Grid data-based rainstorm pattern identification method, apparatus and device, and medium

Abstract

The invention provides a rainstorm pattern identification method and device based on grid data, equipment and a medium. The method comprises the following steps: establishing a rainfall grid based on different to-be-identified areas in a time sequence; extracting rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data; and extracting rainstorm pattern data from the rainfall grid data based on the rainstorm event, performing rainstorm pattern identification on the rainstorm event, and obtaining one or more rainstorm pattern identification results of a constant flow pattern, a peak pattern, a gentle peak pattern and a double-wave pattern. According to the rainstorm pattern recognition method, flexible, efficient and high-adaptability rainstorm pattern recognition can be carried out on rainfall data sets with different temporal-spatial resolutions and rich types at present, and automatic extraction of the rainstorm pattern is achieved.

Description

Technical Field

[0001] This application belongs to the field of remote sensing technology and relates to a method, device, equipment and medium for identifying rainstorm patterns based on grid data. Background Technology

[0002] Ground-based rain gauges are the most common way to obtain rainfall information, and traditional rainfall monitoring mainly relies on rain gauges equipped at ground weather stations to collect rainfall data. However, the layout of this observation network is primarily designed to meet the needs of weather forecasting, rather than being specifically designed for scientific research, resulting in data gaps at rainfall observation stations in remote or harsh environments. Furthermore, the data provided by existing rain gauges often lacks sufficiently long time series, making it difficult to meet the needs of scientific research.

[0003] With the development and improvement of satellite precipitation observation missions such as the Tropical Rainfall Measuring Mission (TRMM), Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), Global Satellite Mapping of Precipitation (GsMAP), and Global Precipitation Measurement (GPM), they have gradually compensated for the shortcomings of ground-based rain gauges in terms of spatial distribution and data continuity by providing long-term gridded precipitation data. While the distribution of rainfall at a single moment can be identified using simple spatial analysis methods, accurately analyzing the rainfall pattern characteristics of heavy rainfall requires long-term precipitation data. Due to the limited efficiency of manual visual interpretation, it is difficult to quickly and accurately identify the rainfall pattern characteristics of each rainfall event.

[0004] Rainfall pattern is a characterization of a rainfall event. For the same amount of rainfall, different rainfall patterns can lead to vastly different destructive forces and degrees of disaster. Current research mainly relies on surface rain gauge data, using methods such as cluster analysis to classify rainfall into several categories, or employing the Chicago Rain Pattern and Huff Rain Pattern to analyze the distribution characteristics of a specific area. However, no research has yet attempted to use long-term gridded rainfall data for rainfall pattern studies. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a method, apparatus, device, and medium for identifying rainstorm patterns based on grid data. It addresses the commonly used grid format for storing rainfall data, enabling automatic identification of the rainstorm pattern for each event from time-series rainfall data.

[0006] In a first aspect, this invention proposes a method for identifying rainstorm patterns based on grid data, specifically including the following steps:

[0007] Establish a rainfall grid based on the time series of different regions to be identified;

[0008] Rainfall identification data is extracted from the rainfall grid to identify all rainfall events in the rainfall grid data;

[0009] Based on the rainstorm event, rainstorm pattern data is extracted from the rainfall grid data, and the rainstorm event is identified to obtain one or more rainstorm pattern identification results among constant flow pattern, peak pattern, gentle peak pattern and double wave pattern.

[0010] Furthermore, the steps for establishing a rainfall grid based on different regions to be identified over time include:

[0011] A time series rainfall grid is established based on multiple different grid data types corresponding to different time points within a preset time interval range;

[0012] The rainfall grid stores grid data corresponding to different regions to be identified.

[0013] The grid data includes the average rainfall G, peak rainfall PR, peak time PD, peak total rainfall Vo, total rainfall duration Du, rainfall intensity RL, rainfall intensity ratio P, and RD values ​​representing 90% and 50% of the total rainfall for each rainstorm, sorted from highest to lowest rainfall amount for each rainstorm. 90 and RD 50 And, the maximum deviation MD between the actual rainfall and the ideal linear cumulative distribution.

[0014] Furthermore, the step of extracting rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data specifically includes:

[0015] Starting from the initial time point, the rainfall grid is traversed to extract rainstorm identification data, which includes the average rainfall G, the peak rainfall PR, the peak time PD, the peak total rainfall Vo, the total rainfall duration Du, and the rainfall intensity RL.

[0016] Determine the average rainfall G at grid i at time t. i With peak rainfall PR i The relationship, if the average rainfall G i Greater than the peak rainfall PR i Update the grid data for grid i: PR i =G i Peak time PD of grid i i=t, the peak total rainfall Vo of grid i i =Vo i +G i *M, Total duration of rainfall (Du) i =Du i +M, where M is the time interval;

[0017] If the average rainfall G i Less than or equal to peak rainfall PR i At the average rainfall G i If the values ​​are greater than 0, update the peak total rainfall and total rainfall duration for grid i.

[0018] After completing the update of grid i at time t, the next update is performed at time t+1 and the next update of grid i+1 until all times and all grid updates are completed, thus completing the process of identifying the rainstorm and storing the updated grid data.

[0019] Furthermore, if the average rainfall G i Less than or equal to peak rainfall PR i The steps also include, on the average rainfall G i When the value equals 0, an independent rainfall event ends. The peak rainfall PR at this point is then determined. i If the product of 2 and 24 is greater than 50, then a rainstorm event has been successfully identified, and the rainfall intensity RL is calculated. i The value of RL i =Vo i / Du i .

[0020] Furthermore, based on the rainstorm event, the rainstorm pattern data is extracted from the rainfall grid data, and the rainstorm pattern identification step for the rainstorm event includes:

[0021] Based on the identified rainstorm events, corresponding rainstorm pattern data is extracted. This rainstorm pattern data includes the rainfall intensity ratio P, the total rainfall duration Du, and RD values ​​representing 90% and 50% of the total rainfall for each rainstorm, sorted from highest to lowest. 90 and RD 50 And, the maximum deviation MD between the actual rainfall and the ideal linear cumulative distribution; the rainfall intensity ratio P is expressed as the ratio of the peak rainfall PR to the rainfall intensity RL;

[0022] Determine the relationship between the rainfall intensity ratio P and 3;

[0023] If the rainfall intensity ratio P is less than or equal to 3, and RD 90 The total duration of rainfall (Du) and the maximum deviation (MD) satisfy the following:

[0024] RD 90 ≥(2 / 3)Du and MD≤15%

[0025] The rainstorm pattern was then identified as a steady flow pattern.

[0026] If the rainfall intensity ratio P is greater than 3, and RD 50 RD 90 The total duration of rainfall, Du, satisfies:

[0027] RD 50 ≤(1 / 6)Du and RD 90 ≤(1 / 2)Du

[0028] The rainstorm pattern was then identified as a spike-type.

[0029] If the rainfall intensity ratio P is greater than 3, and RD 50 RD 90 The total duration of rainfall, Du, satisfies:

[0030] RD 50 >(1 / 6)Du and RD 90 >(1 / 2)Du

[0031] The rainstorm pattern is then identified as a gentle peak type.

[0032] Furthermore, the step of extracting rainstorm pattern data from the rainfall grid data based on the rainstorm event and performing rainstorm pattern identification on the rainstorm event also includes:

[0033] Based on the identified rainstorm events, extract the corresponding rainstorm pattern data, including the peak rainfall PR and the next highest rainfall PR. c If the following conditions are met simultaneously:

[0034] The time interval between the peak rainfall PR and the next peak rainfall PRc is greater than or equal to 3.

[0035] The peak rainfall PRc is greater than or equal to 70% of the peak rainfall PR;

[0036] The rainfall trend between the peak rainfall PRc and the peak rainfall PR follows an M-shaped trend;

[0037] The rainstorm pattern was then identified as a double-wave pattern.

[0038] Furthermore, the grid data in the rainfall grid is stored in text format, and the text format is converted into integer or floating-point numbers when the grid data is extracted.

[0039] Secondly, the present invention also proposes a rainstorm pattern identification device based on grid data. The rainstorm pattern identification device uses the rainstorm pattern identification method described in the first aspect to identify the rainstorm pattern of the area to be identified. The rainstorm pattern identification device includes:

[0040] The grid creation module is used to create rainfall grids based on different regions to be identified over time.

[0041] The identification module is used to extract rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data;

[0042] The processing module extracts rainstorm pattern data from the rainfall grid data based on the rainstorm event, performs rainstorm pattern identification on the rainstorm event, and obtains one or more rainstorm pattern identification results among constant flow pattern, peak pattern, gentle peak pattern and double wave pattern.

[0043] Thirdly, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the rainstorm pattern identification method described in the first aspect.

[0044] Fourthly, the present invention also proposes a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the rainstorm pattern identification method as described in the first aspect.

[0045] The beneficial effects of this invention are:

[0046] For the current diverse rainfall datasets with varying spatiotemporal resolutions, this invention employs a flexible, efficient, and adaptable method for identifying rainstorm patterns, enabling automatic extraction of rainstorm patterns. This rainstorm pattern identification provides technical support for analyzing the spatiotemporal distribution of rainstorm patterns at global and regional scales. Attached Figure Description

[0047] Figure 1 This is a flowchart of the rainstorm pattern identification method based on grid data proposed in this invention.

[0048] Figure 2 This is a schematic diagram showing the results of rainstorm pattern identification based on grid data proposed in this invention.

[0049] Figure 3 This is a framework diagram of the rainstorm pattern identification device based on grid data proposed in this invention.

[0050] Figure 4 This is a schematic diagram of the electronic device proposed in this invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The following is combined Figures 1-4 This invention describes a method, apparatus, device, and medium for identifying rainstorm patterns based on grid data.

[0053] like Figure 1 As shown in the figure, this application provides a method for identifying rainstorm patterns based on grid data. The method specifically includes the following steps:

[0054] S101. Establish a rainfall grid based on different regions to be identified over time;

[0055] S102. Extract rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data;

[0056] S103. Based on the rainstorm event, extract rainstorm pattern data from the rainfall grid data, perform rainstorm pattern identification on the rainstorm event, and obtain one or more rainstorm pattern identification results among constant flow pattern, peak pattern, gentle peak pattern and double wave pattern.

[0057] It should be noted that the purpose of this embodiment is to identify the type of rainstorm based on grid data. Therefore, it is necessary to first confirm whether the rainfall belongs to rainstorm, and then identify the type of rainstorm to achieve the purpose of this embodiment.

[0058] In this embodiment, in order to more accurately identify rainstorms and rainstorm types, a rainfall grid based on time series is established, and the rainstorm identification data and rainstorm type identification data stored in the rainfall grid are extracted and analyzed, thereby achieving the purpose of this embodiment.

[0059] In this embodiment, the step of establishing a rainfall grid based on different regions to be identified over time includes:

[0060] A time series rainfall grid is established based on multiple different grid data types corresponding to different time points within a preset time interval range;

[0061] Store grid data corresponding to different regions to be identified at different time points in the rainfall grid;

[0062] Grid data includes the average rainfall G, peak rainfall PR, peak time PD, peak total rainfall Vo, total rainfall duration Du, rainfall intensity RL, rainfall intensity ratio P, and RD values ​​representing 90% and 50% of the total rainfall for each storm, sorted by rainfall amount from highest to lowest. 90 and RD 50 And, the maximum deviation MD between the actual rainfall and the ideal linear cumulative distribution.

[0063] In this embodiment, the process of establishing a time-series rainfall grid based on multiple different data types corresponding to different time points within a preset time interval is as follows: Assuming the preset time interval is T hours, multiple different types of grid data corresponding to time point t are recorded. The established rainfall grid is represented by rows and columns, and the product of rows and columns represents the total number of grid cells. It should be noted that in reality, there are multiple possible study areas. Each study area is considered a region to be identified. Therefore, it is possible to analyze the rainfall pattern of a region to be identified within a certain time period, or to analyze and identify the rainfall patterns of multiple study areas, thereby providing technical support for analyzing the spatiotemporal distribution of rainfall patterns at global or local scales. Therefore, it can be understood that in this embodiment, a corresponding identifier can be set for each different region to be identified, thereby accurately identifying the region's location.

[0064] In addition, it should be noted that rainfall data for each grid can be collected directly from satellites or weather stations, and no specific limitations are made here.

[0065] The above process enables the establishment of a rainfall grid, and the recording or storage of data in each grid according to time point t, which is then used to analyze the rainfall pattern.

[0066] Step S102 is the process of identifying rainstorms, that is, identifying whether the rainfall belongs to rainstorms. This process specifically includes the following:

[0067] Starting from the initial time point, the rainfall grid is traversed to extract rainstorm identification data, which includes the average rainfall G, the peak rainfall PR, the peak time PD, the peak total rainfall Vo, the total rainfall duration Du, and the rainfall intensity RL.

[0068] Determine the average rainfall G at grid i at time t. i With peak rainfall PR i The relationship, if the average rainfall G i Greater than the peak rainfall PR i Update the grid data for grid i: PR i =G iPeak time PD of grid i i =t, the peak total rainfall Vo of grid i i =Vo i +G i *M, Total duration of rainfall (Du) i =Du i +M, where M is the time interval;

[0069] If the average rainfall G i Less than or equal to peak rainfall PR i At the average rainfall G i If the values ​​are greater than 0, update the peak total rainfall and total rainfall duration for grid i.

[0070] At the average rainfall G i When the value equals 0, an independent rainfall event ends. The peak rainfall PR at this point is then determined. i If the product of 2 and 24 is greater than 50, then a rainstorm event has been successfully identified, and the rainfall intensity RL is calculated. i The value of RL i =Vo i / Du i .

[0071] After completing the update of grid i at time t, the next update is performed at time t+1 and the next update of grid i+1 until all times and all grid updates are completed, thus completing the process of identifying the rainstorm and storing the updated grid data.

[0072] In this embodiment, the time interval can be set to M, the number of time points to m, and the number of grids to be determined by the number of rows and columns. For example, if the number of rows is r and the number of columns is c, then the total number of grids is r*c. That is, the value range of grid i is from 1 to r*c, and the average rainfall of grid i at time t is G. i Millimeters per hour. Additionally, it should be noted that when performing rainfall grid traversal, the peak rainfall PR value will be... i Peak PD i Total duration of rainfall (Du) i and rainfall intensity RL i The initial value is set to 0. Furthermore, since only the peak rainfall PR needs to be extracted during rainstorm identification... i Peak PD i Total duration of rainfall (Du) i and rainfall intensity RL i The rainstorm identification data, therefore, the rainfall intensity ratio P in the grid data, and the RD, which are sorted from high to low according to the rainfall amount of each rainstorm, respectively reach 90% and 50% of the total rainfall. 90 and RD 50The maximum deviation (MD) between actual rainfall and ideal linear cumulative distribution, coefficient of variation (CVT), rainstorm start event (STT), and rainstorm end time (ETT) are set to empty strings.

[0073] Considering that each grid area may experience more than one rainstorm event within the time axis, and the timing of rainstorms varies across grids, rainstorm information, i.e., related grid data, is stored according to the table structure shown in Table 1. It should be noted that the field names in Table 1 are stored in text format and assigned names. During actual grid data extraction, the text format needs to be converted to integer or floating-point numbers, i.e., text-to-numerical conversion. For example, a text-type field named PeakRain represents the peak rainfall (PR); a text-type field named PeakDate represents the peak time (PD); a text-type field named Volume represents the peak total rainfall (Vo); a text-type field named Duration represents the total rainfall duration (Du); and a text-type field named RainLevel represents the rainfall intensity (RL). In addition, the same characters can be used to represent the text type and the converted numerical value. For example, the text type P corresponds to the rainfall intensity ratio P. In fact, it is sufficient to convert the text into a numerical value with a clear meaning. The specific representation of the identifier is not limited.

[0074] Table 1

[0075]

[0076] The above steps have completed the process of identifying rainstorm events. The following describes the identification of rainstorm patterns, such as... Figure 2 As shown, a schematic diagram of the rainstorm pattern identification is given. The obtained rain patterns include steady flow pattern, peak pattern, gentle peak pattern and double wave pattern.

[0077] Based on the identified rainstorm events, corresponding rainstorm pattern data is extracted. This rainstorm pattern data includes the rainfall intensity ratio P, the total rainfall duration Du, and RD values ​​representing 90% and 50% of the total rainfall for each rainstorm, sorted from highest to lowest. 90 and RD 50 And, the maximum deviation MD between the actual rainfall and the ideal linear cumulative distribution; the rainfall intensity ratio P is expressed as the ratio of the peak rainfall PR to the rainfall intensity RL;

[0078] Determine the relationship between the rainfall intensity ratio P and 3;

[0079] If the rainfall intensity ratio P is less than or equal to 3, and RD 90The total duration of rainfall (Du) and the maximum deviation (MD) satisfy the following:

[0080] RD 90 ≥(2 / 3)Du and MD≤15%

[0081] The identified rainstorm pattern is a steady flow pattern;

[0082] If the rainfall intensity ratio P is greater than 3, and RD 50 RD 90 The total duration of rainfall, Du, satisfies:

[0083] RD 50 ≤(1 / 6)Du and RD 90 ≤(1 / 2)Du

[0084] The identified rainstorm pattern is a peak type;

[0085] If the rainfall intensity ratio P is greater than 3, and RD 50 RD 90 The total duration of rainfall, Du, satisfies:

[0086] RD 50 >(1 / 6)Du and RD 90 >(1 / 2)Du

[0087] The identified rainstorm pattern is a gentle peak type.

[0088] Based on the identified rainstorm events, extract the corresponding rainstorm pattern data, including the peak rainfall PR and the next highest rainfall PR. c If all three of the following conditions are met:

[0089] Peak rainfall PR and second-highest rainfall PR c The time interval between them is greater than or equal to 3;

[0090] The peak rainfall was PR. c Peak rainfall (PR) of 70% or more;

[0091] The peak rainfall was PR. c The rainfall trend between the peak rainfall (PR) and the peak rainfall (PR) follows an M-shaped pattern of first decreasing and then increasing.

[0092] The identified rainstorm pattern is a double-wave pattern.

[0093] like Figure 3 As shown, this embodiment also provides a rainstorm pattern identification device 300 based on grid data. The rainstorm pattern identification device identifies the rainstorm pattern of the area to be identified using the rainstorm pattern identification method as described in the first aspect. The rainstorm pattern identification device 300 includes:

[0094] The grid establishment module 301 is used to establish a rainfall grid based on different regions to be identified over time.

[0095] The identification module 302 is used to extract rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data;

[0096] The processing module 303 extracts rainstorm pattern data from the rainfall grid data based on the rainstorm event, performs rainstorm pattern identification on the rainstorm event, and obtains one or more rainstorm pattern identification results among constant flow pattern, peak pattern, gentle peak pattern and double wave pattern.

[0097] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a rainstorm pattern identification method, which includes: establishing a rainfall grid based on different regions to be identified in a time series; extracting rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data; extracting rainstorm pattern data from the rainfall grid data based on the rainstorm events, performing rainstorm pattern identification on the rainstorm events, and obtaining one or more rainstorm pattern identification results among constant flow pattern, spike pattern, gentle peak pattern, and double wave pattern.

[0098] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the rainstorm pattern identification method provided by the above methods. The method includes: establishing a rainfall grid based on different regions to be identified in a time series; extracting rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data; extracting rainstorm pattern data from the rainfall grid data based on the rainstorm events; performing rainstorm pattern identification on the rainstorm events; and obtaining one or more rainstorm pattern identification results among constant flow pattern, spike pattern, gentle peak pattern, and double wave pattern.

[0100] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program is implemented to perform the rainstorm pattern identification method provided by the above methods. The method includes: establishing a rainfall grid based on different regions to be identified in a time series; extracting rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data; extracting rainstorm pattern data from the rainfall grid data based on the rainstorm events; performing rainstorm pattern identification on the rainstorm events; and obtaining one or more rainstorm pattern identification results among constant flow pattern, spike pattern, gentle peak pattern, and double wave pattern.

[0101] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0102] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying rainstorm patterns based on grid data, characterized in that, Specifically, the steps include the following: Establish a rainfall grid based on the time series of different regions to be identified; Rainfall identification data is extracted from the rainfall grid to identify all rainfall events in the rainfall grid data; Based on the rainstorm event, rainstorm pattern data is extracted from the rainfall grid data, and the rainstorm event is identified to obtain one or more rainstorm pattern identification results among constant flow pattern, peak pattern, gentle peak pattern and double wave pattern.

2. The method for identifying rainstorm patterns based on grid data according to claim 1, characterized in that, The steps for establishing a rainfall grid based on different regions to be identified over time include: A time series rainfall grid is established based on multiple different grid data types corresponding to different time points within a preset time interval range; The rainfall grid stores grid data corresponding to different regions to be identified. The grid data includes the average rainfall G, peak rainfall PR, peak time PD, peak total rainfall Vo, total rainfall duration Du, rainfall intensity RL, rainfall intensity ratio P, and RD values ​​representing 90% and 50% of the total rainfall for each rainstorm, sorted from highest to lowest rainfall amount for each rainstorm. 90 and RD 50 And, the maximum deviation MD between the actual rainfall and the ideal linear cumulative distribution.

3. The method for identifying rainstorm patterns based on grid data according to claim 2, characterized in that, The steps of extracting rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data specifically include: Starting from the initial time point, the rainfall grid is traversed to extract rainstorm identification data, which includes the average rainfall G, the peak rainfall PR, the peak time PD, the peak total rainfall Vo, the total rainfall duration Du, and the rainfall intensity RL. Determine the average rainfall G at grid i at time t. i With peak rainfall PR i The relationship, if the average rainfall G i Greater than the peak rainfall PR i Update the grid data for grid i: PR i =G i Peak time PD of grid i i =t, the peak total rainfall Vo of grid i i =Vo i +G i *M, Total duration of rainfall (Du) i =Du i +M, where M is the time interval; If the average rainfall G i Less than or equal to peak rainfall PR i At the average rainfall G i If the values ​​are greater than 0, update the peak total rainfall and total rainfall duration for grid i. After completing the update of grid i at time t, the next update is performed at time t+1 and the next update of grid i+1 until all times and all grid updates are completed, thus completing the process of identifying the rainstorm and storing the updated grid data.

4. The method for identifying rainstorm patterns based on grid data according to claim 3, characterized in that, If the average rainfall G i Less than or equal to peak rainfall PR i The steps also include, on the average rainfall G i When the value equals 0, an independent rainfall event ends. The peak rainfall PR at this point is then determined. i If the product of 2 and 24 is greater than 50, then a rainstorm event has been successfully identified, and the rainfall intensity RL is calculated. i The value of RL i =Vo i / Du i .

5. The method for identifying rainstorm patterns based on grid data according to claim 4, characterized in that, The steps of extracting rainstorm pattern data from the rainfall grid data based on the rainstorm event and performing rainstorm pattern identification on the rainstorm event include: Based on the identified rainstorm events, corresponding rainstorm pattern data is extracted. This rainstorm pattern data includes the rainfall intensity ratio P, the total rainfall duration Du, and RD values ​​representing 90% and 50% of the total rainfall for each rainstorm, sorted from highest to lowest. 90 and RD 50 And, the maximum deviation MD between the actual rainfall and the ideal linear cumulative distribution; the rainfall intensity ratio P is expressed as the ratio of the peak rainfall PR to the rainfall intensity RL; Determine the relationship between the rainfall intensity ratio P and 3; If the rainfall intensity ratio P is less than or equal to 3, and RD 90 The total duration of rainfall (Du) and the maximum deviation (MD) satisfy the following: RD 90 ≥(2 / 3)Du and MD≤15% The rainstorm pattern was then identified as a steady flow pattern. If the rainfall intensity ratio P is greater than 3, and RD 50 RD 90 The total duration of rainfall, Du, satisfies: RD 50 ≤(1 / 6)Du and RD 90 ≤(1 / 2)You The rainstorm pattern was then identified as a spike-type. If the rainfall intensity ratio P is greater than 3, and RD 50 RD 90 The total duration of rainfall, Du, satisfies: RD 50 >(1 / 6)You and RD 90 >(1 / 2)You The rainstorm pattern is then identified as a gentle peak type.

6. The method for identifying rainstorm patterns based on grid data according to claim 5, characterized in that, The step of extracting rainstorm pattern data from the rainfall grid data based on the rainstorm event, and performing rainstorm pattern identification on the rainstorm event, further includes: Based on the identified rainstorm events, extract the peak rainfall PR from the corresponding rainstorm pattern data, and the next highest rainfall PR after the peak rainfall PR. c If the following conditions are met simultaneously: Peak rainfall PR and second-highest rainfall PR c The time interval between them is greater than or equal to 3; The peak rainfall was PR. c Peak rainfall (PR) of 70% or more; The peak rainfall was PR. c The rainfall trend between the peak rainfall (PR) and the peak rainfall (PR) follows an M-shaped pattern. The rainstorm pattern was then identified as a double-wave pattern.

7. The method for identifying rainstorm patterns based on grid data according to claim 2, characterized in that, The grid data in the rainfall grid is stored in text format, and the text format is converted into integer or floating-point numbers when the grid data is extracted.

8. A rainstorm pattern identification device based on grid data, characterized in that, The rainstorm pattern identification device identifies the rainstorm pattern of the area to be identified using the rainstorm pattern identification method as described in any one of claims 1 to 7, and the rainstorm pattern identification device includes: The grid creation module is used to create rainfall grids based on different regions to be identified over time. The identification module is used to extract rainstorm identification data from the rainfall grid to identify all rainstorm events in the rainfall grid data; The processing module extracts rainstorm pattern data from the rainfall grid data based on the rainstorm event, performs rainstorm pattern identification on the rainstorm event, and obtains one or more rainstorm pattern identification results among constant flow pattern, peak pattern, gentle peak pattern and double wave pattern.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the program, it implements the rainstorm pattern identification method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the rainstorm pattern identification method as described in any one of claims 1 to 7.

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