Method and system for analyzing extreme lightning distribution characteristics

Abstract

The invention discloses a method and system for analyzing extreme lightning distribution characteristics, and the method comprises the steps: obtaining multi-year lightning data monitoring result data, carrying out the preprocessing of the multi-year lightning data monitoring result data, and obtaining the preprocessed data; carrying out binning processing on the preprocessed data according to a preset time dimension and a space dimension, and calculating a key statistical magnitude to obtain lightning activity data; performing spatial matching on the lightning activity data and geographic information data to obtain a geographic distribution condition of the lightning activity data; and carrying out visualization processing on the geographical distribution condition of the lightning activity data. According to the method, the extreme value spatial distribution characteristics of the lightning frequency and the current intensity can be accurately identified, and invalid or abnormal data can be effectively eliminated, so that the reliability and the accuracy of an analysis result are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of lightning disaster monitoring and analysis, and particularly relates to a method and system for analyzing the distribution characteristics of extreme lightning. Background Art

[0002] With the development of meteorological science, the monitoring and analysis of lightning activities have become increasingly important. Lightning, as a natural phenomenon, its characteristics such as activity rules, frequency, and current intensity are of great significance for fields such as meteorological forecasting, lightning protection, and disaster reduction. How to extract useful information from a large amount of raw data and accurately display this information is one of the main challenges faced by current lightning disaster monitoring and protection.

[0003] Existing lightning data analysis methods have problems such as low efficiency and incomplete analysis when dealing with a large amount of data, and it is difficult to accurately reveal the extreme characteristics of lightning activities. Specifically, traditional lightning data analysis methods have low spatio-temporal resolution and do not use fine spatio-temporal binning statistics, making it difficult to capture the changes in lightning activities at the hourly level and the details of spatial distribution; the visualization effect is poor, and it is difficult to intuitively display the spatial distribution characteristics of extreme values of lightning frequency and current intensity. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method and system for analyzing the distribution characteristics of extreme lightning. By processing lightning monitoring data over the years and combining lightning feature analysis techniques such as dynamic binning, geographic masking, and heat map rendering, it realizes the efficient statistics and visualization of the extreme characteristics of lightning hourly frequency and current intensity, can intuitively display the spatial distribution characteristics of extreme values of lightning frequency and current intensity, and can be applied to real-time monitoring of lightning disasters, risk warning, and disaster risk assessment.

[0005] On the one hand, to achieve the above object, the present invention provides a method for analyzing the distribution characteristics of extreme lightning, including: obtaining the monitoring result data of lightning data over the years, preprocessing the monitoring result data of the lightning data over the years to obtain preprocessed data;

[0006] Binning the preprocessed data according to a preset time dimension and space dimension, calculating key statistics, and obtaining lightning activity data;

[0007] Performing spatial matching on the lightning activity data and geographic information data to obtain the geographical distribution of the lightning activity data;

[0008] Performing visualization processing on the geographical distribution of the lightning activity data.

[0009] Optionally, obtaining the preprocessed data includes:

[0010] Read the data file of lightning activity data to obtain the monitoring results data of multi-year lightning data, where the monitoring results data of multi-year lightning data includes the lightning occurrence time string, longitude, latitude, current amplitude and steepness;

[0011] Delete the outliers and missing values in the monitoring results data of multi-year lightning data to obtain the denoised data;

[0012] Successively perform format conversion and screening on the denoised data to obtain the preprocessed data.

[0013] Optionally, obtaining the lightning activity data includes:

[0014] Bin the preprocessed data according to the preset time dimension and space dimension to obtain the binned data;

[0015] Statistically calculate the lightning frequency and maximum current amplitude at the preset resolution for the data after each spatio-temporal binning to obtain the statistically processed data;

[0016] Aggregate the statistically processed data to obtain the lightning activity data.

[0017] Optionally, obtaining the geographical distribution of the lightning activity data includes:

[0018] Use a geographic information processing software library to read the geographic information data of the study area;

[0019] Perform spatial matching between the lightning activity data and the geographic information data to obtain the geographical locations corresponding to the lightning activity data;

[0020] Determine the range of the lightning activity geographical area according to the geographical locations corresponding to the lightning activity data, create a mask to exclude the data outside the study area, and obtain the geographical distribution of the lightning activity data.

[0021] Optionally, visualizing the geographical distribution of the lightning activity data includes:

[0022] Based on the geographical distribution of the lightning activity area, use two-dimensional grid data and a plotting library to draw a geographical distribution heat map of the hourly frequency and maximum current amplitude of lightning with different polarities;

[0023] According to the geographical distribution heat map, select a suitable color mapping, and assign corresponding colors to each area according to different numerical ranges to obtain the change trend of the data in different areas;

[0024] Draw the geographical boundary of the study area and add axis labels; add a color bar to indicate the numerical correspondence of the colors.

[0025] On the other hand, to achieve the above object, the present invention also provides a system for analyzing the distribution characteristics of extreme lightning, including: a data preprocessing module, a data binning and aggregation module, a geographic information processing module, and a data visualization module;

[0026] The data preprocessing module is used to obtain the monitoring result data of lightning data over the years, preprocess the monitoring result data of the lightning data over the years, and obtain the preprocessed data;

[0027] The data binning and aggregation module is used to perform binning processing on the preprocessed data according to the preset time dimension and space dimension, calculate key statistics, and obtain lightning activity data;

[0028] The geographic information processing module is used to perform spatial matching on the lightning activity data and the geographic information data to obtain the geographical distribution of the lightning activity data;

[0029] The data visualization module is used to perform visualization processing on the geographical distribution of the lightning activity data.

[0030] Optionally, the data preprocessing module includes a data reading unit, a data denoising unit, and a data conversion and screening unit;

[0031] The data reading unit is used to read the data file of the lightning activity data and obtain the monitoring result data of the lightning data over the years, where the monitoring result data of the lightning data over the years includes the lightning occurrence time string, longitude, latitude, current amplitude, and steepness;

[0032] The data denoising unit is used to delete the outliers and missing values in the monitoring result data of the lightning data over the years to obtain the denoised data;

[0033] The data conversion and screening unit is used to perform format conversion and screening on the denoised data in sequence to obtain the preprocessed data.

[0034] Optionally, the data binning and aggregation module includes a data binning unit, a data statistics unit, and a data aggregation unit;

[0035] The data binning unit is used to perform binning on the preprocessed data according to the preset time dimension and space dimension to obtain the binned data;

[0036] The data statistics unit is used to statistically calculate the lightning frequency and the maximum current amplitude at a preset resolution for the data after each spatio-temporal binning to obtain the statistically processed data;

[0037] The data aggregation unit is used to aggregate the statistically processed data to obtain the lightning activity data.

[0038] Optionally, the geographic information processing module includes an information reading unit, a spatial matching unit, and a mask creating unit;

[0039] The information reading unit is configured to read the geographic information data of the research area by using a geographic information processing software library;

[0040] The spatial matching unit is configured to perform spatial matching between the lightning activity data and the geographic information data to obtain the geographic locations corresponding to the lightning activity data;

[0041] The mask creating unit is configured to determine the range of the lightning activity geographic area according to the geographic locations corresponding to the lightning activity data, create a mask to exclude the data outside the research area, and obtain the geographic distribution of the lightning activity data.

[0042] Optionally, the data visualization module includes a heat map drawing unit, an application color mapping unit, and a graphic element adding unit;

[0043] The heat map drawing unit is configured to draw a heat map of the geographic distribution of the hourly frequency of lightning with different polarities and the maximum current amplitude based on the geographic distribution of the lightning activity area by using two-dimensional grid data and a drawing library;

[0044] The application color mapping unit is configured to select a suitable color mapping according to the heat map of the geographic distribution, assign corresponding colors to each area according to different numerical ranges, and obtain the change trend of the data in different areas;

[0045] The graphic element adding unit is configured to draw the geographic boundary of the research area and add axis labels; add a color bar to indicate the numerical correspondence of the colors.

[0046] Technical effects of the present invention: The present invention discloses a method and a system for analyzing the distribution characteristics of extreme lightning. By combining dynamic binning, geographic masking, and heat map rendering technologies, a more intuitive and accurate understanding of the spatio-temporal distribution characteristics of lightning activities can be obtained. Through efficient data preprocessing, binning aggregation, and geographic information processing steps, not only can the spatial distribution characteristics of extreme values of lightning frequency and current intensity be accurately identified, but also invalid or abnormal data can be effectively excluded, thereby improving the reliability and accuracy of the analysis results. In addition, by using heat map visualization technology, complex lightning activity data can be presented in an intuitive manner, which greatly facilitates decision-makers to quickly grasp key information and supports more scientific and reasonable decision-making. The method and the system can be widely applied to multiple fields such as weather forecasting, disaster warning, and power facility protection. By deeply understanding the lightning activity characteristics of a specific area, effective protection measures can be formulated to reduce losses caused by lightning strikes. Description of the Drawings

[0047] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the accompanying drawings:

[0048] Figure 1 It is a schematic flowchart of a method for analyzing the distribution characteristics of extreme lightning in an embodiment of the present invention;

[0049] Figure 2 It is a schematic structural diagram of a system for analyzing the distribution characteristics of extreme lightning in an embodiment of the present invention;

[0050] Figure 3 It is a schematic diagram of the spatial distribution of the maximum occurrence frequencies of negative cloud-to-ground lightning and positive cloud-to-ground lightning within a 1-hour scale in an embodiment of the present invention, where (a) is negative cloud-to-ground lightning and (b) is positive cloud-to-ground lightning;

[0051] Figure 4 It is a schematic diagram of the spatial distribution of the minimum lightning current of negative cloud-to-ground lightning and the maximum lightning current of positive cloud-to-ground lightning within a 1-hour scale in an embodiment of the present invention, where (a) is negative cloud-to-ground lightning and (b) is positive cloud-to-ground lightning. Detailed implementation manners

[0052] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the accompanying drawings and combine with the embodiments to detail this application.

[0053] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0054] As Figure 1 shown, in this embodiment, a method for analyzing the distribution characteristics of extreme lightning is provided, including:

[0055] Obtain the monitoring result data of lightning data over the years, preprocess the monitoring result data of the lightning data over the years, and obtain the preprocessed data;

[0056] Perform binning processing on the preprocessed data according to the preset time dimension and space dimension, calculate the key statistics, and obtain the lightning activity data;

[0057] Perform spatial matching on the lightning activity data and the geographic information data to obtain the geographic distribution of the lightning activity data;

[0058] Perform visualization processing on the geographic distribution of the lightning activity data.

[0059] Further, obtaining the preprocessed data includes:

[0060] Reading the data file of lightning activity data to obtain the monitoring result data of multi-year lightning data, where the monitoring result data of multi-year lightning data includes the lightning occurrence time string, longitude, latitude, current amplitude, and steepness;

[0061] Deleting the outliers and missing values in the monitoring result data of multi-year lightning data to obtain the denoised data;

[0062] Successively performing format conversion and screening on the denoised data to obtain the preprocessed data.

[0063] Specifically, the purpose of this embodiment is to first perform lightning observation data cleaning and format conversion processing to improve data quality, ensure the accuracy and availability of data, and provide a high-quality data basis for subsequent analysis; specifically, it includes the following processing steps:

[0064] a) Reading the monitoring result data file of multi-year lightning data

[0065] Using specialized programming software and software libraries to read the data file containing lightning activity data, and importing the data into the memory for analysis.

[0066] The lightning data should include the lightning occurrence time string, longitude, latitude, current amplitude, and steepness.

[0067] b) Deleting outliers

[0068] Searching for and deleting the rows with values of specific outlier identifiers in the data columns. These values may be data entry errors or invalid data, which are meaningless or even misleading to the analysis results.

[0069] c) Deleting incomplete data

[0070] Checking each row in the data and deleting the row records with missing key fields to ensure the integrity of the data.

[0071] d) Format conversion

[0072] Merging the time strings and converting them into time variable objects to facilitate subsequent analysis by time dimension;

[0073] Converting relevant numerical fields such as current into appropriate numerical types for numerical operations.

[0074] e) Data screening

[0075] According to the research area, delimiting a rectangular spatial area and screening out the lightning data record rows falling within this spatial range to reduce unnecessary lightning data processing and improve analysis efficiency.

[0076] Further, obtaining lightning activity data includes:

[0077] Binning the preprocessed data according to preset time dimensions and spatial dimensions to obtain binned data;

[0078] Counting the lightning frequency and maximum current amplitude at a preset resolution for the data after each spatio-temporal binning to obtain the counted data;

[0079] Aggregating the counted data to obtain lightning activity data.

[0080] Specifically, the purpose of this embodiment is to bin the data according to specific time and space dimensions through binning and aggregation, and calculate key statistics to reveal the lightning activity laws and extreme characteristics in different regions. The specific processing steps are as follows:

[0081] a) Data binning

[0082] Bin the data according to time, longitude, and latitude at a specific resolution to form multiple data subsets for analyzing lightning activities in different time and space ranges respectively.

[0083] b) Counting lightning frequency and maximum current amplitude at a specific resolution

[0084] For each spatio-temporal bin, calculate the number of lightning occurrences (frequency) during this time period to study the frequency of lightning occurrences at this time scale.

[0085] Calculate the maximum value of the current amplitude with different polarities in each spatio-temporal bin to determine the strongest lightning activity at a specific time and space scale.

[0086] c) Further aggregation

[0087] Further aggregate the binned data, traverse the time dimension, calculate the maximum lightning frequency and maximum current amplitude with different lightning polarities in each longitude-latitude bin, and form a comprehensive statistical data set to reflect the extreme lightning activity conditions in different geographical regions.

[0088] Further, obtaining the geographical distribution of lightning activity data includes:

[0089] Read the geographical information data of the study area using a geographical information processing software library;

[0090] Perform spatial matching on the lightning activity data and the geographical information data to obtain the geographical locations corresponding to the lightning activity data;

[0091] Determine the range of the lightning activity geographical area according to the geographical location corresponding to the lightning activity data, create a mask to exclude the data outside the study area, and obtain the geographical distribution of the lightning activity data.

[0092] Specifically, in this embodiment, by performing geographical information processing on the data, combining the lightning activity data with the geographical environment, realizing the geographical spatial visualization and analysis of the data, and revealing the differences and laws of lightning activities in different geographical areas, the following processing steps are specifically included:

[0093] a) Read geographical information data

[0094] Use the geographical information processing software library to read the geographical information data of the study area, including administrative boundaries, terrain, etc.

[0095] b) Spatial matching

[0096] Perform spatial matching on the aggregated lightning activity data and the geographical information data to ensure that each data point can be accurately corresponding to its geographical location.

[0097] c) Create a mask

[0098] According to the range of the geographical area, create a mask to exclude the data outside the study area, so as to ensure that the subsequent analysis is only carried out on the data within a specific area, improving the accuracy and pertinence of the analysis.

[0099] Furthermore, the visualization processing of the geographical distribution of the lightning activity data includes:

[0100] Based on the geographical distribution of the lightning activity area, use two-dimensional grid data and a plotting library to draw a geographical distribution heat map of the hourly frequency of lightning with different polarities and the maximum current amplitude;

[0101] According to the geographical distribution heat map, select a suitable color mapping, assign corresponding colors to each area according to different numerical ranges, and obtain the change trend of the data in different areas;

[0102] Draw the geographical boundary of the study area and add axis labels; add a color bar to indicate the numerical correspondence of the colors.

[0103] Specifically, this embodiment shows the geographical distribution of the hourly frequency of lightning and the extreme characteristics of the current, presenting the complex data in an intuitive and easy-to-understand way, helping decision-makers quickly grasp the characteristics and laws of lightning activities, and providing strong support for relevant decisions. The following processing steps are specifically included:

[0104] a) Draw a heat map

[0105] Use two-dimensional grid data and a plotting library to draw a geographical distribution heat map of the hourly frequency of lightning with different polarities and the maximum current amplitude.

[0106] b) Apply color mapping

[0107] Select a suitable color mapping, assign corresponding colors to each region according to different numerical ranges, and use the color depth to represent the frequency and current intensity, which can intuitively display the change trend of data in different regions.

[0108] c) Add graphic elements

[0109] Draw the geographical boundary of the study area and add axis labels to clearly understand the coordinate system of the map; add a color bar to indicate the numerical correspondence of the colors to help interpret the graph.

[0110] As Figure 2 shown, in this embodiment, a system for analyzing the distribution characteristics of extreme lightning is also provided, including:

[0111] A data preprocessing module, a data binning and aggregation module, a geographic information processing module, and a data visualization module;

[0112] The data preprocessing module is used to obtain the monitoring result data of lightning data over the years, preprocess the monitoring result data of the lightning data over the years, and obtain the preprocessed data;

[0113] The data binning and aggregation module is used to bin the preprocessed data according to the preset time dimension and space dimension, calculate key statistics, and obtain lightning activity data;

[0114] The geographic information processing module is used to spatially match the lightning activity data with the geographic information data to obtain the geographical distribution of the lightning activity data;

[0115] The data visualization module is used to visually process the geographical distribution of the lightning activity data.

[0116] Further, the data preprocessing module includes a data reading unit, a data denoising unit, and a data conversion and screening unit;

[0117] The data reading unit is used to read the data file of the lightning activity data and obtain the monitoring result data of the lightning data over the years, where the monitoring result data of the lightning data over the years includes the lightning occurrence time string, longitude, latitude, current amplitude, and steepness;

[0118] The data denoising unit is used to delete the outliers and missing values in the monitoring result data of the lightning data over the years to obtain the denoised data;

[0119] The data conversion and screening unit is used to sequentially perform format conversion and screening on the denoised data to obtain preprocessed data.

[0120] Further, the data binning and aggregation module includes a data binning unit, a data statistics unit, and a data aggregation unit;

[0121] The data binning unit is used to bin the preprocessed data according to preset time dimensions and space dimensions to obtain binned data;

[0122] The data statistics unit is used to statistically calculate the lightning frequency and maximum current amplitude at a preset resolution for the data after each spatio-temporal binning to obtain statistically processed data;

[0123] The data aggregation unit is used to aggregate the statistically processed data to obtain lightning activity data.

[0124] Further, the geographic information processing module includes an information reading unit, a spatial matching unit, and a mask creation unit;

[0125] The information reading unit is used to read the geographic information data of the research area by using a geographic information processing software library;

[0126] The spatial matching unit is used to perform spatial matching on the lightning activity data and the geographic information data to obtain the geographical locations corresponding to the lightning activity data;

[0127] The mask creation unit is used to determine the range of the lightning activity geographical area according to the geographical locations corresponding to the lightning activity data, create a mask to exclude the data outside the research area, and obtain the geographical distribution of the lightning activity data.

[0128] Further, the data visualization module includes a heat map drawing unit, an application color mapping unit, and a graphic element adding unit;

[0129] The heat map drawing unit is used to draw a heat map of the geographical distribution of the hourly lightning frequency and maximum current amplitude of different polarities based on the geographical distribution of the lightning activity area, using two-dimensional grid data and a drawing library;

[0130] The application color mapping unit is used to select a suitable color mapping according to the geographical distribution heat map, assign corresponding colors to each area according to different numerical ranges, and obtain the change trend of the data in different areas;

[0131] The graphic element adding unit is used to draw the geographical boundary of the research area and add axis labels; add a color bar to indicate the numerical correspondence of the colors.

[0132] A specific application example of the present invention is as follows:

[0133] (1) Data preprocessing

[0134] This operation first performs lightning observation data cleaning and format conversion to improve data quality, including the following processing steps:

[0135] a) Read the data file of lightning data monitoring results over the years

[0136] Use a dedicated programming software (such as Python) and software libraries (such as pandas) to read the data file containing lightning activity data, and utilize the powerful data processing capabilities of the pandas library to clean and convert the format of the original data.

[0137] The lightning data should include the lightning occurrence time string, longitude, latitude, current amplitude, and steepness as shown in Table 1.

[0138] Table 1

[0139] Year Month Day Hour Minute Second Latitude Longitude Current Gradient 2013 6 23 0 0 52.44185 30.8501 123.3397 -228.2 -57.3 2013 6 23 0 0 52.74769 30.7782 115.4294 -26.1 -6.8 2013 6 23 0 1 18.88955 29.9682 109.575 26.2 4.7 2013 6 23 0 2 9.165302 30.8181 116.4761 -57.8 -9.3 2013 6 23 0 2 9.223165 30.8468 116.714 -61.6 -17.5 2013 6 23 0 4 49.31316 30.7855 115.3253 -29 -6.6 2013 6 23 0 6 6.685317 30.8599 116.6086 -82.2 -17.5 2013 6 23 0 6 6.828925 30.9097 122.997 -225.8 -57.8 2013 6 23 0 9 21.85406 29.9016 109.5098 33.9 5.8 2013 6 23 0 11 53.26796 31.1173 116.4683 -48 -8.7 2013 6 23 0 12 30.04614 29.9842 109.5741 22.2 -9999 2013 6 23 0 12 45.39144 28.7304 108.8982 -68.8 -10.4 2013 6 23 0 13 34.63806 29.8114 109.3435 -22.8 -3.4

[0140] b) Delete data outliers

[0141] Find and delete the rows with values of specific abnormal identifiers (such as -9999, see the row record at 0:12 on June 23, 2013) in the data columns. These values may be data entry errors or invalid data.

[0142] c) Delete incomplete data

[0143] Check each row in the data and delete the row records with missing key fields to ensure data integrity.

[0144] d) Format conversion

[0145] Merge the time strings and convert them into time variable objects (such as 0:12 on June 23, 2013, merged and converted to 2013-6-23 00:12) to facilitate subsequent analysis by time dimension;

[0146] Convert relevant numerical fields such as current into appropriate numerical types (such as integer type or floating-point type) for numerical operations.

[0147] e) Data screening

[0148] The research area of concern is Hubei Province. Define a rectangular spatial area (108°E - 116.5°E, 28.9°N - 33.5°N) that includes Hubei Province, and screen out the lightning data record rows that fall within this spatial range to reduce unnecessary lightning data processing and improve analysis efficiency.

[0149] (2) Data binning and aggregation

[0150] This operation bins the data according to specific time - dimension resolutions (such as 1 hour) and space - dimension resolutions (such as 0.1°), and calculates key statistics (maximum value) to reveal the lightning activity patterns and extreme characteristics in different regions.

[0151] It includes the following processing steps:

[0152] a) Data binning

[0153] Bin the data according to time (1 hour), longitude (0.1°), and latitude (0.1°) at a specific resolution to form multiple data subsets, so as to analyze the lightning activities within different time and space ranges respectively.

[0154] b) Statistically analyze lightning frequency and maximum current amplitude at a specific resolution

[0155] Use the groupby and agg functions in Python to perform binning and aggregation operations on the data.

[0156] For each spatio - temporal bin, calculate the number of lightning occurrences within each 1 - hour period to study the frequency of lightning occurrences at this time scale.

[0157] Calculate the maximum value of the current amplitude with different polarities in each spatio - temporal bin to determine the strongest lightning activity at the spatio - temporal scale of each 1 hour and 0.1°×0.1°.

[0158] c) Further aggregation

[0159] Further aggregate the binned data, traverse the time dimension, calculate the maximum lightning frequency and maximum current amplitude of different lightning polarities within each longitude - latitude bin (0.1°×0.1°) to form a comprehensive statistical data set, reflecting the extreme situations of lightning activities in different geographical regions.

[0160] (3) Geographic information processing

[0161] This operation combines lightning activity data with the geographical environment to achieve geographical - spatial visualization and analysis of the data, revealing the differences and patterns of lightning activities in different geographical regions. It includes the following processing steps:

[0162] a) Read geographic information data

[0163] Use a geographic information processing software library (such as the geopandas library in Python) to read the geographic information data of the Hubei Province region, including administrative boundaries, terrain, etc.

[0164] b) Spatial matching

[0165] Spatially match the aggregated lightning activity data with geographical information data (the administrative boundary and terrain of a certain province) to ensure that each data point can be accurately corresponding to its geographical location.

[0166] c) Create a mask

[0167] Create a mask according to the scope of the geographical area of a certain province to exclude data outside the province, so as to ensure that subsequent analysis is only carried out on data within Hubei Province, improving the accuracy and pertinence of the analysis.

[0168] (3) Data visualization

[0169] This operation presents the geographical distributions of lightning hourly frequency and current extreme characteristics in an intuitive and easy-to-understand way, helping decision-makers quickly grasp the characteristics and laws of lightning activities, including the following processing steps:

[0170] a) Draw a heat map

[0171] Use two-dimensional grid data and a plotting library (such as matplotlib in Python) to draw heat maps of the geographical distributions of hourly frequencies ( Figure 3 a- Figure 3 b) of different polarities of lightning (positive lightning, negative lightning) and maximum current amplitudes ( Figure 4 a- Figure 4 b).

[0172] b) Apply color mapping

[0173] Select a suitable color mapping (such as Spectral_r in Python), assign corresponding colors to each region according to different numerical ranges, and use the shade of the color to represent the frequency and current intensity, which can intuitively display the changing trends of data in different regions.

[0174] c) Add graphic elements

[0175] Draw the geographical boundary of the study area and add axis labels to clearly understand the coordinate system of the map; add a color bar to indicate the numerical correspondence of the colors to help interpret the graph.

[0176] The present invention discloses a method and system for analyzing the distribution characteristics of extreme lightning. By combining dynamic binning, geographic masking, and heat map rendering techniques, a more intuitive and accurate understanding of the spatio-temporal distribution characteristics of lightning activities can be achieved. Through efficient data preprocessing, bin aggregation, and geographic information processing steps, not only can the spatial distribution characteristics of extreme values of lightning frequency and current intensity be accurately identified, but also invalid or abnormal data can be effectively removed, thereby improving the reliability and accuracy of the analysis results. In addition, using heat map visualization technology, complex lightning activity data can be presented in an intuitive manner, greatly facilitating decision-makers to quickly grasp key information and supporting more scientific and reasonable decision-making. This method and system can be widely applied to multiple fields such as weather forecasting, disaster warning, and power facility protection. By deeply understanding the lightning activity characteristics in a specific area, effective protection measures can be formulated to reduce losses caused by lightning strikes.

[0177] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for analyzing the distribution characteristics of extreme lightning, characterized in that, Including: Obtain the monitoring result data of lightning data over the years, preprocess the monitoring result data of the lightning data over the years to obtain preprocessed data; Perform binning on the preprocessed data according to preset time dimensions and spatial dimensions, calculate key statistics, and obtain lightning activity data; Perform spatial matching on the lightning activity data and the geographic information data to obtain the geographic distribution of the lightning activity data; Visualize the geographic distribution of the lightning activity data.

2. The method for analyzing the distribution characteristics of extreme lightning as claimed in claim 1, wherein Obtaining the preprocessed data includes: Read the data file of the lightning activity data to obtain the monitoring result data of the lightning data over the years, wherein the monitoring result data of the lightning data over the years includes the lightning occurrence time string, longitude, latitude, current amplitude and steepness; Delete the outliers and missing values in the monitoring result data of the lightning data over the years to obtain denoised data; Successively perform format conversion and screening on the denoised data to obtain the preprocessed data.

3. The method for analyzing the distribution characteristics of extreme lightning as claimed in claim 1, wherein Obtaining the lightning activity data includes: Bin the preprocessed data according to preset time dimensions and spatial dimensions to obtain binned data; Statistically count the lightning frequency and the maximum current amplitude at a preset resolution for the data after each space-time binning to obtain the statistically processed data; Aggregate the statistically processed data to obtain the lightning activity data.

4. The method for analyzing the distribution characteristics of extreme lightning as claimed in claim 1, wherein Obtaining the geographic distribution of the lightning activity data includes: Use a geographic information processing software library to read the geographic information data of the research area; Perform spatial matching on the lightning activity data and the geographic information data to obtain the corresponding geographical locations of the lightning activity data; Determine the range of the lightning activity geographical area according to the corresponding geographical locations of the lightning activity data, create a mask to exclude the data outside the research area, and obtain the geographic distribution of the lightning activity data.

5. The method for analyzing the distribution characteristics of extreme lightning as claimed in claim 1, wherein Visualizing the geographic distribution of the lightning activity data includes: Based on the geographic distribution of the lightning activity area, use two-dimensional grid data and a plotting library to draw a geographic distribution heat map of the hourly frequency and maximum current amplitude of lightning with different polarities; According to the geographic distribution heat map, select a suitable color mapping, and assign corresponding colors to each area according to different numerical ranges to obtain the change trend of the data in different areas; Draw the geographic boundary of the research area and add axis labels; add a color bar to indicate the numerical correspondence of the colors.

6. A system for analyzing the distribution characteristics of extreme lightning, which is applied to the method according to any one of claims 1-5, characterized in that, Including: A data preprocessing module, a data binning and aggregation module, a geographic information processing module, and a data visualization module; The data preprocessing module is used to obtain the monitoring result data of the lightning data over the years, preprocess the monitoring result data of the lightning data over the years to obtain the preprocessed data; The data binning and aggregation module is used to perform binning on the preprocessed data according to preset time dimensions and spatial dimensions, calculate key statistics, and obtain lightning activity data; The geographic information processing module is used to perform spatial matching on the lightning activity data and geographic information data to obtain the geographic distribution of the lightning activity data; The data visualization module is used to perform visualization processing on the geographic distribution of the lightning activity data.

7. The system for analyzing extreme lightning distribution characteristics according to claim 6, wherein The data preprocessing module includes a data reading unit, a data denoising unit, and a data conversion and screening unit; The data reading unit is used to read the data file of the lightning activity data and obtain the monitoring results data of lightning data over the years, where the monitoring results data of lightning data over the years includes the lightning occurrence time string, longitude, latitude, current amplitude, and steepness; The data denoising unit is used to delete the outliers and missing values in the monitoring results data of lightning data over the years to obtain the denoised data; The data conversion and screening unit is used to perform format conversion and screening on the denoised data in sequence to obtain the preprocessed data.

8. The system for analyzing extreme lightning distribution characteristics according to claim 6, wherein The data binning and aggregation module includes a data binning unit, a data statistics unit, and a data aggregation unit; The data binning unit is used to perform binning on the preprocessed data according to preset time dimensions and spatial dimensions to obtain the binned data; The data statistics unit is used to statistically calculate the lightning frequency and the maximum current amplitude at a preset resolution for the data in each spatio-temporal bin to obtain the statistically processed data; The data aggregation unit is used to aggregate the statistically processed data to obtain the lightning activity data.

9. The system for analyzing extreme lightning distribution characteristics according to claim 6, wherein The geographic information processing module includes an information reading unit, a spatial matching unit, and a mask creation unit; The information reading unit is used to read the geographic information data of the research area by using a geographic information processing software library; The spatial matching unit is used to perform spatial matching on the lightning activity data and the geographic information data to obtain the geographical location corresponding to the lightning activity data; The mask creation unit is used to determine the range of the lightning activity geographical area according to the geographical location corresponding to the lightning activity data, create a mask, exclude the data outside the research area, and obtain the geographic distribution of the lightning activity data.

10. The system for analyzing extreme lightning distribution characteristics according to claim 6, wherein The data visualization module includes a heat map drawing unit, an application color mapping unit, and a graphic element adding unit; The heat map drawing unit is used to draw a heat map of the geographic distribution of the hourly lightning frequency and the maximum current amplitude of different polarities based on the geographic distribution of the lightning activity area, using two-dimensional grid data and a drawing library; The application color mapping unit is used to select a suitable color mapping according to the geographical distribution heat map, assign corresponding colors to each area according to different numerical ranges, and obtain the change trend of data in different areas; The graphic element adding unit is used to draw the geographical boundary of the research area and add axis labels; Add a color bar to indicate the numerical correspondence of the colors.

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