Typhoon database construction method

Abstract

The invention relates to a typhoon database construction method. The method is suitable for the technical field of offshore wind power structure wind resistance design. The technical problem to be solved by the invention is to provide a typhoon database construction method. According to the technical scheme, the typhoon database construction method comprises the steps that S100, an optimal typhoon path data set is acquired; s200, based on the typhoon optimal path data set, a wind field model is adopted to simulate a wind field of the target area, wind speed data of all simulation points in the target area are obtained, and a simulation data set is formed; s300, establishing a plurality of data tables in the object-relational database for storing typhoon names, typhoon paths and strength data in the typhoon optimal path data set, and typhoon names, simulation point longitudes and latitudes, wind speed data and typhoon time data in the simulation data set; and S400, importing the data of the typhoon optimal path data set in the step S100 and the data of the simulation data set in the step S200 into each data table in the step S300.

Description

Technical Field

[0001] The present invention relates to a typhoon database construction method, which is applicable to the field of wind-resistant design of offshore wind power structures. Background Art

[0002] Accurately simulating and managing typhoon path data is crucial for the wind-resistant design of offshore wind turbine structures. This data is a crucial prerequisite for studying the wind load distribution and wind resistance of offshore wind turbine structures. Effectively managing and utilizing typhoon path data can provide a scientific basis for wind turbine design, improving their wind resistance and safety.

[0003] The construction and operation of offshore wind farms face complex environmental conditions, particularly the impact of strong typhoons. Typhoons, as extreme weather phenomena, are extremely destructive and pose a serious threat to the safety and stability of offshore wind turbine structures. Therefore, effective typhoon path prediction and data management are crucial for the wind-resistant design of offshore wind turbine structures.

[0004] Currently, the wind-resistant design of offshore wind farms primarily relies on the analysis and research of historical typhoon path data. Traditional typhoon path data management methods often rely on manual compilation, resulting in fragmented and difficult-to-share data and a lack of unified data standards and efficient management methods, leading to inefficient data utilization. Furthermore, existing typhoon path datasets are mostly stored in relational databases. However, with the increasing volume of typhoon data, traditional relational databases are struggling to handle large amounts of data and are unable to meet the demands of fast query and data analysis. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: to provide a typhoon database construction method in response to the above-mentioned problems.

[0006] The technical solution adopted by the present invention is: a typhoon database construction method, comprising:

[0007] S100, obtain the best typhoon path dataset;

[0008] S200, based on the typhoon optimal path data set, using a wind field model to simulate the wind field in the target area, obtaining wind speed data of each simulation point in the target area, and forming a simulation data set;

[0009] S300, establishing multiple data tables in an object-relational database for storing typhoon names, typhoon paths, and intensity data in the typhoon optimal path dataset, and typhoon names, simulation point latitude and longitude, wind speed data, and typhoon time data in the simulation dataset;

[0010] S400 , importing the typhoon optimal path dataset data in step S100 and the simulation dataset data in step S200 into the data tables in step S300 .

[0011] The step S200 includes:

[0012] S210, determining a plurality of evenly distributed simulation points in the target area;

[0013] S220, constructing a simulated circle with the simulated point as the center, and the radius of the simulated circle is a preset value;

[0014] S230: Filter the typhoons that have passed through the simulation circle, and perform wind field simulation based on the filtered typhoons to obtain wind speed data at each typhoon-related simulation point and at each typhoon time in the target area.

[0015] The step S210 includes:

[0016] Based on the preset size, the target area is evenly divided into multiple blocks, and the center point of each block is used as the simulation point.

[0017] The wind field simulation is performed based on the screened typhoons to obtain wind speed data at each typhoon time simulation point in the target area, including:

[0018] The Meng wind field model is used to simulate the wind field in the target area, taking the typhoon center longitude and latitude, near-center wind speed, and center pressure data of the typhoon best path dataset as input;

[0019] In the Meng wind field model, the tangential gradient wind speed V is obtained considering the pressure reduction along the height. θg and radial gradient wind speed V rg :

[0020]

[0021] c r =-V T cos(θ-β′)

[0022] c θ =-V T sin(θ-β′)

[0023] Where θ is the angle between the line connecting the simulation point and the center of the tropical cyclone and the east direction, with the counterclockwise direction being positive; β′ is the angle between the moving direction of the tropical cyclone and the east direction, with the counterclockwise direction being positive; ρ is the air density; f c =2×7.273×10 -5 sinψ represents the Coriolis parameter, ψ is the latitude of the simulation point; r is the radial distance between the simulation point and the typhoon center, and p(r,z) represents the ocean surface wind pressure at the height z of the simulation point;

[0024] The obtained gradient wind speed is used as the initial value for the near-ground wind speed calculation and substituted into the following formula to calculate the surface friction wind speed V θ ′ and V r ′:

[0025]

[0026] Where k m is the kinematic viscosity; D1 and D2 can be calculated by the following formula:

[0027]

[0028] Where k is the Karman constant; Cd is the drag coefficient; h = Az00.86, is the average roughness unit height, where A = 11.4; zero plane displacement d = 0.75h; z 10 is h+10m; z0 is the surface roughness length;

[0029] The surface friction wind speed V θ ′ and V r ′ and gradient wind speed V θg and V rg The new near-ground wind speed is obtained by superposition, and the iterative calculation is repeated many times until convergence, and the wind speed at a height of 10m at the simulation point is calculated.

[0030] The method of establishing multiple data tables in the object-relational database includes:

[0031] Data Table 1, used to store typhoon names in the typhoon best path dataset;

[0032] Data Table 2 is used to store typhoon path and intensity data in the typhoon best path dataset;

[0033] Data Table 3 is used to store the typhoon names in the simulation data set;

[0034] Data table 4 is used to store the latitude and longitude of the simulation points in the simulation data set;

[0035] Data Table 5 is used to store the wind speed data of the simulation points in the simulation data set;

[0036] Data Table 6 is used to store typhoon time data in the simulation data set.

[0037] A typhoon database construction device, comprising:

[0038] Data acquisition module, used to obtain the typhoon best path data set;

[0039] The wind field simulation module is used to simulate the wind field in the target area using a wind field model based on the typhoon optimal path dataset, obtain the wind speed data of each simulation point in the target area, and form a simulation dataset;

[0040] A data table creation module is used to create multiple data tables in an object-relational database to store typhoon names, typhoon paths, and intensity data in the typhoon optimal path dataset, as well as typhoon names, simulation point latitude and longitude, wind speed data, and typhoon time data in the simulation dataset;

[0041] The data import module is used to import the typhoon optimal path dataset data and simulation dataset data into various data tables.

[0042] A storage medium stores a computer program that can be executed by a processor, and when the computer program is executed, the steps of the typhoon database construction method are implemented.

[0043] A typhoon database construction device comprises a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the typhoon database construction method are implemented.

[0044] A typhoon database is constructed based on the typhoon database construction method.

[0045] A typhoon data interaction method based on the typhoon database.

[0046] The beneficial effects of the present invention are as follows: the present invention adopts the typhoon optimal path data set as the basic data source of the typhoon database, and also simulates the wind speed in the typhoon-affected area by introducing a wind field model, and combines the simulation results with the actual path data to provide users with more accurate and comprehensive typhoon data.

[0047] In order to solve the storage and query problems of large-scale typhoon data, the present invention adopts an object-relational database on the PostgreSQL platform to manage typhoon data. Multiple data tables are established in the database, and partition table technology is used to partition and store data according to time, typhoon name, etc., thereby reducing the data scanning range during query, providing a storage structure for the typhoon optimal path data set, and also supporting the storage of wind field simulation data sets. The present invention supports distributed storage architecture to ensure efficient storage and access of large-scale data, especially when the storage and query involve a large number of path points, wind speed and time data, the system can process them efficiently. An efficient indexing mechanism is established for typhoon path data, wind speed data and time data. Large-scale data is indexed using efficient indexing methods such as B-tree index and spatial index, making data query more efficient. During the data import process, batch insertion is adopted to achieve batch processing of data through the integration of SQLAlchemy and Flask framework. By reducing the number of database interactions for each operation, the speed of data import is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of an embodiment.

[0049] Figure 2 This is a simulated diagram of the path and wind field of Typhoon Lekima (2019).

[0050] Figure 3 This figure compares the simulated wind speed results and the measured results of Typhoon Lekima.

[0051] Figure 4 Schematic diagram of the front-end user interaction interface in the embodiment.

[0052] Figure 5 This is a diagram showing typhoon path and typhoon intensity data displayed on the front-end user interaction interface in the embodiment. DETAILED DESCRIPTION

[0053] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0054] In the description of the present invention, "a plurality" means two or more. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly indicating the number of the indicated technical features, or as implicitly indicating the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art.

[0055] Example 1: Figure 1 As shown, this embodiment is a method for constructing a typhoon database, which specifically includes the following steps:

[0056] S100. Obtain the typhoon optimal path dataset.

[0057] Download the CMA best track dataset for the target time period from the official website of the Tropical Cyclone Data Center of the China Meteorological Administration. The CMA best track dataset records tropical cyclones in the northwest Pacific region, located north of the equator and west of 180°E, including the South China Sea. The best track records include date and time, intensity category, latitude, longitude, minimum central pressure, and maximum near-center wind speed. Intensity categories are based on the Chinese Tropical Cyclone Scale. Latitude and longitude are expressed in units of 0.1°, pressure is expressed in hectopascals (hPa), and wind speed is expressed in meters per second (m / s).

[0058] S200: Based on the typhoon optimal path data set, a wind field model is used to simulate the wind field in the target area, and wind speed data of each simulation point in the target area is obtained to form a simulation data set.

[0059] S210: Determine a plurality of evenly distributed simulation points in the target area.

[0060] The target area is evenly divided into multiple blocks with a resolution of 10 km, and the center point of each block is used as the simulation point.

[0061] S220. Establish a simulation circle with the simulation point as the center and a radius of 250 km.

[0062] S230: Filter the typhoons that have passed through the simulation circle, and perform wind field simulation based on the filtered typhoons to obtain wind speed data at each typhoon-related simulation point and at each typhoon time in the target area.

[0063] In this embodiment, the typhoon center longitude and latitude, near-center wind speed, and center pressure data from the typhoon optimal path dataset are used as input, and the Meng wind field model is used to simulate the wind field in the target area. Specifically, the following steps are performed:

[0064] The wind field simulation of the selected typhoons is carried out in turn, and the wind field model is selected as the Meng wind field model. In the Meng wind field model, the tangential gradient wind speed V considering the pressure reduction along the height is first obtained by the following formula:θg and radial gradient wind speed V rg :

[0065]

[0066] Where: c r =-V T cos(θ-β′), c θ =-V T sin(θ-β′), where θ is the angle between the line connecting the simulation point and the center of the tropical cyclone and the east direction, with the counterclockwise direction being positive, and β′ is the angle between the direction of movement of the tropical cyclone and the east direction, also with the counterclockwise direction being positive. ρ is the air density, which is 1.2 kg / m3; f c =2×7.273×10 -5 sinψ represents the Coriolis parameter, ψ is the latitude of the simulation point; r is the radial distance between the simulation point and the center of the tropical cyclone, and p(r,z) represents the ocean surface wind pressure at the height z of the simulation point.

[0067] The obtained gradient wind speed is used as the initial value for the near-ground wind speed calculation and substituted into the following formula to calculate the surface friction wind speed V θ ′ and V r ′:

[0068]

[0069] Where k m is the kinematic viscosity, generally taken as 100m2 / s. D1 and D2 can be calculated using the following formula:

[0070]

[0071] Where k = 0.4, is the Karman constant; Cd is the drag coefficient; h = Az00.86, is the average roughness unit height, where A = 11.4; zero plane displacement d = 0.75h; z 10 is h+10m; z0 is the surface roughness length, which can be taken as 0.0001m on the ocean surface. θ ′ and V r ′ and gradient wind speed V θg and V rg The new near-surface wind speed is obtained by superimposing the above values. Repeating the calculations repeatedly until convergence allows the wind speed at a height of 10m at the simulation point to be calculated. Wind speed simulations are performed for all simulation points within the simulation area. The wind speeds at all simulation points within the target area are obtained, forming the Meng wind field model simulation dataset.

[0072] Taking the 2019 super typhoon "Lekima" as the simulation object, Figure 2The following chart shows Lekima's trajectory and central intensity before and after landfall in China. Simulated wind field results at a height of 10 meters at different stages of the typhoon are also presented. It can be seen that when Lekima was developing its intensity over the ocean surface, its eye was very compact. Using a surface roughness length z0 of 0.0001 meters, the simulated maximum wind speed near the center reached 61 meters per second (CMA recorded winds of 62 meters per second). 26 hours after landfall, Lekima weakened significantly to tropical storm intensity as it reached the border between Jiangsu and Shanghai. Using a surface roughness length z0 of 0.05 meters, the simulated maximum wind speed near the center reached 24 meters per second (CMA recorded winds of 23 meters per second). Due to the northward movement of the cyclone at this time, the maximum wind speed occurred east of the cyclone center, and the asymmetry of its wind field structure was evident.

[0073] from Figure 3 It can be seen that the simulated wind speeds at each moment are quite close to the wind speeds measured at the meteorological station. The wind field model established in this example can well capture the intensity evolution process of historical typhoons.

[0074] S300 . Establish multiple data tables in an object-relational database for storing typhoon and path data, including typhoon optimal path data sets and simulation data sets.

[0075] Using the PostgreSQL platform as the database, multiple data tables are established, including data tables one, two, three, four, five, and six.

[0076] In this embodiment, Data Table 1 (Typhoon) and Data Table 2 (TyphoonPath) are respectively used to store the typhoon name, typhoon path, and intensity data in the CMA optimal path dataset; Data Table 3 (CMAV_Typhoon_Name), Data Table 4 (CMAV_SpatialPoints), Data Table 5 (CMAV_Data), and Data Table 6 (CMAV_Time) are respectively used to store the typhoon name, simulation point latitude and longitude, wind speed data, and typhoon time data in the Meng wind field model simulation dataset.

[0077] This embodiment uses PostgreSQL as the database platform, which has good data processing capabilities and scalability, can support the storage, query and management of large-scale typhoon data, and through the integration of the Flask framework, can effectively handle complex back-end tasks.

[0078] S400 , importing the typhoon optimal path dataset data in step S100 and the simulation dataset data in step S200 into the data tables in step S300 .

[0079] This example uses the Flask framework to handle HTTP requests, and defines API endpoints for front-end interaction through a routing mechanism, such as the / typhoon endpoint for obtaining and adding typhoon information, the / typhoon_path endpoint for obtaining typhoon path data, and the / export_typhoon_data_years endpoint for exporting typhoon data for a specific year. SQLAlchemy is used for ORM (Object-Relational Mapping) mapping, simplifying the writing of interface programs in Python.

[0080] The CMA best path dataset and Meng wind model simulation data are imported into the PostgreSQL database through the Flask framework. The program first defines the database connection configuration and creates a database connection and session. When processing the CMA best path data, the typhoon ID, name, date, and path data are extracted by parsing the input data file and stored in the Typhoon and TyphoonPath tables. For each typhoon, if there is no record with the same name and source in the database, a new Typhoon record is added. When processing the Meng wind model simulation data, the CSV file content is read, the timestamp is converted to a datetime object, and the columns are renamed to match the column names in the database table. Then, spatial points, typhoon names, and wind field data are batch inserted.

[0081] Example 2: This example is a typhoon database construction device, including: a data acquisition module, a wind field simulation module, a data table establishment module and a data import module, etc.

[0082] In this example, the data acquisition module is used to obtain the typhoon optimal path data set; the wind field simulation module is used to simulate the wind field in the target area based on the typhoon optimal path data set using a wind field model, obtain the wind speed data of each simulation point in the target area, and form a simulation data set; the data table establishment module is used to establish multiple data tables in the object-relational database to store the typhoon name, typhoon path and intensity data in the typhoon optimal path data set, as well as the typhoon name, latitude and longitude of the simulation point, wind speed data, and typhoon time data in the simulation data set; the data import module is used to import the typhoon optimal path data set and the simulation data set data into each data table.

[0083] Example 3: This example is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the typhoon database construction method in Example 1 are implemented.

[0084] Example 4: This example is a typhoon database construction device having a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the typhoon database construction method described in Example 1 are implemented.

[0085] Example 5: This example is a typhoon database, which is constructed based on the typhoon database construction method described in Example 1.

[0086] Example 6: This example is a typhoon data interaction method. This interaction method is based on the typhoon database in Example 5. It uses the PyQt5 framework to create a desktop application as the front-end user interaction interface, which realizes the query and display of CMA data. Figure 4 As shown, the interface is divided into two parts: the map display area on the left and the control panel on the right. QHBoxLayout is used to implement the main layout, the QWebEngineView component loads and displays the map, and QVBoxLayout is used for the layout of the control panel.

[0087] The map display area uses the QWebEngineView component to load a local HTML file to display typhoon path information. The typhoon path on the map is dynamically updated by calling JavaScript functions. The path data comes from a backend API, which uses the requests library to send HTTP requests to obtain the data and pass it to the JavaScript code for rendering.

[0088] The control panel includes functions such as data source selection, year selection, typhoon selection, and typhoon query. The QComboBox component is used to implement data source and year selection, and these controls are laid out through QFormLayout. The year options are listed through QComboBox. After the user selects a year, the program calls the backend API to obtain the typhoon data for that year and updates the typhoon selection control. Typhoon selection is also implemented using QComboBox to list the typhoons for the selected year. After the user selects a specific typhoon, the program will call the backend API to obtain the path data of the typhoon and display it on the map. Figure 5 This example shows a typhoon path displayed on a map. After the user selects the year and typhoon in the control panel, the program calls the backend API to obtain the typhoon path data. The path data includes information such as time, latitude, longitude, air pressure, and wind speed. The path data is not only displayed on the map, but also in a QTableWidget. The QTableWidget is used to display detailed information about each typhoon path point, making it easier for users to view and analyze. Figure 5An example of using a QTableWidget to display typhoon path data is also shown. Data export and batch download functions are implemented using QPushButton and QFileDialog components. Users can export queried typhoon data as text files or batch download typhoon data for multiple years.

[0089] This embodiment uses the PyQt5 framework to create a front-end interface, providing functions such as map display and data query. It can easily interact through desktop applications to query typhoon paths and wind field information. The operation is simple and intuitive, greatly improving the user experience.

[0090] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present invention set forth in the claims using ordinary skills without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0091] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0092] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0093] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0094] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0095] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0096] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0097] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for constructing a typhoon database, characterized in that: include: S100, obtain the best typhoon path dataset; S200, based on the typhoon optimal path data set, using a wind field model to simulate the wind field in the target area, obtaining wind speed data of each simulation point in the target area, and forming a simulation data set; S300, establishing multiple data tables in an object-relational database for storing typhoon names, typhoon paths, and intensity data in the typhoon optimal path dataset, and typhoon names, simulation point latitude and longitude, wind speed data, and typhoon time data in the simulation dataset; S400 , importing the typhoon optimal path dataset data in step S100 and the simulation dataset data in step S200 into the data tables in step S300 .

2. The method for constructing a typhoon database according to claim 1, characterized in that: The step S200 includes: S210, determining a plurality of evenly distributed simulation points in the target area; S220, constructing a simulated circle with the simulated point as the center, and the radius of the simulated circle is a preset value; S230: Filter the typhoons that have passed through the simulation circle, and perform wind field simulation based on the filtered typhoons to obtain wind speed data at each typhoon-related simulation point and at each typhoon time in the target area.

3. The method for constructing a typhoon database according to claim 2, wherein: The step S210 includes: Based on the preset size, the target area is evenly divided into multiple blocks, and the center point of each block is used as the simulation point.

4. The method for constructing a typhoon database according to claim 2, wherein: The wind field simulation is performed based on the screened typhoons to obtain wind speed data at each typhoon time simulation point in the target area, including: The Meng wind field model is used to simulate the wind field in the target area, taking the typhoon center longitude and latitude, near-center wind speed, and center pressure data of the typhoon best path dataset as input; In the Meng wind field model, the tangential gradient wind speed V is obtained considering the pressure reduction along the height. θg and radial gradient wind speed V rg : c r =-V T cos(θ-β′) c θ =-V T sin(θ-β′) Where θ is the angle between the line connecting the simulation point and the center of the tropical cyclone and the east direction, with the counterclockwise direction being positive; β′ is the angle between the moving direction of the tropical cyclone and the east direction, with the counterclockwise direction being positive; ρ is the air density; f c =2×7.273×10 -5 sinψ represents the Coriolis parameter, ψ is the latitude of the simulation point; r is the radial distance between the simulation point and the typhoon center, and p(r,z) represents the ocean surface wind pressure at the height z of the simulation point; The obtained gradient wind speed is used as the initial value for the near-ground wind speed calculation and substituted into the following formula to calculate the surface friction wind speed V θ ′ and V r ′: Where k m is the kinematic viscosity; D1 and D2 can be calculated by the following formula: Where k is the Karman constant; Cd is the drag coefficient; h = Az00.86, is the average roughness unit height, where A = 11.4; zero plane displacement d = 0.75h; z 10 is h+10m; z0 is the surface roughness length; The surface friction wind speed V θ ′ and V r ′ and gradient wind speed V θg and V rg The new near-ground wind speed is obtained by superposition, and the iterative calculation is repeated many times until convergence, and the wind speed at a height of 10m at the simulation point is calculated.

5. The method for constructing a typhoon database according to claim 1, wherein: The method of establishing multiple data tables in the object-relational database includes: Data Table 1, used to store typhoon names in the typhoon best path dataset; Data Table 2 is used to store typhoon path and intensity data in the typhoon best path dataset; Data Table 3 is used to store the typhoon names in the simulation data set; Data table 4 is used to store the latitude and longitude of the simulation points in the simulation data set; Data Table 5 is used to store the wind speed data of the simulation points in the simulation data set; Data Table 6 is used to store typhoon time data in the simulation data set.

6. A typhoon database construction device, characterized in that: include: Data acquisition module, used to obtain the typhoon best path data set; The wind field simulation module is used to simulate the wind field in the target area using a wind field model based on the typhoon optimal path dataset, obtain the wind speed data of each simulation point in the target area, and form a simulation dataset; A data table creation module is used to create multiple data tables in an object-relational database to store typhoon names, typhoon paths, and intensity data in the typhoon optimal path dataset, as well as typhoon names, simulation point latitude and longitude, wind speed data, and typhoon time data in the simulation dataset; The data import module is used to import the typhoon optimal path dataset data and simulation dataset data into various data tables.

7. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the typhoon database construction method according to any one of claims 1 to 5 are implemented.

8. A typhoon database construction device comprising a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, characterized in that: When the computer program is executed, the steps of the typhoon database construction method according to any one of claims 1 to 5 are implemented.

9. A typhoon database, characterized in that: The database is constructed based on the typhoon database construction method described in any one of claims 1 to 5.

10. A typhoon data interaction method based on the typhoon database according to claim 9.

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