A typhoon weather-based automatic warning method, device and medium

Abstract

The application discloses a kind of automatic warning method, equipment and medium based on typhoon weather, belong to data analysis and geographic information technical field, for solving the technical problems in the existing typhoon weather warning, it is difficult to accurately disaster warning for the ground object and facilities that are greatly influenced by typhoon and its accompanying weather, it is not conducive to provide early warning decision support for anti typhoon flood control work, it is easy to waste a lot of manpower and material resources to warn and analyze typhoon disaster.The method comprises the following steps: obtaining typhoon path data and rainfall data corresponding to the typhoon path data;According to the typhoon path data and rainfall data, the key ground objects in the disaster area are subjected to spatial determination screening processing to obtain a warning data set;The warning data set is subjected to data weight allocation calculation to determine the warning level data of the key ground objects;The typhoon path data and warning level data are combined to obtain typhoon warning display data.

Description

Technical Field

[0001] This application relates to the fields of data analysis and geographic information, and in particular to an automatic early warning method, device and medium based on typhoon weather. Background Technology

[0002] Typhoons are a type of tropical cyclone. Typhoons often bring a lot of disasters, which mainly occur before and after the typhoon makes landfall. The direct damage is usually caused by the strong winds and heavy rains that accompany the typhoon.

[0003] Current efforts focus primarily on typhoons and rainfall themselves. While real-time monitoring data and relatively accurate forecasts for typhoons and rainfall are available, there is a lack of systematic and comprehensive early warning measures for key geographical features and facilities affected by typhoons. Effective early warning based on geographic information about key geographical features and facilities is difficult to conduct; that is, it is challenging to quickly analyze features and facilities significantly impacted by typhoons and their accompanying weather. This hinders the provision of concise and clear typhoon and flood prevention data for staff and increases the human and material costs associated with typhoon weather early warning. Summary of the Invention

[0004] This application provides an automatic early warning method, device, and medium based on typhoon weather to solve the following technical problems: In existing typhoon weather early warning systems, it is difficult to provide accurate disaster warnings for ground features and facilities that are significantly affected by typhoons and their accompanying weather, which is not conducive to providing early warning decision support for typhoon and flood prevention work, and easily wastes a lot of manpower and material resources to analyze and warn of typhoon disasters.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] On one hand, this application provides an automatic early warning method based on typhoon weather, including: acquiring typhoon path data and rainfall data corresponding to the typhoon path data; wherein the rainfall data includes: real-time rainfall data and future rainfall data; based on the typhoon path data and the rainfall data, performing spatial determination filtering on key features in the disaster area to obtain an early warning data group; calculating the weight allocation of the early warning data group based on weight parameters to determine the early warning level data of the key features; combining the typhoon path data and the early warning level data to obtain typhoon early warning display data, so as to display the key features and facilities affected by typhoon and rainfall that generate early warnings.

[0007] This application embodiment uses typhoon path data and rainfall data to calculate the real-time impact of extreme weather such as typhoons and rainfall on key features, issue warnings of different levels, predict the impact of typhoons and rainfall on key features or facilities, provide reference for similar situations, and more quickly analyze features and facilities that are significantly affected by typhoons and their accompanying weather. It can also provide data support for other users, and by combining the warning data with typhoon path data after weight allocation, it can ultimately provide decision support for typhoon and flood prevention work, while also saving manpower and resources.

[0008] In one feasible implementation, acquiring typhoon path data and corresponding rainfall data specifically includes: acquiring wind circle data and affected area data of typhoon path points using GIS (Geographic Information System) technology; wherein the wind circle data includes real-time wind circle data and predicted wind circle data; the affected area data refers to data on land features or facilities affected by typhoons and rainstorms, including at least one of the following: reservoirs, river stations, rain gauges, pumping stations, sluice gates, hydropower stations, and flood-prone areas; determining the wind circle data and affected area data as the typhoon path data; acquiring real-time rainfall grid data and predicted rainfall grid data corresponding to the typhoon path data; performing vector conversion of the real-time rainfall grid data and predicted rainfall grid data into rainfall contour lines to obtain real-time rainfall vector data and predicted rainfall vector data; and determining the real-time rainfall vector data as the real-time rainfall data and the predicted rainfall vector data as the future rainfall data.

[0009] In one feasible implementation, based on the typhoon path data and the rainfall data, a spatial determination screening process is performed on key features in the disaster-stricken area, including: determining the warning level of the overlapping area range between the wind circle data in the typhoon path data and the key features to obtain first warning level data; wherein, the wind circle data includes: the wind circle range and the corresponding wind force; wherein, the key features are the features and facilities corresponding to the affected area data in the typhoon path data; based on the first warning level data, the corresponding affected area data in the key features are labeled with the corresponding warning level to obtain a first set of warning data; wherein, the first set of warning data is used to represent the feature data affected by the typhoon.

[0010] In one feasible implementation, the spatial determination and filtering process for key features in the disaster area based on the typhoon path data and the rainfall data further includes: using the area vector data of the real-time rainfall vector data in the real-time rainfall data to perform spatial inclusion filtering of key features in the adjacent affected area of ​​the typhoon path data, obtaining real-time key feature data exceeding the rainfall warning value; and using the rainfall contour lines of the real-time rainfall data to label the filtered real-time key feature data with real-time rainfall warning levels, obtaining a second set of warning data; wherein, the second set of warning data is used to represent the warning of real-time rainfall to the adjacent affected area of ​​the typhoon.

[0011] In one feasible implementation, based on the typhoon path data and the rainfall data, key features in the disaster-stricken area are spatially determined and filtered to obtain a warning data set. Specifically, this includes: using the area vector data of the predicted rainfall vector data in the future rainfall data to spatially filter key features in the expected affected area data of the typhoon path data, obtaining predicted key feature data exceeding the rainfall warning value; using the rainfall contour lines of the future rainfall data to label the filtered predicted key feature data with predicted rainfall warning levels, obtaining a third set of warning data; wherein the third set of warning data represents the warning of future rainfall on key features at typhoon path points; comparing the key feature data with the second set of warning data to filter out identical key feature data; and highlighting the identical key feature data in the third set of warning data and the second set of warning data to obtain high-warning-level data; updating the high-warning-level data to the corresponding positions of the third set of warning data and the second set of warning data; wherein the warning data set includes: the first set of warning data, the updated second set of warning data, and the updated third set of warning data.

[0012] In one feasible implementation, after labeling the selected key land cover data for predicted rainfall warning levels based on the rainfall isolines of the future rainfall data to obtain a third set of warning data, the method further includes: calculating the catchment volume of the catchment area based on the real-time rainfall data, the future rainfall data, the predicted rainfall duration, and the surface vector data of the rainfall to obtain the catchment prediction data of the catchment area; wherein the catchment area includes at least: reservoirs, rivers, and lakes; making a warning judgment on the catchment prediction data based on the flood control water level standard corresponding to the catchment area to obtain the catchment warning data; and incrementally updating the catchment warning data to the third set of warning data.

[0013] In one feasible implementation, the warning data group is weighted based on weight parameters to determine the warning level data of the key features. Specifically, this includes: weighting the warning levels of the key features in the warning data group according to the proportion of typhoon disaster levels in different periods of the typhoon path data, obtaining the weight parameters of each key feature; and reclassifying the warning levels of each key feature according to the weight parameters to determine the warning level data of each key feature.

[0014] In one feasible implementation, the typhoon path data and the warning level data are combined to obtain typhoon warning display data. Specifically, this includes: using the Vue framework and the Cesium framework in GIS geographic information system technology, based on the running path of the typhoon path points in the typhoon path data, performing one-to-one matching of the warning level data, the real-time rainfall data, and the future rainfall data to obtain the typhoon warning display data.

[0015] Secondly, embodiments of this application also provide an automatic early warning device based on typhoon weather, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to execute an automatic early warning method based on typhoon weather as described in any of the above embodiments.

[0016] Thirdly, embodiments of this application also provide a non-volatile computer storage medium, characterized in that the storage medium is a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium stores at least one program, each program including instructions, and when the instructions are executed by a terminal, the terminal executes an automatic early warning method based on typhoon weather as described in any of the above embodiments.

[0017] This application provides an automatic early warning method, device, and medium based on typhoon weather. By using typhoon path data and rainfall data, it can calculate in real time the impact of extreme weather such as typhoons and rainfall on key features, issue early warnings of different levels, predict the impact of typhoons and rainfall on key features or facilities, provide reference for similar situations, and more quickly analyze features and facilities that are significantly affected by typhoons and their accompanying weather. It can also provide data support for other users, and by combining the weighted early warning data with typhoon path data, it can ultimately provide decision support for typhoon and flood prevention work, while also saving manpower and resources. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0019] Figure 1 A flowchart of an automatic early warning method based on typhoon weather provided for an embodiment of this application;

[0020] Figure 2 A flowchart of an automatic early warning system based on typhoon weather provided for embodiments of this application;

[0021] Figure 3 This is a schematic diagram of an automatic early warning device based on typhoon weather, provided as an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0023] This application provides an automatic early warning method based on typhoon weather, such as... Figure 1 As shown, the automatic early warning method based on typhoon weather specifically includes steps S101-S104:

[0024] S101. Obtain typhoon path data and corresponding rainfall data. Rainfall data includes real-time rainfall data and future rainfall data.

[0025] Specifically, using GIS (Geographic Information System) technology, wind circle data and affected area data of typhoon path points are acquired. The wind circle data includes real-time wind circle data and predicted wind circle data; the affected area data consists of ground features or facilities affected by the typhoon and heavy rain, including at least one of the following: reservoirs, river monitoring stations, rain gauges, pumping stations, sluice gates, hydropower stations, and flood-prone areas. The wind circle data and affected area data are then used to determine the typhoon path data.

[0026] Furthermore, real-time rainfall grid data and predicted rainfall grid data corresponding to the typhoon path data are acquired. Vector transformation of the rainfall contour lines is performed on the real-time rainfall grid data and predicted rainfall grid data to obtain real-time rainfall vector data and predicted rainfall vector data. The real-time rainfall vector data is then identified as real-time rainfall data, and the predicted rainfall vector data is identified as future rainfall data.

[0027] In one embodiment, Figure 2 A flowchart of an automatic early warning system based on typhoon weather is provided for embodiments of this application, as follows: Figure 2 As shown, firstly, typhoon path data is acquired using GIS (Geographic Information System) technology and stored in a MySQL database. Then, grid data of real-time and forecasted rainfall corresponding to the typhoon path data is obtained and converted into vector data. Finally, real-time rainfall data, including rainfall amount, area, and duration, as well as future rainfall data, are determined. Next, spatial data of key features susceptible to typhoons and heavy rains within a designated area, such as reservoirs, river stations, rain gauges, pumping stations, sluice gates, hydropower stations, ports, flood-prone areas, and wind power stations, are stored in the MySQL database.

[0028] S102. Based on typhoon path data and rainfall data, key features in the disaster area are spatially identified and filtered to obtain early warning data sets.

[0029] Specifically, the wind circle data in the typhoon path data is compared with key features to determine the warning level for the overlapping area, resulting in the first warning level data. The wind circle data includes the wind circle's extent and the corresponding wind speed. Key features are the features and facilities corresponding to the affected area data in the typhoon path data.

[0030] Based on the first warning level data, the corresponding affected areas within key ground features are labeled with the corresponding warning level to obtain the first set of warning data. This first set of warning data represents the ground features affected by the typhoon.

[0031] In one embodiment, such as Figure 2 As shown, the typhoon's wind circle and key features are spatially included to filter out the key features that may be affected. The wind circle range and wind force are used as the criteria for judging the warning level. Different warning levels are marked for the affected features, resulting in the first set of warning data (warning data group 1) for key features that may be affected by the typhoon.

[0032] Simultaneously, based on the area vector data of real-time rainfall vector data, the spatial inclusion of key features in the adjacent affected area is filtered through the typhoon path data to obtain real-time key feature data exceeding the rainfall warning value. Then, based on the rainfall isolines in the real-time rainfall data, the selected real-time key feature data is labeled with real-time rainfall warning levels, resulting in a second set of warning data. This second set of warning data represents the warning of real-time rainfall affecting the typhoon's adjacent affected area.

[0033] In one embodiment, such as Figure 2 As shown, when a typhoon approaches and affects a designated area, the rainfall brought by the typhoon is used as the basis for early warning. The surface vector data of real-time rainfall is spatially contained with key features, and real-time key feature data exceeding the rainfall warning value is filtered out. Then, based on the rainfall contour lines of the real-time rainfall data, the selected real-time key feature data is marked with the disaster level of the real-time rainfall warning, and the second set of early warning data (early warning data group 2) of the typhoon is passing through is obtained.

[0034] Finally, based on the area vector data of the predicted rainfall vector data in the future rainfall data, the key features in the expected affected area data of the typhoon path data are spatially filtered to obtain the predicted key feature data exceeding the rainfall warning value. Based on the rainfall isolines of the future rainfall data, the selected predicted key feature data are labeled with the predicted rainfall warning level, resulting in the third set of warning data. This third set of warning data represents the warning effect of future rainfall on key features at the typhoon path points.

[0035] Furthermore, by comparing the third set of early warning data with the second set of early warning data, key feature data was identified and identical key feature data was filtered out. The identical key feature data in the third and second sets of early warning data were then highlighted to obtain high-alert data. This high-alert data was then updated to the corresponding positions in the third and second sets of early warning data. The early warning data sets include: the first set of early warning data, the updated second set of early warning data, and the updated third set of early warning data.

[0036] In one implementation, the area vector data of the predicted rainfall vector data and key features are spatially included and filtered to obtain the predicted key feature data that exceeds the rainfall warning value. Then, the rainfall contour lines of the predicted future rainfall data are used to mark the predicted rainfall warning level of the filtered predicted key feature data, and the third set of warning data (warning data group 3) is determined on the typhoon path.

[0037] After obtaining the third set of early warning data, the catchment volume of the watershed is calculated based on real-time rainfall data, future rainfall data, predicted rainfall duration, and rainfall surface vector data to obtain the watershed forecast data for the watershed area. The watershed area includes at least reservoirs, rivers, and lakes. Then, based on the corresponding flood control water level standards for the watershed area, an early warning judgment is made on the watershed forecast data to obtain the watershed early warning data; this watershed early warning data is then incrementally updated into the third set of early warning data.

[0038] In one embodiment, for facilities such as reservoirs and rivers, which are greatly affected by regional rainwater runoff, the water volume of reservoirs and rivers in the future can be roughly calculated based on the rainwater runoff area (area vector data), real-time rainfall data, and forecasted future rainfall data. The expected water level in the future can be obtained through calculation, i.e., the water runoff prediction data. Then, the water runoff warning data is incrementally updated into the third set of warning data by comparing it with the flood limit water level and other warning standards.

[0039] S103. Based on the weight parameters, the early warning data group is weighted and calculated to determine the early warning level data of key features.

[0040] Specifically, based on the proportion of typhoon disaster severity at different times in the typhoon path data, the warning levels corresponding to key features in the warning data set are weighted and assigned, resulting in weight parameters for each key feature. Based on these weight parameters, the warning levels for each key feature are reclassified, thus determining the warning level data for each key feature.

[0041] In one embodiment, such as Figure 2 As shown, the first set of early warning data, the updated second set of early warning data, and the updated third set of early warning data in the early warning data group are analyzed and integrated. Different weights are assigned to various influencing factors or disaster levels at different times of the typhoon on several key features in the designated area. The early warning level of the affected key features is recalculated according to the weights, and the early warning level data of each key feature after analysis and calculation is stored in a MySQL database.

[0042] S104. Combine typhoon path data and warning level data to obtain typhoon warning display data, so as to show the key features and facilities that are affected by typhoon and rainfall and generate warnings.

[0043] Specifically, using the Vue framework and the Cesium framework in GIS geographic information system technology, the typhoon path data is matched one-to-one with the warning level data, real-time rainfall data, and future rainfall data based on the typhoon path data. This results in typhoon warning display data.

[0044] In one embodiment, such as Figure 2As shown, after reading the warning level data through the provided interface, a backend calculation program is built using the Java Spring Boot framework, Vue framework, and the Cesium framework in GIS technology. This program matches the specific data of the typhoon's path points with the warning level data, real-time rainfall data, and future rainfall data. It also combines this with corresponding key geographical features to display the typhoon warning data along the typhoon's path. For general facilities, wind force and rainfall are used as warning standards. For facilities such as reservoirs and river stations, the amount of rainwater inflow within a given period can be calculated based on the catchment area as a warning rule. The analysis results are stored in a database, and interfaces are provided to other users as data support.

[0045] In one embodiment, such as Figure 2 As shown, the typhoon warning display system built using the Cesium framework also includes: a typhoon data storage module, a data processing module, a backend warning calculation module, and a frontend display module.

[0046] The typhoon data storage module stores typhoon path data in the form of a MySQL database, including a typhoon basic information table, a typhoon path location table, a typhoon forecast information table, and a typhoon landfall information table.

[0047] The data processing module obtains vector data of rainfall isolines during the current typhoon's operation by calling external interfaces for real-time and forecast rainfall. It then filters out isoline ranges exceeding rainfall warning values ​​and performs spatial analysis on rainfall isolines and key features or facilities with different rainfall amounts to identify affected key features. For facilities with catchment areas, such as reservoirs and river stations, rainfall and catchment area are used as parameters. By calling the catchment volume interface, the catchment volume for a future period is obtained, and future water level changes are calculated based on the catchment volume and simulated water level curves. Parameters exceeding flood control limits are added to the third set of warning data.

[0048] The background early warning calculation module uses factors such as typhoon wind circle, rainfall isosurface, and water level changes as early warning judgment conditions. It assigns different weights according to different stages of typhoon operation, recalculates the early warning level of ground facilities, and saves the early warning in the database.

[0049] The front-end display module provides other users with APIs for accessing key geographic features that triggered early warnings, obtained from the back-end early warning calculation module. The front-end system uses the typhoon's trajectory from a GIS system as its display format, showing historical and real-time early warning data for various time periods.

[0050] In addition, this application also provides an automatic early warning device based on typhoon weather, such as... Figure 3As shown, the automatic early warning device 300 based on typhoon weather specifically includes:

[0051] At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301; wherein the memory 302 stores instructions executable by the at least one processor 301 to enable the at least one processor 301 to execute:

[0052] Acquire typhoon path data and corresponding rainfall data; the rainfall data includes real-time rainfall data and future rainfall data.

[0053] Based on typhoon path data and rainfall data, key features in the disaster-stricken area are spatially identified and filtered to obtain early warning data sets.

[0054] Based on the weight parameters, the early warning data group is weighted and calculated to determine the early warning level data of key features;

[0055] By combining typhoon path data and warning level data, typhoon warning display data is obtained, which shows the key features and facilities that are affected by typhoons and rainfall and generate warnings.

[0056] This application embodiment uses typhoon path data and rainfall data to calculate the real-time impact of extreme weather such as typhoons and rainfall on key features, issue warnings of different levels, predict the impact of typhoons and rainfall on key features or facilities, provide reference for similar situations, and more quickly analyze features and facilities that are significantly affected by typhoons and their accompanying weather. It can also provide data support for other users, and by combining the warning data with typhoon path data after weight allocation, it can ultimately provide decision support for typhoon and flood prevention work, while also saving manpower and resources.

[0057] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for devices and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0058] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0059] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0062] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0063] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The above description is merely an embodiment of this application and is not intended to limit this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this application should be included within the scope of the claims of this application.

Claims

1. An automatic early warning method based on typhoon weather, characterized in that, The method includes: Acquire typhoon path data and corresponding rainfall data; wherein the rainfall data includes: real-time rainfall data and future rainfall data; Based on the typhoon path data and the rainfall data, key features in the disaster-stricken area are spatially identified and filtered to obtain a warning data set, including: The wind circle data in the typhoon path data is compared with the key features to determine the warning level of the overlapping area, and a first warning level data is obtained; wherein, the wind circle data includes: the wind circle range and the corresponding wind force; wherein, the key features are the features and facilities corresponding to the affected area data in the typhoon path data; Based on the first warning level data, the corresponding warning level is marked on the data of the corresponding affected areas in the key features to obtain the first set of warning data; wherein, the first set of warning data is used to represent the data of features affected by the typhoon. Also includes: Based on the area vector data of the real-time rainfall vector data in the real-time rainfall data, the spatial inclusion filtering of key features in the adjacent affected area is performed on the typhoon path data to obtain real-time key feature data that exceeds the rainfall warning value. Based on the rainfall contour lines of the real-time rainfall data, the selected real-time key ground feature data are labeled with real-time rainfall warning levels to obtain a second set of warning data; wherein, the second set of warning data is used to represent the warning of real-time rainfall on the typhoon's adjacent impact area; Specifically, it includes: Based on the area vector data of the predicted rainfall vector data in the future rainfall data, the key features in the expected affected area data of the typhoon path data are spatially contained and filtered to obtain the predicted key feature data that exceeds the rainfall warning value. Based on the rainfall contour lines of the future rainfall data, the selected key ground feature data are labeled with the predicted rainfall warning level to obtain the third set of warning data; wherein, the third set of warning data is used to represent the warning of future rainfall on key ground features at the typhoon path points; By comparing the third set of early warning data with the second set of early warning data, the key feature data is selected to identify the same key feature data; and the key feature data that are the same in the third set of early warning data and the second set of early warning data are highlighted to obtain high early warning data. Update the high-alert data to the corresponding positions of the third set of alert data and the second set of alert data; The warning data group includes: the first group of warning data, the updated second group of warning data, and the updated third group of warning data; Based on the weight parameters, the early warning data group is weighted and calculated to determine the early warning level data of the key features; By combining the typhoon path data and the warning level data, typhoon warning display data is obtained, which shows the key features and facilities that are affected by the typhoon and rainfall and generate warnings.

2. The automatic early warning method based on typhoon weather according to claim 1, characterized in that, Obtaining typhoon path data and corresponding rainfall data specifically includes: Using GIS (Geographic Information System) technology, wind circle data and affected area data of typhoon path points are obtained; wherein, the wind circle data includes: real-time wind circle data and predicted wind circle data; the affected area data are data on land features or facilities affected by typhoons and rainstorms, including at least any one of the following: reservoirs, river stations, rain gauges, pumping stations, sluice gates, hydropower stations, and flood-prone areas; The wind circle data and the affected area data are determined as the typhoon path data; Obtain real-time rainfall grid data and predicted rainfall grid data corresponding to the typhoon path data; Vector transformation of the real-time rainfall grid data and the predicted rainfall grid data is performed on the rainfall contour lines to obtain real-time rainfall vector data and predicted rainfall vector data; the real-time rainfall vector data is determined as the real-time rainfall data, and the predicted rainfall vector data is determined as the future rainfall data.

3. The automatic early warning method based on typhoon weather according to claim 1, characterized in that, After labeling the selected key ground feature data for predicted rainfall with predicted rainfall warning levels based on the rainfall isolines of the future rainfall data to obtain the third set of warning data, the method further includes: Based on the real-time rainfall data, the future rainfall data, the predicted rainfall duration, and the surface vector data of the rainfall, the catchment volume of the catchment area is calculated to obtain the predicted catchment data of the catchment area; wherein, the catchment area includes at least: reservoirs, rivers, and lakes; Based on the flood control water level standard corresponding to the catchment area, the water flow prediction data is used to make an early warning judgment to obtain water flow early warning data; The water catchment warning data is incrementally updated into the third set of warning data.

4. The automatic early warning method for typhoon weather according to claim 1, characterized in that, Based on weight parameters, the warning data group is weighted and calculated to determine the warning level data of the key features, specifically including: Based on the proportion of typhoon disaster levels at different times in the typhoon path data, the warning levels corresponding to key features in the warning data group are weighted and assigned to obtain the weight parameters of each key feature. Based on the weight parameters, the warning level is reclassified for each key feature, and the warning level data for each key feature is determined.

5. The automatic early warning method based on typhoon weather according to claim 1, characterized in that, By combining the typhoon path data and the warning level data, typhoon warning display data is obtained, specifically including: Using the Vue framework and the Cesium framework in GIS geographic information system technology, the typhoon warning display data is obtained by matching the warning level data, the real-time rainfall data, and the future rainfall data one by one based on the running path of the typhoon path points in the typhoon path data.

6. An automatic early warning device based on typhoon weather, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform an automatic early warning method based on typhoon weather according to any one of claims 1-5.

7. A non-volatile computer storage medium, characterized in that, The storage medium is a non-volatile computer-readable storage medium that stores at least one program, each program including instructions that, when executed by a terminal, cause the terminal to perform an automatic early warning method based on typhoon weather according to any one of claims 1-5.

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