Method and device for objectively dividing meteorological event intensity grade, and storage medium

Abstract

The invention discloses a method and device for objectively dividing meteorological event intensity grades and a storage medium, and the method comprises the steps: carrying out the sorting processing of a meteorological event data set of a to-be-divided region, obtaining a linearized data sequence, endowing each data sample of the data sequence with a corresponding serial number, and obtaining the serial number of each data sample; establishing a fitting model of the data sequence by adopting a least square method and taking the serial number of each data sample as an independent variable, and substituting the serial number of each data sample of the data sequence into the fitting model for calculation to obtain a fitting sequence; calculating a difference value between each data sample of the data sequence and each data sample of the fitting sequence to obtain a difference sequence; analyzing a data sample of the difference sequence, and determining an intersection point set; and dividing the meteorological event data set according to the intersection point set to obtain a series of intervals of meteorological event intensity levels. The method can objectively analyze the internal statistical law and sudden change characteristics of the data from the data, and achieves the objective division of the meteorological event intensity grade.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meteorological monitoring and evaluation and meteorological risk assessment, and relates to, but is not limited to, a method, device and storage medium for objectively dividing meteorological event intensity levels. BACKGROUND

[0002] Human production and economic activities are closely related to weather and climate, and different meteorological data have significant differences in the impact on human production and economic activities. Normalized meteorological fluctuations are usually within the range of social adaptation and within the range of ecological system regulation; however, when the intensity of a specific meteorological event reaches or exceeds a certain critical degree, it may break through the defense capacity of the disaster-bearing body, causing significant impact on social stability, economic operation and ecological environment, and even disastrous consequences. Therefore, how to scientifically and accurately identify and divide the intensity levels of meteorological events has become a key link and a preliminary basis for effectively utilizing meteorological resources, predicting the pros and cons of events, and formulating fine disaster prevention and mitigation strategies on this basis. SUMMARY

[0003] To solve the above problems of the prior art, the embodiments of the present application provide a method, device and storage medium for objectively dividing meteorological event intensity levels, which are designed to objectively and stably divide meteorological event intensity levels by establishing a fitting model and difference analysis from the data itself.

[0004] The technical solution of the embodiments of the present application is as follows: In a first aspect, the embodiments of the present application provide a method for objectively dividing meteorological event intensity levels, which comprises: sorting meteorological event data sets of a region to be divided to obtain a linearized data sequence, and assigning corresponding serial numbers to each data sample of the data sequence to obtain serial numbers of the data samples; using the least square method, taking the serial numbers of the data samples as independent variables, establishing a fitting model of the data sequence, and substituting the serial numbers of the data samples into the fitting model for calculation to obtain a fitting sequence; calculating the difference between each data sample of the data sequence and the corresponding data sample of the fitting sequence to obtain a difference sequence; performing symbol analysis on the data samples of the difference sequence to determine a preliminary intersection point set, and performing stability analysis on the preliminary intersection point set to obtain a final intersection point set; and dividing the meteorological event data sets according to the final intersection point set to obtain a series of intervals of the meteorological event intensity levels of the region to be divided.

[0005] In some embodiments, before the weather event data set of the to-be-divided region is sorted to obtain the linearized data sequence, the method further comprises: obtaining an initial weather data set of the to-be-divided region; if a skewness coefficient of the initial weather data set is greater than a preset threshold, performing a normalization transformation on the initial weather data set to obtain the weather event data set; and if the skewness coefficient is less than or equal to the preset threshold, directly taking the initial weather data set as the weather event data set.

[0006] In some embodiments, the symbol analysis on the data samples of the difference sequence to determine the preliminary intersection point set comprises: for each two adjacent data samples of the difference sequence, performing symbol analysis on the two data samples, and if the two data samples are of different signs, taking the larger sequence number corresponding to the two data samples as a preliminary intersection point; and integrating the obtained preliminary intersection points to obtain the preliminary intersection point set.

[0007] In some embodiments, the stability analysis on the preliminary intersection point set to obtain the final intersection point set comprises: for each intersection point of the preliminary intersection point set, performing symbol analysis on the data samples corresponding to a previous sequence number and a next sequence number of the intersection point in the difference sequence, and if the data samples are of different signs, retaining the intersection point to obtain a first intersection point set; taking a first intersection point of the first intersection point set as a starting point, for each two adjacent intersection points of the first intersection point set, dynamically counting the number of data samples between the two intersection points to obtain a data sample number between the two intersection points; integrating the obtained data sample numbers to obtain a sample data set; for each data in the sample data set, determining an unstable intersection point based on a comparison result between the data and a sample threshold to obtain a second intersection point set; wherein the sample threshold is ten percent of the total number of data samples of the weather event data set; taking a first intersection point of the second intersection point set as a starting point, for each two adjacent intersection points of the second intersection point set, performing symbol analysis on the data samples of the difference sequence corresponding to the two intersection points, and if the data samples are of different signs, retaining the two intersection points; and if the data samples are of the same sign, removing either of the two intersection points to obtain a third intersection point set; taking a first intersection point of the third intersection point set as a starting point, for each two adjacent intersection points of the third intersection point set, performing symbol analysis on the data sample corresponding to a previous sequence number of the two intersection points in the difference sequence, and if the data sample is of a different sign, retaining the two intersection points; and if the data sample is of the same sign, removing either of the two intersection points to obtain the final intersection point set.

[0008] In some embodiments, the determination of the unstable intersection point based on the comparison result between the data and the sample threshold for each data in the sample data set to obtain the second intersection point set comprises: For each data in the sample set, if the data is greater than the sample threshold, two intersection points corresponding to the data are retained; if the data is less than or equal to the sample threshold, a larger intersection point of two intersection points corresponding to the data is determined as the unstable intersection point, and the unstable intersection point is removed, to obtain the second intersection point set.

[0009] In some embodiments, the dividing the meteorological event data set according to the final intersection point set to obtain a series of intervals of the meteorological event intensity level of the to-be-divided region comprises: sorting the final intersection point set according to the sorting manner of the data sequence to obtain a sorted intersection point set; in a case where the meteorological event data set is the initial meteorological data set, for each intersection point of the sorted intersection point set, a condition value of a division interval is obtained according to data samples of the meteorological event data set corresponding to the intersection point, and a series of intervals of the meteorological event intensity level of the to-be-divided region is determined according to the condition value; in a case where the meteorological event data set is the initial meteorological data set after normalization transformation, firstly, the initial meteorological data set after normalization transformation is subjected to inverse normalization transformation to obtain the meteorological event data set; secondly, for each intersection point of the sorted intersection point set, a condition value of a division interval is obtained according to data samples of the meteorological event data set corresponding to the intersection point; finally, a series of intervals of the meteorological event intensity level of the to-be-divided region is determined according to the condition value.

[0010] In some embodiments, after the dividing the meteorological event data set according to the final intersection point set to obtain a series of intervals of the meteorological event intensity level of the to-be-divided region, the method further comprises: For each of two adjacent intervals of the series of intervals of the meteorological event intensity level, a test statistic of the two intervals is obtained according to data samples corresponding to the two intervals in the data sequence and a calculation formula of the test statistic of the two intervals. The calculation formula of the test statistic of the two intervals is: ; wherein, is the test statistic of the two intervals; and are numbers of data samples corresponding to the two intervals in the data sequence, respectively; and are average values of data samples corresponding to the two intervals in the data sequence, respectively. and a variance of data samples corresponding to the two intervals in the data sequence respectively; determine a U test result of the two intervals according to a comparison result of an absolute value of the test statistic and a check threshold value, wherein the check threshold value is obtained by looking up a table according to a preset significance level and a value of the test statistic.

[0011] In some embodiments, the determining the U test result of the two intervals according to the comparison result of the absolute value of the test statistic and the check threshold value comprises: if the absolute value of the test statistic is greater than or equal to the check threshold value, the two intervals corresponding to the test statistic pass the U test; and if the absolute value of the test statistic is less than the check threshold value, the two intervals corresponding to the test statistic do not pass the U test.

[0012] In a second aspect, the embodiments of the present application provide a computer device, comprising a memory and a processor, the memory stores a computer program, and the computer program performs the method for objectively dividing the intensity grades of meteorological events when running on the processor.

[0013] In a third aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and the computer program is loaded and executed by a processor to implement the method for objectively dividing the intensity grades of meteorological events.

[0014] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects: ​The embodiment of the present application provides a method, device and storage medium for objectively dividing the intensity level of a meteorological event. In the method, first, meteorological event data sets of a region to be divided are sorted to obtain linearized data sequences, and each data sample of the data sequences is assigned a corresponding serial number to obtain the serial number of each data sample. Then, a fitting model of the data sequences is established by taking the serial number of each data sample as an independent variable by using the least square method, and the serial number of each data sample is substituted into the fitting model for calculation to obtain a fitting sequence. Then, the difference between each data sample of the data sequences and the corresponding data sample of the fitting sequence is calculated to obtain a difference sequence. Subsequently, the data samples of the difference sequence are subjected to symbol analysis to determine a preliminary intersection point set, and the preliminary intersection point set is subjected to stability analysis to obtain a final intersection point set. Finally, the meteorological event data sets are divided according to the final intersection point set to obtain a series of intervals of the intensity level of the meteorological event of the region to be divided. In this way, the linear fitting model of the meteorological data sequence is established, and the difference sequence between the actual meteorological data and the fitting value is analyzed to identify the key intersection points. After the stability of the intersection points is optimized, the intensity level of the meteorological event is objectively divided according to the intersection points. In this way, the present application can objectively analyze the internal statistical rules and mutation characteristics of the data itself to realize the objective division of the intensity level of the meteorological event. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings. Figure 1 A flowchart of a method for objectively dividing the intensity level of a meteorological event provided by the embodiment of the present application; Figure 2 A flowchart of a method for objectively dividing the intensity level of a meteorological event provided by the embodiment of the present application applied to practical application; Figure 3 A structural diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The following embodiments are used to describe the present application, but not to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of the present application.

[0017] In the following description, “some embodiments” are related to a subset of all possible embodiments, but it can be understood that “some embodiments” can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0018] It should be noted that the terms “first\second\third” involved in the embodiments of the present application are only to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that “first\second\third” can be interchanged with a specific order or sequence as allowed, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0019] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as generally understood by those skilled in the art to which the embodiments of the present application belong. It should also be understood that terms such as those defined in general dictionaries should be understood as having meanings consistent with those in the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as such.

[0020] Embodiment one: Please refer to Figure 1 , Figure 1 The flowchart of a method for objectively dividing the intensity level of a meteorological event provided by the embodiments of the present application, the method comprises: Step 101, sorting the meteorological event data set of the region to be divided to obtain a linearized data sequence, and assigning a corresponding serial number to each data sample of the data sequence to obtain the serial number of each data sample.

[0021] In some embodiments, the meteorological event data set of the region to be divided is sorted, that is, the data samples of the meteorological event data set of the region to be divided can be sorted from large to small to obtain a linearized data sequence; or the data samples of the meteorological event data set of the region to be divided can be sorted from small to large to obtain a linearized data sequence; the actual situation can be selected, which is not limited here.

[0022] It should be noted that the data samples of the meteorological event data set can be temperature data, rainfall data, and Camellia oleifera meteorological drought event index, etc. A serial number is assigned to each data sample of the data sequence to obtain the serial number of each data sample, and the serial number here is Arabic data: 1, 2, 3, 4, and the like.

[0023] In some embodiments, the above-mentioned acquisition of the meteorological event data set can be realized by the following process: First, an initial meteorological data set of the region to be divided is acquired.

[0024] In some embodiments, the initial meteorological data set of the region to be divided can be a single meteorological element, a combination of multiple meteorological elements, an applied meteorological event index, or a newly defined meteorological event index, which is not limited here.

[0025] Then, if the skewness coefficient of the initial meteorological data set is greater than a preset threshold, the initial meteorological data set is subjected to a normal transformation process to obtain the meteorological event data set.

[0026] Or, if the skewness coefficient is less than or equal to the preset threshold, the initial meteorological data set is directly taken as the meteorological event data set.

[0027] In some embodiments, the skewness coefficient of the initial meteorological data set is obtained according to the acquired initial meteorological data set of the region to be divided and a skewness coefficient calculation formula.

[0028] The skewness coefficient calculation formula is: Formula (1); Formula (2); Formula (3); Wherein, is the skewness coefficient of the initial meteorological data set; is the total number of data samples of the initial meteorological data set; is the i-th data sample of the initial meteorological data set; is the average value of the data samples of the initial meteorological data set; is the standard deviation of the data samples of the initial meteorological data set.

[0029] It should be noted that the preset threshold is generally 1, which can be adjusted according to actual conditions, and is not limited here. The normal transformation process is a data preprocessing method of converting non-normal distribution data into approximately normal distribution data through mathematical transformation.

[0030] Step 102: Using the least squares method, a fitting model for the data sequence is established with the index of each data sample as the independent variable. The index of each data sample is then substituted into the fitting model for calculation to obtain the fitted sequence.

[0031] In some embodiments, the least squares method is used, with the index of each data sample in the data sequence (e.g., , Using (i = 1, 2, 3, ..., n) as independent variables, a fitting model for the data sequence is established. The sequence number of each data sample is then substituted into the fitting model for calculation, yielding the fitted sequence. The formula used in this process is as follows: Formula (4); Formula (5); Formula (6); Formula (7); Formula (8); Formula (9); Formula (10); Formula (11); in, For the fitted sequence of the th One data sample; For the linearized data sequence, the first... One data sample; For the linearized data sequence, the first... One serial number; The slope is the fitted slope; The intercept of the fitted model; This represents the sequence number of each data sample in the linearized data sequence and the covariance of the data samples in the linearized data sequence. The variance of the index of each data sample in the linearized data sequence; The variance of the data samples in the linearized data sequence; This is the average of the indexes of each data sample in the linearized data sequence; This represents the average value of the data samples in the linearized data sequence. This represents the total number of data samples in the linearized data sequence.

[0032] It should be noted that, here, the intersection point of the linearized data sequence and the fitting sequence is found, because the slope of the fitting sequence is fixed, so if there is an intersection point, it means that the slope of the linearized data sequence has changed, and the intersection point can be established as the transition point of the linearized data sequence.

[0033] In step 103, the difference between each data sample of the data sequence and the corresponding data sample of the fitting sequence is calculated to obtain a difference sequence.

[0034] In some embodiments, the difference between and can be calculated by using the following formula (12): Formula (12); wherein, is the i-th data sample of the difference sequence. Correspondingly, by analogy, the difference between each data sample of the data sequence, for example, = 1, 2, 3, …, n, and the corresponding data sample of the fitting sequence, for example, = 1, 2, 3, …, n, is calculated to obtain n difference values, and the n difference values are integrated to obtain the difference sequence.

[0035] In step 104, the data samples of the difference sequence are subjected to sign analysis to determine a preliminary intersection point set, and the preliminary intersection point set is subjected to stability analysis to obtain a final intersection point set.

[0036] It should be noted that sign analysis is an analysis of the positive and negative signs of two data. If the two data are the same in sign, they are determined to be of the same sign. If the two data are one positive and one negative, they are determined to be of different signs. By performing sign analysis on the data samples of the difference sequence, the intersection point of the data sequence and the fitting sequence is found. Because the slope of the fitting sequence is fixed, if there is an intersection point between the data sequence and the fitting sequence, it is considered that the slope of the data sequence has changed, and the intersection point is the turning point confirming the change in the slope of the data sequence.

[0037] In some embodiments, the "sign analysis of the data samples of the difference sequence to determine a preliminary intersection point set" in step 104 described above can be realized by the following steps A1 to A2: In step A1, for each adjacent two data samples of the difference sequence, the two data samples are subjected to sign analysis, and if they are of different signs, the larger sequence number corresponding to the two data samples is taken as a preliminary intersection point.

[0038] ​​​​​In some embodiments, for each pair of adjacent data samples in the difference sequence, n-1 sets of adjacent pairs of data samples are obtained, for example... =k (k is...) =a value from 1, 2, 3, ..., n) and =k+1 ;like and If the signs are opposite, then there is an intersection between k and k+1, and k+1 is defined as the initial intersection point; and so on, the sign analysis is performed on two adjacent data samples in n-1 groups to obtain M initial intersection points.

[0039] It should be noted that M is an integer greater than 0.

[0040] Step A2: Integrate the obtained preliminary intersection points to obtain the preliminary intersection point set.

[0041] In some embodiments, the step 104 above, "performing stability analysis on the preliminary intersection set to obtain the final intersection set", can be achieved through the following steps B1 to B6: Step B1: For each intersection point in the preliminary intersection point set, perform sign analysis on the data samples of the preceding and following indices of the intersection point in the difference sequence. If they are opposite signs, retain the intersection point to obtain the first intersection point set.

[0042] In some embodiments, for each intersection point in the initial intersection point set, such as k+1, the data samples corresponding to the preceding index k and the following index k+2 in the difference sequence for intersection point k+1 are... and Perform symbolic analysis, if and If the signs are opposite, then retain the intersection point k+1; if and If the signs are the same, remove intersection point k+1. Repeat this process for the M intersection points in the initial intersection point set to obtain M1 intersection points. Integrate these M1 intersection points to obtain the first intersection point set.

[0043] It should be noted that M1 is an integer greater than 0 and less than or equal to M.

[0044] Step B2: Starting from the first intersection point of the first intersection point set, dynamically count the number of data samples between each pair of adjacent intersection points in the first intersection point set to obtain the number of data samples between the two intersection points.

[0045] In some embodiments, starting from the first intersection point of the first intersection point set, for each adjacent two intersection points of the first intersection point set, a M1-1 group of adjacent two intersection points is obtained. The number of data samples between each group of intersection points is dynamically counted to obtain M1-1 sample numbers.

[0046] Step B3, integrating the obtained data sample numbers to obtain a sample data set.

[0047] Step B4, for each data in the sample data set, based on the comparison result between the data and the sample threshold, determining the unstable intersection point to obtain the second intersection point set.

[0048] Wherein, the sample threshold is ten percent of the total number of data samples of the meteorological event data set.

[0049] In some embodiments, for each data in the sample data set, i.e. M1-1 sample numbers, M1-1 sample numbers are compared with the sample threshold respectively to obtain M1-1 comparison results, and according to the M1-1 comparison results, the unstable intersection point is confirmed one by one to obtain the second intersection point set.

[0050] It should be noted that the sample threshold = , wherein, is the total number of data samples of the meteorological event data set, that is, the numerical value is also equal to the total number of data samples of the initial meteorological data set.

[0051] In some embodiments, the above step B4 can be realized by the following step C1: Step C1, for each data of the sample number set, if the data is greater than the sample threshold, the two intersection points corresponding to the data are retained; if the data is less than or equal to the sample threshold, the larger intersection point of the two intersection points corresponding to the data is determined as the unstable intersection point, and the unstable intersection point is removed to obtain the second intersection point set.

[0052] In some embodiments, for each data in the sample data set, i.e. M1-1 sample numbers. For example, if the mth sample number is 7 (i.e. the number of sample numbers between intersection point k and intersection point k+8), it is greater than the sample threshold, then the intersection point k and the intersection point k+8 corresponding to the mth sample number are retained; if the mth sample number is 7 (i.e. the number of sample numbers between intersection point k and intersection point k+8), it is less than or equal to the sample threshold, then the larger intersection point k+8 corresponding to the mth sample number is determined as the unstable intersection point, and the intersection point k+8 is removed. By analogy, the above analysis is carried out on M1-1 sample numbers to obtain M1-1 comparison results, and according to the M1-1 comparison results, the unstable intersection point is confirmed one by one to obtain the second intersection point set.

[0053] Step B5, starting from the first cross point of the second cross point set, for each adjacent two cross points of the second cross point set, if the signs of the data samples corresponding to the two cross points in the difference sequence are different, the two cross points are retained; if the signs are the same, either of the two cross points is removed, to obtain a third cross point set.

[0054] In some embodiments, starting from the first cross point of the second cross point set, for each adjacent two cross points of the second cross point set, for example, the second cross point set has M2 cross points, there are M2-1 groups of adjacent two cross points, for the data samples corresponding to each group of cross points in the difference sequence (for example: and ), symbol analysis is performed, if and are different, the cross point k and the cross point k+8 are retained; if and are the same, either the cross point k or the cross point k+8 is removed. By analogy, the above analysis is performed on M2-1 groups of adjacent two cross points, to obtain M3 cross points, and the M3 cross points are integrated to obtain a third cross point set.

[0055] It should be noted that M2 is an integer greater than 0 and less than or equal to M1; M3 is an integer greater than 0 and less than or equal to M2.

[0056] Step B6, starting from the first cross point of the third cross point set, for each adjacent two cross points of the third cross point set, symbol analysis is performed on the data samples corresponding to the previous sequence number of the two cross points in the difference sequence, if the signs are different, the two cross points are retained; if the signs are the same, either of the two cross points is removed, to obtain the final cross point set.

[0057] In some embodiments, starting from the first cross point of the third cross point set, for each adjacent two cross points of the third cross point set, the third cross point set has M3 cross points, there are M3-1 groups of adjacent two cross points, for the data samples corresponding to the previous sequence number of each group of cross points in the difference sequence (for example: and ), symbol analysis is performed, if and are different, the cross point k and the cross point k+8 are retained; if and are the same, either the cross point k or the cross point k+8 is removed. By analogy, the above analysis is performed on M3-1 groups of adjacent two cross points, to obtain M4 cross points, and the M4 cross points are integrated to obtain the final cross point set.

[0058] It should be noted that M4 is an integer greater than 0 and less than or equal to M3.

[0059] In step 105, the meteorological event data set is divided according to the final intersection point set to obtain a series of intervals of the meteorological event intensity level of the region to be divided.

[0060] In some embodiments, the final intersection point set is sorted according to the sorting manner of the data sequence, and the meteorological event data set is divided according to the sorted intersection points to obtain a series of intervals of the meteorological event intensity level of the region to be divided.

[0061] In some embodiments, the above step 105 can be implemented by the following step D1 and step D2, or step D1 and step D3: In step D1, the final intersection point set is sorted according to the sorting manner of the data sequence to obtain a sorted intersection point set.

[0062] In some embodiments, the final intersection point set is sorted according to the sorting manner of the data sequence (for example, the sorting manner from small to large) to obtain a sorted intersection point set. Generally, the final intersection point set has 2 to 3 intersection points (generally, 3 intersection points are the most common), and 2 intersection points can be divided into three levels, and 3 intersection points can be divided into four levels. The following description takes the final intersection point set with 3 intersection points as an example for description.

[0063] In step D2, in the case that the meteorological event data set is the initial meteorological data set, for each intersection point of the sorted intersection point set, a condition value of a division interval is obtained according to the data sample of the meteorological event data set corresponding to the intersection point, and a series of intervals of the meteorological event intensity level of the region to be divided is determined according to the condition value.

[0064] In some embodiments, in the case that the meteorological event data set is the initial meteorological data set, for each intersection point of the sorted intersection point set, for example, the sorted intersection point set is {k, k+8, k+12}, the data samples of the meteorological event data set corresponding to the intersection points k, k+8 and k+12 are 、 and , the condition values of the division intervals 、 and are obtained, and the series of intervals of the meteorological event intensity level of the region to be divided is determined according to 、 and , and the series of intervals of the meteorological event intensity level of the region to be divided is as follows in Table 1 from low to high.

[0065] Table 1 Series of intervals of meteorological event intensity level Step D3, in the case that the weather event data set is the initial weather data set after normalization transformation, first, the initial weather data set after normalization transformation is subjected to inverse normalization transformation to obtain the weather event data set; second, for each intersection point of the sorted intersection point set, a condition value of a division interval is obtained according to the data sample of the weather event data set corresponding to the intersection point; finally, the series interval of the weather event intensity level of the region to be divided is determined according to the condition value.

[0066] In some embodiments, the inverse normalization transformation is the inverse process of normalization transformation. Here, after the initial weather data set after normalization transformation is subjected to inverse normalization transformation to obtain the weather event data set, the operation is the same as described above, and is not repeated here.

[0067] The embodiments of the present application provide a method for objectively dividing the intensity level of a weather event. In the execution process of the method, first, the weather event data set of the region to be divided is subjected to sorting processing to obtain a linearized data sequence, and each data sample of the data sequence is assigned a corresponding serial number to obtain the serial number of each data sample. Then, the least squares method is used to establish a fitting model of the data sequence with the serial number of each data sample as the independent variable, and the serial number of each data sample is substituted into the fitting model for calculation to obtain a fitting sequence. Then, the difference between each data sample of the data sequence and the corresponding data sample of the fitting sequence is calculated to obtain a difference sequence. Subsequently, the data samples of the difference sequence are subjected to symbol analysis to determine a preliminary intersection point set, and the preliminary intersection point set is subjected to stability analysis to obtain a final intersection point set. Finally, the weather event data set is divided according to the final intersection point set to obtain a series interval of the intensity level of the weather event of the region to be divided. In this way, by establishing a linear fitting model of the weather data sequence and analyzing the difference sequence between the actual weather data and the fitting value, the key intersection points are identified. After stability optimization of the intersection points, the intensity level of the weather event is objectively divided according to these intersection points. In this way, the embodiments of the present application can objectively analyze the internal statistical law and mutation characteristics of the data itself to achieve the objective division of the intensity level of the weather event.

[0068] In some embodiments, after step 105 is executed, the method further includes steps E1 and E2: Step E1, for each two adjacent intervals of the series interval of the weather event intensity level, the test statistic of the two intervals is obtained according to the data samples corresponding to the two intervals in the data sequence and the calculation formula of the test statistic of the two intervals.

[0069] In some embodiments, the test statistic of the two intervals is calculated according to the following formula: Formula (13); wherein, is the test statistic of the two intervals; and are the number of data samples corresponding to the two intervals in the data sequence, respectively; and are the average values of the data samples corresponding to the two intervals in the data sequence, respectively; and are the variances of the data samples corresponding to the two intervals in the data sequence, respectively.

[0070] In some embodiments, for each pair of adjacent intervals, for example, the intervals in Table 1 above, three pairs of adjacent intervals are obtained, i.e., the first and second intervals, the second and third intervals, and the third and fourth intervals. According to the data samples corresponding to the first and second intervals in the data sequence, and Formula (13) above, the test statistic of the first and second intervals is calculated as According to the data samples corresponding to the second and third intervals in the data sequence, and Formula (13) above, the test statistic of the second and third intervals is calculated as and according to the data samples corresponding to the third and fourth intervals in the data sequence, and Formula (13) above, the test statistic of the third and fourth intervals is calculated as .

[0071] Step E2, determining the U test result of the two intervals according to the comparison result of the absolute value of the test statistic and the check threshold.

[0072] wherein, the check threshold is obtained by looking up the u- table according to the preset significance level.

[0073] In some embodiments, the u- is a numerical table representing the relationship between the significance level and the check threshold; for example: according to the comparison result of and the check threshold, the U test result of the first and second intervals is determined; according to the comparison result of and the check threshold, the U test result of the second and third intervals is determined; according to the comparison result of and the check threshold, the U test result of the third and fourth intervals is determined.

[0074] It should be noted that the check threshold is obtained according to the preset significance level , by looking up the table of u , i.e. according to , by looking up the table of u , i.e. according to , the is taken as the check threshold. Generally, the value is 0.05, which can be adjusted according to the actual situation, and is not limited here.

[0075] In some embodiments, the above step E2 is realized by the following two cases: The first case, if the absolute value of the test statistic is greater than or equal to the check threshold, then the two intervals corresponding to the test statistic pass the U test.

[0076] The second case, if the absolute value of the test statistic is less than the check threshold, then the two intervals corresponding to the test statistic do not pass the U test.

[0077] In some embodiments, if ≥ , then the first and second level intervals pass the U test, indicating that there is a significant difference in the mean of the data samples corresponding to the first and second level intervals in the data sequence, and it can be inferred that there is also a significant difference in the mean of the data samples corresponding to the non-adjacent intervals in the data sequence; if , then the first and second level intervals do not pass the U test, indicating that there is no significant difference in the mean of the data samples corresponding to the first and second level intervals in the data sequence, and it can be inferred that there is also no significant difference in the mean of the data samples corresponding to the non-adjacent intervals in the data sequence.

[0078] In some embodiments, if ≥ , then the second and third level intervals pass the U test, indicating that there is a significant difference in the mean of the data samples corresponding to the second and third level intervals in the data sequence, and it can be inferred that there is also a significant difference in the mean of the data samples corresponding to the non-adjacent intervals in the data sequence; if , then the second and third level intervals do not pass the U test, indicating that there is no significant difference in the mean of the data samples corresponding to the second and third level intervals in the data sequence, and it can be inferred that there is also no significant difference in the mean of the data samples corresponding to the non-adjacent intervals in the data sequence.

[0079] In some embodiments, if ≥ , then the third-level interval and the fourth-level interval pass the U test, indicating that the mean values of the data samples corresponding to the third-level interval and the fourth-level interval in the data sequence have significant differences, and it can be inferred that the mean values of the data samples corresponding to the two intervals and the non-adjacent interval in the data sequence also have significant differences; if , then the third-level interval and the fourth-level interval do not pass the U test, indicating that the mean values of the data samples corresponding to the third-level interval and the fourth-level interval in the data sequence do not have significant differences, and it can be inferred that the mean values of the data samples corresponding to the two intervals and the non-adjacent interval in the data sequence also do not have significant differences.

[0080] The embodiment of the present application discloses a method for objectively dividing the intensity level of a meteorological event, relates to the technical fields of meteorological evaluation, disaster prevention and reduction, meteorological application, and the like, and comprises the following steps: obtaining an initial meteorological data set; calculating a skewness coefficient; if the skewness coefficient is greater than 1, performing a normalization transformation on the initial meteorological data set to obtain a meteorological event data set; if the skewness coefficient is less than or equal to 1, directly taking the initial meteorological data set as the meteorological event data set; performing linearization processing on the meteorological event data set, sorting the data after linearization processing in an increasing or decreasing manner, and assigning each data a corresponding serial number; constructing a fitting sequence (also referred to as a reference sequence) of the data after linearization processing, and calculating to find the intersection points of the data after linearization processing and the fitting sequence; performing stability analysis on the initially selected intersection points, and removing unstable intersection points; based on the intersection points after stability analysis, finding the meteorological event data corresponding to the serial numbers of the data after linearization processing (if the data has been normalized and transformed in the foregoing step, the inverse transformation of the normalization needs to be performed on the data), sorting the intersection points in an increasing or decreasing manner, obtaining the meteorological event intensity level division condition, and dividing the data after linearization processing into intervals according to the intensity level condition; and performing U test on adjacent intervals to determine whether the intervals of the meteorological event intensity level division pass the U test. The technical solution provided by the present application is easy to realize through a computer program, can also be realized through related tool software (for example, excel), has universality, and objectively and quantitatively obtains results, which facilitates further manual analysis.

[0081] The method for objectively dividing the intensity level of a meteorological event provided by the embodiment of the present application can make breakthrough progress in objectively and quantitatively dividing the intensity level of a meteorological event. Through a flowable technical analysis structure, the meteorological event intensity level analysis process is simplified, and automatic division of the intensity level of a meteorological event can be realized.

[0082] Based on the above description, the method for objectively dividing the intensity level of a meteorological event provided by the embodiment of the present application is applied to actual application, and the corresponding steps can be referred to the method shown in FIG. 1, wherein: Figure 2 201. Obtain an initial meteorological data set.

[0083] ​202. Calculate the skewness coefficient g of the initial meteorological data set.

[0084] Specifically, the number of data samples of the initial meteorological data set is counted, and the skewness coefficient g of the initial meteorological data set is obtained according to the counted number of data samples of the initial meteorological data set, each data sample of the initial meteorological data set, and the above formulas (1), (2), and (3). .

[0085] 203. Determine whether g ≤ a preset threshold value?

[0086] When yes, that is, g ≤ the preset threshold value, then perform 204 to take the initial meteorological data set as a meteorological event data set, and perform 206.

[0087] When no, that is, g > the preset threshold value, then perform 205 to perform a normalization transformation on the initial meteorological data set to obtain a meteorological event data set, and perform 206.

[0088] 206. Sort the meteorological event data set to obtain a linearized data sequence, and assign each data sample of the data sequence a corresponding serial number to obtain the serial number of each data sample.

[0089] 207. Use the least squares method to establish a fitting model of the data sequence with the serial number of each data sample as the independent variable.

[0090] Specifically, the serial number of each data sample obtained in 206 is used as the independent variable, and the fitting model of the data sequence is established according to each data sample of the linearized data sequence and the above formulas (4) to (11).

[0091] 208. Substitute the serial number of each data sample of the data sequence into the fitting model to obtain a fitting sequence of the data sequence.

[0092] 209. Calculate the difference between each data sample of the data sequence and the corresponding data sample of the fitting sequence to obtain a difference sequence.

[0093] 210. Cross-point diagnosis to obtain a preliminary cross-point set.

[0094] Specifically, first, for each adjacent two data samples of the difference sequence, the two data samples are subjected to sign analysis, and if they are of different signs, then the larger serial number corresponding to the two data samples is taken as a preliminary cross-point. Then, the obtained preliminary cross-points are integrated to obtain a preliminary cross-point set.

[0095] 211. Stability analysis is performed on the preliminary cross-point set to remove unstable cross-points to obtain a final cross-point set.

[0096] Specifically, firstly, for each intersection point in the preliminary intersection point set, sign analysis is performed on the data samples with the preceding and following indices in the difference sequence corresponding to the intersection point. If they are opposite signs, the intersection point is retained, resulting in the first intersection point set. Secondly, starting from the first intersection point in the first intersection point set, the number of data samples between each pair of adjacent intersection points in the first intersection point set is dynamically counted, resulting in the number of data samples between the two intersection points. Next, the obtained number of data samples is integrated to obtain the sample dataset. Then, for each data point in the sample dataset, if the data is greater than a sample threshold, the two intersection points corresponding to the data are retained; if the data is less than or equal to the sample threshold, the larger intersection point among the two intersection points corresponding to the data is determined. Unstable crossover points are identified and removed to obtain a second set of crossover points. Then, starting from the first crossover point in the second set, for each pair of adjacent crossover points, sign analysis is performed on the data samples of the difference sequence corresponding to the two crossover points. If the signs are opposite, both crossover points are retained; if the signs are the same, either crossover point is removed, resulting in a third set of crossover points. Finally, starting from the first crossover point in the third set, for each pair of adjacent crossover points in the third set, sign analysis is performed on the data samples of the preceding sequence number corresponding to the two crossover points. If the signs are opposite, both crossover points are retained; if the signs are the same, either crossover point is removed, resulting in the final set of crossover points.

[0097] 212. Sort the final set of intersection points according to the sorting method of the data sequence to obtain the sorted set of intersection points.

[0098] 213. Based on the sorted set of intersection points, determine the condition values ​​for dividing the intervals to obtain a series of intervals for the intensity level of meteorological events.

[0099] Specifically, when the meteorological event dataset is the initial meteorological dataset, for each intersection of the sorted intersection set, the condition value for dividing the interval is obtained based on the data sample of the meteorological event dataset corresponding to the intersection point, and the series of intervals of meteorological event intensity level are determined based on the condition value.

[0100] Given that the meteorological event dataset is an initial meteorological dataset after normalization transformation, the following steps are taken: First, the initial meteorological dataset after normalization transformation is subjected to inverse normalization transformation to obtain the meteorological event dataset. Second, for each intersection point in the sorted intersection point set, the condition value for dividing the interval is obtained based on the data sample of the meteorological event dataset corresponding to the intersection point. Finally, the series of intervals for the intensity level of meteorological events are determined based on the condition value.

[0101] After the step 213, the embodiment of the present application can further perform U test on the series intervals of the meteorological event intensity grades, and the specific process is as follows: First, for each adjacent two intervals of the series intervals of the meteorological event intensity grades, the test statistic of the two intervals is obtained according to the data samples corresponding to the two intervals in the data sequence and the formula (13). Then, for the test statistic of each adjacent interval, if the absolute value of the test statistic is greater than or equal to the threshold value, the two adjacent intervals corresponding to the test statistic pass the U test; if the absolute value of the test statistic is less than the threshold value, the two intervals corresponding to the test statistic do not pass the U test.

[0102] The method for objectively dividing meteorological event intensity grades provided by the embodiment of the present application can objectively analyze the internal statistical rules and mutation characteristics of data by establishing a linear fitting model of the meteorological data sequence and analyzing the difference sequence of the actual data and the fitting value to identify the key cross points. After the stability optimization of the cross points, the intensity grades of the meteorological events are finally objectively divided according to the cross points. In this way, the embodiment of the present application can objectively analyze the internal statistical rules and mutation characteristics of data from the data itself, and realize the objective division of the intensity grades of meteorological events.

[0103] Correspondingly, the method for objectively dividing meteorological event intensity grades provided by the embodiment of the present application is applied to specific examples, and the corresponding processes are as follows two examples, in which: Example 1: In the first step, the meteorological drought index of camellia oleifera in region A from 1990 to 2022 is obtained, as shown in the second column of Table 2.

[0104] In the second step, the skewness coefficient of the meteorological drought index of camellia oleifera in region A is calculated, and the skewness coefficient is 0.786.

[0105] In the third step, 0.786 < 1, and the preset threshold value is 1, so the meteorological drought index of camellia oleifera in region A is taken as the meteorological event data set. In the fourth step, the meteorological event data set is sorted to obtain the linearized data sequence, as shown in the third column of Table 2, and each data sample in the third column of Table 2 is assigned a corresponding serial number to obtain the serial number of each data sample, as shown in the fourth column of Table 2.

[0106] In the fifth step, the least square method is used to take each data sample in the fourth column of Table 2 as the independent variable to establish a fitting model of the data in the third column of Table 2, and the fitting model is 0.016400x + 2.423882.

[0107] In the sixth step, the data in the fourth column of Table 2 is substituted into 0.016400x + 2.423882 to obtain the fitting sequence of the data sequence, as shown in the fifth column of Table 2.

[0108] Step 7, calculate the difference between each data sample of the third column data in Table 2 and the corresponding data sample of the fifth column data in Table 2, to obtain the difference sequence, as shown in the sixth column data in Table 2.

[0109] Step 8, cross-point diagnosis, to obtain the preliminary cross-point set: 6, 17, and 31, as shown in the seventh column data in Table 2.

[0110] Step 9, stability analysis of the preliminary cross-point set: 6, 17, and 31, to remove unstable cross-points, to obtain the final cross-point set: 6, 17, and 31, as shown in the eighth column data in Table 2.

[0111] Step 10, sort the final cross-point set: 6, 17, and 31, to obtain the sorted cross-point set: 6, 17, 31.

[0112] Step 11, determine the condition values 2.5678, 2.6974, and 2.9567 for dividing intervals according to the sorted cross-point set: 6, 17, 31, as shown in the ninth column data in Table 2, to obtain the series of intervals for the meteorological event intensity level, as shown in Table 3.

[0113] Table 2 Process data for objectively dividing the intensity level of the camellia meteorological drought index in region A Table 3 Series of intervals for objectively dividing the intensity level of the camellia meteorological drought index in region A Step 12, calculate the test statistics for the first and second level intervals, the second and third level intervals, and the third and fourth level intervals, respectively, as shown in the third column data U in Table 4.

[0114] Table 4 Test statistics for the series of intervals for objectively dividing the intensity level of the camellia meteorological drought index in region A Step 13, Take the value 0.05, pass the u- Table lookup to obtain 1.96, =2.9020 is greater than 1.96, so the first and second level intervals pass the U test; =2.0279 is greater than 1.96, so the second and third level intervals pass the U test; =2.7110 is greater than 1.96, so the third and fourth level intervals pass the U test.

[0115] Example 2: Step 1, obtain the camellia meteorological drought index describing region B from 1990 to 2022, as shown in the second column data in Table 5.

[0116] Second step, calculate the skewness coefficient of the meteorological drought index of oil tea in B area from 1990 to 2022, and get the skewness coefficient of 0.596.

[0117] Third step, 0.596 < 1, here the preset threshold is 1, then the meteorological drought index of oil tea in B area from 1990 to 2022 is taken as the meteorological event data set.

[0118] Fourth step, sort the meteorological event data set to get the linearized data sequence, such as the data in the third column of Table 5, and assign the corresponding serial number to each data sample of the data in the third column of Table 5 to get the serial number of each data sample, such as the data in the fourth column of Table 5.

[0119] Fifth step, using the least square method, taking each data sample of the data in the fourth column of Table 5 as the independent variable, a fitting model of the data in the third column of Table 5 is established, and the fitting model is 0.014880x + 2.441388.

[0120] Sixth step, substitute the data in the fourth column of Table 5 into 0.014880x + 2.441388 to get the fitting sequence of the data sequence, such as the data in the fifth column of Table 5.

[0121] Seventh step, calculate the difference between each data sample of the data in the third column of Table 5 and the corresponding data sample of the data in the fifth column of Table 5 to get the difference sequence, such as the data in the sixth column of Table 5.

[0122] Eighth step, cross point diagnosis, get the preliminary cross point set: 2, 3, 6, 17, 20, 21 and 31, such as the data in the seventh column of Table 5.

[0123] Table 5 Process data for objectively dividing the intensity level of the meteorological drought index of oil tea in B area from 1990 to 2022 Table 6 is a series of intervals for objectively dividing the intensity level of the meteorological drought index of oil tea in B area from 1990 to 2022 Ninth step, stability analysis is performed on the preliminary cross point set: 2, 3, 6, 17, 20, 21 and 31, and unstable cross points are removed to get the final cross point set: 6, 17 and 31, such as the data in the eighth column of Table 5.

[0124] Tenth step, sort the final cross point set: 6, 17 and 31 to get the sorted cross point set: 6, 17, 31.

[0125] The tenth step is to determine the condition values 2.53067, 2.69435, 2.90266 of the division interval according to the sorted intersection set: 6, 17, 31, as shown in the ninth column of Table 5, to obtain a series of intervals of the meteorological event intensity level, as shown in Table 6.

[0126] The twelfth step is to calculate the test statistics of the first and second intervals, the second and third intervals, and the third and fourth intervals, respectively, as shown in the third column of Table 7.

[0127] Table 7 is the test statistics of the series of intervals of the meteorological drought index intensity level of the Camellia oleifera in the B region from 1990 to 2022 The thirteenth step, The value is 0.05, and the U- Table is looked up to obtain 1.96, =2.6721 is greater than 1.96, so the first and second intervals pass the U test; =2.0841 is greater than 1.96, so the second and third intervals pass the U test; =2.1946 is greater than 1.96, so the third and fourth intervals pass the U test.

[0128] Example two: Please refer to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device provided by the embodiment of the application. The electronic device 300 comprises a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 complete mutual communication through the communication bus 304; The memory 303 is configured to store a computer program. The processor 301 is configured to execute the computer program to implement the method steps for objectively dividing the meteorological event intensity level.

[0129] The electronic device provided by the embodiment of the application can execute the above-mentioned method embodiments, and the implementation principles and technical effects are similar, which will not be described here.

[0130] Example three: Another embodiment of the application provides a computer readable storage medium having a computer program stored thereon, and the above-mentioned computer program is executed by a processor to implement the above-mentioned method embodiments, and the implementation principles and technical effects are similar, which will not be described here.

[0131] It should be understood that every feature, structure, or characteristic described herein is within a preferred embodiment of the present application. It should be noted that the foregoing embodiments are merely exemplary and are not to be construed as limiting the present application. It should also be noted that features described in the foregoing relate to both structural and method aspects of the application. Accordingly, the terminology in use has a multi-use effect: to the extent there is structural correspondence between a feature described herein and terminology conventionally used to describe corresponding structure in the art, that correspondence is intended. To the extent terminology is used in a manner other than that conventionally used to describe corresponding structure in the art, that terminology is intended to refer to that which is described herein, whether or not there is a corresponding structure in the art to which that terminology conventionally applies.

[0132] It should be noted that, as used in this document, the terms "comprises" or "comprising," or "includes" or "including" or "has" or "having" or "contains" or "containing" or variants thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless otherwise specified, terms of degree such as "substantially", "approximately", and "comprises substantially" or any other similar term are used on a meaning of allowing for a certain imprecision in a recited value or characteristic. Unless otherwise specified, such an imprecision can also apply to a claim. .

[0133] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The embodiments described above are merely exemplary, and the unit division is only a logical function division, and other division manners can be adopted during actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling, direct coupling, or communication connection between the components can be indirect coupling or communication connection through some interface, device, or unit, and can be electrical, mechanical, or in other forms.

[0134] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units; they can be located in one place, or distributed on a plurality of network units; and some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0135] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be separately taken as one unit, or two or more units can be integrated in one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software function unit.

[0136] Alternatively, the integrated unit of the present application, if realized in the form of software function module and sold or used as an independent product, can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing the equipment automatic test line to execute the methods described in each embodiment of the present application. The foregoing storage medium includes mobile storage device, ROM, magnetic disc or optical disc and various storage medium capable of storing program codes.

[0137] The methods disclosed in several method embodiments of the present application can be combined arbitrarily to obtain new method embodiments without conflict.

[0138] The features disclosed in several method or device embodiments of the present application can be combined arbitrarily to obtain new method embodiments or device embodiments without conflict.

[0139] The above is only the implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application.

Claims

1. A method for objectively classifying the intensity levels of meteorological events, characterized in that, The method includes: The meteorological event dataset of the region to be divided is sorted to obtain a linearized data sequence, and each data sample in the data sequence is assigned a corresponding number to obtain the number of each data sample. Using the least squares method, a fitting model for the data sequence is established with the index of each data sample as the independent variable. The index of each data sample is then substituted into the fitting model for calculation to obtain the fitted sequence. Calculate the difference between each data sample in the data sequence and the corresponding data sample in the fitted sequence to obtain the difference sequence; A sign analysis is performed on the data samples of the difference sequence to determine the preliminary crossover point set, and a stability analysis is performed on the preliminary crossover point set to obtain the final crossover point set; Based on the final set of intersections, the meteorological event dataset is divided to obtain a series of intervals for the intensity level of the meteorological events in the region to be divided.

2. The method for objectively classifying the intensity level of meteorological events according to claim 1, characterized in that, Before sorting the meteorological event dataset of the region to be divided to obtain the linearized data sequence, the method further includes: Obtain the initial meteorological dataset for the region to be divided; If the skewness coefficient of the initial meteorological dataset is greater than a preset threshold, then the initial meteorological dataset is subjected to normalization transformation to obtain the meteorological event dataset. If the skewness coefficient is less than or equal to the preset threshold, then the initial meteorological dataset is directly used as the meteorological event dataset.

3. The method for objectively classifying the intensity level of meteorological events according to claim 1, characterized in that, The step of performing sign analysis on the data samples of the difference sequence to determine the preliminary crossover point set includes: For each pair of adjacent data samples in the difference sequence, sign analysis is performed on the two data samples. If they have opposite signs, the larger sequence number corresponding to the two data samples is taken as the initial crossover point. The initial intersection points are integrated to obtain the initial intersection point set.

4. The method for objectively classifying the intensity level of meteorological events according to claim 1, characterized in that, The process of performing stability analysis on the preliminary set of intersections to obtain the final set of intersections includes: For each intersection point in the preliminary intersection point set, sign analysis is performed on the data samples of the preceding and following indices of the intersection point in the difference sequence. If they are opposite signs, the intersection point is retained to obtain the first intersection point set. Starting from the first intersection point of the first intersection point set, for each pair of adjacent intersection points in the first intersection point set, the number of data samples between the two intersection points is dynamically counted to obtain the number of data samples between the two intersection points. The obtained data samples are integrated to obtain a sample dataset; For each data point in the sample dataset, unstable crossover points are determined based on the comparison between the data point and a sample threshold, resulting in a second set of crossover points; wherein the sample threshold is 10% of the total number of data samples in the meteorological event dataset. Starting from the first intersection point of the second intersection point set, for each pair of adjacent intersection points in the second intersection point set, sign analysis is performed on the data samples of the difference sequence corresponding to the two intersection points. If they are opposite signs, the two intersection points are retained; if they are the same sign, either of the two intersection points is removed to obtain the third intersection point set. Starting from the first intersection point of the third intersection point set, for each pair of adjacent intersection points in the third intersection point set, perform sign analysis on the data sample corresponding to the previous index in the difference sequence. If they are different signs, retain the two intersection points; if they are the same sign, remove either of the two intersection points to obtain the final intersection point set.

5. The method for objectively classifying the intensity level of meteorological events according to claim 4, characterized in that, For each data point in the sample dataset, based on the comparison result between the data point and a sample threshold, unstable crossover points are determined to obtain a second crossover point set, including: For each data point in the sample set, if the data point is greater than the sample threshold, the two intersection points corresponding to the data point are retained; if the data point is less than or equal to the sample threshold, the larger intersection point among the two intersection points corresponding to the data point is determined as the unstable intersection point, and the unstable intersection point is removed to obtain the second intersection point set.

6. The method for objectively classifying the intensity level of meteorological events according to claim 2, characterized in that, The step of dividing the meteorological event dataset based on the final intersection point set to obtain a series of intervals for the intensity level of the meteorological events in the region to be divided includes: The final set of intersection points is sorted according to the sorting method of the data sequence to obtain the sorted set of intersection points; When the meteorological event dataset is the initial meteorological dataset, for each intersection of the sorted intersection set, based on the data sample of the meteorological event dataset corresponding to the intersection, a condition value for dividing the interval is obtained, and based on the condition value, a series of intervals of the meteorological event intensity level of the region to be divided are determined; When the meteorological event dataset is the initial meteorological dataset after normalization transformation, firstly, the initial meteorological dataset after normalization transformation is subjected to inverse normalization transformation to obtain the meteorological event dataset; secondly, for each intersection of the sorted intersection set, a condition value for dividing the interval is obtained based on the data sample of the meteorological event dataset corresponding to the intersection point; finally, a series of intervals of the meteorological event intensity level of the region to be divided are determined based on the condition value.

7. The method for objectively classifying the intensity level of meteorological events according to claim 1, characterized in that, After dividing the meteorological event dataset according to the final crossover point set to obtain a series of intervals for the intensity level of the meteorological events in the region to be divided, the method further includes: For each pair of adjacent intervals in the series of intervals of the intensity level of the meteorological event, the test statistic of the two intervals is obtained based on the data samples corresponding to the two intervals in the data sequence and the calculation formula of the test statistic of the two intervals. The formulas for calculating the test statistics for the two intervals are as follows: ； in, Let be the test statistic for the two intervals; and These represent the number of data samples corresponding to the two intervals in the data sequence, respectively. and These are the average values ​​of the data samples corresponding to the two intervals in the data sequence, respectively; and These are the variances of the data samples corresponding to the two intervals in the data sequence, respectively. The U-test results for the two intervals are determined based on the comparison between the absolute value of the test statistic and the validation threshold; wherein, the validation threshold is determined by comparing the u-... The table was searched.

8. The method for objectively classifying the intensity level of meteorological events according to claim 7, characterized in that, The step of determining the U-test result for the two intervals based on the comparison between the absolute value of the test statistic and the test threshold includes: If the absolute value of the test statistic is greater than or equal to the verification threshold, then the two intervals corresponding to the test statistic pass the U test; If the absolute value of the test statistic is less than the verification threshold, then the two intervals corresponding to the test statistic fail the U test.

9. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed on the processor, performs the method for objectively classifying the intensity level of meteorological events as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when run on a processor, executes the method for objectively classifying the intensity level of meteorological events as described in any one of claims 1 to 8.

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