Lightning flash analysis method and device, terminal equipment and storage medium

Abstract

The present application relates to the technical field of lightning disaster assessment, and particularly relates to a lightning ground flash analysis method and device, a terminal device and a storage medium. The present application is used for studying the multi-year trend change and its significance of lightning in a grid scale. After obtaining M years of lightning monitoring data, the lightning monitoring data is processed in time and space interval to obtain the spatial distribution relationship between the longitude and the latitude of thunderstorm hours. Unlike the traditional method of directly investigating the change of lightning ground flash density, the present application processes the wide-area lightning ground flash monitoring data into lightning hours for investigation, which can avoid the error caused by the different detection efficiencies of stations in different years to a certain extent and is more accurate. On this basis, by analyzing the significance of the slope of all spatial grids in the multi-year longitude and latitude spatial distribution matrix of lightning hours and the slope of all spatial grids, the multi-year change trend of lightning hours can be effectively analyzed, and the accuracy and reliability of the analysis result can be improved.

Description

Technical Field

[0001] This invention relates to the field of lightning disaster assessment technology, specifically to a lightning flash analysis method, apparatus, terminal equipment, and storage medium. Background Technology

[0002] Lightning is a common natural discharge phenomenon with high frequency and intensity, significantly impacting fields such as power, forestry, and transportation. Studying the multi-year variation trend of grid scale in lightning strike data is of great significance for lightning protection and disaster prevention.

[0003] In the meteorological field, existing data for assessing long-term lightning trends mainly comes from daily thunderstorm data recorded manually at meteorological stations. This method relies heavily on manual observation, and insufficient or uneven distribution of observation stations can lead to missed measurements or misjudgments, resulting in significant errors in the assessment of lightning trend changes. Furthermore, daily thunderstorm data reflects the number of thunderstorm days in a year, with a coarse temporal resolution, failing to reflect the hourly-scale variations in lightning disasters. In the power grid field, methods mainly rely on drawing annual lightning density distribution maps or calculating the temporal variation characteristics of annual lightning quantity by province. The former only qualitatively reflects lightning trends, while the latter has too large a spatial scale to reflect local spatial characteristics.

[0004] Currently, there are many methods for trend analysis of lightning and lightning data, including statistical methods and neural networks. However, these methods often have some problems when processing and analyzing lightning and lightning data. For example, traditional statistical methods such as linear slope are easily affected by outliers and noise when dealing with complex and nonlinear data, leading to inaccurate analysis results. Neural network methods, on the other hand, require a large amount of sample data and high computational resources, making them difficult to widely apply in practice. Summary of the Invention

[0005] In view of this, embodiments of this application provide a lightning flash analysis method, apparatus, terminal device, and storage medium to solve the problems of low accuracy and difficulty in widespread application of traditional lightning flash data trend analysis methods in practical applications.

[0006] The first aspect of this application provides a lightning flash analysis method, including:

[0007] Obtain lightning monitoring data for year M;

[0008] The lightning monitoring data for year i is sequentially processed into a spatiotemporal grid with a preset spatial interval resolution and a preset temporal interval resolution to obtain a first matrix from year i to year M. The first matrix for year i records data representing the spatial distribution relationship between longitude and latitude of thunderstorm hours in year i, where i = 1, 2, ..., M.

[0009] A second matrix is ​​calculated based on the first matrix from the first year to the Mth year. The second matrix records data representing the spatial distribution relationship between longitude and latitude of lightning hours from the first year to the Mth year.

[0010] Calculate the forest slope for all spatial grids in the second matrix, and construct the third matrix based on the forest slope;

[0011] The significance of the slope of all spatial grids is tested to obtain the significance value, and a fourth matrix is ​​constructed based on the significance value.

[0012] Multiplying the third matrix and the fourth matrix yields the fifth matrix, which records data reflecting the M-year trend change level of lightning duration within the grid scale.

[0013] A preferred method for calculating the first matrix in the i-th year includes:

[0014] The lightning monitoring data for year i is denoted as the sixth matrix for year i.

[0015] The sixth matrix of the i-th year is subjected to spatiotemporal gridding with a preset spatial interval resolution and a preset time interval resolution to obtain the seventh matrix of the i-th year;

[0016] The lightning time values ​​within different latitude and longitude grid intervals are calculated based on the seventh matrix of the i-th year, and the eighth matrix of the i-th year is established. The eighth matrix of the i-th year records the lightning time values ​​within different latitude and longitude grid intervals.

[0017] A two-dimensional empty matrix is ​​established, and the eighth matrix of the i-th year is assigned to the two-dimensional empty matrix according to the latitude and longitude interval index to obtain the first matrix of the i-th year.

[0018] A preferred embodiment of the method for constructing the eighth matrix for the i-th year includes:

[0019] For lightning monitoring data that appears multiple times in the seventh matrix, only the lightning monitoring data that appears the first time is retained to obtain the ninth matrix;

[0020] The number of times the lightning monitoring data in the ninth matrix appears in the same longitude grid interval and latitude grid interval is summed to obtain the lightning time value in different longitude and latitude grid intervals;

[0021] Based on the lightning duration values ​​within different latitude and longitude grid intervals, construct the eighth matrix for the i-th year.

[0022] Preferably, the method for calculating the second matrix based on the first matrix from the first year to the Mth year includes:

[0023] A three-dimensional empty matrix is ​​established, wherein the first and second dimensions of the three-dimensional empty matrix are the number of latitudinal and longitudinal grids after the lightning spatial region under investigation is gridded, respectively, and the third dimension is M in size;

[0024] The first matrix from the first year to the Mth year is assigned to the three-dimensional empty matrix to obtain the second matrix.

[0025] More preferably, the method for calculating the forest slope for all spatial grids in the second matrix includes:

[0026] Take the third-dimensional data of the j-th spatial grid in the second matrix to obtain the first time series of the j-th spatial grid. The first time series of the j-th spatial grid is the thunderstorm hour series of the j-th spatial grid over M years, where j = 1, 2, ..., N, and N is the number of spatial grids in the second matrix.

[0027] The Thyssen slope is calculated for the first time series of the j-th spatial grid using the Thyssen slope estimation method.

[0028] More preferably, the method for testing the significance of the slopes of all spatial grids to obtain the fourth matrix includes:

[0029] Take the third-dimensional data of the j-th spatial grid in the second matrix to obtain the first time series of the j-th spatial grid. The first time series is the thunderstorm hour series of the spatial grid over M years, where j = 1, 2, ..., N, and N is the number of spatial grids in the second matrix.

[0030] The significance level α matrix of the first time series of the j-th spatial grid is calculated using the Mankendall test, and the fourth matrix is ​​obtained.

[0031] When α in the significance level α matrix is ​​less than a preset value, the first time series of the j-th spatial grid is determined to have a significant trend;

[0032] When α in the significance level α matrix is ​​greater than or equal to a preset value, it is determined that the first time series of the j-th spatial grid has no significant trend.

[0033] A second aspect of this application provides a lightning flash analysis device, comprising:

[0034] The data acquisition unit is used to acquire lightning monitoring data for year M.

[0035] The first matrix calculation unit is used to sequentially perform spatiotemporal interval gridding processing on the lightning monitoring data of the i-th year with a preset spatial interval resolution and a preset time interval resolution to obtain the first matrix from the first year to the M-th year. The first matrix of the i-th year records data representing the spatial distribution relationship between longitude and latitude of the thunderstorm hours in the i-th year, i = 1, 2, ..., M.

[0036] The second matrix calculation unit is used to calculate the second matrix based on the first matrix from the first year to the Mth year. The second matrix records data representing the spatial distribution relationship between longitude and latitude of lightning hours from the first year to the Mth year.

[0037] The third matrix calculation unit is used to calculate the forest slope of all spatial grids in the second matrix and construct the third matrix based on the forest slope.

[0038] The fourth matrix calculation unit is used to test the significance of the slope of all spatial grids, obtain the significance value, and construct the fourth matrix based on the significance value;

[0039] The fifth matrix calculation unit is used to multiply the third matrix and the fourth matrix to obtain the fifth matrix, which records data reflecting the M-year trend change level of lightning hours within the grid scale.

[0040] A third aspect of this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the lightning flash analysis method described above.

[0041] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, characterized in that: when the computer program is executed by a processor, it implements the steps of the method described above.

[0042] The lightning flashover analysis method provided in the first aspect of this application is used to study the multi-year trend changes and significance of lightning hours (the number of hours of lightning occurring in a year) within a grid scale. After acquiring M years of lightning monitoring data, the data is processed into spatiotemporal intervals to obtain the spatial distribution relationship between longitude and latitude of thunderstorm hours. Unlike traditional methods that directly examine changes in lightning flashover density, this method processes wide-area lightning flashover monitoring data into lightning hours for examination, which can, to some extent, avoid errors caused by inconsistent detection efficiency of monitoring stations over many years, resulting in higher accuracy. Based on this, by combining the forest slope of all spatial grids in the multi-year longitude and latitude spatial distribution matrix of lightning hours with the significance of the forest slope of all spatial grids, the multi-year trend of lightning hours can be effectively analyzed, improving the accuracy and reliability of the analysis results and providing more scientific and reliable data support for lightning protection and disaster prevention. This method has advantages such as robustness, no need to assume data distribution, and computational simplicity.

[0043] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A schematic flowchart illustrating a lightning flashover analysis method provided in this application embodiment;

[0046] Figure 2 A flowchart illustrating the first matrix calculation method provided in an embodiment of this application;

[0047] Figure 3 A flowchart illustrating the eighth matrix calculation method provided in this application embodiment;

[0048] Figure 4 A flowchart illustrating the second matrix calculation method provided in this application embodiment;

[0049] Figure 5 A flowchart illustrating the forest slope calculation method provided in this application embodiment;

[0050] Figure 6 This is a schematic flowchart of a method for calculating the Sen slope using the Tylsen slope estimation method provided in an embodiment of this application.

[0051] Figure 7 A flowchart illustrating the fourth matrix calculation method provided in this application embodiment;

[0052] Figure 8 This is a schematic diagram of the structure of the lightning flashover analysis device provided in the embodiments of this application;

[0053] Figure 9 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application;

[0054] Figure 10 A schematic diagram of forest slope at the grid scale in Shandong Province, used to characterize the 15-year variation trend of lightning at the grid scale, provided for embodiments of this application;

[0055] Figure 11 A schematic diagram of the significance level α of the grid-scale trend of lightning in Shandong Province, used to characterize the 15-year variation trend of grid-scale lightning, provided for an embodiment of this application;

[0056] Figure 12 A schematic diagram illustrating the 15-year lightning variation trend and significance classification results at the grid scale in Shandong Province, provided as an embodiment of this application. Detailed Implementation

[0057] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0058] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0059] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0060] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0062] The lightning flashover analysis method provided in this application can be executed by the processor of a terminal device when running a computer program with corresponding functions. After acquiring M years of lightning monitoring data, the lightning monitoring data is processed into spatiotemporal intervals to obtain the spatial distribution relationship between longitude and latitude of thunderstorm hours. Unlike traditional methods that directly examine changes in lightning flashover density, this method processes wide-area lightning flashover monitoring data into lightning hours for examination, which can avoid errors caused by inconsistent detection efficiency of stations over many years to a certain extent, resulting in higher accuracy. In addition, by combining the forest slope of all spatial grids and the significance of the forest slope of all spatial grids for analysis, it has advantages such as robustness, no need to assume data distribution, and computational simplicity. This method can effectively analyze the multi-year variation trend of lightning hours, improve the accuracy and reliability of the analysis results, and provide more scientific and reliable data support for lightning protection and disaster prevention.

[0063] The terminal device can be a computing device with at least one function such as lightning data acquisition, lightning analysis, and lightning early warning, such as a lightning protection testing instrument, lightning protection device, and lightning early warning device. This application embodiment does not limit the specific type of terminal device.

[0064] Example 1

[0065] like Figure 1 As shown, the lightning flashover analysis method provided in this application includes the following steps S10 to S60:

[0066] Step S10: Obtain lightning monitoring data for year M, then proceed to step S20.

[0067] In application, M years of lightning monitoring data specifically refers to M years of wide-area lightning monitoring data within the investigated lightning spatial region. To achieve the required data volume for calculation, the value of M can be appropriately increased. To reduce the computational load, the value of M can also be appropriately decreased. The specific value of M can be adjusted according to the actual situation. For example, when the Theil-Sen Median slope estimation method and the Mann-Kendall test are used in the lightning-to-ground lightning analysis method of this embodiment, the required amount of time series data is greater than or equal to 8 years. Therefore, at least 8 years of wide-area lightning monitoring data are required, i.e., M≥8 is required.

[0068] Step S20: Sequentially perform spatiotemporal gridding processing on the lightning monitoring data of year i with preset spatial interval resolution and preset time interval resolution to obtain the first matrix from year i to year M. The first matrix of year i records the data used to represent the spatial distribution relationship between longitude and latitude of thunderstorm hours in year i, i = 1, 2, ..., M. Proceed to step S30.

[0069] In this application, i = 1, 2, ..., M represents i taking values ​​of 1, 2, ..., M sequentially. That is, first, i is set to 1. This involves performing spatiotemporal interval processing on the lightning monitoring data of the first year, resulting in the first matrix for that year, representing the spatial distribution of thunderstorm hours between longitude and latitude. Then, i is set to 2. This involves performing spatiotemporal interval processing on the lightning monitoring data of the second year, resulting in the first matrix for that year, representing the spatial distribution of thunderstorm hours between longitude and latitude. This calculation is repeated until i is set to M. At this point, the lightning monitoring data of the Mth year is spatiotemporally interval processed, resulting in the first matrix for that year, representing the spatial distribution of thunderstorm hours between longitude and latitude.

[0070] Step S30: Calculate the second matrix based on the first matrix from the first year to the Mth year. The second matrix records data representing the spatial distribution relationship between longitude and latitude of lightning hours from the first year to the Mth year. Proceed to step S40.

[0071] In application, since the first matrix for years 1 to M was calculated in step S20, there are M such first matrices. In this step, the second matrix is ​​calculated based on the M first matrices. This calculation method can be achieved by defining an empty matrix and then assigning values ​​to it, thus forming a new matrix, namely the second matrix.

[0072] Step S40: Calculate the forest slope for all spatial grids in the second matrix, construct a third matrix based on the forest slope, the third matrix records data for representing the forest slope of all spatial grids in the second matrix, and proceed to step S50.

[0073] In application, the forest slope can be calculated using the Telsen slope estimation method. After calculating the forest slope, by comparing the slope value with a preset value, the trend of the M-year thunderstorm duration sequence can be determined. This trend can be any of the following: upward, stable, or downward. The preset value can be set based on experience.

[0074] Step S50: Test the significance of the forest slope of all spatial grids, obtain the significance value, construct a fourth matrix based on the significance value, the fourth matrix is ​​used to represent the significance of the forest slope of all spatial grids, and proceed to step S60.

[0075] In applications, the significance of the slope of all spatial grids can be tested using the Menkendall test. When using the Menkendall test, the significance is determined by the significance level α matrix. The significance can be judged based on the relationship between the α values ​​in the significance level α matrix and preset values. This significance includes two types: significant trend and no significant trend.

[0076] Step S60: Multiply the third matrix and the fourth matrix to obtain the fifth matrix, which records data reflecting the M-year trend change level of lightning hours within the grid scale.

[0077] In application, the third and fourth matrices are multiplied to obtain a fifth matrix, which records numerical values ​​reflecting trends and significance. Different values ​​correspond to different trends and significance levels. These levels can include: significant decrease, decrease but not significant, stable, increase but not significant, and significant increase.

[0078] like Figure 2 As shown, in one embodiment, step S20 includes the following steps S201 to S204:

[0079] Step S201: Record the lightning monitoring data of year i as the sixth matrix of year i.

[0080] In application, each row of the sixth matrix for year i records one lightning monitoring data point, with the number of rows equal to the number of lightning records for year i. Each column records the longitude, latitude, and time (year-month-day-hour-minute-second) data from one lightning monitoring data point, where i can be any number from 1 to M. For example, the lightning monitoring data for year i (with a time resolution of microseconds and a spatial resolution of hundreds of meters) can be recorded in the sixth matrix A. i A iIt contains 3 columns and P rows. The 3 columns record the longitude, latitude and time (year-month-day-hour-minute-second) data of a lightning monitoring data. P is the number of lightning records in the i-th year.

[0081] Step S202: Perform spatiotemporal interval gridding processing on the sixth matrix of the i-th year with a preset spatial interval resolution and a preset time interval resolution to obtain the seventh matrix of the i-th year.

[0082] In application, when spatially and temporally intervalizing lightning data, the spatial interval resolution is m km. Referring to the detectable range of conventional daily manual observations of thunderstorms, m can be taken as 10, and the temporal resolution is 1 hour. The seventh matrix for the i-th year obtained after spatial and temporal intervalization can be denoted as B. i The data is still 3 columns and P rows. The 3 columns are the longitude grid intervals, latitude grid intervals and time (year-month-day-hour) data of lightning monitoring records, respectively, and P is the number of lightning records in the i-th year.

[0083] Step S203: Calculate the lightning time values ​​in different latitude and longitude grid intervals based on the seventh matrix of year i, and establish the eighth matrix of year i.

[0084] In application, the eighth matrix for year i records the lightning time values ​​within different latitude and longitude grid intervals. For example, the eighth matrix for year i is D. i The number of rows represents the number of spatial grids where lightning occurred in the i-th year. Each row records one lightning monitoring data point, and each column records the longitude grid interval, latitude grid interval, and lightning duration within the longitude and latitude spatial intervals of one lightning monitoring data point.

[0085] Step S204: Establish a two-dimensional empty matrix, and assign the eighth matrix of the i-th year to the two-dimensional empty matrix according to the latitude and longitude interval index to obtain the first matrix of the i-th year.

[0086] In applications, a two-dimensional empty matrix E can be constructed. i (lats,lons), where lats and lons are the latitudinal and meridional grid numbers of the investigated lightning spatial region after gridding with a spatial resolution of m km. Matrix D... i Assign values ​​to E according to latitude and longitude interval index. i In the middle, then E i Let be the first matrix for year i, used to represent the spatial distribution relationship between longitude and latitude of thunderstorm hours in year i.

[0087] like Figure 3 As shown, in one embodiment, step S203 includes the following steps S2031 to S2033:

[0088] Step S2031: For the lightning monitoring data that appears repeatedly in the seventh matrix, only the lightning monitoring data that appears for the first time is retained to obtain the ninth matrix.

[0089] In application, the seventh matrix B i The P rows of data are checked row by row. If several rows of data have the same longitude grid interval, latitude grid interval, and time, then these rows are considered to have appeared multiple times. Only the row that appears first (i.e., the first row of these rows) is retained, and the rest are deleted. The remaining data forms the ninth matrix C. i C i The size is 3 columns and Q rows, and generally, Q is much smaller than P.

[0090] Step S2032: For the ninth matrix C i The number of times lightning monitoring data appeared in the same longitude and latitude grid intervals was summed to obtain the lightning duration values ​​in different longitude and latitude grid intervals.

[0091] In application, the same longitude grid interval and latitude grid interval refer to the ninth matrix C. i In a lightning monitoring data point, the longitude and latitude grid segments have the same values. Only when the longitude and latitude grid segments have the same values ​​is this lightning monitoring data point considered a valid data point for summation. If the longitude and latitude grid segments of a lightning monitoring data point have different values, then this lightning monitoring data point is not included in the summation. For the ninth matrix C... i By performing the above checks on all lightning monitoring data, one or more valid data points can be obtained. Summing these data points yields a numerical value, which represents the lightning duration within different latitude and longitude grid intervals.

[0092] Step S2033: Based on the lightning time values ​​within different latitude and longitude grid intervals, establish the eighth matrix for the i-th year.

[0093] In application, the eighth matrix D in year i i It is used to record the lightning duration values ​​within the different latitude and longitude grid intervals. It has 3 columns and R rows. The number of rows R of the eighth matrix in the i-th year is the number of spatial grids where lightning occurred in the i-th year. Each row records one lightning monitoring data, and each column records the lightning duration within the latitude grid interval, latitude grid interval, and the spatial interval between the latitude and longitude grid intervals in one lightning monitoring data.

[0094] like Figure 4 As shown, in one embodiment, step S30 includes the following steps S301 to S302:

[0095] Step S301: Establish a three-dimensional empty matrix. The first and second dimensions of the three-dimensional empty matrix are the latitudinal and meridional grid numbers of the examined lightning spatial region, respectively, and the third dimension is M in size.

[0096] In applications, a three-dimensional empty matrix F(lats,lons,M) can be established, where lats and lons are the number of latitudinal and longitudinal grids in the investigated lightning spatial region after being gridded with a spatial resolution of m km.

[0097] Step S302: Assign the first matrix from the first year to the Mth year to the three-dimensional empty matrix to obtain the second matrix F(lats,lons,M).

[0098] In application, after assigning the first matrix from the first year to the Mth year to the three-dimensional empty matrix F(lats,lons,i), the second matrix F(lats,lons,M) can be obtained.

[0099] like Figure 5 As shown, in one embodiment, step S40 includes the following steps S401 to S402:

[0100] Step S401: Take the third-dimensional data of the j-th spatial grid in the second matrix to obtain the first time series of the j-th spatial grid. The first time series of the j-th spatial grid is the thunderstorm hour sequence of the j-th spatial grid over M years, j = 1, 2, ..., N, where N is the number of spatial grids in the second matrix.

[0101] In application, in the second matrix F(lats,lons,M), the third dimension F(j,k,M) of the j-th spatial grid (j,k) is taken, which is the M-year thunderstorm hour sequence of that grid, with a length of M. j = 1, 2, ..., N means that j takes the values ​​1, 2, ..., N in sequence.

[0102] Step S402: Calculate the Thyssen slope of the first time series of the j-th spatial grid using the Thyssen slope estimation method.

[0103] The Theil-Sen slope estimation method is a non-parametric test method that does not require the time series to satisfy assumptions such as serial autocorrelation and normal distribution, and can effectively handle small outliers and missing value noises. Different from the least squares method and some other regression methods, the Theil-Sen slope estimation method uses the median slope statistic for parameter estimation, thus improving the robustness of the regression model. In the application, for the first time series (i.e., the annual thunderstorm hour series for M years) of the j-th spatial grid (j, k), the Theil-Sen slope is calculated using the Theil-Sen slope estimation method. For the M data of the F(j, k, M) time series, denoted as x, the slopes of all pairs of points (two-by-two data points) are calculated. Then, the median of all the slopes is found, and this median is the estimated slope of the Theil-Sen estimation method, reflecting the overall trend of the change in the annual thunderstorm hour time series on the spatial grid (j, k). The calculation formula is as follows:

[0104]

[0105] In the formula: x i1 and x i2 are the thunderstorm hours in the i1-th and i2-th years on the (j, k) grid, where 1 < i1 < i2 < M. β is the median slope obtained from M(M - 1) / 2 combinations of data. When β is greater than 0.0005, it indicates that the E(j, k, M) time series shows an upward trend and is reclassified as 1; when β is less than 0.0005, it indicates that the E(j, k, M) time series shows a downward trend and is reclassified as -1; when β is between -0.0005 and 0.0005, it indicates that the change in the E(j, k, M) time series is relatively stable and is reclassified as 0.

[0106] By adjusting the value of j, all spatial grids in the second matrix F(lats, lons, M) can be selected in sequence, so that the Theil-Sen slopes of all spatial grids in the second matrix can be obtained, and then the third matrix G can be obtained.

[0107] As Figure 6 shown, in one embodiment, step S50 includes the following steps S501 to S502:

[0108] Step S501: Take the third-dimensional data of the j-th spatial grid in the second matrix to obtain the first time series of the j-th spatial grid. The first time series is the annual thunderstorm hour series for M years of the spatial grid, where j = 1, 2,..., N, and N is the number of spatial grids in the second matrix.

[0109] In the application, the method for obtaining the first time series of the j-th spatial grid can be performed by referring to step S401 and will not be elaborated here.

[0110] Step S502: Calculate the significance level α matrix of the first time series of the j-th spatial grid using the Mankendall test to obtain the fourth matrix; when α in the significance level α matrix is ​​less than a preset value, it is determined that the first time series of the j-th spatial grid has a significant trend; when α in the significance level α matrix is ​​greater than or equal to the preset value, it is determined that the first time series of the j-th spatial grid has no significant trend.

[0111] The Mankendall test is a nonparametric statistical test method. Its advantages include not requiring the measured values ​​to follow a normal distribution, not requiring a linear trend, and being unaffected by missing or outlier values. It has been widely used in testing the significance of trends in long-term data series. In application, for M data points of a time series F(j,k,M), denoted as x, the specific process of calculating the significance level α matrix of the first time series in the j-th spatial grid using the Mankendall test is as follows:

[0112] The Mankendall test statistic (S) is defined as follows:

[0113]

[0114] The sign function sgn is calculated as follows:

[0115]

[0116] When M≥8, the statistic S approximates a normal distribution, and the variance of S is as follows:

[0117]

[0118] When a tie group exists in sequence x, meaning there are multiple identical values ​​in the data sequence, Var(S) is calculated as follows:

[0119]

[0120] Here, p represents the number of tie groups in sequence x, and t i This represents the number of data items in the i-th tie group.

[0121] The standardized MK test statistic Z is calculated as follows:

[0122]

[0123] The corresponding α value can be obtained by looking up a table. When the absolute value of Z is greater than 1.65, 1.96, and 2.58, it indicates that the trend has passed the significance tests with a reliability of 90%, 95%, and 99%, respectively, and the null hypothesis that the series has no trend is rejected. Here, using the 95% reliability test, when the absolute value of Z is greater than or equal to 1.96 (i.e., when α is less than 0.05), there is a significant trend, and the reclassification is 2; when the absolute value of Z is less than 1.96 (α is greater than or equal to 0.05), there is no significant trend, and the reclassification is 1.

[0124] By adjusting the value of j, the significance of the slope of all spatial grids is determined, and the fourth matrix H is obtained.

[0125] After obtaining the fifth matrix I, which reflects the M-year trend variation level of lightning duration within the grid scale, the obtained multi-year lightning variation trend and its significance distribution map at the grid scale within the region can be transmitted and displayed in the system, which can be accessed by lightning disaster protection assessment personnel. This system can be a relevant system in the lightning analysis industry.

[0126] Example 2

[0127] like Figure 8 As shown, in one embodiment, the lightning flashover analysis device provided in this embodiment includes:

[0128] The data acquisition unit is used to acquire lightning monitoring data for M years, where M is greater than 1.

[0129] The first matrix calculation unit is used to perform spatiotemporal interval processing on the lightning monitoring data of the i-th year to obtain the first matrix of the i-th year. The first matrix of the i-th year records data representing the spatial distribution relationship between longitude and latitude of thunderstorm hours in the i-th year, i = 1, 2, ..., M.

[0130] The second matrix calculation unit is used to calculate the second matrix from the first to the Mth year. The second matrix is ​​used to represent the spatial distribution relationship between the longitude and latitude of the lightning hours in the Mth year.

[0131] The third matrix calculation unit is used to calculate the forest slope of all spatial grids in the second matrix to obtain the third matrix. The third matrix records data representing the forest slope of all spatial grids in the second matrix.

[0132] The fourth matrix calculation unit is used to test the significance of the forest slope of all spatial grids and obtain the fourth matrix, which is used to represent the significance of the forest slope of all spatial grids.

[0133] The fifth matrix calculation unit is used to multiply the third matrix and the fourth matrix to obtain the fifth matrix, which records data reflecting the M-year trend change level of lightning hours within the grid scale.

[0134] In one embodiment, to assess the multi-year variation trend of lightning at the grid scale in Shandong Province, the lightning-to-ground flashover analysis method provided in this embodiment includes the following steps S10 to S60:

[0135] Step S10: Obtain wide-area lightning monitoring data for Shandong Province from 2008 to 2022, a total of 15 years (M=15).

[0136] Step S20: Perform spatiotemporal interval processing on the lightning monitoring data of year i to obtain the first matrix of year i. The first matrix of year i records the spatial distribution relationship between longitude and latitude used to represent the thunderstorm hours of year i, i = 1, 2, ..., M. Proceed to step S30.

[0137] For example, the discrete lightning monitoring data of 2008 (with a time resolution of microseconds and a spatial resolution of hundreds of meters) is denoted as the sixth matrix A1 of 2008. In 2008, a total of 813,201 wide-area lightning monitoring data were monitored in Shandong Province, so the size of matrix A1 is 3 columns and 813,201 rows.

[0138] The data in matrix A1 was spatiotemporally processed with a spatial resolution of 10 km and a time resolution of 1 hour to obtain the seventh matrix B1 for 2008. The size remains 3 columns and 813,201 rows, with each column containing longitude grid segments, latitude grid segments, and time (year-month-day-hour) data from lightning monitoring records.

[0139] The 813201 rows of data in B1 are examined row by row. For rows that appear multiple times, only the first occurrence of the row is retained, resulting in the ninth matrix C1 with 3 columns and 82955 rows.

[0140] Next, examine the longitude and latitude columns in the ninth matrix C1 as a whole. Sum the number of times the lightning monitoring data in the ninth matrix C1 appears in the same longitude and latitude grid segments to obtain the lightning duration values ​​in different longitude and latitude grid segments. Establish the eighth matrix D1 for 2008. The three columns of data in D1 are the longitude grid segment, the latitude grid segment, and the lightning duration in that longitude and latitude grid segment.

[0141] Step S30: Calculate the second matrix based on the first matrix from the first year to the Mth year. The second matrix records data representing the spatial distribution relationship between longitude and latitude of lightning hours from the first year to the Mth year. Proceed to step S40.

[0142] A two-dimensional spatial matrix E1 (lats=44, lons=87) is established, where lats=44 and lons=87 are the latitudinal and meridional grid numbers after Shandong Province is gridded with a spatial resolution of 10km, respectively. Matrix D1 is assigned to E1 according to the latitude and longitude interval index. Thus, E1 is the latitude-longitude spatial distribution matrix of thunderstorm hours in 2008 (the first matrix), used to represent the spatial distribution relationship between the longitude and latitude of thunderstorm hours in 2008.

[0143] A three-dimensional empty matrix F(44,87,15) is constructed, and the values ​​of the first matrix for the 15 years from 2008 to 2022 are assigned to F(44,87,i). Finally, matrix F is obtained, which is the 15-year longitude-latitude spatial distribution matrix of lightning hours, i.e., the second matrix.

[0144] Step S40: Calculate the forest slope for all spatial grids in matrix F(44,87,15) to obtain the third matrix. This specifically includes the following steps:

[0145] For matrix F(44,87,15), taking the third dimension data F(1,1,15) of the first spatial grid (1,1) gives the 15-year thunderstorm hour sequence of that grid, with a length of 15.

[0146] For the time series F(1,1,15), the Sen slope is calculated using the Tilsen estimation method. The steps of the Tilsen estimation method are as follows:

[0147] For 15 data points in the F(1,1,15) time series, denoted as x, calculate the slope of all pairs of points, and then find the median of all slopes. This median is the estimated slope β obtained using the Tilson estimation method. The formula for calculating β is as follows:

[0148]

[0149] Performing the above operation on all spatial grids in matrix F yields the Sen slopes for all spatial grids, forming a new matrix G(44,87), see [link to matrix F]. Figure 1044 and 87 represent the latitudinal and meridional grid numbers of the investigated lightning spatial region after gridding with a spatial resolution of 10 km, respectively. β greater than 0.0005 indicates that the E(1,1,15) time series shows an upward trend, and is reclassified as 1; β less than 0.0005 indicates that the E(1,1,15) time series shows a downward trend, and is reclassified as -1; β between -0.0005 and 0.0005 indicates that the 05 time series is relatively stable, and is reclassified as 0.

[0150] Step S50: Test the significance of the slope of all spatial grids to obtain the fourth matrix. This specifically includes the following steps:

[0151] For a time series F(1,1,15), denoted as x, the significance of the result is calculated using the Mankendall test. The Mankendall test statistic (S) is defined as follows:

[0152]

[0153] The sign function sgn is calculated as follows:

[0154]

[0155] The variance of S, Var(S), is calculated using the following formula:

[0156]

[0157] When a tie group exists in sequence x, meaning there are multiple identical values ​​in the data sequence, Var(S) is calculated as follows:

[0158]

[0159] Here, p represents the number of tie groups in sequence x, and t i This represents the number of data items in the i-th tie group.

[0160] Specifically, x = [12, 56, 23, 12, 67, 45, 56, 56, 10, 33, 42, 26, 38, 18, 27], has two tiegroups, p = 2, namely tie group {12}, t1 = 2; and tie group {56}, t1 = 3.

[0161] The standardized MK test statistic Z is calculated as follows:

[0162]

[0163] The corresponding α value can be obtained by looking up a table. When the absolute value of Z is greater than 1.96 (i.e., α is less than 0.05), it means that the trend has passed the significance test at a significance level of α of 0.05, and the null hypothesis that the series has no trend is rejected.

[0164] Performing the above operations on all spatial grids in matrix F yields the significance level α of the MK test for all spatial grids, forming a new matrix H(44,87), see [link to matrix F]. Figure 11 When α is less than 0.05, there is a significant trend, and the reclassification is 2; when α is greater than or equal to 0.05, there is no significant trend, and the reclassification is 1.

[0165] Multiplying matrices G and H yields the trend change level classification matrix I. The trend and significance of the values ​​on each grid in matrix H are as follows: -2 significantly decreasing, -1 decreasing but not significant, 0 stable, 1 increasing but not significant, 2 significantly increasing. This allows us to obtain the multi-year trend and significance of lightning variations at the grid scale within Shandong Province. (See...) Figure 12 This shows that over the past 15 years, the number of lightning hours in most parts of northern Shandong has been on the rise, with some areas showing a more significant upward trend, while the number of lightning hours in most parts of southern Shandong has been on the decline.

[0166] Example 3

[0167] like Figure 9 As shown, this application embodiment also provides a terminal device 11, including: at least one processor 221 ( Figure 9 The diagram shows only one processor, memory 222, and computer program 223 stored in memory 222 and executable on at least one processor 221. When processor 221 executes computer program 223, it implements the steps in the various method embodiments described above.

[0168] In applications, terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that... Figure 9 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0169] In applications, the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0170] In applications, the memory may be an internal storage unit of the terminal device in some embodiments, such as the hard drive or RAM of the terminal device. In other embodiments, the memory may be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the terminal device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of a computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0171] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0173] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, it implements the steps in the above-described method embodiments.

[0174] Example 4

[0175] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0176] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0178] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0179] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0180] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0181] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0182] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application, and should all be included within the protection scope of this application.

Claims

1. A method of lightning ground flash analysis, characterized by, The method comprises the following steps: obtaining lightning monitoring data of M years; performing space-time interval gridding processing on the lightning monitoring data of the i-th year in sequence at a preset space interval resolution and a preset time interval resolution to obtain a first matrix of the first year to the M-th year, the first matrix of the i-th year recording data for representing a spatial distribution relationship between longitude and latitude of thunderstorm hours of the i-th year, i = 1, 2, …, M; calculating a second matrix based on the first matrix of the first year to the M-th year, the second matrix recording data for representing a spatial distribution relationship between longitude and latitude of lightning hours of the first year to the M-th year; calculating a Sen slope for all spatial grids in the second matrix, and constructing a third matrix according to the Sen slope; checking the significance of the Sen slope of all spatial grids to obtain a significance value, and constructing a fourth matrix according to the significance value; multiplying the third matrix and the fourth matrix to obtain a fifth matrix, the fifth matrix recording data for reflecting a trend change level of lightning hours in a grid scale in M years.

2. The lightning ground flash analysis method of claim 1, wherein, The method for calculating the first matrix of the i-th year comprises the following steps: record the lightning monitoring data of the i-th year as a sixth matrix of the i-th year; performing space-time interval gridding processing on the sixth matrix of the i-th year at a preset space interval resolution and a preset time interval resolution to obtain a seventh matrix of the i-th year; calculating lightning hour values in different longitude-latitude grid interval segments according to the seventh matrix of the i-th year, and establishing an eighth matrix of the i-th year, the eighth matrix of the i-th year recording lightning hour values in different longitude-latitude grid interval segments; establishing a two-dimensional empty matrix, and assigning the eighth matrix of the i-th year to the two-dimensional empty matrix according to longitude-latitude interval indexes to obtain the first matrix of the i-th year.

3. The lightning ground flash analysis method of claim 2, wherein, The method for establishing the eighth matrix of the i-th year comprises the following steps: only retaining lightning monitoring data appearing for the first time from lightning monitoring data appearing multiple times in the seventh matrix to obtain a ninth matrix; summing the number of times that lightning monitoring data in the ninth matrix appears in the same longitude grid interval segment and latitude grid interval segment to obtain lightning hour values in different longitude-latitude grid interval segments; establishing the eighth matrix of the i-th year according to the lightning hour values in different longitude-latitude grid interval segments.

4. The lightning ground flash analysis method of claim 1, wherein, The method for calculating the second matrix based on the first matrix of the first year to the M-th year comprises the following steps: establishing a three-dimensional empty matrix, the first and second dimensions of the three-dimensional empty matrix having sizes of the number of latitude grids and the number of longitude grids after lightning space region gridding, and the third dimension having a size of M; assigning the first matrix of the first year to the M-th year to the three-dimensional empty matrix to obtain the second matrix.

5. The lightning ground flash analysis method of claim 4, wherein, The method for calculating the Sen slope for all spatial grids in the second matrix comprises the following steps: taking third dimension data of a j-th spatial grid in the second matrix to obtain a first time sequence of the j-th spatial grid, the first time sequence of the j-th spatial grid being a thunderstorm hour sequence of the j-th spatial grid in M years, j = 1, 2, …, N, N being the number of spatial grids in the second matrix; calculating a Sen slope of the first time sequence of the j-th spatial grid by using a Theil Sen slope estimation method.

6. The lightning ground flash analysis method of claim 5, wherein, The method for calculating the Theil's slope of the first time sequence of the jth spatial grid comprises the following steps: calculating the slope of each two data points in the M data of the first time sequence of the jth spatial grid; taking the median of all the calculated slopes as the Theil's slope of the first time sequence of the jth spatial grid.

7. The lightning ground flash analysis method of claim 4, wherein, The method for testing the significance of the Theil's slope of all spatial grids to obtain the fourth matrix comprises the following steps: taking the third-dimensional data of the jth spatial grid in the second matrix to obtain the first time sequence of the jth spatial grid, the first time sequence being the thunderstorm hour sequence of the spatial grid in M years, j = 1, 2, …, N, N being the number of spatial grids in the second matrix; calculating the significance level matrix of the first time sequence of the jth spatial grid by using the Mann-Kendall test method to obtain the fourth matrix; when the alpha in the significance level matrix is less than the preset value, it is determined that the first time sequence of the jth spatial grid has a significant trend; when the alpha in the significance level matrix is greater than or equal to the preset value, it is determined that the first time sequence of the jth spatial grid has no significant trend.

8. A lightning ground flash analysis apparatus characterized by comprising: comprise: a data acquisition unit configured to acquire lightning monitoring data in M years; a first matrix calculation unit configured to sequentially perform spatial and temporal interval gridding processing on the lightning monitoring data in the ith year at a preset spatial interval resolution and a preset time interval resolution to obtain a first matrix of the first year to the Mth year, the first matrix of the ith year recording data for representing the spatial distribution relationship between longitude and latitude of thunderstorm hours in the ith year, i = 1, 2, …, M; a second matrix calculation unit configured to calculate a second matrix based on the first matrix of the first year to the Mth year, the second matrix recording data for representing the spatial distribution relationship between longitude and latitude of lightning hours in the first year to the Mth year; a third matrix calculation unit configured to calculate Theil's slopes for all spatial grids in the second matrix and construct a third matrix according to the Theil's slopes; a fourth matrix calculation unit configured to test the significance of the Theil's slopes of all spatial grids to obtain a significance value and construct a fourth matrix according to the significance value; a fifth matrix calculation unit configured to multiply the third matrix and the fourth matrix to obtain a fifth matrix, the fifth matrix recording data for reflecting the trend change level of lightning hours in M years in a grid scale.

9. The lightning ground flash analysis apparatus of claim 8, wherein, The first matrix calculation unit is further configured to: record the lightning monitoring data in the ith year as a sixth matrix of the ith year; perform spatial and temporal interval gridding processing on the sixth matrix of the ith year at a preset spatial interval resolution and a preset time interval resolution to obtain a seventh matrix of the ith year; calculate lightning hour values in different longitude and latitude grid interval segments according to the seventh matrix of the ith year, and establish an eighth matrix of the ith year, the eighth matrix of the ith year recording the lightning hour values in different longitude and latitude grid interval segments; establish a two-dimensional empty matrix, and assign the eighth matrix of the ith year to the two-dimensional empty matrix according to longitude and latitude interval indexes to obtain the first matrix of the ith year.

10. The lightning ground flash analysis apparatus of claim 9, wherein, The first matrix calculation unit is further configured to: The lightning monitoring data appearing multiple times in the seventh matrix is retained only once to obtain a ninth matrix; The lightning monitoring data appearing in the same longitude grid interval and latitude grid interval in the ninth matrix is summed to obtain a lightning duration value in different longitude and latitude grid interval segments; An eighth matrix of the i-th year is established according to the lightning duration value in different longitude and latitude grid interval segments.

11. The lightning ground flash analysis apparatus of claim 8, wherein, The second matrix calculation unit is further configured to: A three-dimensional empty matrix is established, the first and second dimensions of the three-dimensional empty matrix have sizes of the latitude and longitude grid numbers of the lightning space region after being gridded, and the third dimension has a size of M; The first matrix of the first year to the M-th year is assigned to the three-dimensional empty matrix to obtain the second matrix.

12. The lightning ground flash analysis apparatus of claim 11, wherein, The third matrix calculation unit is further configured to: The third dimension data of the j-th space grid in the second matrix is taken to obtain a first time sequence of the j-th space grid, the first time sequence of the j-th space grid being a thunderstorm duration sequence of the j-th space grid in M years, j = 1, 2, …, N, N being the number of space grids in the second matrix; The Theil slope of the first time sequence of the j-th space grid is calculated by using the Theil slope estimation method.

13. The lightning ground flash analysis apparatus of claim 12, wherein, The method for calculating the Theil slope of the first time sequence of the j-th space grid by using the Theil slope estimation method comprises: The slope of each two data points in the M data of the first time sequence of the j-th space grid is calculated; The median of all the calculated slopes is taken as the Theil slope of the first time sequence of the j-th space grid.

14. The cloud-to-ground lightning analysis apparatus of claim 11, wherein, The method for testing the significance of the Theil slope of all the space grids to obtain a fourth matrix comprises: The third dimension data of the j-th space grid in the second matrix is taken to obtain a first time sequence of the j-th space grid, the first time sequence being a thunderstorm duration sequence of the space grid in M years, j = 1, 2, …, N, N being the number of space grids in the second matrix; The significance level matrix of the first time sequence of the j-th space grid is calculated by using the Mann-Kendall test method to obtain the fourth matrix; When the significance level in the significance level matrix is less than a preset value, it is determined that the first time sequence of the j-th space grid has a significant trend; When the significance level in the significance level matrix is greater than or equal to the preset value, it is determined that the first time sequence of the j-th space grid has no significant trend.

15. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the lightning ground flash analysis method according to any one of claims 1 to 7. 16.A computer readable storage medium, storing a computer program, and the computer program comprises the following steps: The computer program is executed by the processor to realize the steps of the lightning ground flash analysis method according to any one of claims 1 to 7.

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