Meteorological disaster early warning method for point, line and surface bearing structures

Abstract

This application relates to a meteorological disaster early warning method and system for point, line, and surface carriers, belonging to the field of meteorological disaster early warning technology. The method includes: compensating for positioning errors at the coordinates of each point within the carrier to obtain a set of carrier point coordinates; obtaining the arrival time of the user at each point coordinate based on the carrier point coordinate set and the user's preset movement speed to obtain a carrier meteorological dataset; obtaining the meteorological sparsity coefficient of each carrier meteorological data within the carrier meteorological dataset to obtain a meteorological sparsity coefficient set; obtaining the user's movement speed error coefficient, performing meteorological disaster analysis, obtaining the meteorological disaster rate, and issuing a meteorological disaster early warning. This invention solves the problem that traditional meteorological disaster early warning systems cannot accurately warn of point, line, and surface carriers. Through steps such as positioning error compensation, spatiotemporal indexing of meteorological data, and sparsity assessment, it achieves personalized meteorological disaster early warning for different carriers, improving the accuracy and targeting of the early warning.

Description

Technical Field

[0001] This application relates to the field of meteorological disaster early warning, and in particular to meteorological disaster early warning methods and systems for point, line and surface carriers. Background Technology

[0002] With the intelligent development of meteorological early warning technology, precise early warning for point, line, and area carriers has become crucial for improving disaster prevention efficiency. Currently, traditional meteorological disaster early warning mostly adopts a unified regional early warning model, which suffers from insufficient personalization and poor adaptability, making it difficult to meet the precise early warning needs of shipping, agriculture, and other fields for specific locations, routes, or areas.

[0003] Existing early warning methods rely solely on routine regional data for warnings, resulting in insufficient accuracy in warning specific areas of interest to users. This fails to meet the differentiated needs of different scenarios and is ill-suited to the requirements of precision and real-time performance in the intelligent transformation of meteorological early warning systems. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a meteorological disaster early warning method and system for point, line, and surface carriers. This method solves the problems of insufficient personalization, poor adaptability, and low accuracy of traditional meteorological disaster early warning systems for specific carriers. It achieves precise early warning based on point, line, and surface customization, thereby improving the pertinence and effectiveness of meteorological disaster early warning.

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

[0006] In a first aspect, embodiments of this application provide a meteorological disaster early warning method for point, line, and surface bearing structures, the method comprising:

[0007] The user-selected carrier is obtained, and the positioning error compensation is performed on the coordinates of each point within the carrier to obtain the carrier point coordinate set. The carrier is a point, line, or surface, and the carrier includes the coordinates of at least one point.

[0008] Based on the set of coordinates of the carrier points, and according to the user's preset moving speed, the arrival time of the user at each point coordinate is obtained. The meteorological data corresponding to each point coordinate and arrival time is obtained by indexing within the meteorological data sequence, and the carrier meteorological dataset is obtained.

[0009] Obtain the meteorological sparsity coefficients of each meteorological data in the meteorological dataset of the carrier, and obtain the meteorological sparsity coefficient set.

[0010] The user's movement speed error coefficient is obtained, and combined with the meteorological sparse coefficient set and the carrier meteorological dataset, meteorological disaster analysis is performed to obtain the meteorological disaster rate and conduct meteorological disaster early warning.

[0011] Secondly, embodiments of this application provide a meteorological disaster early warning system for point, line, and surface bearing structures, the system comprising:

[0012] The carrier coordinate compensation module is used to obtain the carrier selected by the user, perform positioning error compensation on the coordinates of each point in the carrier, and obtain the carrier point coordinate set. The carrier is a point, line or surface, and the carrier includes the point coordinates of at least one point.

[0013] The meteorological data spatiotemporal indexing module is used to obtain the arrival time of the user at each point coordinate based on the set of coordinates of the carrier and the user's preset movement speed, and to obtain the meteorological data corresponding to each point coordinate and arrival time within the meteorological data sequence, thereby obtaining the carrier meteorological dataset.

[0014] The data sparsity assessment module is used to obtain the meteorological sparsity coefficient of each meteorological data in the meteorological dataset of the carrier, and obtain the meteorological sparsity coefficient set.

[0015] The disaster rate comprehensive early warning module is used to obtain the user's movement speed error coefficient, combine it with the meteorological sparse coefficient set and the carrier meteorological dataset, perform meteorological disaster analysis, obtain the meteorological disaster rate, and conduct meteorological disaster early warning.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] This application proposes a meteorological disaster early warning method and system. By collecting multi-source meteorological data, movement speed error coefficients, and meteorological sparsity coefficients, and comprehensively analyzing basic meteorological disaster rates, speed compensation parameters, and regional environmental characteristics, the system dynamically compensates and optimizes multi-dimensional monitoring data. Simultaneously, based on real-time meteorological elements, equipment movement errors, regional monitoring density, and other types of information, the system dynamically corrects the basic disaster risk assessment results by combining speed compensation coefficients and meteorological sparsity coefficients. This effectively improves the accuracy and reliability of meteorological disaster early warnings, enabling precise risk prediction under complex meteorological conditions and dynamic movement of monitoring equipment, avoiding inaccurate warnings caused by data bias or environmental factors. Through multi-source data fusion, dynamic coefficient compensation, and regional risk correction, the system integrates monitoring information from multiple channels and quantifies multiple influencing factors, effectively avoiding early warning bias caused by single data sources and static analysis.

[0018] The technical solution of this application achieves accurate assessment and early warning of meteorological disaster risks by integrating dynamic parameters such as real-time meteorological data, moving speed error coefficient, and meteorological sparsity coefficient. It solves the problems of misjudgment and missed judgment caused by equipment movement error and uneven distribution of monitoring stations in traditional early warning, improves the timeliness and effectiveness of disaster early warning, and avoids the delay in emergency response and misallocation of disaster prevention resources caused by inaccurate early warning. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a meteorological disaster early warning method for point, line, and surface bearing structures provided in this application embodiment;

[0020] Figure 2 A schematic diagram of the structure of a meteorological disaster early warning system for point, line, and surface bearing bodies provided in this application embodiment;

[0021] The components represented by each number in the attached diagram are explained below:

[0022] Carrier coordinate compensation module 01, meteorological data spatiotemporal index module 02, data sparsity assessment module 03, disaster rate comprehensive early warning module 04. Detailed Implementation

[0023] This application provides a meteorological disaster early warning method and system for point, line and surface carriers, which is used to solve the technical problems of insufficient personalization, poor adaptability and low accuracy of early warning for specific carriers in the existing technology.

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0027] Example 1, as shown in the appendix Figure 1 As shown, this application provides a meteorological disaster early warning method for point, line, and surface bearing structures. The method includes the following steps:

[0028] S100: Obtain the carrier selected by the user, perform positioning error compensation on the coordinates of each point within the carrier, and obtain the carrier point coordinate set, wherein the carrier is one of a point, a line, or a surface, and the carrier includes the point coordinates of at least one point.

[0029] In this embodiment of the application, during the meteorological disaster early warning process, it is necessary to obtain the carrier selected by the user in the meteorological map through the interactive interface (a point carrier contains one point coordinate, and a line / area carrier contains multiple point coordinates) and obtain the positioning error parameters (such as parameter values ​​that characterize the error range).

[0030] Furthermore, the positioning error parameter is used to compensate for the coordinates of each original point, generating multiple compensated point coordinates within its nearby error range. The original point coordinates are then integrated with all the compensated point coordinates to form a set of carrier point coordinates.

[0031] This process covers the coordinate range that may be affected by positioning errors by matching the error compensation logic of the carrier type (point / line / surface), providing a coordinate reference that includes the possibility of error for subsequent data processing.

[0032] Step S100 in the method provided in this application embodiment includes:

[0033] Obtain the carrier selected by the user within the weather map. The carrier can be a point, a line, or a surface. A point carrier includes the coordinates of a single point, while a line carrier and a surface carrier include the coordinates of multiple points.

[0034] Obtain positioning error parameters;

[0035] Using the aforementioned positioning error parameters, error compensation is performed on the coordinates of each point within the carrier body to obtain multiple compensated point coordinates, and then all point coordinates and all compensated point coordinates are obtained to obtain the carrier body point coordinate set.

[0036] In this embodiment of the application, in order to achieve accurate positioning of the area of ​​interest to the user during the meteorological disaster early warning process, it is necessary to first obtain the carrier selected by the user through the meteorological map interactive interface.

[0037] Specifically, the coordinate data of point / line / area shapes can be obtained by calling the point selection, line drawing or circle selection functions of the map API, and stored as structured data (such as latitude and longitude pairs of point coordinates, coordinate sequences of lines / areas) in a preset format.

[0038] For example, if a user selects Xuanwu District of Nanjing City, Jiangsu Province (surface carrier) on a weather map, the coordinates of its boundary vertex are recorded as ("Type": "surface", "coordinate ring": [[118.812, 32.065], [118.837, 32.088], [118.861, 32.072], [118.812, 32.065]], "Number": "Region 001"), forming a closed region data structure through coordinates with the same beginning and end.

[0039] At the same time, positioning error parameters (such as the standard error range of satellite positioning, terrain influence coefficient, etc.) are retrieved from the parameter library. These parameters can be dynamically matched according to different carrier types (such as mountain locations and sea routes).

[0040] For example, when the positioning error parameter corresponds to the distance between two point coordinates, the coordinates of points within the distance between the two point coordinates near each original point coordinate are used together with the original coordinates as the coordinates of the carrier point. This covers the deviation range that may occur in actual positioning, provides a more reliable location reference for the spatiotemporal index of subsequent meteorological data, and avoids the offset of early warning data caused by coordinate errors.

[0041] For example, if a user draws a sea route (line carrier) from Lianyungang to Yancheng Port on a weather map, the coordinates of the starting point and passing points of the route are obtained through the map API and recorded as ("Type": "line", "Coordinate sequence": [[119.16, 34.60], [120.03, 33.78], [120.03, 33.78], [120.23, 33.50]], "Number": "line segment 002").

[0042] Furthermore, positioning error parameters applicable to maritime routes are retrieved from the parameter library, such as the standard error range of satellite positioning being ±80 meters and the offset range caused by ocean current influence coefficient being ±50 meters. The two errors are then combined and quantified into the range of the coordinate distance between two points.

[0043] Based on these parameters, the coordinates of all points within a two-point distance range near each original point coordinate of the route are integrated with the original coordinates to form the final set of carrier point coordinates. This ensures that when retrieving meteorological data for the route later, the range caused by ship navigation deviation and positioning errors can be effectively covered, thus guaranteeing the accuracy of meteorological disaster early warning.

[0044] S200: Based on the set of coordinates of the carrier points and the user's preset moving speed, obtain the arrival time of the user at each point coordinate, index the meteorological data corresponding to each point coordinate and arrival time in the meteorological data sequence, and obtain the carrier meteorological dataset.

[0045] In this embodiment of the application, during the meteorological disaster early warning process, it is necessary to further obtain corresponding meteorological data based on the coordinate set of the carrier point.

[0046] Specifically, the system first obtains the preset movement speed input by the user. When the carrier is a point, the default preset movement speed is 0. When the carrier is a line or surface, the preset movement speed can be set according to the actual scenario (such as standard driving speed, ship sailing speed, etc.).

[0047] Furthermore, the user's current location is obtained, and combined with the preset movement speed and the coordinate set of the carrier points, the estimated time for the user to reach each point coordinate is calculated using the distance-speed formula.

[0048] If the carrier is a point, the point arrival time is the current time; if it is a line or a surface, the estimated arrival time of each point is calculated sequentially according to the movement path, forming a set of point arrival times.

[0049] Finally, based on the correspondence between the arrival time set and the coordinates of the points in the meteorological data sequence, the meteorological data (such as wind speed, rainfall, temperature, etc.) of each coordinate point at the corresponding time are accurately indexed. These data are then integrated into a carrier meteorological dataset to provide data support for subsequent meteorological disaster risk assessment and early warning.

[0050] Step S200 in the method provided in this application embodiment includes:

[0051] Obtain the user's preset movement speed, where the preset movement speed is 0 when the carrier is a point;

[0052] Obtain the user's current location, and combine it with the preset moving speed and the coordinate set of the carrier point to obtain the arrival time of the user to each point coordinate, thus obtaining the arrival time set;

[0053] Obtain the meteorological data sequence, and based on the arrival time set, index the meteorological data corresponding to the coordinates and arrival time of each point to obtain the meteorological dataset of the carrier.

[0054] In this embodiment of the application, in order to achieve accurate matching between meteorological data and the area of ​​interest to the user during the meteorological disaster early warning process, it is necessary to further obtain the corresponding meteorological data based on the coordinate set of the carrier point.

[0055] First, in order to achieve accurate indexing of meteorological data by combining the time dimension, it is necessary to obtain the user's preset movement speed.

[0056] If the carrier is a point (such as a fixed monitoring station), the default preset movement speed is 0, and the time when the user arrives at the point is the current time. If the carrier is a line (such as a shipping route) or a surface (such as an urban area), the movement speed input by the user is obtained through the interactive interface. For example, the speed limit is used in the driving scenario, and the speed is matched according to the ship type in the shipping scenario.

[0057] Furthermore, the user's current location coordinates are obtained through the mobile terminal's positioning function or map API and used as the starting reference for time calculation.

[0058] For point carriers with a preset movement speed of 0, the current system time is directly marked as the arrival time; while for line or surface carriers, the estimated arrival time of the starting point is calculated based on the spatial distance between the user's current position and the starting point of the carrier, combined with the preset movement speed, using the formula "time = distance / speed".

[0059] Furthermore, along the path or boundary of the carrier, coordinate points are extracted at preset distance intervals (e.g., 500 meters). Based on the calculated arrival time of the previous point, the estimated arrival time of each coordinate point is calculated sequentially, ultimately forming a set of arrival times covering the coordinates of all carrier points, providing an accurate time dimension reference for subsequent meteorological data retrieval.

[0060] The method provided in this application embodiment includes the following steps: "obtaining a meteorological data sequence, indexing and obtaining meteorological data corresponding to the coordinates and arrival time of each point based on the arrival time set, and obtaining a meteorological dataset of the carrier."

[0061] According to the meteorological data monitoring system, a meteorological data sequence is obtained, wherein the meteorological data sequence includes meteorological data for multiple future times, and each meteorological data includes meteorological data for all coordinates within the meteorological map;

[0062] Based on the set of coordinates and arrival times of the carrier points, the meteorological data for each point coordinate and arrival time is indexed in the meteorological data sequence to obtain the carrier meteorological dataset.

[0063] In this embodiment of the application, in order to achieve precise coupling between meteorological data and the spatial location and time dimension of the area of ​​interest to the user during the meteorological disaster early warning process, it is necessary to implement spatiotemporal joint retrieval and acquisition of corresponding meteorological data based on the coordinate set of carrier points that have completed positioning error compensation.

[0064] Specifically, the process begins by connecting to a meteorological data monitoring system to obtain a multi-source meteorological data sequence. This sequence contains meteorological data for multiple future moments, and each moment covers all meteorological elements (such as wind speed, precipitation, and temperature) at all coordinates within the meteorological map.

[0065] Furthermore, based on the obtained arrival time set, a spatiotemporal joint indexing operation is performed on the meteorological data sequence to obtain a carrier meteorological dataset that reflects the dynamic meteorological characteristics of the user's area of ​​interest throughout the entire time period.

[0066] Specifically, the spatial scope is first defined. That is, the coordinate set of the carrier points is used as the spatial retrieval benchmark. If the carrier is a linear structure (such as a marathon track, highway, or waterway), a linear spatial scope is formed based on its coordinate point sequence; if it is a planar structure (such as a factory area, urban area, or watershed), a closed spatial boundary is constructed by the coordinates of the polygon vertices, thereby clarifying the spatial matching range of meteorological data.

[0067] Furthermore, corresponding time anchors are generated. This involves aligning the arrival time set with the timestamps of the meteorological data sequence. The arrival time set contains the estimated arrival time (accurate to the minute) of the coordinates of each carrier point. For example, the 10:30 arrival time calculated for a certain highway coordinate point is used as the retrieval anchor point in the time dimension.

[0068] Furthermore, a spatiotemporal joint retrieval is performed. This involves executing a composite conditional query based on "coordinate point + timestamp" in the meteorological database. Specifically, the retrieval area is formed by taking the coordinates of a single carrier point as the center and combining it with a preset spatial error compensation radius (such as ±100 meters). The meteorological observation data within this area that is closest to the arrival time of the target is then matched.

[0069] If there is a time sampling interval (e.g., data is updated every 15 minutes), the observation with the closest timestamp is selected as the matching result.

[0070] Finally, data structure integration is implemented, which involves combining the coordinates of each carrier point with the meteorological data at the corresponding time to generate standardized data records containing latitude and longitude coordinates, timestamps, and meteorological parameters (wind speed, precipitation intensity, temperature, etc.), forming a carrier meteorological dataset, which presents the dynamic meteorological conditions of the area of ​​interest to the user in a temporal and spatial manner.

[0071] For example, when a user selects the highway from Nanjing Xinjiekou to Lukou Airport as the area of ​​interest (line carrier), the coordinate point set of the route is first corrected based on the positioning error compensation mechanism.

[0072] Furthermore, by connecting to the meteorological data monitoring system, meteorological data sequences with 15-minute intervals will be obtained for the next 6 hours, covering meteorological elements such as wind speed, precipitation, and visibility at coordinate points along the entire route.

[0073] Furthermore, based on the preset driving speed of 80km / h, combined with the user's current location and route coordinates, the estimated arrival time of each key node (such as toll stations and interchanges) is calculated, forming an arrival time set.

[0074] Finally, using the corrected route coordinate point set as the spatial range and the arrival time set as the time anchor, a spatiotemporal joint indexing operation is performed in the meteorological data sequence to retrieve the meteorological data at the corresponding time of each node. After integration, a carrier meteorological dataset is generated to provide users with dynamic meteorological early warning information throughout the entire process.

[0075] S300: Obtain the meteorological sparsity coefficient of each meteorological data in the meteorological dataset of the carrier, and obtain the meteorological sparsity coefficient set;

[0076] In this embodiment of the application, in order to evaluate the reliability of the meteorological data, it is necessary to further calculate the meteorological sparsity coefficient of each data.

[0077] Specifically, the coordinates of meteorological stations within the meteorological map are first obtained. Then, using a spatial distance algorithm, the spatial distance between the coordinates of each point in the meteorological dataset and the coordinates of its nearest meteorological station is calculated, forming a meteorological station distance set. This distance intuitively reflects the proximity of each point's coordinates to the actual source of the observation data.

[0078] Furthermore, a preset standard meteorological station distance is set as a reference threshold, which can be flexibly configured according to the meteorological data monitoring accuracy requirements or application scenarios (such as cities, mountainous areas, etc.). The ratio of each distance value in the meteorological station distance set to the preset standard meteorological station distance is calculated, and the resulting ratio is the meteorological sparsity coefficient of the corresponding point coordinates.

[0079] The greater the distance, the larger the ratio, indicating that the accuracy of meteorological data is reduced due to the lack of nearby observation support, and the corresponding meteorological sparsity coefficient is larger; conversely, the closer the distance, the smaller the coefficient.

[0080] Finally, through the above calculation process, each meteorological data point in the meteorological dataset is assigned a corresponding meteorological sparsity coefficient, which is then integrated to form a meteorological sparsity coefficient set, providing a quantitative basis for subsequent meteorological disaster risk assessment and early warning information classification based on data reliability.

[0081] Step S300 in the method provided in this application embodiment includes:

[0082] Obtain the coordinates of meteorological stations within the meteorological map, calculate the distance between the coordinates of each point carrying meteorological data and the coordinates of the nearest meteorological station, and obtain the meteorological station distance set;

[0083] The meteorological sparsity coefficient set is calculated based on the distance set of meteorological stations.

[0084] In this embodiment of the application, in order to assess the reliability of the data in the meteorological dataset of the carrier, it is necessary to further obtain the meteorological sparsity coefficients corresponding to each data point during the meteorological disaster early warning process.

[0085] Specifically, in order to accurately calculate the meteorological sparsity coefficient, it is first necessary to obtain the coordinates of meteorological stations within the meteorological map.

[0086] If the meteorological map uses a standard geographic coordinate system (such as WGS84), the latitude and longitude coordinates of the meteorological station in that coordinate system can be obtained directly. If the meteorological map uses a custom projection coordinate system (such as a local independent coordinate system), the coordinates of the meteorological station need to be converted to coordinates in the same coordinate system as the coordinates of the meteorological data points through coordinate transformation.

[0087] Furthermore, for each point coordinate in the meteorological dataset of the carrier, a spatial distance metric algorithm (such as the Euclidean distance formula or the Haversine formula) is used to calculate the spatial distance between it and the coordinates of all meteorological stations one by one, and the minimum distance is selected as the distance between the point coordinate and the nearest meteorological station.

[0088] For two-dimensional planar coordinates, the Euclidean distance formula is used; for latitude and longitude coordinates, the Haversine formula is used. The values ​​obtained from these two formulas are then integrated to form a set of meteorological station distances.

[0089] Furthermore, based on the existing set of meteorological station distances, a set of meteorological sparsity coefficients is calculated.

[0090] The step of "calculating a set of meteorological sparsity coefficients based on a set of meteorological station distances" in the method provided in this application includes:

[0091] Obtain the distance to the preset standard meteorological station;

[0092] Calculate the ratio of the distance of each meteorological station in the meteorological station distance set to the distance of a preset standard meteorological station to obtain a meteorological sparsity coefficient set.

[0093] Specifically, the distance to a preset standard meteorological station is first obtained. This distance serves as a benchmark threshold for measuring the reliability of meteorological data and can be flexibly set according to the actual application scenario.

[0094] If applied to urban areas with dense meteorological monitoring stations, considering the need for high-precision data monitoring, the distance between the preset standard meteorological stations can be set to a small value (e.g., 500 meters); if applied to mountainous or remote areas with sparse meteorological monitoring stations, the distance between the preset standard meteorological stations can be appropriately increased (e.g., 5 kilometers) to adapt to their monitoring conditions.

[0095] Furthermore, based on the acquired set of meteorological station distances, the distance data of each meteorological station in the set is compared with the distance of a preset standard meteorological station to obtain a set of meteorological sparsity coefficients.

[0096] Specifically, the meteorological sparsity coefficient = distance to meteorological station / distance to preset standard meteorological station. Through this calculation, the distance between each point's coordinates and the nearest meteorological station can be standardized, transforming the distance information into a value within the range of 0 to positive infinity.

[0097] Among them, when the distance between meteorological stations is less than or equal to the distance between preset standard meteorological stations, that is, the meteorological sparsity coefficient is less than or equal to 1, it indicates that the meteorological data at that point is highly reliable; when the distance between meteorological stations is greater than or equal to the distance between preset standard meteorological stations, that is, the meteorological sparsity coefficient is greater than 1, it indicates that the meteorological data at that point is less reliable, and the farther the distance, the larger the coefficient and the lower the data accuracy.

[0098] Furthermore, all meteorological sparse coefficients obtained through ratio calculations are systematically integrated to form a set of meteorological sparse coefficients that correspond one-to-one with the data in the meteorological dataset of the carrier.

[0099] This dataset provides a quantitative basis for subsequent operations such as meteorological data quality assessment, early warning information weight allocation, and risk level classification, ensuring the scientific validity and reliability of data application during meteorological disaster early warning.

[0100] S400: Obtain the user's movement speed error coefficient, combine it with the meteorological sparse coefficient set and the carrier meteorological dataset, perform meteorological disaster analysis, obtain the meteorological disaster rate, and conduct meteorological disaster early warning.

[0101] In this embodiment of the application, in order to improve the accuracy of meteorological disaster early warning, it is necessary to comprehensively consider the impact of user movement speed error and the spatial distribution sparsity of meteorological data on disaster prediction.

[0102] Specifically, the user's movement speed error coefficient is first obtained, which is related to the type of carrier. When the carrier is a point, the default movement speed error coefficient is 0; when the carrier is a line or surface, the coefficient is calculated using historical speed fluctuation data.

[0103] Furthermore, the meteorological dataset of the carrier is input into the meteorological disaster predictor, which outputs multiple basic meteorological disaster rates and calculates the average value as the initial disaster risk assessment value.

[0104] Furthermore, compensation and correction are performed by combining the moving speed error coefficient and the meteorological sparsity coefficient set. Specifically, the speed compensation coefficient is obtained by summing 1 with the moving speed error coefficient, and then multiplied by the basic meteorological disaster rate using the sum of the moving speed error coefficient and the meteorological sparsity coefficient set, and the average is taken to obtain the final meteorological disaster rate. The larger the moving speed error and the sparser the meteorological data, the higher the corrected meteorological disaster rate.

[0105] Ultimately, the meteorological disaster rate is compared with a preset threshold. If the rate exceeds the threshold, a corresponding warning is triggered; otherwise, it is not. This mechanism allows for dynamic adjustment of disaster risk assessments in mobile scenarios, thereby improving the accuracy of warnings.

[0106] Step S400 in the method provided in this application embodiment includes:

[0107] Obtain the user's movement speed error coefficient, where the movement speed error coefficient is the magnitude of the movement speed error, and the movement speed error coefficient is 0 when the carrier is a point;

[0108] Based on the meteorological data of each carrier in the aforementioned carrier meteorological dataset, meteorological disaster prediction is performed, and the basic meteorological disaster rate is obtained through processing.

[0109] Based on the moving speed error coefficient, a speed compensation coefficient is calculated. Combined with the meteorological sparse coefficient set, the basic meteorological disaster rate is compensated to obtain the meteorological disaster rate, and meteorological disaster early warning judgment is performed.

[0110] In this embodiment of the application, in order to accurately assess the risk of meteorological disasters, it is necessary to obtain the user's movement speed error coefficient. This coefficient is used to characterize the magnitude of the movement speed error, and its value is closely related to the type of carrier.

[0111] When the carrier is a point (such as a fixed monitoring station or a stationary target area), the default movement speed error coefficient is 0 since there is no movement. When the carrier is a line (such as a highway route or air route) or a surface (such as an urban area or an activity range area), the movement speed error coefficient can be calculated based on the user-defined standard movement speed, the real-time measured actual movement speed, and arithmetic operations.

[0112] Specifically, when calculating the movement speed error coefficient, first obtain the user-set standard movement speed (e.g., 60 km / h), then measure the user's actual movement speed in real time (e.g., 50 km / h), subtract the actual movement speed from the standard movement speed to obtain the speed difference (60-50=10 km / h), divide the absolute value of the speed difference by the standard movement speed, and the final result is the movement speed error coefficient (movement speed error coefficient = |10| / 60≈0.17).

[0113] The method provided in this application embodiment includes the step of "predicting meteorological disasters based on each meteorological data point in the meteorological dataset of the carrier and processing it to obtain the basic meteorological disaster rate" as follows:

[0114] Each meteorological data point within the meteorological dataset of the carrier is input into the meteorological disaster predictor, and multiple basic meteorological disaster rates are obtained from the prediction output. The meteorological disaster predictor is trained using a sample carrier meteorological data set and a sample meteorological disaster rate set. The sample meteorological disaster rate is the proportion of meteorological disasters sent under different sample carrier meteorological data.

[0115] The average of the multiple predicted basic meteorological disaster rates is calculated to obtain the basic meteorological disaster rate.

[0116] In this embodiment of the application, in order to accurately assess the risk of meteorological disasters and obtain a reliable basic meteorological disaster rate, it is necessary to first construct a meteorological disaster predictor.

[0117] During the training process of the meteorological disaster predictor, historical meteorological monitoring archives are reviewed, and meteorological data from different years, seasons, and regions are selected from the meteorological data center database to form a sample carrier meteorological data set.

[0118] The meteorological data includes multiple meteorological elements such as temperature, humidity, wind speed, air pressure, and precipitation, as well as geographical environmental parameters such as altitude, terrain slope, and vegetation coverage. Data collection was conducted using a uniform 1-hour time resolution, simultaneously acquired from multiple sources including ground weather stations, meteorological satellites, and radar monitoring, to ensure the integrity and accuracy of the sample data.

[0119] For example, monitoring data on typical meteorological disasters such as heavy rain (precipitation ≥ 50 mm / 24 h), strong winds (wind speed ≥ 17.2 m / s), and high temperatures (temperature ≥ 35 °C) are selected from summer meteorological data of a certain region. These data exhibit specific combination patterns in the feature space; for example, heavy rain is usually accompanied by phenomena such as relative humidity ≥ 90% and a sudden drop in air pressure.

[0120] Furthermore, an analysis team composed of meteorological experts used professional data analysis tools to manually label the meteorological disaster occurrence corresponding to the meteorological data carried by each sample.

[0121] During the labeling process, a unified disaster determination standard is followed. For example, rainfall exceeding 50 mm for 3 consecutive hours is defined as a rainstorm disaster, and daily maximum temperature ≥ 35℃ for 3 consecutive days is defined as a high temperature disaster. The disaster type and probability of occurrence corresponding to each data sample are recorded to form a sample meteorological disaster rate set.

[0122] Furthermore, after completing the construction of the meteorological data set of the sample carrier and the meteorological disaster rate set of the sample, a meteorological disaster predictor is constructed using the random forest algorithm as the basic architecture.

[0123] The Random Forest algorithm takes a set of meteorological data as input and, through parallel computation of multiple decision trees, directly outputs a set of corresponding meteorological disaster rates, thus mapping meteorological data to disaster probabilities. By constructing multiple decision trees, the Random Forest algorithm can automatically extract complex feature relationships from the data, adapting to the multi-factor influence characteristics of meteorological disaster prediction.

[0124] Specifically, the algorithm's input layer receives standardized meteorological data (containing 10 meteorological elements and 5 geographic parameters). Each decision tree extracts approximately 65% ​​of the data from the original samples as a training set through bootstrapping, and randomly selects some features for node splitting. During node splitting, the Gini coefficient is used as an evaluation metric. After 10 splits, 5 decision tree branches are cascaded to capture multi-dimensional features such as the changing trends of meteorological elements and the influence of the geographic environment.

[0125] Specifically, at the end of the algorithm, the final probability of meteorological disasters is output through a voting mechanism or average prediction value, which corresponds to the sample meteorological disaster rate set.

[0126] During the model training phase, the meteorological data set of the sample carriers was divided into a training set and a validation set in an 8:2 ratio. During training, mean squared error was used as the optimization objective, and iterative optimization was performed by adjusting parameters such as the number of decision trees and the maximum depth. The initial number of trees was set to 50, and a performance evaluation was conducted every time 10 trees were added.

[0127] For example, in the first round of training, a batch (e.g., 100 sets) of sample data was input, and the average error between the model's output meteorological disaster probability prediction value and the labeled value was 0.15. After 30 rounds of training, the average error on the validation set dropped to 0.05, and the error curve tended to be stable, indicating that the model had converged.

[0128] During training, an early stopping strategy is used to avoid overfitting, that is, training is automatically stopped when the error value on the validation set does not decrease for 5 consecutive rounds.

[0129] Meanwhile, to enhance the model's adaptability to different meteorological environments, data augmentation strategies were employed to expand the meteorological data set of the sample carrier.

[0130] Specifically, by performing feature perturbation operations on the original data, such as adding ±2℃ random noise to the temperature data, scaling the wind speed data by ±10%, and simulating the changing patterns of meteorological elements under different terrain conditions, diverse derivative data samples are generated.

[0131] This method exposes the model to more diverse meteorological data features during the training phase, effectively improving its adaptability to data differences caused by monitoring equipment errors and environmental changes in practical applications, and ensuring accurate prediction of meteorological disasters under different climatic conditions.

[0132] Ultimately, after multiple rounds of iterative training and parameter optimization, the obtained meteorological disaster predictor achieved an accuracy of 90% and a recall rate of 85% on the test set. This predictor can accurately predict the probability of meteorological disasters such as rainstorms, strong winds, and high temperatures, providing reliable technical support for subsequent meteorological disaster early warning.

[0133] Furthermore, multiple real-time collected meteorological data are sequentially input into the pre-trained meteorological disaster predictor. The predictor, through the feature analysis capabilities of the random forest algorithm, automatically analyzes the combined characteristics of meteorological elements in the data and, based on preset disaster determination rules, accurately predicts the probability of meteorological disasters occurring.

[0134] Furthermore, after obtaining multiple predicted basic meteorological disaster rates, in order to form a unified and comprehensive risk assessment indicator, it is necessary to calculate the average of multiple predicted basic meteorological disaster rates to obtain the basic meteorological disaster rate.

[0135] Specifically, the validity of multiple predicted basic meteorological disaster rates output by the forecaster is first verified, and outliers caused by abnormal data transmission or monitoring equipment failure are removed. For example, when the predicted probability for a certain area exceeds 100% or is less than 0%, it is corrected by interpolation of data from neighboring stations to ensure the rationality of the data.

[0136] Furthermore, the basic meteorological disaster rate is calculated using the arithmetic mean method, which involves summing all valid predicted probabilities and then dividing by the number of stations.

[0137] For example, the predicted probabilities of rainstorm disasters output by 10 monitoring stations in a certain river basin are 82%, 78%, 85%, 90%, 75%, 88%, 80%, 92%, 76%, and 83%, respectively. Adding these values ​​together and dividing by 10, we get (82+78+85+90+75+88+80+92+76+83) / 10=83.9%, which gives the basic meteorological disaster rate of the river basin as 83.9%, providing effective data reference for the local emergency management department to activate the evacuation plan.

[0138] The method provided in this application includes the following steps: "calculating a speed compensation coefficient based on the moving speed error coefficient, compensating the basic meteorological disaster rate by combining it with the meteorological sparse coefficient set, obtaining the meteorological disaster rate, and performing meteorological disaster early warning judgment."

[0139] Calculate the sum of 1 and the moving speed error coefficient to obtain the speed compensation coefficient;

[0140] The basic meteorological disaster rate is compensated by using the speed compensation coefficient and each meteorological sparse coefficient in the meteorological sparse coefficient set to obtain multiple compensated meteorological disaster rates. The average value is calculated to obtain the meteorological disaster rate. It is then determined whether the rate is greater than or equal to the meteorological disaster rate threshold to perform meteorological disaster early warning judgment.

[0141] In this embodiment of the application, in order to further improve the prediction accuracy and optimize the early warning results in combination with actual environmental factors, it is necessary to perform compensation processing based on the moving speed error coefficient and the meteorological sparsity coefficient set to achieve accurate calculation and early warning judgment of meteorological disaster rate.

[0142] Specifically, the speed compensation coefficient is obtained through linear calculation logic, that is, the speed compensation coefficient can be directly obtained by adding 1 to the movement speed error coefficient.

[0143] This coefficient is used to quantify the impact of speed deviation on data accuracy of mobile observation equipment (such as weather drones and vehicle-mounted radar). For example, when the actual moving speed of the equipment is higher than the preset value, resulting in a 5% error in the monitoring data, the corresponding speed compensation coefficient is 1.05 (1 + 5% = 1.05).

[0144] Furthermore, the basic meteorological disaster rate is compensated based on the velocity compensation coefficient and the meteorological sparsity coefficient set to obtain multiple compensated meteorological disaster rates.

[0145] Specifically, the velocity compensation coefficient is multiplied by each element in the meteorological sparse coefficient set, and then multiplied by the basic meteorological disaster rate. That is, multiple compensation meteorological disaster rates are calculated by the formula: Compensation meteorological disaster rate = (velocity compensation coefficient × corresponding element in the meteorological sparse coefficient set) / basic meteorological disaster rate.

[0146] The compensation process takes into account the impact of differences in the distribution density of meteorological monitoring stations in different regions (such as insufficient data representativeness due to sparse stations in mountainous areas) on the prediction results.

[0147] For example, in a sparse area of ​​a certain meteorological station, the meteorological sparsity coefficient is 0.8. Combined with the velocity compensation coefficient of 1.05 and the basic meteorological disaster rate of 80%, the compensated meteorological disaster rate of the area is calculated to be 67.2% (1.05×0.8×80%=67.2%).

[0148] Furthermore, after completing the multi-regional compensation calculation, the meteorological disaster rate is obtained by calculating the arithmetic mean.

[0149] Specifically, the meteorological disaster rate is calculated by adding up all the compensated meteorological disaster rates and dividing by the total number of regions, i.e., by using the formula: meteorological disaster rate = sum of all meteorological disaster rates / total number of regions, in order to further achieve accurate meteorological disaster identification.

[0150] Furthermore, the calculated meteorological disaster rate is compared with a preset meteorological disaster rate threshold (e.g., 70%). If the meteorological disaster rate is greater than or equal to the threshold, the early warning mechanism is immediately triggered, a red warning is sent to the emergency management department, and high-risk areas are marked. If the meteorological disaster rate is less than the threshold, the data is continuously monitored and the current warning level is maintained.

[0151] For example, a region comprises five monitoring areas with compensation rates for meteorological disasters of 67.2%, 75.0%, 82.1%, 69.8%, and 78.5%, respectively, and a meteorological disaster rate threshold of 70%. Substituting these values ​​into the formula, we obtain the meteorological disaster rate = (67.2 + 75.0 + 82.1 + 69.8 + 78.5) / 5 = 74.52%. Since 74.52% ≥ the threshold of 70%, an orange rainstorm warning will be automatically activated, and a disaster impact range will be generated simultaneously. This provides intuitive and accurate data support for emergency departments to make disaster prevention decisions such as allocating relief supplies and organizing the evacuation of the public.

[0152] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects:

[0153] This application provides a meteorological disaster early warning method for point, line, and surface carriers. First, it acquires the carriers selected by the user on a meteorological map. Positioning error parameters are used to compensate for the carrier point coordinates, constructing a carrier point coordinate set containing the error range, thus solving the problem of early warning data offset caused by positioning deviation. Second, combining the user's preset movement speed and current location, the estimated time to reach each point is calculated. Meteorological data sequences are retrieved through spatiotemporal joint retrieval to obtain the carrier meteorological dataset, achieving accurate matching of meteorological data with the user's area of ​​interest. Then, the distance between the carrier meteorological data points and the nearest meteorological station is calculated and compared with a preset standard distance to obtain a meteorological sparsity coefficient set, quantifying the reliability of the data. Finally, the movement speed error coefficient is obtained, and the carrier meteorological data is input into the predictor to calculate the basic meteorological disaster rate. This rate is then compensated and corrected using a speed compensation coefficient and a meteorological sparsity coefficient to obtain the final meteorological disaster rate. This rate is compared with a preset threshold to trigger a corresponding early warning.

[0154] The method provided in this application solves the problems of false alarms and missed detections caused by positioning deviations, movement speed errors, and sparse monitoring data in traditional meteorological disaster early warning systems. Through a multi-dimensional processing mechanism involving positioning error compensation, dynamic movement speed correction, and meteorological data sparsity assessment, it overcomes the limitations of traditional unified regional early warning models, achieving personalized and accurate early warnings for point, line, and surface carriers, improving early warning adaptability, and promoting the development of intelligent and precise meteorological disaster prevention.

[0155] Example 2, as shown in the appendix Figure 2 As shown, based on the inventive concept of the meteorological disaster early warning method for point, line, and surface bearing bodies provided in Embodiment 1, this application also provides a meteorological disaster early warning system for point, line, and surface bearing bodies, specifically including:

[0156] The carrier coordinate compensation module 01 is used to obtain the carrier selected by the user, perform positioning error compensation on the coordinates of each point in the carrier, and obtain the carrier point coordinate set. The carrier is a point, line or surface, and the carrier includes the point coordinates of at least one point.

[0157] The meteorological data spatiotemporal index module 02 is used to obtain the arrival time of the user at each point coordinate based on the set of coordinates of the carrier and the user's preset movement speed, and to obtain the meteorological data corresponding to each point coordinate and arrival time in the meteorological data sequence, thereby obtaining the carrier meteorological dataset.

[0158] The data sparsity assessment module 03 is used to obtain the meteorological sparsity coefficient of each meteorological data in the meteorological dataset of the carrier, and to obtain a set of meteorological sparsity coefficients.

[0159] The disaster rate comprehensive early warning module 04 is used to obtain the user's movement speed error coefficient, combine it with the meteorological sparse coefficient set and the carrier meteorological dataset, perform meteorological disaster analysis, obtain the meteorological disaster rate, and conduct meteorological disaster early warning.

[0160] In one embodiment, the carrier coordinate compensation module 01 is further configured to:

[0161] Obtain the carrier selected by the user within the weather map. The carrier can be a point, a line, or a surface. A point carrier includes the coordinates of a single point, while a line carrier and a surface carrier include the coordinates of multiple points.

[0162] Obtain positioning error parameters;

[0163] Using the aforementioned positioning error parameters, error compensation is performed on the coordinates of each point within the carrier body to obtain multiple compensated point coordinates, and then all point coordinates and all compensated point coordinates are obtained to obtain the carrier body point coordinate set.

[0164] In one embodiment, the meteorological data spatiotemporal indexing module 02 is also used for:

[0165] Obtain the user's preset movement speed, where the preset movement speed is 0 when the carrier is a point;

[0166] Obtain the user's current location, and combine it with the preset moving speed and the coordinate set of the carrier point to obtain the arrival time of the user to each point coordinate, thus obtaining the arrival time set;

[0167] Obtain the meteorological data sequence, and based on the arrival time set, index the meteorological data corresponding to the coordinates and arrival time of each point to obtain the meteorological dataset of the carrier.

[0168] In one embodiment, the data sparsity assessment module 03 is further used for:

[0169] Obtain the coordinates of meteorological stations within the meteorological map, calculate the distance between the coordinates of each point carrying meteorological data and the coordinates of the nearest meteorological station, and obtain the meteorological station distance set;

[0170] The meteorological sparsity coefficient set is calculated based on the distance set of meteorological stations.

[0171] In one embodiment, the disaster rate comprehensive early warning module 04 is also used for:

[0172] Obtain the user's movement speed error coefficient, where the movement speed error coefficient is the magnitude of the movement speed error, and the movement speed error coefficient is 0 when the carrier is a point;

[0173] Based on the meteorological data of each carrier in the aforementioned carrier meteorological dataset, meteorological disaster prediction is performed, and the basic meteorological disaster rate is obtained through processing.

[0174] Based on the moving speed error coefficient, a speed compensation coefficient is calculated. Combined with the meteorological sparse coefficient set, the basic meteorological disaster rate is compensated to obtain the meteorological disaster rate, and meteorological disaster early warning judgment is performed.

[0175] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0176] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0177] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A meteorological disaster early warning method for point, line, and surface bearing bodies, characterized in that, include: The user-selected carrier is obtained, and the positioning error compensation is performed on the coordinates of each point within the carrier to obtain the carrier point coordinate set. The carrier is a point, line, or surface, and the carrier includes the coordinates of at least one point. Based on the set of coordinates of the carrier points, and according to the user's preset moving speed, the arrival time of the user at each point coordinate is obtained. The meteorological data corresponding to each point coordinate and arrival time is obtained by indexing within the meteorological data sequence, and the carrier meteorological dataset is obtained. Obtain the meteorological sparse coefficients for each meteorological data point within the meteorological dataset of the carrier, thus obtaining a set of meteorological sparse coefficients, including: Obtain the coordinates of meteorological stations within the meteorological map, calculate the distance between the coordinates of each point carrying meteorological data and the coordinates of the nearest meteorological station, and obtain the meteorological station distance set; Based on the distance set of meteorological stations, a set of meteorological sparsity coefficients is calculated, including: Obtain the distance to the preset standard meteorological station; Calculate the ratio of the distance of each meteorological station in the meteorological station distance set to the distance of a preset standard meteorological station to obtain a meteorological sparsity coefficient set; The user's movement speed error coefficient is obtained, and combined with the meteorological sparse coefficient set and the carrier meteorological dataset, meteorological disaster analysis is performed to obtain the meteorological disaster rate and conduct meteorological disaster early warning, including: Obtain the user's movement speed error coefficient, where the movement speed error coefficient is the magnitude of the movement speed error, and the movement speed error coefficient is 0 when the carrier is a point; Based on the meteorological data of each carrier in the aforementioned carrier meteorological dataset, meteorological disaster prediction is performed, and the basic meteorological disaster rate is obtained through processing. Based on the moving speed error coefficient, a speed compensation coefficient is calculated. Combined with the meteorological sparse coefficient set, the basic meteorological disaster rate is compensated to obtain the meteorological disaster rate, and meteorological disaster early warning judgment is performed.

2. The meteorological disaster early warning method for point, line, and surface bearing bodies according to claim 1, characterized in that, The system retrieves the user-selected carrier, performs positioning error compensation on the coordinates of each point within the carrier, and obtains a set of carrier point coordinates, including: Obtain the carrier selected by the user within the weather map. The carrier can be a point, a line, or a surface. A point carrier includes the coordinates of a single point, while a line carrier and a surface carrier include the coordinates of multiple points. Obtain positioning error parameters; Using the aforementioned positioning error parameters, error compensation is performed on the coordinates of each point within the carrier body to obtain multiple compensated point coordinates, and then all point coordinates and all compensated point coordinates are obtained to obtain the carrier body point coordinate set.

3. The meteorological disaster early warning method for point, line, and surface bearing bodies according to claim 1, characterized in that, Based on the set of coordinates of the carrier points, and according to the user's preset movement speed, the arrival time of the user at each point coordinate is obtained. The meteorological data corresponding to each point coordinate and arrival time is then indexed within the meteorological data sequence to obtain the carrier meteorological dataset, including: Obtain the user's preset movement speed, where the preset movement speed is 0 when the carrier is a point; Obtain the user's current location, and combine it with the preset moving speed and the coordinate set of the carrier point to obtain the arrival time of the user to each point coordinate, thus obtaining the arrival time set; Obtain the meteorological data sequence, and based on the arrival time set, index the meteorological data corresponding to the coordinates and arrival time of each point to obtain the meteorological dataset of the carrier.

4. The meteorological disaster early warning method for point, line, and surface bearing structures according to claim 3, characterized in that, Obtain the meteorological data sequence, and based on the arrival time set, index the meteorological data corresponding to the coordinates and arrival time of each point to obtain the carrier meteorological dataset, including: According to the meteorological data monitoring system, a meteorological data sequence is obtained, wherein the meteorological data sequence includes meteorological data for multiple future times, and each meteorological data includes meteorological data for all coordinates within the meteorological map; Based on the set of coordinates and arrival times of the carrier points, the meteorological data for each point coordinate and arrival time is indexed in the meteorological data sequence to obtain the carrier meteorological dataset.

5. The meteorological disaster early warning method for point, line, and surface bearing bodies according to claim 1, characterized in that, Based on the meteorological data of each carrier within the aforementioned carrier meteorological dataset, meteorological disaster prediction is performed, and the basic meteorological disaster rate is obtained through processing, including: Each meteorological data point within the meteorological dataset of the carrier is input into the meteorological disaster predictor, and multiple basic meteorological disaster rates are obtained from the prediction output. The meteorological disaster predictor is trained using a sample carrier meteorological data set and a sample meteorological disaster rate set. The sample meteorological disaster rate is the proportion of meteorological disasters sent under different sample carrier meteorological data. The average of the multiple predicted basic meteorological disaster rates is calculated to obtain the basic meteorological disaster rate.

6. The meteorological disaster early warning method for point, line, and surface bearing structures according to claim 1, characterized in that, Based on the movement speed error coefficient, a speed compensation coefficient is calculated. Combined with the meteorological sparse coefficient set, the basic meteorological disaster rate is compensated to obtain the meteorological disaster rate. Meteorological disaster early warning judgment is then performed, including: Calculate the sum of 1 and the moving speed error coefficient to obtain the speed compensation coefficient; The basic meteorological disaster rate is compensated by using the speed compensation coefficient and each meteorological sparse coefficient in the meteorological sparse coefficient set to obtain multiple compensated meteorological disaster rates. The average value is calculated to obtain the meteorological disaster rate. It is then determined whether the rate is greater than or equal to the meteorological disaster rate threshold to perform meteorological disaster early warning judgment.

7. A meteorological disaster early warning system for point, line, and surface bearing bodies, characterized in that, The system is used to execute the meteorological disaster early warning method for point-line-surface bearing bodies according to any one of claims 1-6, the system comprising: The carrier coordinate compensation module is used to obtain the carrier selected by the user, perform positioning error compensation on the coordinates of each point in the carrier, and obtain the carrier point coordinate set. The carrier is a point, line or surface, and the carrier includes the point coordinates of at least one point. The meteorological data spatiotemporal indexing module is used to obtain the arrival time of the user at each point coordinate based on the set of coordinates of the carrier and the user's preset movement speed, and to obtain the meteorological data corresponding to each point coordinate and arrival time within the meteorological data sequence, thereby obtaining the carrier meteorological dataset. The data sparsity assessment module is used to obtain the meteorological sparsity coefficient of each meteorological data in the meteorological dataset of the carrier, and obtain the meteorological sparsity coefficient set. The disaster rate comprehensive early warning module is used to obtain the user's movement speed error coefficient, combine it with the meteorological sparse coefficient set and the carrier meteorological dataset, perform meteorological disaster analysis, obtain the meteorological disaster rate, and conduct meteorological disaster early warning.

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