A typhoon identification method, system, device and medium

Abstract

The application discloses a typhoon identification method, system, device and medium. The method comprises the following steps: acquiring meteorological data of a target area, wherein the meteorological data comprises sea level pressure, wind field and potential height; comparing each coordinate point of the target area with the sea level pressure in the preset neighborhood around the coordinate point, and screening out a coordinate point of local minimum pressure; if the sea level pressure value of the coordinate point of local minimum pressure is lower than a preset pressure threshold, marking the corresponding coordinate point of local minimum pressure as a candidate typhoon center of the target area; and determining the candidate typhoon center as a typhoon center if the candidate typhoon center meets preset conditions. The preset conditions comprise that the wind field data of the candidate typhoon center meets a first preset condition, the potential height data of the candidate typhoon center meets a second preset condition, and the coordinate point of the candidate typhoon center meets a third preset condition. The method can accurately identify the position, intensity and future path change of a typhoon, and improves the typhoon identification efficiency.

Description

Technical Field

[0001] This application relates to the field of meteorological management technology, and in particular to a typhoon identification method, system, device and medium. Background Technology

[0002] In meteorology, a typhoon is a powerful tropical cyclone that brings strong winds, torrential rains, storm surges, and other disasters, causing severe damage to coastal areas and threatening human life and property. Therefore, typhoon monitoring and forecasting are crucial for reducing disaster losses.

[0003] Existing typhoon monitoring technologies mainly rely on manual methods or image processing algorithms to find low-pressure areas in satellite images or use radar echo signals to determine the location of the typhoon center. The accuracy of these methods is not high, and the predictions are made based on static observation data, resulting in poor timeliness and an inability to accurately track typhoon changes. This makes it difficult to achieve efficient, real-time and accurate typhoon identification. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0005] The main objective of this disclosure is to provide a typhoon identification method, system, device, and storage medium that can quickly and accurately identify the location, intensity, and future path changes of typhoons, thereby improving typhoon identification efficiency.

[0006] A first aspect of this application provides a typhoon identification method for a central controller, the method comprising: Acquire meteorological data for the target area, including sea level pressure, wind field, and geopotential height; By comparing the sea level air pressure of each coordinate point in the target area with the sea level air pressure in the surrounding preset neighborhood, the coordinate points with local air pressure minimum values ​​are selected. If the sea level pressure value at the coordinate point of the local pressure minimum is lower than the preset pressure threshold, then the coordinate point of the corresponding local pressure minimum is marked as the candidate typhoon center of the target area. If the candidate typhoon center meets the preset conditions, the candidate typhoon center is determined as the typhoon center; The preset conditions include the wind field data of the candidate typhoon center satisfying the first preset condition, the geopotential height data of the candidate typhoon center satisfying the second preset condition, and the coordinates of the candidate typhoon center satisfying the third preset condition.

[0007] In some embodiments of this application, the first preset condition includes: Calculate the absolute vorticity value of the corresponding candidate typhoon center based on the wind field data of the candidate typhoon center; The absolute vorticity value of the candidate typhoon center is greater than the preset maximum vorticity value.

[0008] In some embodiments of this application, the formula for calculating the absolute vorticity value includes: ; in, The absolute vorticity value, and The current coordinates are in the wind field. shaft and Wind speed in the axial direction.

[0009] In some embodiments of this application, the second preset condition includes: within any two altitude layers, the geopotential height difference of the candidate typhoon center is greater than or equal to the geopotential height difference of any coordinate within a preset first radius range, wherein the geopotential height difference is calculated based on the geopotential height.

[0010] In some embodiments of this application, the third preset condition includes: when the coordinate point of the candidate typhoon center is within a preset land boundary range, the wind field data of the candidate typhoon center within a preset second radius range is greater than a preset threshold, and the coordinate point of the candidate typhoon center is within a preset elevation range.

[0011] In some embodiments of this application, after determining the candidate typhoon center as a typhoon center when the candidate typhoon center meets preset conditions, the method further includes: Obtain the typhoon center of the target area at multiple consecutive time points; The typhoon's movement path is generated based on the typhoon centers in the target areas corresponding to the multiple consecutive time points.

[0012] In some embodiments of this application, after generating the typhoon movement path based on the typhoon centers corresponding to the target areas at the multiple consecutive time points, the method further includes: Calculate the typhoon intensity at the corresponding typhoon center based on the meteorological data of the typhoon center at each consecutive time. Based on the typhoon intensities and movement paths of each typhoon, the evolution of typhoon movement direction, speed, and intensity is determined according to meteorological data from the typhoon center at each consecutive moment.

[0013] To achieve the above objectives, a second aspect of the present invention provides a typhoon identification system, the system comprising: The acquisition module is used to acquire meteorological data of the target area, including sea level pressure, wind field, and geopotential height. The filtering module is used to filter out the coordinate points of local pressure minima by comparing each coordinate point in the target area with the sea level air pressure in its surrounding preset neighborhood. The marking module is used to mark the coordinate point of the local pressure minimum as the candidate typhoon center of the target area if the sea level pressure value of the coordinate point of the local pressure minimum is lower than a preset pressure threshold. The identification module is used to determine the candidate typhoon center as a typhoon center when the candidate typhoon center meets the preset conditions. The preset conditions include the wind field data of the candidate typhoon center satisfying the first preset condition, the geopotential height data of the candidate typhoon center satisfying the second preset condition, and the coordinates of the candidate typhoon center satisfying the third preset condition.

[0014] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the typhoon identification method described above.

[0015] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the typhoon identification method described above.

[0016] This application provides a typhoon identification method. It acquires meteorological data of a target area, including sea level pressure, wind field, and geopotential height. By comparing the sea level pressure of each coordinate point in the target area with its surrounding preset neighborhood, it filters out coordinate points with local pressure minima. If the sea level pressure value of the coordinate point with the local pressure minima is lower than a preset pressure threshold, the corresponding coordinate point with the local pressure minima is marked as a candidate typhoon center in the target area. If the candidate typhoon center meets preset conditions, it is determined as the typhoon center. The preset conditions include the candidate typhoon center's wind field data meeting a first preset condition, the candidate typhoon center's geopotential height data meeting a second preset condition, and the candidate typhoon center's coordinate points meeting a third preset condition. This method can accurately identify the typhoon's location, intensity, and future path changes, improving typhoon identification efficiency.

[0017] It is understood that the beneficial effects of the second to fourth aspects compared with the related technologies are the same as the beneficial effects of the first aspect compared with the related technologies. Please refer to the relevant description in the first aspect above, which will not be repeated here. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating a typhoon identification method provided in an embodiment of this application; Figure 2 This is a visualization diagram of typhoon identification results provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a typhoon recognition training system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0020] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0021] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0022] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0023] In meteorology, a typhoon is a powerful tropical cyclone that brings strong winds, torrential rains, storm surges, and other disasters, causing severe damage to coastal areas and threatening human life and property. Therefore, typhoon monitoring and forecasting are crucial for reducing disaster losses.

[0024] Existing typhoon monitoring technologies mainly rely on manual methods or image processing algorithms to find low-pressure areas in satellite images or use radar echo signals to determine the location of the typhoon center. The accuracy of these methods is not high, and the predictions are made based on static observation data, resulting in poor timeliness and an inability to accurately track typhoon changes. This makes it difficult to achieve efficient, real-time and accurate typhoon identification.

[0025] Based on this, embodiments of this application provide a typhoon identification method, system, electronic device, and medium, aiming to quickly and accurately identify the location, intensity, and future path changes of typhoons, thereby improving typhoon identification efficiency.

[0026] The typhoon identification method, system, electronic device and medium provided in the embodiments of this application are specifically described through the following embodiments. First, the typhoon identification method in the embodiments of this application is described.

[0027] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0028] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0029] The typhoon identification method provided in this application relates to the field of meteorological management technology. The typhoon identification method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the typhoon identification method, but is not limited to the above forms.

[0030] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0031] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0032] Therefore, referring to Figure 1 This application provides a typhoon identification method. This method is applied to a central controller, which can be a server, an electronic device, or a mobile terminal, etc., without specific limitations. The method includes the following steps S110 to S140: Step S110: Obtain meteorological data for the target area, including sea level pressure, wind field, and geopotential height; Step S120: By comparing the sea level air pressure of each coordinate point in the target area with the sea level air pressure in the surrounding preset neighborhood, the coordinate points of the local air pressure minimum value are selected. Step S130: If the sea level pressure value of the coordinate point of the local pressure minimum is lower than the preset pressure threshold, then mark the coordinate point of the corresponding local pressure minimum as the candidate typhoon center of the target area. Step S140: If the candidate typhoon center meets the preset conditions, determine the candidate typhoon center as the typhoon center; The preset conditions include that the wind field data of the candidate typhoon center meets the first preset condition, the geopotential height data of the candidate typhoon center meets the second preset condition, and the coordinates of the candidate typhoon center meet the third preset condition.

[0033] In this step, the target area refers to the geographical range where typhoon identification and monitoring are required. Specifically, this can be achieved by dividing the area into latitude and longitude grids using a Geographic Information System (GIS). Limiting the monitoring range can improve data processing efficiency and avoid interference from invalid areas. Meteorological data includes sea level pressure, wind field, and geopotential height, which can be obtained through meteorological satellites, ground meteorological stations, or buoy sensors. Multi-dimensional data fusion can more comprehensively reflect the thermal and dynamic conditions for typhoon formation. The local minimum pressure value refers to the point in the target area where the pressure is lowest compared to its surrounding preset neighborhood. This can be achieved by traversing grid points using a sliding window algorithm, used to initially screen areas that may form typhoon low-pressure centers. The preset pressure threshold is a critical value derived from historical typhoon data. Specifically, it can be dynamically adjusted using different marine climate models. Judging by the absolute pressure value can eliminate interference from weak low-pressure systems.

[0034] The preset conditions include joint verification of wind field data, geopotential height data, and coordinate point attributes. Specifically, this can be achieved through absolute vorticity calculation, geopotential height difference comparison, and land boundary condition constraints. Multi-condition collaborative screening can improve the accuracy and anti-interference ability of typhoon center identification.

[0035] Specifically, meteorological data for the target area is first acquired, including sea level pressure, wind field, and geopotential height. By comparing the sea level pressure of each coordinate point in the target area with its surrounding preset neighborhood, coordinate points with local pressure minima are selected. If the sea level pressure at the coordinate point of a local pressure minima is lower than a preset pressure threshold, the corresponding coordinate point is marked as a candidate typhoon center for the target area. If the candidate typhoon center meets preset conditions, it is determined as the typhoon center. This achieves the construction of a dynamic typhoon identification model through multi-source meteorological data fusion and multi-dimensional conditional verification. Furthermore, candidate centers are selected based on local pressure minima, and a joint judgment is made combining absolute vorticity of the wind field, geopotential height gradient, and land boundary effects. This solves the problems of low identification accuracy and high false positive rate caused by traditional methods relying on a single data source. Simultaneously, by continuously tracking the typhoon center to generate its movement path, real-time monitoring and prediction of the typhoon's dynamic evolution are achieved.

[0036] In this step, a multi-screening and verification mechanism is used to identify the typhoon center. First, air pressure data is used to initially screen out possible typhoon centers. Then, the center is further verified through multiple dimensions such as wind field, geopotential height, and geographical location to ensure the accuracy of the identification. This can effectively distinguish typhoons from other meteorological systems and reduce misjudgments and omissions.

[0037] In some embodiments, meteorological data of the target area is first acquired, including sea level pressure, wind field and geopotential height data, which can be obtained through various channels such as meteorological satellites, meteorological radars or ground observation stations. Then, the target area is gridded and divided into several grid points.

[0038] Furthermore, for each grid point, its sea level pressure value is compared with the pressure values ​​of other points within a preset neighborhood (e.g., a 3x3 or 5x5 grid area). If the pressure value of that point is lower than all other points in the neighborhood, it is marked as a local pressure minimum point. Additionally, a preset pressure threshold is set, such as 1000 hPa. For each local pressure minimum point, if its pressure value is lower than this threshold, it is marked as a candidate typhoon center.

[0039] Furthermore, multiple conditions were verified for each candidate typhoon center, including: First preset condition (wind field data verification): Calculate the absolute vorticity value of the candidate center point. If it exceeds the preset maximum vorticity value, then the first condition is met.

[0040] The second preset condition (potential height data verification): Select two different height layers (e.g., 850 hPa and 500 hPa) and calculate the potential height difference between the candidate center point and these two height layers. If this difference is greater than or equal to the potential height difference between any point within a preset first radius range (e.g., 500 km), then the second condition is satisfied.

[0041] The third preset condition (geographical location verification): If the candidate center point is located within the preset land boundary (e.g., within 100km of the coastline), it is necessary to further verify whether the wind field intensity within the preset second radius (e.g., 200km) exceeds the preset threshold (e.g., 17m / s) and whether the altitude of the point is within the preset elevation range (e.g., below 1000m).

[0042] Only candidate typhoon centers that simultaneously meet these three preset conditions are ultimately identified as typhoon centers, thus effectively improving the accuracy of typhoon center identification. This embodiment, through multi-dimensional meteorological data analysis and verification mechanisms, can effectively distinguish typhoons from other meteorological systems, reducing misjudgments and omissions. It considers not only air pressure characteristics but also wind field and geopotential height, making the identification results more reliable. Furthermore, by introducing geographical location verification, areas that do not meet the conditions for typhoon formation can be excluded, further improving the accuracy of identification and providing more accurate information for typhoon monitoring and early warning, thus contributing to improved efficiency and effectiveness in disaster prevention and mitigation.

[0043] In some embodiments, in step S140, if the candidate typhoon center meets the preset conditions, the candidate typhoon center is determined as the typhoon center, including the following steps S210 to S220: Step S210: Calculate the absolute vorticity value of the corresponding candidate typhoon center based on the wind field data of the candidate typhoon center; Step S220: The absolute vorticity value of the candidate typhoon center is greater than the preset maximum vorticity value.

[0044] In this embodiment, the absolute vorticity value of the candidate typhoon center is calculated based on the wind field data of the candidate typhoon center. Then, by checking whether the absolute vorticity value of the candidate typhoon center is greater than the preset maximum vorticity value, the true typhoon center can be effectively distinguished from other meteorological phenomena, thus reducing the misjudgment rate.

[0045] Specifically, the absolute vorticity value is calculated by observing the spatial variation of wind speed within the wind field. and These represent the current coordinates of the point in the wind field. shaft and Wind speed along the axial direction, through calculation Direction partial derivatives and Direction The difference between the partial derivatives yields the absolute vorticity value.

[0046] The formula for calculating the absolute vorticity value includes: ; In the formula, This is the absolute vorticity value. and The current coordinates are in the wind field. shaft and Wind speed in the axial direction, Indicates along Wind speed in the axial direction Gradient that varies with spatial location Indicates along Wind speed in the axial direction Gradient that varies with spatial location; shaft and Wind speed data in the axial direction is acquired through wind field observation equipment, including weather radar or satellite remote sensing devices. The calculation of absolute vorticity is preferably performed by numerical differentiation. The wind field data is discretized using the central difference method to obtain the partial derivative values ​​at each coordinate point.

[0047] Specifically, after selecting the current coordinate point in the wind field, a two-dimensional plane coordinate system is established with that point as the center, along... Wind speed at two adjacent coordinate points along the axis The value is calculated, the difference is divided by the coordinate spacing, and the result is obtained. Approximate value; similarly, along Wind speed at two adjacent coordinate points along the axis The value is calculated, the difference is divided by the coordinate spacing, and the result is obtained. The approximate value is obtained by subtracting the two partial derivative values ​​to get the absolute vorticity value. This allows for the precise characterization of the intensity of cyclone rotation by quantifying the rate of change of the wind speed field in the horizontal and vertical directions.

[0048] In some embodiments, when there is strong rotational motion in the central region of a cyclone, the spatial gradient of wind speed in both the horizontal and vertical directions increases significantly, and the calculated wind speed... The value will be significantly higher than that of the surrounding area. Therefore, by setting a threshold condition for the maximum value of vorticity, the interference of non-typhoon systems can be effectively eliminated, ensuring the reliability of typhoon center identification. Furthermore, by accurately quantifying the airflow rotation intensity near the candidate typhoon center, an important basis can be provided for subsequent judgment on whether the typhoon center conditions are met.

[0049] Furthermore, a preset maximum vorticity value can be determined through historical typhoon data statistics or meteorological model simulation. For example, it can be set to 0.0001 / s to ensure that the wind field data of the candidate typhoon center meets the requirement that the absolute vorticity value exceeds the preset vorticity threshold, thereby excluding the influence of weak vortices or non-rotating meteorological systems.

[0050] Specifically, for the coordinates of the candidate typhoon center, wind field data around it is extracted, and calculations are performed based on the distribution of wind speed in the wind field. shaft and axial wind speed component and And then through the Component along rate of change of direction and Component along The rate of change of direction is differentially calculated to obtain the absolute vorticity value, which characterizes the rotational intensity. If the calculated absolute vorticity value exceeds a preset threshold, it indicates that the candidate typhoon center possesses the strong rotational characteristics unique to typhoons, thus confirming that it meets the first preset condition. By introducing quantitative calculation of absolute vorticity values, meteorological data with core typhoon characteristics can be screened more accurately, avoiding misjudgments caused by relying solely on wind speed or air pressure data in traditional methods, and improving the accuracy of typhoon center identification.

[0051] In some embodiments, wind field data within a certain range around the candidate typhoon center is first acquired, including the horizontal and vertical wind speeds at each grid point. Then, the spatial derivative of the wind speed is calculated using the finite difference method to obtain the absolute vorticity value. Finally, the calculated absolute vorticity value is compared with a preset maximum vorticity value. If the absolute vorticity value is greater than the maximum vorticity value, it is considered that the first preset condition is met.

[0052] Specifically, a 100km×100km area around the center of a candidate typhoon is selected, and wind field data is acquired at 10km intervals. The maximum vorticity is set to 1×10^(-4), which can effectively identify typhoon systems with strong rotational characteristics.

[0053] In this embodiment, vorticity information from wind field data is used to identify the typhoon center, improving the accuracy of typhoon identification. Furthermore, since vorticity is an important physical quantity describing atmospheric rotation, using it as a criterion can effectively distinguish typhoons from other weather systems. In addition, by setting a vorticity threshold, the identification sensitivity can be flexibly adjusted to adapt to typhoon systems of different intensities, thereby effectively avoiding misjudgments that may arise from relying solely on air pressure data, and improving the reliability and robustness of typhoon identification.

[0054] In some embodiments, in step S140, if the candidate typhoon center meets the preset conditions, determining the candidate typhoon center as the typhoon center includes the following step S310: Step S310: Within any two height layers, the geopotential height difference of the candidate typhoon center is greater than or equal to the geopotential height difference of any coordinate within the preset first radius range. The geopotential height difference is calculated based on the geopotential height.

[0055] In this embodiment, the typhoon center is identified by comparing the geopotential height difference between candidate typhoon centers within any two altitude layers to see if it is greater than or equal to the geopotential height difference at any coordinate within a preset first radius range. The geopotential height difference is calculated based on the geopotential height.

[0056] Specifically, the two altitude layers are preferably selected from different pressure layers within the troposphere, such as the 850 hPa layer and the 500 hPa layer; the geopotential height difference is preferably obtained by subtracting the geopotential height value of the coordinates of the candidate typhoon center from the corresponding coordinate geopotential height value of the other altitude layer; the preset first radius range is preferably set to 500 kilometers around the typhoon center; the geopotential height difference comparison logic is to traverse the geopotential height differences of all coordinate points within the preset first radius range and determine whether the difference value corresponding to the candidate typhoon center has reached the maximum.

[0057] Furthermore, by acquiring geopotential height data of two different height layers at the candidate typhoon center in the vertical direction, calculating their difference, and comparing this difference with the difference at other coordinate points within a preset radius in the horizontal direction.

[0058] In some embodiments, when the geopotential height of a candidate typhoon center is 1500 geopotential meters at 850 hPa and 5600 geopotential meters at 500 hPa, the difference is 4100 geopotential meters. If the geopotential height difference between the 850 hPa and 500 hPa layers for other coordinate points within a preset first radius is less than or equal to 4100 geopotential meters, then the current candidate typhoon center meets the criteria. Through dual constraints in both the vertical and horizontal directions, weather systems with geopotential anomalies at only a single layer, such as frontal cyclones or localized low pressure systems, can be excluded, thereby improving the accuracy of typhoon center identification.

[0059] Specifically, the 850 hPa and 500 hPa altitude layers are selected as reference altitude layers. The geopotential height values ​​of the candidate typhoon center at these two altitude layers are obtained, and the difference between them is calculated as the geopotential height difference of the candidate typhoon center. Then, within a preset first radius (e.g., 500 km), the geopotential height values ​​of all coordinate points within this range at the 850 hPa and 500 hPa altitude layers are obtained, and the geopotential height difference for each coordinate point is calculated. Finally, the geopotential height difference of the candidate typhoon center is compared with the geopotential height differences of all coordinate points within the preset first radius. If the geopotential height difference of the candidate typhoon center is greater than or equal to the geopotential height difference of any coordinate point within the preset first radius, then the candidate typhoon center is considered to meet the second preset condition. Therefore, by comparing the geopotential height difference between the candidate typhoon center and the surrounding area, the location of the typhoon center can be effectively identified, eliminating some candidate points that do not meet the characteristics of a typhoon, thus improving the accuracy of typhoon identification.

[0060] Furthermore, by setting a preset first radius range, the size of the comparison area can be flexibly adjusted to adapt to typhoon systems of different scales. Moreover, this judgment method based on geopotential height difference, combined with other preset conditions, can more comprehensively evaluate the characteristics of candidate typhoon centers, thereby improving the reliability and accuracy of typhoon identification.

[0061] In some embodiments, in step S140, if the candidate typhoon center meets the preset conditions, determining the candidate typhoon center as the typhoon center includes the following step S410: Step S410: When the coordinates of the candidate typhoon center are within the preset land boundary range, the wind field data of the candidate typhoon center within the preset second radius range is greater than the preset threshold, and the coordinates of the candidate typhoon center are within the preset elevation range.

[0062] In this embodiment, when the coordinates of the candidate typhoon center are within a preset land boundary, the typhoon center is identified based on whether the wind field data of the candidate typhoon center within a preset second radius is greater than a preset threshold and whether the coordinates of the candidate typhoon center are within a preset elevation range. Thus, the preset elevation range is determined by geographic elevation data, eliminating the interference of high-altitude areas on the identification of the typhoon center. By combining the wind field data with the elevation range, it is ensured that the candidate typhoon center near the land boundary simultaneously meets the requirements of wind intensity and terrain conditions.

[0063] The preset land boundary range is pre-defined based on geographic data to distinguish between marine and land areas; the preset second radius range defines the wind field data collection area around the candidate typhoon center; and the preset threshold corresponds to the minimum wind speed or wind field intensity required near the typhoon center.

[0064] Specifically, since the current candidate typhoon center is located within the land boundary, wind field data at each coordinate point within a preset second radius around it is further analyzed. If the average or maximum value of the wind field data within this range exceeds a preset threshold, it indicates that there is a wind field of sufficient intensity to support the determination of the typhoon center. At the same time, the elevation data of the candidate typhoon center coordinate points is obtained. If the elevation data of the current candidate typhoon center is within a preset elevation range, it indicates that the current candidate typhoon center is located in the altitude range where a typhoon may exist.

[0065] In some embodiments, the preset land boundary range is set to an area within 100 kilometers of the coastline, the preset second radius range is set to a circular area with a radius of 300 kilometers centered on the candidate typhoon center, the preset threshold is set to 10 meters per second, and the preset elevation range is set to an altitude between 0 and 1000 meters.

[0066] Furthermore, when the coordinates of the current candidate typhoon center are located within a preset land boundary, the wind field data of the current candidate typhoon center's coordinates within a preset second radius is checked. If the average wind speed within this radius is greater than 10 m / s, and the altitude of the current candidate typhoon center's coordinates is between 0 and 1000 meters, then the current candidate typhoon center is considered to meet the third preset condition. This effectively filters out the true typhoon center and eliminates some false typhoon centers that may be caused by factors such as terrain, thereby effectively improving the accuracy of typhoon identification, especially for typhoons close to land. Moreover, by considering multiple factors such as land boundaries, wind field intensity, and terrain altitude, the typhoon center can be located more accurately, reducing misjudgments and providing favorable data support for monitoring landfalling typhoons or those about to make landfall. This helps to issue timely warnings and provides important support for disaster prevention and mitigation.

[0067] In some embodiments, after determining the candidate typhoon center as the typhoon center in step S140 when the candidate typhoon center meets the preset conditions, the method further includes the following steps S510 to S540: Step S510: Obtain the typhoon center of the target area at multiple consecutive time points; Step S520: Generate the typhoon's movement path based on the typhoon centers of the target area at multiple consecutive time points; Step S530: Calculate the typhoon intensity at the typhoon center at the corresponding time based on the meteorological data of the typhoon center at each consecutive time. Step S540: Based on the intensity and path of each typhoon, determine the evolution of the typhoon's direction of movement, speed, and intensity according to the meteorological data of the typhoon center at each consecutive moment.

[0068] In this embodiment, after determining the typhoon center, the typhoon centers of the target area at multiple consecutive time points are obtained. Based on these typhoon centers, a typhoon movement path is generated. The typhoon intensity at each time point is calculated using meteorological data from the typhoon centers at each consecutive time point. Based on the typhoon intensity and movement path, the evolution of the typhoon's movement direction, speed, and intensity is determined using meteorological data from the typhoon centers at each consecutive time point. This continuous analysis of meteorological data across multiple time points enables the synchronous calculation of the typhoon's movement path and intensity evolution, creating a dynamic correlation between the predicted typhoon center location, movement direction, and speed. This effectively improves the continuity of typhoon path prediction and the accuracy of intensity change identification, providing real-time data support for disaster early warning.

[0069] The typhoon's path is formed by connecting multiple typhoon centers at consecutive moments, and the spatial position changes of the typhoon center are analyzed through time series analysis. The typhoon intensity is calculated using meteorological data at the current moment, including at least one parameter among sea level pressure, wind field data, and geopotential height difference. The typhoon's direction of movement is determined based on the coordinate changes of the typhoon center at adjacent moments in the path, and the movement speed is calculated by the ratio of the time interval to the displacement distance. The evolution of the typhoon's intensity is assessed by analyzing the trend of intensity change over time, combined with the spatial distribution characteristics of the path, to evaluate the typhoon's development stage.

[0070] Specifically, the typhoon center of the target area is obtained at multiple consecutive time points. For example, the typhoon center location of the target area can be obtained every hour, and the typhoon center location data at 24 consecutive time points can be obtained. Then, based on the typhoon center of the target area at multiple consecutive time points, the typhoon movement path can be generated.

[0071] Furthermore, after generating the typhoon's path, meteorological data from the typhoon's center at each moment is extracted, and the intensity value is calculated by weighting wind speed and air pressure parameters using a preset algorithm. For example, typhoon intensity can be quantified by the difference between sea level air pressure and a preset benchmark value; the larger the difference, the higher the intensity level.

[0072] Furthermore, the azimuth angle is calculated by the difference in center coordinates between adjacent moments in the path to obtain the typhoon's movement direction, for example, by using a vector direction calculation method under a geographic coordinate system.

[0073] Furthermore, the typhoon speed is determined by dividing the typhoon center displacement distance at adjacent times by the time interval, for example, by converting the latitude and longitude difference into actual distance.

[0074] Furthermore, by inputting the intensity data at each moment into a time series analysis model, the evolution of typhoon intensity can be obtained, and the future trend of intensity change can be predicted. The time series analysis model can use linear regression or neural network algorithms, thereby dynamically tracking the correlation between typhoon intensity and path, improving the timeliness and accuracy of prediction.

[0075] In some embodiments, by synchronously collecting sea level pressure data updated every six hours from meteorological satellites, the typhoon center coordinates corresponding to different time nodes are extracted. Then, the discrete coordinate points are smoothed using cubic spline interpolation to generate a continuous movement path curve. The lowest air pressure value within a 50-kilometer radius around the typhoon center is then input into a pre-trained wind speed prediction model to output the typhoon intensity level. Subsequently, the movement direction angle is calculated based on the center coordinates of three consecutive time points. The instantaneous movement speed is obtained by differentiating the coordinates of adjacent time points using the path curve. Finally, combined with the typhoon intensity change range within 24 hours, an intensity evolution trend function is established to predict the typhoon development status in the next six hours. This method can accurately capture the changing trend of the typhoon's movement trajectory and the intensity fluctuation characteristics, effectively solving the prediction lag problem caused by traditional static observation methods. It provides continuous evolution data support for typhoon early warning and significantly improves the timeliness and scientific nature of disaster prevention decisions.

[0076] In some embodiments, by solving atmospheric motion equations using numerical weather prediction products, and utilizing high-resolution observational data and advanced physical parameterization schemes, the three-dimensional structure and dynamic changes of the atmosphere can be simulated. This provides more detailed and accurate information on wind fields, pressure fields, temperature fields, etc., offering strong data support for typhoon path identification and extraction. Furthermore, it can effectively utilize high-resolution meteorological data to accurately identify the location, intensity, and future path changes of typhoons. The specific steps are as follows: Step 1: Data Acquisition and Preprocessing First, the sea level pressure, wind field, 10m wind field, land boundary, elevation, and geopotential height data output by the T1279 numerical weather prediction model were obtained. The T1279 numerical weather prediction model provides high spatiotemporal resolution meteorological data, which provides a detailed meteorological background for typhoon identification and can reflect the dynamic evolution of typhoons in real time.

[0077] Furthermore, the raw meteorological data output from the T1279 numerical weather prediction model were cleaned to remove missing or outlier values, and the data were converted into a uniform format to ensure data quality and consistency, laying the foundation for subsequent analysis.

[0078] Step 2: Feature Extraction and Typhoon Identification After data preprocessing, typhoon identification is achieved by extracting key features from meteorological data (sea level pressure, wind field, 10m wind speed, land boundary, elevation, and geopotential height data).

[0079] First, forecast data (sea level pressure, wind field data, and 10m wind speed data) within a preset reporting time range are selected. Using a preset latitude and longitude range, the spatial range of forecast data for typhoon identification is determined, i.e., the target area is identified. Then, the minimum value of the local mean sea level pressure (MSLP) is searched within the preset data range. The sea level pressure data is iterated, and for each coordinate point, its pressure value is compared with that of surrounding points to determine if it is a local minimum. Simultaneously, the minimum value within this data range must be lower than a preset standard, thus initially determining the candidate latitude and longitude coordinates of the typhoon center. Finally, the absolute vorticity value is calculated based on the wind field and corresponding latitude and longitude coordinates of the candidate typhoon center. Absolute vorticity refers to the intensity of vortices in a fluid within an absolute coordinate system. In two-dimensional planar flow, the formula for calculating absolute vorticity is: ; In the formula, This is the absolute vorticity value. and The current coordinates are in the wind field. shaft and Wind speed in the axial direction.

[0080] Furthermore, within a certain radius of the initially determined candidate latitude and longitude coordinates, the following conditions are also met to further verify the extracted points: 1) The candidate latitude and longitude coordinates must have an absolute vorticity value greater than the preset maximum vorticity value within a preset height layer (hPa); 2) Within the preset radius of the candidate latitude and longitude coordinates, the maximum value of the geopotential height difference between two different pressure surfaces must coincide with the candidate latitude and longitude coordinates; 3) When the candidate latitude and longitude coordinates are located within the land boundary data, the wind speed of the maximum 10m wind field within the set radius should be greater than the preset wind speed. If the elevation data of the current typhoon center candidate latitude and longitude coordinates exceeds 1000m, the conditions are not met. By using multiple verification methods to identify the typhoon center, the spatial and temporal characteristics of the typhoon can be extracted from high-quality meteorological data through numerical forecast data analysis, avoiding the manual intervention and image recognition errors in traditional methods.

[0081] Step 3: Typhoon Path and Intensity Forecast: After typhoon identification, numerical weather prediction data is used to predict the typhoon's future path and intensity changes. By analyzing the changing trends of pressure fields and wind fields at future time steps, the typhoon's direction of movement, speed, and intensity changes are predicted. When predicting the typhoon's direction of movement, speed, and intensity changes, the forecast data for each time point is processed. The typhoon center is identified based on extracted key meteorological features, and these center points are recorded chronologically to form the typhoon path prediction. The typhoon intensity is calculated based on the wind speed data at the typhoon center. When new forecast data arrives at the new forecast time, the above steps are repeated to generate new typhoon center and intensity prediction results. Multiple sets of results within the same forecast period are then analyzed and compared, and the optimal result is selected to improve prediction accuracy. Moreover, by using high-resolution T1279 numerical weather prediction data combined with typhoon identification information, the future development trend of typhoons can be predicted in a shorter time, thereby improving prediction accuracy and timeliness.

[0082] Step 4: Output and visualization of typhoon identification results: This system visualizes typhoon identification and forecasting results. Typhoon data at each point in time (including longitude, latitude, wind speed, sea level pressure, typhoon radius, wind force level, typhoon intensity, and time) is compiled into a JSON file. Based on WebGL, this data is loaded onto a map base map, and rendering technology is used to display the wind circle, center trajectory, and predicted path. Simultaneously, it generates charts and maps showing the typhoon's location, intensity, path, and future changes. This information can be promptly transmitted to meteorologists and disaster prevention departments to facilitate appropriate emergency measures. By transforming typhoon identification results into visual charts or maps, combined with path and intensity predictions, the transmission of typhoon information becomes more intuitive, enhancing the response speed and accuracy of the early warning system.

[0083] In some embodiments, high-resolution T1279 numerical forecast data is first used to identify the center location, intensity, and path of the typhoon through an automated algorithm, avoiding the shortcomings of traditional methods that rely on satellite images or radar data. Specifically, T1279 forecast data is processed to generate wind field and sea level pressure data.

[0084] Furthermore, the coordinates corresponding to the local minimum points of the MSLP field in the typhoon area are extracted. Within the predefined data range, the sea level pressure values ​​of each coordinate point are traversed and compared. Specifically, by comparing the values ​​of the target coordinate point with all points in its surrounding neighborhood, it is determined whether the point is a local minimum, and the latitude and longitude coordinates of the typhoon center are preliminarily determined.

[0085] Furthermore, based on the latitude and longitude coordinates of the typhoon center and the European Centre for Medium-Range Weather Forecasts (ECMWF) regulations for typhoon track tracking, it is determined whether the maximum vorticity within a 278 km radius around the typhoon center is greater than 5 × 10^(-5). If it is greater, then it is considered the typhoon center.

[0086] Furthermore, combined Figure 2 As shown, the maximum wind speed within a 96-kilometer radius of the typhoon center is extracted as the maximum wind speed at the typhoon center. According to the Beaufort scale, this maximum wind speed is compared with the wind speed thresholds of each level to determine the intensity level of the typhoon (including tropical depression, tropical storm, severe tropical storm, typhoon, severe typhoon, and super typhoon). This process is then repeated to generate typhoon identification data at different times. Finally, the typhoon identification and prediction results are output in intuitive forms such as maps and charts, making it easier for meteorological experts and the public to quickly obtain typhoon information and improve decision-making efficiency.

[0087] like Figure 3 As shown in some embodiments of this application, a typhoon identification system is provided. The system includes an acquisition module 310, a filtering module 320, a marking module 330, and an identification module 340. Specifically: The acquisition module 310 is used to acquire meteorological data of the target area, including sea level pressure, wind field and geopotential height. The filtering module 320 is used to filter out the coordinate points of local pressure minima by comparing the sea level air pressure in the target area with each coordinate point of the target area and its surrounding preset neighborhood. The marking module 330 is used to mark the coordinate point of the local pressure minimum as the candidate typhoon center of the target area if the sea level pressure value of the coordinate point of the local pressure minimum is lower than the preset pressure threshold. The identification module 340 is used to determine the candidate typhoon center as the typhoon center when the candidate typhoon center meets the preset conditions. The preset conditions include that the wind field data of the candidate typhoon center meets a first preset condition, the geopotential height data of the candidate typhoon center meets a second preset condition, and the coordinates of the candidate typhoon center meet a third preset condition. In some embodiments, the identification module 340 may include: calculating the absolute vorticity value of the corresponding candidate typhoon center based on the wind field data of the candidate typhoon center.

[0088] In some implementations, the identification module 340 may include: the absolute vorticity value of the candidate typhoon center is greater than a preset maximum vorticity value.

[0089] In some implementations, the identification module 340 may include: ; in, This is the absolute vorticity value. and The current coordinates are in the wind field. shaft and Wind speed in the axial direction.

[0090] In some implementations, the identification module 340 may include: within any two height layers, the geopotential height difference between the candidate typhoon centers is greater than or equal to the geopotential height difference between any coordinates within a preset first radius range, wherein the geopotential height difference is calculated based on the geopotential height.

[0091] In some implementations, the identification module 340 may include: when the coordinates of the candidate typhoon center are within a preset land boundary range, the wind field data of the candidate typhoon center within a preset second radius range is greater than a preset threshold, and the coordinates of the candidate typhoon center are within a preset elevation range.

[0092] In some implementations, the identification module 340 may include: acquiring the typhoon center of the target area at multiple consecutive times.

[0093] In some implementations, the identification module 340 may include generating a typhoon movement path based on the typhoon center corresponding to the target area at multiple consecutive time points.

[0094] In some implementations, the identification module 340 may include: calculating the typhoon intensity at the typhoon center at a corresponding time based on meteorological data of the typhoon center at each consecutive time.

[0095] In some implementations, the identification module 340 may include: determining the evolution of the typhoon's direction of movement, speed, and intensity based on meteorological data from the typhoon center at each consecutive moment, according to the typhoon's intensity and movement path.

[0096] It should be noted that the typhoon identification system provided in this embodiment is based on the same inventive concept as the typhoon identification method described above. Therefore, the relevant content of the typhoon identification method described above is also applicable to the content of the typhoon identification system, and will not be repeated here.

[0097] To achieve this, the system acquires meteorological data for the target area, including sea level pressure, wind field, and geopotential height. It then compares the sea level pressure of each coordinate point in the target area with its surrounding preset neighborhood to identify local pressure minima. If the sea level pressure at a local pressure minima is lower than a preset pressure threshold, the corresponding local pressure minima are marked as candidate typhoon centers for the target area. If a candidate typhoon center meets preset conditions, it is confirmed as the typhoon center. These preset conditions include the candidate typhoon center's wind field data meeting a first preset condition, its geopotential height data meeting a second preset condition, and its coordinates meeting a third preset condition. This allows for accurate identification of typhoon location, intensity, and future path changes, improving typhoon identification efficiency.

[0098] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described typhoon identification method.

[0099] like Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device includes: At least one battery; At least one memory; At least one processor; At least one program; The program is stored in memory, and the processor executes at least one program to implement the typhoon identification method described above in this disclosure.

[0100] This electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0101] The electronic devices according to embodiments of this application will now be described in detail.

[0102] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure. The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and called and executed by the processor 1600 to execute a typhoon identification method according to an embodiment of this disclosure.

[0103] The input / output interface 1800 is used to implement information input and output. The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900); The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0104] This disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the aforementioned typhoon identification method.

[0105] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0106] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0107] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0110] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0111] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

[0117] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A typhoon identification method, characterized in that, The method includes: Acquire meteorological data for the target area, including sea level pressure, wind field, and geopotential height; By comparing the sea level air pressure of each coordinate point in the target area with the sea level air pressure in the surrounding preset neighborhood, the coordinate points with local air pressure minimum values ​​are selected. If the sea level pressure value at the coordinate point of the local pressure minimum is lower than the preset pressure threshold, then the coordinate point of the corresponding local pressure minimum is marked as the candidate typhoon center of the target area. If the candidate typhoon center meets the preset conditions, the candidate typhoon center is determined as the typhoon center; The preset conditions include the wind field data of the candidate typhoon center satisfying the first preset condition, the geopotential height data of the candidate typhoon center satisfying the second preset condition, and the coordinates of the candidate typhoon center satisfying the third preset condition.

2. The typhoon identification method according to claim 1, characterized in that, The first preset condition includes: Calculate the absolute vorticity value of the corresponding candidate typhoon center based on the wind field data of the candidate typhoon center; The absolute vorticity value of the candidate typhoon center is greater than the preset maximum vorticity value.

3. The typhoon identification method according to claim 2, characterized in that, The formula for calculating the absolute vorticity value includes: ； in, The absolute vorticity value. and The current coordinates are in the wind field. shaft and Wind speed in the axial direction.

4. The typhoon identification method according to claim 1, characterized in that, The second preset condition includes: within any two altitude layers, the geopotential height difference between the centers of the candidate typhoon is greater than or equal to the geopotential height difference between any coordinates within a preset first radius range, wherein the geopotential height difference is calculated based on the geopotential height.

5. The typhoon identification method according to claim 1, characterized in that, The third preset condition includes: when the coordinates of the candidate typhoon center are within a preset land boundary range, the wind field data of the candidate typhoon center within a preset second radius range is greater than a preset threshold, and the coordinates of the candidate typhoon center are within a preset elevation range.

6. The typhoon identification method according to claim 1, characterized in that, After determining the candidate typhoon center as a typhoon center when the candidate typhoon center meets the preset conditions, the method further includes: Obtain the typhoon center of the target area at multiple consecutive time points; The typhoon's movement path is generated based on the typhoon centers in the target areas corresponding to the multiple consecutive time points.

7. The typhoon identification method according to claim 6, characterized in that, After generating the typhoon movement path based on the typhoon centers of the target areas corresponding to the multiple consecutive time points, the method further includes: Calculate the typhoon intensity at the corresponding typhoon center based on the meteorological data of the typhoon center at each consecutive time. Based on the typhoon intensities and movement paths of each typhoon, the evolution of typhoon movement direction, speed, and intensity is determined according to meteorological data from the typhoon center at each consecutive moment.

8. A typhoon identification system, characterized in that, The system includes: The acquisition module is used to acquire meteorological data of the target area, including sea level pressure, wind field, and geopotential height. The filtering module is used to filter out the coordinate points of local pressure minima by comparing each coordinate point in the target area with the sea level air pressure in its surrounding preset neighborhood. The marking module is used to mark the coordinate point of the local pressure minimum as the candidate typhoon center of the target area if the sea level pressure value of the coordinate point of the local pressure minimum is lower than a preset pressure threshold. The identification module is used to determine the candidate typhoon center as a typhoon center when the candidate typhoon center meets the preset conditions. The preset conditions include the wind field data of the candidate typhoon center satisfying the first preset condition, the geopotential height data of the candidate typhoon center satisfying the second preset condition, and the coordinates of the candidate typhoon center satisfying the third preset condition.

9. An electronic device, characterized in that: It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform a typhoon identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a typhoon identification method according to any one of claims 1 to 7.

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