhoon impact range is established. Typhoons are classified into six levels (TD to SuperTY) based on their maximum near-center wind speed, and a fixed impact radius (150km-400km) is assigned to each level. The distance between grid points and the typhoon center is calculated using the spherical distance formula to generate a dynamic spatial mask of the impact domain. The core calculation formula utilizes the latitude-longitude trigonometric relationship to accurately reflect the influence of the Earth's curvature. This model breaks through the limitations of traditional fixed radius models, enabling the typhoon impact range to adaptively adjust with intensity, significantly improving the coverage accuracy of strong typhoon impact domains and the sensitivity of weak typhoon identification.

[0077] Nearshore disaster identification module: Based on 10m resolution global coastline data, the spherical distance from grid points to the nearest coastline is calculated to construct a 100km nearshore buffer mask. The intersection of the dynamic influence domain mask and the nearshore buffer mask yields the effective identification area. Within this area, a threshold judgment is performed, marking grid points with storm surge anomalies ≥1.0 meter as disaster areas. Through a spatial dual constraint mechanism, interference from invalid areas in the open ocean is eliminated, focusing on disaster-sensitive areas in the coastal zone and solving the spatial misalignment problem in nearshore disaster identification using traditional methods.

[0078] Disaster Risk Output Module: Enables multi-dimensional output of risk products. Dynamic visualization: Draws anomaly distribution maps of storm surge (red-blue gradient), overlaid with the typhoon path evolution and a 100km nearshore buffer zone boundary. Product generation: Outputs storm surge anomaly data in NetCDF format, disaster distribution maps in raster image format, and daily risk evolution sequences. Statistical analysis: Calculates the maximum storm surge value and its location coordinates, calculates the total disaster area, and generates a risk level assessment report (yellow / orange / red three-level warning). This forms a three-in-one output system of "data-image-report," directly supporting disaster prevention decision-making.

[0079] This application aims to solve three major problems: background field interference, typhoon dynamic impact domain modeling, and nearshore disaster spatial identification, so as to achieve more accurate storm surge disaster identification.

[0080] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores storm surge nearshore hazard identification data based on the fusion of typhoon dynamic impact domains. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a storm surge nearshore hazard identification method based on the fusion of typhoon dynamic impact domains.

[0081] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0082] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0083] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0084] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0087] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0088] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A storm surge nearshore disaster identification method fusing the dynamic influence domain of a typhoon, characterized in that, The method comprises the following steps: acquire a meteorological and ocean reanalysis dataset within a historical set period in a target area; preprocess multi-source data in the meteorological and ocean reanalysis dataset; the multi-source data comprises sea surface height data and CMA data; based on the preprocessed multi-source data, perform background field elimination processing to determine storm surge anomaly data; based on the intensity grade of the typhoon and the storm surge anomaly data, determine a dynamic influence radius; determine the center position of the typhoon according to the dynamic influence radius, and generate a typhoon dynamic influence domain spatial mask based on the center position of the typhoon by using a spherical distance calculation method; determine a nearshore buffer zone according to the typhoon dynamic influence domain spatial mask, and determine a storm surge disaster area based on a nearshore buffer zone constraint and a surge threshold determination method; perform storm surge nearshore disaster identification based on the storm surge disaster area by using a geographic spatial visualization method to obtain an identification result; the identification result is used to generate a risk evolution sequence and determine a risk assessment.

2. The storm surge nearshore disaster identification method of fusing the dynamic influence domain of a typhoon according to claim 1, characterized in that, The preprocessing of the multi-source data in the meteorological and ocean reanalysis dataset comprises the following steps: the multi-source data is processed by using a spatiotemporal alignment method and a bilinear interpolation algorithm to obtain preprocessed multi-source data.

3. The method according to claim 1, wherein, based on the preprocessed multi-source data, perform background field elimination processing to determine storm surge anomaly data, which comprises the following steps: determine a typhoon influence period; the typhoon influence period is determined based on the typhoon start date to the end date in the meteorological and ocean reanalysis dataset; determine a background period according to the typhoon influence period; the background period is determined based on a preset time period before the typhoon start date to a preset time period after the end date in the typhoon influence period; calculate the background field of the sea surface height data at all time points in the background period to obtain a spatial average value; calculate the storm surge anomaly value based on the actual sea surface height in the typhoon influence period and the spatial average value to obtain storm surge anomaly data.

4. The storm surge nearshore disaster identification method of fusing the dynamic influence domain of a typhoon according to claim 3, characterized in that, The calculation formula of the spatial average value is as follows: ； wherein, is the spatial average; is the time point; is the background period time set; is the time point total data; is the time point corresponding sea surface height data.

5. The method according to claim 3, wherein, The calculation formula of the storm surge anomaly value is as follows: ； wherein, is the storm surge anomaly value; is the spatial mean of sea surface height during the typhoon period; is the spatial mean.

6. The method according to claim 1, wherein, The mathematical expression of the spherical distance calculation method is as follows: ； wherein, is the spherical distance; is the radius of the earth; is the latitude corresponding to the grid point; is the latitude corresponding to the typhoon center; is the longitude; is the longitude increment.

7. A storm surge nearshore disaster identification device that fuses the dynamic influence domain of a typhoon, characterized by, The method comprises the following steps: a data acquisition module is configured to acquire a meteorological and ocean reanalysis dataset within a historical set period in a target area; a preprocessing module is configured to preprocess multi-source data in the meteorological and ocean reanalysis dataset; the multi-source data comprises sea surface height data and CMA data; an elimination module is configured to perform background field elimination processing based on the preprocessed multi-source data to determine storm surge anomaly data; a dynamic influence radius determination module is configured to determine a dynamic influence radius based on the intensity grade of the typhoon and the storm surge anomaly data; a typhoon dynamic influence domain spatial mask generation module is configured to determine the center position of the typhoon according to the dynamic influence radius, and generate a typhoon dynamic influence domain spatial mask based on the center position of the typhoon by using a spherical distance calculation method; a storm surge disaster area determination module is configured to determine a nearshore buffer zone according to the typhoon dynamic influence domain spatial mask, and determine a storm surge disaster area based on a nearshore buffer zone constraint and a surge threshold determination method; An identification module is configured to identify storm surge near-shore disasters based on the storm surge disaster area by using a geospatial visualization method, and obtain an identification result; the identification result is used to generate a risk evolution sequence and determine a risk assessment.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for identifying storm surge near-shore disasters in a dynamic influence domain of a typhoon according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for identifying storm surge near-shore disasters in a dynamic influence domain of a typhoon according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the method for identifying storm surge near-shore disasters in a dynamic influence domain of a typhoon according to any one of claims 1-6. The computer program is executed by the processor to implement the method for identifying storm surge near-shore disasters in a dynamic influence domain of a typhoon according to any one of claims 1-6.

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