A rainstorm warning method and related equipment

Abstract

The embodiments of the present application belong to the field and relate to a rainstorm warning method, including obtaining a rainstorm rainfall threshold and multiple grid points, gathering the multiple grid points to form a warning area; monitoring the rainfall for each grid point, and determining the area where the grid points exceeding the rainstorm rainfall threshold converge in the warning area as a rainstorm area; obtaining the proportion of the rainstorm area to the warning area and a preset alarm threshold, and judging whether the proportion reaches the preset alarm threshold; if so, issuing a rainstorm warning to all warning objects in the warning area. The present application also provides a rainstorm warning system, a computer device, and a computer-readable storage medium. The present application avoids the deviation of rainfall at a single grid point, reduces the occurrence of invalid warnings, and has a higher accuracy rate of rainstorm warnings overall.

Description

Technical Field

[0001] The present application relates to the field of hydrology and meteorology, and in particular to a rainstorm warning method and related equipment for the insurance industry. Background Art

[0002] When a rainstorm is about to hit, insurance companies need to issue a rainstorm warning to their policyholders so they can take appropriate precautions. The insurance industry's existing rainstorm warning method relies on forecast rainfall data at grid points. The daily rainfall at each grid point determines whether the policyholders at that grid point need a warning.

[0003] Since each grid point has a certain area range, but the rainfall at any location within the area is not the rainfall corresponding to the grid point, there are deviations in the rainfall within the same grid point, which will result in many invalid warnings. The accuracy of heavy rain warnings (accuracy refers to the amount of data that needs to be warned and is warned by the heavy rain system, plus the amount of data that does not need to be warned and is not warned by the heavy rain system, as a percentage of the total data volume) is low. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a high-precision rainstorm warning method and related equipment.

[0005] In order to solve the above technical problems, the present application provides a rainstorm warning method, which adopts the following technical solutions:

[0006] A rainstorm early warning method comprises the following steps:

[0007] Obtaining a rainstorm rainfall threshold and a plurality of grid points, and aggregating the plurality of grid points to form a warning area;

[0008] Monitoring rainfall at each grid point, and determining an area within the warning area where grid points exceeding the rainstorm rainfall threshold converge as a rainstorm area;

[0009] Obtaining the ratio of the rainstorm area to the warning area and a preset alarm threshold, and determining whether the ratio reaches the preset alarm threshold;

[0010] If so, a heavy rain warning will be issued to all warning objects in the warning area.

[0011] Furthermore, the step of monitoring the rainfall at each grid point and determining the area where the grid points exceeding the rainstorm rainfall threshold converge in the warning area as a rainstorm area includes:

[0012] Obtaining a preset domain radius value, a minimum grid point value, and a grid point value where rainfall exceeds the rainstorm rainfall threshold;

[0013] Input the domain radius value, the minimum grid point value, the grid point value, and the grid point value whose rainfall exceeds the rainstorm rainfall threshold as input parameters into the DBSCAN algorithm model, and output a plurality of cluster points;

[0014] A rainstorm area is formed based on the plurality of cluster points.

[0015] Furthermore, the specific steps of forming a rainstorm area based on the multiple cluster points include:

[0016] Get the preset rolling ball radius;

[0017] Input the obtained multiple cluster points and the rolling ball radius as input parameters into the ALPHASHAPE algorithm model, and output the enclosed area;

[0018] The enclosed area is regarded as a rainstorm area.

[0019] Furthermore, after the step of setting the enclosed area as a rainstorm area, the method further includes:

[0020] A preset boundary extension value is obtained, and the boundary of the rainstorm area is extended outward based on the boundary extension value.

[0021] Furthermore, before the step of obtaining the ratio of the rainstorm area to the warning area and the preset alarm threshold, and determining whether the ratio reaches the preset alarm threshold, the method further includes:

[0022] Obtaining historical rainstorm warning data, and obtaining the rainstorm warning data of the previous year from the historical rainstorm warning data based on the historical rainstorm warning data;

[0023] Calculating the accuracy rate of the previous year's heavy rain warning based on the previous year's heavy rain warning data;

[0024] Determine whether the accuracy rate of the rainstorm warning in the previous year is greater than a preset threshold;

[0025] If yes, obtain the proportion of the rainstorm area to the warning area;

[0026] Otherwise, the proportion of the rainstorm area to the warning area is not obtained and the rainstorm warning is terminated.

[0027] Furthermore, the rainstorm warning data of the previous year includes four groups of data: the number of times warnings were needed and actually issued TP, the number of times warnings were needed but not actually issued FP, the number of times warnings were not needed but actually issued FN, and the number of times warnings were not needed and not actually issued TN;

[0028] The specific steps of calculating the accuracy of the previous year's rainstorm warning data include: substituting the four groups of data of TP, FP, FN and TN into the formula Obtain the accuracy rate P of heavy rain warning in the previous year.

[0029] Furthermore, if there are multiple warning objects, the step of obtaining the rainstorm rainfall threshold includes:

[0030] Obtain warning levels corresponding to a plurality of the warning objects and a rainstorm rainfall threshold corresponding to each warning level.

[0031] In order to solve the above technical problems, the embodiment of the present application further provides a rainstorm warning system, which adopts the following technical solutions:

[0032] A rainstorm warning system includes: an identification module, a calculation module, a judgment module and an output module;

[0033] The identification module is used to obtain a rainstorm rainfall threshold and a plurality of grid points, and to aggregate the plurality of grid points to form a warning area;

[0034] The calculation module is used to monitor the rainfall at each grid point, and determine the area where the grid points exceeding the rainstorm rainfall threshold converge in the warning area as a rainstorm area;

[0035] The judgment module is used to obtain the ratio of the rainstorm area to the warning area and the preset alarm threshold, and judge whether the ratio reaches the preset alarm threshold;

[0036] The output module is used to issue a heavy rain warning to all warning objects in the warning area when the ratio reaches a preset alarm threshold.

[0037] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0038] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the heavy rain warning method when executing the computer-readable instructions.

[0039] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0040] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the rainstorm warning method.

[0041] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0042] Through the rainstorm warning method applied in this application, a unified judgment method is adopted for all warning objects in the warning area, instead of the original individual reports of the warning objects in each grid point. When the proportion of the rainstorm area in the warning area reaches the preset threshold, an alarm is issued to all warning objects. When the preset threshold is not reached, no alarm is issued to any object. Compared with the judgment of a single grid point, this method avoids the deviation of the rainfall at a single grid point, reduces the occurrence of invalid warnings, and has a higher overall accuracy of rainstorm warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 A flowchart of a rainstorm warning method provided in an embodiment of the present application;

[0045] Figure 2 A schematic structural diagram of a rainstorm warning system provided in an embodiment of the present application;

[0046] Figure 3 It is a structural diagram of an embodiment of a computer device according to the present application.

[0047] Reference numerals:

[0048] Computer device 6 , memory 61 , processor 62 , network interface 63 , rainstorm warning system 100 , identification module 101 , calculation module 102 , judgment module 103 , and output module 104 . DETAILED DESCRIPTION

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0050] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0052] See also Figure 1 , a rainstorm early warning method, comprising the following steps:

[0053] S01: Obtain a rainstorm rainfall threshold and multiple grid points, and aggregate the multiple grid points to form a warning area;

[0054] S02: monitoring rainfall at each grid point, and determining the area within the warning area where the grid points exceeding the rainstorm rainfall threshold converge as a rainstorm area;

[0055] S03: Obtaining the ratio of the rainstorm area to the warning area and a preset alarm threshold, and determining whether the ratio reaches the preset alarm threshold;

[0056] S04: If yes, a rainstorm warning is issued to all warning objects in the warning area.

[0057] The grid point is the smallest unit of the regional range. The range of the grid points is selected based on the range of the grid points provided by the forecast site. In the embodiment of the present application, the range of the grid points is selected to be 3 kilometers. The rainfall monitored by the grid points is provided by the forecast site. For example, the warning area consists of 10 grid points in rows and 10 grid points in columns, so the number of grid points is 100. The warning area formed by the collection of 100 grid points is 30 kilometers long and 30 kilometers wide. The preset heavy rain rainfall threshold is that the rainfall exceeds 50 mm within 24 hours. When the heavy rain rainfall within a grid point exceeds 50 mm within 24 hours, the grid point is determined to be a heavy rain grid point. All heavy rain grid points are merged to form a heavy rain area.

[0058] The preset alarm threshold can be 80% or 90%, which is set according to the actual situation and region. In the embodiment of the present application, the alarm threshold is 80%. When 80 of the above 100 grid points are determined to be rainstorm grid points, the rainstorm area formed by the rainstorm grid points occupies 80 units of grid points, and the proportion of the rainstorm area to the warning area is 80%. When the proportion of the rainstorm area to the warning area reaches the preset alarm threshold of 80%, a rainstorm warning is issued for all warning objects in the warning area.

[0059] When the proportion of heavy rain area to warning area does not reach the preset alarm threshold, this heavy rain warning method will not issue a warning to any warning object. Whether to use it in conjunction with other warning methods will be determined based on actual conditions.

[0060] Through the rainstorm warning method of the present application, a unified judgment method is adopted for all warning objects in the warning area, instead of the original individual reports of the warning objects in each grid point. When the proportion of the rainstorm area to the warning area reaches a preset threshold, an alarm is issued to all warning objects. When the preset threshold is not reached, no alarm is issued to any object. Compared with the judgment of a single grid point, this method avoids the deviation of the rainfall at a single grid point, reduces the occurrence of invalid warnings, and has a higher overall accuracy of rainstorm warnings (the accuracy rate refers to the amount of data that needs to be warned and is warned by the rainstorm system, plus the amount of data that does not need to be warned and is not warned by the rainstorm system, accounting for the total data volume).

[0061] In an embodiment of the present application, the step of monitoring the rainfall for each grid point and determining the area where the grid points in the warning area that exceed the rainstorm rainfall threshold converge as a rainstorm area includes: obtaining a preset domain radius value, a minimum grid point value, and a value of the number of grid points whose rainfall exceeds the rainstorm rainfall threshold; inputting the domain radius value, the minimum grid point value, the grid point value, and the grid point value whose rainfall exceeds the rainstorm rainfall threshold as input parameters into a DBSCAN algorithm model, and outputting multiple cluster points; and forming a rainstorm area based on the multiple cluster points.

[0062] The DBSCAN algorithm is a density-based spatial clustering algorithm that can obtain more accurate rainstorm data within a range. It initially forms cluster points and then encloses the cluster points to form an area. The input parameters include neighborhood radius, minimum grid point value, multiple grid points, and multiple grid points that exceed the rainstorm rainfall threshold. They are set according to actual needs.

[0063] The neighborhood radius is used to determine the range of grid points that form a cluster when initially forming cluster points. For example, when a neighborhood radius of 3 is selected, when calculating the first grid point, a circle with a radius of 3 grid points is swept from the first grid point as the center, and the calculation is performed on the swept area.

[0064] The minimum number of grid points is used as a reference value when initially forming clusters. A cluster is formed only when the number of grid points in the swept area that exceed the required heavy rainfall threshold is greater than or equal to the minimum number of grid points. For example, if the minimum number of grid points is 2, the swept area consists of 10 grid points, and 3 of the 10 grid points reach the heavy rainfall threshold, then the number of grid points exceeding the minimum number is 2, and the first grid point forms the first cluster.

[0065] The DBSCAN algorithm calculates all grid points to form multiple cluster points, and then encloses all cluster points to form a rainstorm area.

[0066] In an embodiment of the present application, the multiple grid points, the domain radius value, the minimum grid point value, the multiple grid points, and the multiple grid points exceeding the rainstorm rainfall threshold are input as input parameters into the DBSCAN algorithm model, and the specific steps of outputting multiple cluster points include: obtaining a preset rolling ball radius; inputting the obtained multiple cluster points and the rolling ball radius as input parameters into the ALPHASHAPE algorithm model, and outputting an enclosed area; and using the enclosed area as a rainstorm area.

[0067] The ALPHASHAPE algorithm is a scattered point contouring algorithm that transforms multiple clusters into a rainstorm area. Input parameters include a rolling ball radius and a number of cluster points. The rolling ball radius is set as needed. The algorithm calculates N*(N-1) / 2 lines connecting any two cluster points. Assuming the rolling ball radius is R, all connections with a distance greater than 2R are disconnected. Because the ball must roll inward, a circumcircle of radius R is drawn around any two remaining connected cluster points. This results in at most two feasible solutions. Any circumcircle with a third point is considered invalid, and only circumcircles without any third point are retained. The ALPHASHAPE algorithm transforms multiple cluster points into a rainstorm area, resulting in more accurate coverage of the area.

[0068] In an embodiment of the present application, after the step of using the enclosed area as a rainstorm area, the method further includes: obtaining a preset boundary extension value, and extending the boundary of the rainstorm area outward based on the boundary extension value.

[0069] The boundary extension value can be selected as a proportional value or a fixed value; the proportional value is that the radius of the rainstorm area covered is extended outward according to a certain proportion, for example, when the proportional value is 10%, when the radius of the rainstorm area covered is 30km, the boundary extension value is 3km, then after the boundary extension value extends the boundary of the rainstorm area outward, the radius of the rainstorm area covered is 33km; the fixed value is that the radius of the rainstorm area covered is extended outward according to a fixed value, for example, when the fixed value is 2km, when the radius of the rainstorm area covered is 30km, the boundary extension value is 2km, then after the boundary extension value extends the boundary of the rainstorm area outward, the radius of the rainstorm area covered is 32km; the specific proportional value or fixed value and specific parameters are selected according to actual needs.

[0070] In an embodiment of the present application, before the step of obtaining the proportion of the rainstorm area to the warning area and the preset alarm threshold, and judging whether the proportion reaches the preset alarm threshold, the step also includes: obtaining historical rainstorm warning data, and obtaining the rainstorm warning data of the previous year in the historical rainstorm warning data based on the historical rainstorm warning data; calculating the accuracy of the rainstorm warning of the previous year based on the rainstorm warning data of the previous year; judging whether the accuracy of the rainstorm warning of the previous year is greater than the preset threshold; if so, obtaining the proportion of the rainstorm area to the warning area; otherwise, not obtaining the proportion of the rainstorm area to the warning area and terminating the rainstorm warning.

[0071] This application for heavy rain warning method requires pre-calculation to verify whether the accuracy of this method in the previous year meets the standard based on the heavy rain warning data of the previous year. If it is determined to be not up to standard, the application for heavy rain warning method needs to be terminated.

[0072] In the embodiment of the present application, the rainstorm warning data of the previous year includes four groups of data: the number of times warnings were needed and actually issued TP, the number of times warnings were needed but not actually issued FP, the number of times warnings were not needed but actually issued FN, and the number of times warnings were not needed and not actually issued TN; the specific steps of calculating the accuracy of rainstorm warnings of the previous year based on the rainstorm warning data of the previous year include: substituting the four groups of data TP, FP, FN and TN into the formula Obtain the accuracy rate P of heavy rain warning in the previous year.

[0073] In an embodiment of the present application, there are multiple warning objects, and the step of obtaining a rainstorm rainfall threshold includes: obtaining warning levels corresponding to the multiple warning objects, and a rainstorm rainfall threshold corresponding to each warning level.

[0074] The recognition of heavy rain varies for different warning objects. For example, the degree of recognition of heavy rain for fisheries and forestry is different. Therefore, the warning objects are divided into multiple warning levels. Each warning level has a corresponding heavy rain rainfall threshold, which helps to accurately deliver heavy rain warning information.

[0075] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0076] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0077] Please refer to Figure 2 , as a response to the above Figure 1 In addition to the implementation of the method shown in FIG, the present application also provides an embodiment of a rainstorm warning system 100. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0078] The rainstorm warning system 100 of this embodiment includes: an identification module 101, a calculation module 102, a judgment module 103 and an output module 104;

[0079] The identification module 101 is used to obtain a rainstorm rainfall threshold and a plurality of grid points, and aggregate the plurality of grid points to form a warning area;

[0080] The calculation module 102 is configured to monitor the rainfall at each grid point, and determine that the area where the grid points exceeding the rainstorm rainfall threshold converge in the warning area is a rainstorm area;

[0081] The judgment module 103 is used to obtain the ratio of the rainstorm area to the warning area and the preset alarm threshold, and judge whether the ratio reaches the preset alarm threshold;

[0082] The output module 104 is configured to issue a rainstorm warning to all warning objects in the warning area when the ratio reaches a preset alarm threshold.

[0083] In an embodiment of the present application, the calculation module 102 monitors the rainfall for each grid point, and when determining that the area where the grid points exceeding the heavy rain rainfall threshold converge in the warning area is a heavy rain area, it is specifically used to obtain the domain radius value, the minimum grid point value, multiple grid points, and multiple grid points exceeding the heavy rain rainfall threshold; the multiple grid points, the domain radius value, the minimum grid point value, multiple grid points, and multiple grid points exceeding the heavy rain rainfall threshold are input as input parameters into the DBSCAN algorithm model, and multiple cluster points are output; and a heavy rain area is formed based on the multiple cluster points.

[0084] In an embodiment of the present application, when the calculation module 102 forms a rainstorm area based on the multiple cluster points, it is specifically used to: obtain a rolling ball radius; input the obtained multiple cluster points and the rolling ball radius as input parameters into the ALPHASHAPE algorithm model, and output an enclosed area; and use the enclosed area as a rainstorm area.

[0085] In the embodiment of the present application, after determining the enclosed area as a rainstorm area, the calculation module 102 is further configured to: obtain a boundary extension value, and extend the boundary of the rainstorm area outward based on the boundary extension value.

[0086] In the embodiment of the present application, before the judgment module 103 obtains the ratio of the rainstorm area to the warning area and the preset alarm threshold, and determines whether the ratio reaches the preset alarm threshold, the identification module 101 is further configured to obtain historical rainstorm warning data, and obtain the rainstorm warning data of the previous year in the historical rainstorm warning data based on the historical rainstorm warning data;

[0087] The calculation module 102 is further configured to calculate the accuracy of the previous year's heavy rain warning based on the previous year's heavy rain warning data;

[0088] The judgment module 103 is further configured to judge whether the accuracy rate of the rainstorm warning in the previous year is greater than a preset threshold;

[0089] If so, the output module 104 is further used to obtain the proportion of the heavy rain area to the warning area when the accuracy rate of the heavy rain warning in the previous year is greater than a preset threshold; and when the accuracy rate of the heavy rain warning in the previous year is not greater than the preset threshold, the proportion of the heavy rain area to the warning area is not obtained and the heavy rain warning is terminated.

[0090] In the embodiment of the present application, the rainstorm warning data of the previous year includes four groups of data: the number of times warnings were needed and actually issued TP, the number of times warnings were needed but not actually issued FP, the number of times warnings were not needed but actually issued FN, and the number of times warnings were not needed and not actually issued TN; when the calculation module 102 calculates the accuracy of the rainstorm warning of the previous year based on the rainstorm warning data of the previous year, it is specifically used to: substitute the four groups of data TP, FP, FN and TN into the formula Obtain the accuracy rate P of heavy rain warning in the previous year.

[0091] In an embodiment of the present application, there are multiple warning objects. When the identification module 101 obtains the rainstorm rainfall threshold, it is specifically used to: obtain the warning levels corresponding to the multiple warning objects and the rainstorm rainfall threshold corresponding to each warning level.

[0092] When each module in the above embodiment performs related operations, the specific execution process can refer to the relevant content in the above method embodiment. When each module performs related operations, it has the same technical effect as the above method embodiment.

[0093] An embodiment of the present application also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the heavy rain warning method are implemented, and the technical effect is the same as the above-mentioned method embodiment.

[0094] Specific as Figure 3 As shown, it is a basic structural block diagram of the computer device of this embodiment.

[0095] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 6 with components 61-63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0096] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0097] The memory 61 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions for the rainstorm warning method. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0098] In some embodiments, the processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions stored in the memory 61 or process data, such as computer-readable instructions for executing the rainstorm warning method.

[0099] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0100] An embodiment of the present application also provides a computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, the steps of the heavy rain warning method described in the above method embodiment are implemented and have the same technical effect as the above method embodiment.

[0101] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0102] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A rainstorm early warning method, characterized in that: The steps include: Obtaining a rainstorm rainfall threshold and a plurality of grid points, and aggregating the plurality of grid points to form a warning area; Monitoring rainfall at each grid point, and determining an area within the warning area where grid points exceeding the rainstorm rainfall threshold converge as a rainstorm area; Obtaining the ratio of the rainstorm area to the warning area and a preset alarm threshold, and determining whether the ratio reaches the preset alarm threshold; If so, a rainstorm warning is issued to all warning objects in the warning area; The step of monitoring the rainfall at each grid point and determining the area where the grid points exceeding the rainstorm rainfall threshold converge within the warning area as a rainstorm area includes: Obtaining a preset domain radius value, a minimum grid point value, and a grid point value where rainfall exceeds the rainstorm rainfall threshold; Input the domain radius value, the minimum grid point value, the grid point value, and the grid point value whose rainfall exceeds the rainstorm rainfall threshold as input parameters into the DBSCAN algorithm model, and output a plurality of cluster points; forming a rainstorm area based on the plurality of cluster points; Among them, the neighborhood radius is used to determine the range value of grid points to form cluster points when initially forming cluster points; the minimum number of grid points is used as a reference value when initially forming cluster points. A cluster point can only be formed when the number of grid points in the swept area that exceeds the required heavy rain rainfall threshold is greater than or equal to the minimum number of grid points.

2. The rainstorm early warning method according to claim 1, characterized in that: The specific steps of forming a rainstorm area based on the multiple cluster points include: Get the preset rolling ball radius; Input the obtained multiple cluster points and the rolling ball radius as input parameters into the ALPHASHAPE algorithm model, and output the enclosed area; The enclosed area is regarded as a rainstorm area.

3. The rainstorm early warning method according to claim 2, characterized in that: After the step of setting the enclosed area as a rainstorm area, the method further includes: A preset boundary extension value is obtained, and the boundary of the rainstorm area is extended outward based on the boundary extension value.

4. The rainstorm early warning method according to claim 1, characterized in that: Before the step of obtaining the ratio of the rainstorm area to the warning area and the preset alarm threshold, and determining whether the ratio reaches the preset alarm threshold, the method further includes: Obtaining historical rainstorm warning data, and obtaining the rainstorm warning data of the previous year from the historical rainstorm warning data based on the historical rainstorm warning data; Calculating the accuracy rate of the previous year's heavy rain warning based on the previous year's heavy rain warning data; Determine whether the accuracy rate of the rainstorm warning in the previous year is greater than a preset threshold; If yes, obtain the proportion of the rainstorm area to the warning area; Otherwise, the proportion of the rainstorm area to the warning area is not obtained and the rainstorm warning is terminated.

5. The rainstorm early warning method according to claim 4, characterized in that: The rainstorm warning data of the previous year include four groups of data: the number of times warnings were needed and actually issued TP, the number of times warnings were needed but not actually issued FP, the number of times warnings were not needed but actually issued FN, and the number of times warnings were not needed and not actually issued TN; The specific steps of calculating the accuracy of the previous year's heavy rain warning data include: substituting the four groups of TP, FP, FN and TN data into the formula The accuracy rate P of heavy rain warning in the previous year is obtained.

6. The rainstorm early warning method according to claim 1, characterized in that: There are multiple warning objects, and the step of obtaining a rainstorm rainfall threshold comprises: Obtain warning levels corresponding to a plurality of the warning objects and a rainstorm rainfall threshold corresponding to each warning level.

7. A rainstorm warning system, characterized in that: It includes recognition module, calculation module, judgment module and output module; The identification module is used to obtain a rainstorm rainfall threshold and a plurality of grid points, and to aggregate the plurality of grid points to form a warning area; The calculation module is used to monitor the rainfall at each grid point, and determine the area where the grid points exceeding the rainstorm rainfall threshold converge in the warning area as a rainstorm area; The judgment module is used to obtain the ratio of the rainstorm area to the warning area and the preset alarm threshold, and judge whether the ratio reaches the preset alarm threshold; The output module is used to issue a heavy rain warning to all warning objects in the warning area when the ratio reaches a preset alarm threshold; The calculation module is specifically used to obtain a preset domain radius value, a minimum grid point value, and a value of the number of grid points whose rainfall exceeds the rainstorm rainfall threshold; input the domain radius value, the minimum grid point value, the grid point value, and the value of the number of grid points whose rainfall exceeds the rainstorm rainfall threshold as input parameters into a DBSCAN algorithm model, and output a plurality of cluster points; and form a rainstorm area based on the plurality of cluster points; Among them, the neighborhood radius is used to determine the range value of grid points to form cluster points when initially forming cluster points; the minimum number of grid points is used as a reference value when initially forming cluster points. A cluster point can only be formed when the number of grid points in the swept area that exceeds the required heavy rain rainfall threshold is greater than or equal to the minimum number of grid points.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the heavy rain warning method according to any one of claims 1 to 6 when executing the computer-readable instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the heavy rain warning method according to any one of claims 1 to 6.

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