Multi-series short-duration rainstorm data frequency analysis method

Abstract

The invention relates to the technical field of data processing, in particular to a multi-series short-duration rainstorm data frequency analysis method. The method comprises the following steps: obtaining rainfall original data of multiple series of short duration, and carrying out missing data interpolation and sequence extension processing, duration consistency and rainfall intensity rule check to obtain a rainstorm data set; respectively performing reliability, representativeness and consistency review and sample validity screening on the rainstorm data set; a Pearson III type probability density function model is constructed, probability distribution fitting is carried out, and a frequency distribution curve is generated; and carrying out extra heavy rain value analysis and empirical frequency adaptive line analysis adjustment, generating a corrected frequency parameter, further calculating a design heavy rain value of each design return period, and carrying out duration consistency and spatial rationality check to obtain a final design result output data set. According to the invention, through multi-dimensional data complementation, checking and optimization fitting, the system improves the accuracy, reliability and engineering applicability of short-duration rainstorm frequency analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a frequency analysis method for multiple series of short-duration rainstorm data. Background Art

[0002] To effectively support the planning and design of small-scale water conservancy projects, urban drainage systems, and flood control facilities in mountainous areas, there is an urgent need for multi-period, regionalized, and refined rainstorm frequency analysis. Traditional rainstorm frequency analysis methods often rely on single-period data and simplified frequency distribution models, which are difficult to cover the statistical characteristics of multi-scale rainstorm processes, such as those lasting from 10 minutes to 3 days. Furthermore, when data is scarce or missing, the analysis results often suffer from large errors.

[0003] At present, the existing technologies have the following major problems: First, there is a lack of a systematic consistency verification mechanism between multi-period data, which may lead to distortion of rainfall intensity patterns; second, the processing of missing values mostly relies on empirical interpolation, and lacks standardized sliding synthesis and temporal regularization processes; third, in the treatment of extremely heavy rainstorms, its offset effect on the frequency curve is often ignored, and there is a lack of a recurrence period reconstruction and frequency reordering mechanism for survey samples; fourth, the model fitting process is highly dependent on manual labor, and there is a lack of a parameter adjustment system that integrates optimization fitting and visual estimation; fifth, the verification dimension of analysis results is single, and a dual verification mechanism based on temporal consistency and spatial trend has not been formed. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a frequency analysis method for multiple series of short-duration rainstorm data to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a frequency analysis method for multiple series of short-duration rainstorm data is proposed, which includes the following steps: Step S1: Acquire multiple series of short-duration rainfall original data; perform missing data interpolation and sequence extension processing on the rainfall original data to obtain completed rainfall series data; perform duration consistency and rainfall intensity regularity inspection on the completed rainfall series data to obtain multiple series of short-duration heavy rain data sets; Step S2: Reliability review, representativeness review, and consistency review are performed on multiple series of short-duration rainstorm datasets, and the results are integrated into the three-quality review results. Based on the three-quality review results, the sample validity of the multiple series of short-duration rainstorm datasets is screened to obtain a valid rainstorm frequency analysis sample sequence. Step S3: construct a Pearson III probability density function model based on the effective rainstorm frequency analysis sample sequence, perform probability distribution fitting and verify the goodness of fit, and generate a frequency distribution curve; Step S4: Perform heavy rain value analysis and empirical frequency fitting analysis on the frequency distribution curve to generate corrected frequency parameters; Step S5: Calculate the design rainstorm value of each design return period based on the modified frequency parameter, and obtain a result table of design rainstorm values under multiple durations and multiple frequencies; Step S6: Check the temporal consistency and spatial rationality of the design rainstorm value result table to obtain the final design result output data set.

[0006] By systematically processing multiple series of short-duration rainstorm data, this method effectively completes and extends rainstorm data, significantly improving data integrity and reliability. By combining rigorous verification of temporal consistency and rainfall intensity patterns, the method ensures the scientific rationality of rainstorm data and effectively avoids distortion of rainfall intensity characteristics. Multi-dimensional reliability, representativeness, and consistency reviews ensure the quality of the analysis samples and improve the accuracy and stability of frequency analysis. The method uses a Pearson III probability density function model for fitting and goodness-of-fit verification, enhancing the fitting accuracy and applicability of the rainstorm frequency distribution. By empirically adjusting for the offset effect of extremely heavy rainstorms, the method innovatively addresses the insufficient impact of extreme events on the frequency curve and achieves more accurate reconstruction and ranking of return period estimates. The calculation of multi-frequency and multi-duration design rainstorm values covers a wider range of application needs, meeting the design requirements of different project scales and regional characteristics. Finally, through dual verification of temporal consistency and spatial rationality, the method enhances the scientific nature and engineering practicality of the design results, ensuring the reliable application of frequency analysis results in actual planning and design. This holistic approach breaks through the limitations of traditional technologies and significantly improves the refinement and application value of rainstorm frequency analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 This is a schematic flow chart of the steps of a frequency analysis method for multiple series of short-duration rainstorm data according to the present invention; Figure 2 for Figure 1 Detailed step flow diagram of step S1; Figure 3 for Figure 1 Detailed step flow chart of step S3 in FIG. DETAILED DESCRIPTION

[0008] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0009] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0010] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0011] To achieve this, please refer to Figures 1 to 3 The present invention provides a frequency analysis method for multiple series of short-duration rainstorm data, the method comprising the following steps: Step S1: Acquire multiple series of short-duration rainfall original data; perform missing data interpolation and sequence extension processing on the rainfall original data to obtain completed rainfall series data; perform duration consistency and rainfall intensity regularity inspection on the completed rainfall series data to obtain multiple series of short-duration heavy rain data sets; Step S2: Reliability review, representativeness review, and consistency review are performed on multiple series of short-duration rainstorm datasets, and the results are integrated into the three-quality review results. Based on the three-quality review results, the sample validity of the multiple series of short-duration rainstorm datasets is screened to obtain a valid rainstorm frequency analysis sample sequence. Step S3: construct a Pearson III probability density function model based on the effective rainstorm frequency analysis sample sequence, perform probability distribution fitting and verify the goodness of fit, and generate a frequency distribution curve; Step S4: Perform heavy rain value analysis and empirical frequency fitting analysis on the frequency distribution curve to generate corrected frequency parameters; Step S5: Calculate the design rainstorm value of each design return period based on the modified frequency parameter, and obtain a result table of design rainstorm values under multiple durations and multiple frequencies; Step S6: Check the temporal consistency and spatial rationality of the design rainstorm value result table to obtain the final design result output data set.

[0012] In the embodiment of the present invention, reference Figure 1The above is a schematic flow chart of the steps of a method for frequency analysis of multiple series of short-duration rainstorm data according to the present invention. In this example, the method for frequency analysis of multiple series of short-duration rainstorm data includes the following steps: Step S1: Acquire multiple series of short-duration rainfall original data; perform missing data interpolation and sequence extension processing on the rainfall original data to obtain completed rainfall series data; perform duration consistency and rainfall intensity regularity inspection on the completed rainfall series data to obtain multiple series of short-duration heavy rain data sets; In an embodiment of the present invention, no fewer than 10 self-recording rain gauges and automatic meteorological rain gauges are deployed in the target analysis area, and rainfall data are continuously collected with a time resolution of no more than 5 minutes, ensuring that the duration coverage includes 8 short duration segments of 10 minutes, 30 minutes, 60 minutes, 3 hours, 6 hours, 12 hours, 24 hours and 3 days, and the data collection time is no less than 15 consecutive years; the collected raw rainfall data are constructed into a time series according to the station number and duration sequence; for the missing segments in the data, linear interpolation combined with a time sliding window method is used to perform numerical completion using data from three neighboring rain gauges no more than 20 kilometers away from the target station, and the sliding window length is fixed to 3 times the target duration. The weighted average of the contemporaneous sequences of the neighboring stations is used as the missing point estimate using a sliding step of one minute; when the sequence length is less than 8 years, the peak data of the same duration from historical high-frequency observation stations in the same climate zone are used to extend the sequence, and the length of the extended sequence is controlled within 10 years. The original observation and extended data are compared with each other. The time tags are distinguished and recorded; after completion, the total rainfall array of each duration series is constructed in order from short to long, and the incremental verification is performed item by item to ensure that the total rainfall of 10 minutes in any station in any year does not exceed 30 minutes, 30 minutes does not exceed 60 minutes, and so on until the duration of 3 days; if a record that violates this incremental rule is found, the corresponding observation record is traced back and replaced with the average of the actual records at the same station within 1 hour; after completing the incremental verification of rainfall, the average rainfall intensity (unit time) of each duration series is further calculated. The rainfall intensity is calculated based on the rainfall data (the rainfall amount is calculated based on the rainfall amount), and a proportional test is performed to verify whether the decreasing relationship of average rainfall intensity is satisfied: 10-minute intensity ≥ 3 times of 30-minute intensity, 30-minute intensity ≥ 2 times of 60-minute intensity, 60-minute intensity ≥ 3 times of 3-hour intensity, and so on for 24 hours and 3 days. Samples that do not meet the proportional relationship are marked as abnormal, and their corresponding rainfall intensities are corrected according to the proportional relationship fitting value. Finally, the rainfall series data that pass the increasing verification of total rainfall and the decreasing test of average rainfall intensity are used as the valid rainstorm data set.

[0013] Step S2: Reliability review, representativeness review, and consistency review are performed on multiple series of short-duration rainstorm datasets, and the results are integrated into the three-quality review results. Based on the three-quality review results, the sample validity of the multiple series of short-duration rainstorm datasets is screened to obtain a valid rainstorm frequency analysis sample sequence. In the embodiment of the present invention, first, the reliability of the valid rainstorm data set obtained in step S1 is reviewed, and the site number, equipment type, recording years, collection time accuracy and data unit standardization are checked in turn using a five-level indicator system to confirm that the observation equipment is a self-recording rain gauge or a telemetered automatic rain gauge station with no less than 30 years of continuous recording; the timestamp accuracy of each recorded data is unified to the minute level, the numerical format is unified to two decimal places, and the unit is millimeter. After completion, the upper and lower bounds of the maximum and minimum values of each duration are checked to determine whether there are abnormal values that exceed the upper and lower limits of the regional historical extreme values. The over-limit values are marked as suspicious points and eliminated; then, a trend analysis is performed on the annual rainfall total series of each site, and the annual average method is used to calculate the annual rainfall modulus coefficient of each year. ,in is the annual rainfall in year i, is the average annual rainfall of the series, and then plotted in the form of difference accumulation Sequence curve, identify the slope mutation section in the curve, if there is a station with a single-direction increase or decrease in the difference value for more than 3 consecutive years, it is determined to be a mutation sample and removed; then a representative review is conducted, and the mean and coefficient of variation of each station sample are compared with three long-series reference stations (with a recording period of no less than 50 years) in the same rainstorm zone. and skewness coefficient The comparative analysis of the limited error ranges are mean error ≤ 5%, Error ≤ 10%, If the error is ≤15%, all samples of the exceeding stations will be eliminated. Then the consistency test of the annual maximum rainstorm series is carried out, and the extreme value time series of rainstorm years is constructed according to the duration classification. Each duration series is numbered in sequence to construct the order series. ,statistics The value sequence is normalized to the UFk sequence, and the UBk sequence is constructed in reverse time order. UFk and UBk are superimposed on the time axis to determine whether there is an intersection that falls within the significance level. =0.05, if there is any sample, it is determined to be a sample with a mutation in the distribution structure, and cross-validation is performed based on whether the mutation year of the duration is consistent with that of other durations. If there is a consistent mutation in multiple durations, it will be eliminated; after completing the above three reviews, the samples that have passed all the tests of reliability, representativeness and consistency will be merged and numbered to construct a sample sequence for effective heavy rain frequency analysis, and organized into a duration-year two-dimensional matrix.

[0014] Step S3: construct a Pearson III probability density function model based on the effective rainstorm frequency analysis sample sequence, perform probability distribution fitting and verify the goodness of fit, and generate a frequency distribution curve; The embodiment of the present invention first groups the effective rainstorm frequency analysis sample sequence obtained in step S2 according to the duration type, and arranges each group of samples in descending order to construct an empirical frequency point data set. The empirical frequency is calculated using the formula ,in is the total number of samples, is the order number of the sample in the sequence; then the statistical parameters of each set of effective rainstorm frequency analysis sample sequence are calculated separately, including the mean , standard deviation , coefficient of variation , skewness coefficient , and based on this, the shape parameter of the Pearson type III distribution is derived , scale parameters , positional parameters After the parameter estimation is completed, according to 、 、 Constructing the cumulative distribution function of the Pearson type III distribution The theoretical distribution function is compared with the empirical frequency point data set item by item, and the maximum vertical difference D between the two is calculated as the KS test statistic; the KS critical value table is checked, and if the D value is less than the corresponding significance level, =0.05 critical value , then it is determined that the Pearson III distribution and the empirical distribution are fitted successfully; under the premise of passing the verification, the theoretical probability quantile function of the Pearson III distribution is used ,in It is the inverse function of the gamma distribution. The cumulative probability P = 1-1 / T of heavy rainfall values corresponding to the return periods T = 5 years, 10 years, 20 years, 50 years, 100 years, 200 years, 500 years, 1000 years, 5000 years, 1000 years, 5000 years and 10000 years are calculated in sequence. The coordinate points of the frequency distribution curve are constructed by the pairs of heavy rainfall values of each return period and the corresponding duration. The coordinate points are plotted in a semi-logarithmic coordinate system to finally complete the generation of the frequency distribution curve for each duration.

[0015] Step S4: Perform heavy rain value analysis and empirical frequency fitting analysis on the frequency distribution curve to generate corrected frequency parameters; In the embodiment of the present invention, the upper segment of the empirical frequency points of all rainstorm samples in the frequency distribution curve generated in step S3 is first intercepted, and samples with an empirical frequency less than 0.02 (corresponding to a return period greater than 50 years) are selected as suspected points of heavy rainstorms, and the modulus ratio coefficient of each sample in the segment is calculated respectively. ,in For large value samples, is the sample mean of the time series, when the modulus ratio coefficient If the value of the frequency point is greater than 2.0 and the longitudinal deviation of the corresponding frequency point from the Pearson III fitting curve exceeds 20%, it is determined to be a preliminary judgment sample of heavy rain. After extracting such samples, they are compared with the historical heavy rain survey data and disaster literature records in the area where the target site is located. Based on the scope, duration, rainfall estimation value and corresponding social impact level of the flood disaster described in the literature, and referring to the recurrence period classification standard in the Manual of Heavy Rain and Flood Calculation, the historical recurrence period value T of the heavy rain is manually determined, and its corresponding cumulative probability P = 1-1 / T is calculated. The P value is used as the corrected frequency value of the survey sample. All survey samples were uniformly included in the original sample sequence and re-sorted in descending order, and the extended empirical frequency sequence was reconstructed. The frequency weights of the original samples and the survey samples were redistributed using the piecewise weighted average method, where the frequency weights of the original measured samples were set to 1.0 and the survey samples were set to 0.6. The frequency sequence points were reconstructed based on the cumulative position; a revised frequency distribution point sequence was generated based on the revised frequency sequence, and the point sequence was input into the Pearson III distribution function to re-estimate the statistical parameters. First, with the goal of minimizing the sum of squared residuals, the longitudinal difference residuals between the original frequency curve and the revised point sequence were compared, and the shape parameters were adjusted respectively. , scale parameters , positional parameters The value of is selected to minimize the sum of squared residuals and obtain a preliminary fitting parameter set; then, according to the trend of fit between the correction point series and the actual experience points, professionals compare the curve offset direction, manually judge the rationality of the fit near the maximum value, and propose an estimated adjustment value; the weighted average of the preliminary fitting parameters and the estimated adjustment value is used as the final correction frequency parameter, where the manual weight is set to 0.4 and the numerical optimization weight is set to 0.6. The final constructed correction frequency parameter is used for subsequent design value calculation and serves as the basic input data for re-fitting the frequency distribution curve.

[0016] Step S5: Calculate the design rainstorm value of each design return period based on the modified frequency parameter, and obtain a result table of design rainstorm values under multiple durations and multiple frequencies; The embodiment of the present invention first converts the Pearson III distribution parameters in the modified frequency parameters obtained in step S4 into a unified format, and the conversion content includes converting the shape parameters of each duration into , scale parameters and positional parameters Normalization is performed according to the standard unit (mm), ensuring that all parameter formats are retained to three decimal places, and then a standardized frequency parameter data set is constructed; according to the set 10 design return period T values (T = 5 years, 10 years, 20 years, 50 years, 100 years, 200 years, 500 years, 1000 years, 5000 years, 10000 years), the corresponding cumulative probability P = 1-1 / T is calculated, and then the frequency factor Kf value is calculated. The frequency factor calculation adopts the following empirical calculation method: when the skewness coefficient When it is less than 0.3, ,in is the quantile of the standard normal distribution, The value is obtained from the table based on the cumulative probability P, and the skewness coefficient is the corrected frequency parameter =3.5· ; The corresponding The corresponding mean of the value and each time correction parameter and standard deviation Perform combined calculations and design rainstorm values The calculation formula is , the unit is millimeter; perform the above formula calculation for each duration and each recurrence period respectively to form a two-dimensional matrix of 10 rows × 8 columns, where the rows represent the recurrence period and the columns represent the duration (corresponding to 10min, 30min, 60min, 3h, 6h, 12h, 24h, 3d), fill in the corresponding calculation result in each cell and retain two decimal places; output the above calculation matrix in a structured manner as a "design rainstorm value result table", indicate the site number, statistical year, and source method of the revised parameters (optimization + visual estimation) at the top of the table, and indicate the duration unit and recurrence period identifier by column.

[0017] Step S6: Check the temporal consistency and spatial rationality of the design rainstorm value result table to obtain the final design result output data set.

[0018] In the embodiment of the present invention, the design rainstorm value result table generated in step S5 is first expanded according to the site dimension, and the 8 different duration values (10 minutes, 30 minutes, 60 minutes, 3 hours, 6 hours, 12 hours, 24 hours, and 3 days) of each recurrence period at the same site are subjected to a row-by-row incremental analysis, and the difference between adjacent duration rainstorm values is calculated. It is limited that the short-duration design value shall not be greater than the long-duration value. If a numerical value decreases or an abnormal fluctuation occurs across time periods, it is determined to be a duration cross-abnormality. According to the processing principle of "upper section control and lower section backtracking", the unreasonable value is corrected by comparing it with the average value of adjacent duration samples. After completion, the duration verification results of each site are summarized into a "duration consistency verification mark table", and then a secondary screening is performed based on the table to eliminate the site data whose three consecutive duration values have not passed the verification. Subsequently, the design values of all sites under the same duration and the same recurrence period are compared horizontally, and the data are sorted by site. The spatial distribution map is constructed by coordinates, and the design heavy rainfall value is mapped into a contour map. The spatial gradient of heavy rainfall values in the region is analyzed, and the maximum difference between adjacent stations is limited to no more than 15%. If the value of a station deviates from the gradient change trend and forms an isolated point, it is determined to be a spatial mutation sample; the duration and frequency values corresponding to the station are fitted and adjusted, and the weighted average method of three adjacent stations is used for correction. The weight is determined according to the inverse of the distance, the minimum weight is not less than 0.2, and the maximum weight is not more than 0.5; after completing the correction of all abnormal stations, the "optimized design heavy rainfall value data set" is regenerated and merged with the duration consistency verification mark table and spatial trend comparison results, duplicate abnormal points are eliminated, and only the values that have passed the double verification are retained. The final output is the "final design result output data set". This data set is indexed by the station, with statistical parameter records and verification marks attached, as the final data output of the heavy rainfall frequency analysis results.

[0019] By systematically processing multiple series of short-duration rainstorm data, this method effectively completes and extends rainstorm data, significantly improving data integrity and reliability. By combining rigorous verification of temporal consistency and rainfall intensity patterns, the method ensures the scientific rationality of rainstorm data and effectively avoids distortion of rainfall intensity characteristics. Multi-dimensional reliability, representativeness, and consistency reviews ensure the quality of the analysis samples and improve the accuracy and stability of frequency analysis. The method uses a Pearson III probability density function model for fitting and goodness-of-fit verification, enhancing the fitting accuracy and applicability of the rainstorm frequency distribution. By empirically adjusting for the offset effect of extremely heavy rainstorms, the method innovatively addresses the insufficient impact of extreme events on the frequency curve and achieves more accurate reconstruction and ranking of return period estimates. The calculation of multi-frequency and multi-duration design rainstorm values covers a wider range of application needs, meeting the design requirements of different project scales and regional characteristics. Finally, through dual verification of temporal consistency and spatial rationality, the method enhances the scientific nature and engineering practicality of the design results, ensuring the reliable application of frequency analysis results in actual planning and design. This holistic approach breaks through the limitations of traditional technologies and significantly improves the refinement and application value of rainstorm frequency analysis.

[0020] Preferably, step S1 includes the following steps: Step S11: collecting long-term rainfall monitoring data from automatic rain gauges, self-recording rain gauges, and remotely sensed rainfall monitoring stations deployed in the target area to obtain multiple series of short-term rainfall raw data; Step S12: Identify the rainfall sequence with missing data in the original rainfall data, and perform interpolation and sliding sequence synthesis processing of adjacent stations to obtain the completed rainfall sequence data; Step S13: reconstructing the time correspondence relationship and regularizing the data time axis of the completed rainfall series data to obtain a multi-duration rainfall time series matrix; Step S14: Based on the multi-duration rainfall time series matrix, the consistency of the total rainfall relationship in each duration period is judged to verify the regularity that the short-duration rainfall is not greater than the long-duration rainfall, and the duration consistency verification result is obtained; Step S15: Based on the multi-duration rainfall time series matrix, the gradient of the average rainfall intensity of each duration is judged, and the proportional relationship is calculated to verify whether it conforms to the intensity decreasing law that the short-duration rainfall intensity is higher than the long-duration rainfall intensity, and obtain the rainfall intensity law verification result; Step S16: Based on the duration consistency verification results and the rainfall intensity law verification results, the validity of the supplemented rainfall series data is screened to obtain multiple series of short-duration rainstorm data sets.

[0021] In an embodiment of the present invention, automatic meteorological rain gauges, self-recording rain gauges, and telemetered rainfall monitoring nodes are first deployed in the target area. The total number of stations is no less than 10, and the spacing between the stations is controlled between 5 kilometers and 10 kilometers. The sampling interval of the equipment is uniformly set to 5 minutes to ensure that the collection duration covers 8 standard short durations, namely 10 minutes, 30 minutes, 60 minutes, 3 hours, 6 hours, 12 hours, 24 hours, and 3 days. After the collection is completed, the rainfall data of all stations are preliminarily screened to identify sequence segments with null values or abnormal zero values. After confirming the missing segments, the linear weighted average completion processing is performed using the rainfall data of the same period of three neighboring stations. The weight is calculated according to the inverse of the distance, with the maximum weight not exceeding 0.5 and the minimum weight not less than 0.2. For the completed sequence, in order to prevent time period confusion, all data are uniformly adjusted to a time axis format based on Coordinated Universal Time (UTC) + 8 time zones, and a multi-duration rainfall time series matrix is generated by sliding windows on a duration-by-duration basis. The sliding window lengths are the corresponding duration lengths. , the sliding step is fixed at 5 minutes; after the matrix generation is completed, the total amount of heavy rainfall in each period is judged in turn. The verification rule is that the rainfall in 10 minutes shall not be greater than that in 30 minutes, and the rainfall in 30 minutes shall not be greater than that in 60 minutes, and so on for 3 days. If a sample that violates the relationship is found, its position is recorded and the average total rainfall of the adjacent duration of the station on that day is retrospectively corrected; then the intensity gradient check is performed on the rainfall time series matrix, and the average rainfall intensity value of each duration period is calculated (rainfall divided by duration), and each item is judged to see if it meets the requirements. The decreasing relationship of sufficient rainfall intensity is as follows: the average rainfall intensity of 10 minutes ≥ 3 times of 30 minutes, 30 minutes ≥ 2 times of 60 minutes, 60 minutes ≥ 3 times of 3 hours, 3 hours ≥ 2 times of 6 hours, and so on up to 3 days. If it is not satisfied, the rainfall intensity deviation is corrected according to the fitting value of the adjacent duration; finally, all samples that pass the total rainfall consistency check and the average rainfall intensity decreasing test are constructed into a multi-series short-duration multi-series short-duration rainstorm dataset, and a three-dimensional structure data block is formed with the station number, time start and end, and duration type as the index.

[0022] By deploying multiple types of high-precision rainfall monitoring equipment, the present invention realizes the real-time and comprehensive collection of multiple series of short-duration rainstorm data in the target area, greatly improving the timeliness and coverage of data acquisition; combining the interpolation of neighboring station data with sliding sequence synthesis, it effectively solves the problem of missing data and ensures the continuity and integrity of the rainfall series; the reconstruction of the duration correspondence and the regularization of the time axis standardize the structure of multi-duration rainfall data, and improves the accuracy and data consistency of subsequent analysis; through the dual regularity verification of the duration total rainfall relationship and the average rainfall intensity gradient, the logical rationality between rainfalls of different durations is scientifically verified to ensure that the data conforms to the physical characteristics of natural rainfall; finally, based on the multiple verification results, validity screening is performed to eliminate abnormal and unreasonable data, significantly improving the quality and reliability of the rainstorm data set, and laying a solid data foundation for subsequent rainstorm frequency analysis. The overall method effectively overcomes the technical difficulties caused by traditional data missing and data inconsistency, and enhances the scientificity and practicality of short-duration rainstorm monitoring and analysis.

[0023] Preferably, the reliability review of the multiple series of short-duration rainstorm datasets in step S2 includes: Data source identification verification and collection equipment information verification were performed on multiple series of short-duration rainstorm datasets to obtain site equipment integrity confirmation results; Based on the site equipment integrity confirmation results, a consistency check of observation time accuracy, decimal uniformity, and unit standardization was performed on multiple series of short-duration rainstorm datasets to obtain format integrity verification results. Based on the format integrity verification results, the maximum and minimum values in the multi-series short-duration rainstorm data set are logically judged, and the rationality of the extreme values is reviewed to obtain the extreme value validity identification results; Multi-year trend fluctuation analysis was conducted on multiple series of short-duration rainstorm data sets to identify years with sudden changes and years with abnormal missing data, and to obtain stability test results for daily rainfall series. Based on the comparative analysis of multiple series of short-duration rainstorm datasets and the preset meteorological station data in the same area, the spatial consistency deviation is determined and the station spatial consistency test results are obtained; Based on the site equipment integrity confirmation results, format integrity verification results, extreme value validity identification results, daily rainfall series stability detection results and site spatial consistency test results, abnormal data entries in multiple series of short-duration rainstorm data sets are identified and marked to generate reliability review results.

[0024] In the embodiment of the present invention, the site number, equipment type and collection method of each rainfall record are first verified. The equipment must be an automatic meteorological rain gauge, a self-recording rain gauge or a remote rainfall monitoring node. The record source must be marked with the collection time, equipment number and transmission method. It is confirmed that the site equipment has been in continuous operation for no less than 15 years without manual interruption of the record, and the site equipment integrity confirmation result is generated. On this basis, the timestamp format of all data is uniformly checked, and the time format is unified as "YYYY-MM-DD HH:MM", the accuracy is not less than minute level, two decimal places are uniformly retained, and the rainfall unit is unified as millimeters. Any records with abnormal time format, mixed decimal places or missing units are all eliminated to generate the format integrity verification result; further logical judgment is made on the maximum and minimum values in each duration rainfall series of each station, and the 10-minute maximum value is limited to not more than 150mm, the 3-day maximum value is not more than 800mm, and the minimum value is not less than 0. After the over-limit records are extracted, they are cross-compared with the data of neighboring stations in the same period. If there is no support, it is determined to be an invalid extreme value and invalidated, and the extreme value validity identification result is obtained; then, based on the daily time resolution, the total annual rainfall of each station is calculated as the modulus coefficient ,in is the total rainfall in year i, is the multi-year average, and the yearly difference series is drawn The curve was used to identify the years with the same change in the direction of the difference accumulation for more than three consecutive years as mutation years, and the years with less than 70% of the normal rainfall records as missing years to generate the stability test results of the daily rainfall series; then three meteorological stations with a historical observation period of more than 30 years in the same area were selected, and rainfall records of the same period and length were extracted, and the records were matched with the target station data by year, and the Pearson correlation coefficient was used to calculate the stability test results of the daily rainfall series. Perform comparative calculations, If the value is lower than 0.6, it is judged that the spatial consistency deviation is too large. The site and time period are recorded, and the site spatial consistency test results are output. Finally, the above five test results are merged one by one, using site, time, and duration as key values. The data entries that fail any check are marked as abnormal samples, forming a mark field "REJECT = 1", and the rest are marked as "REJECT = 0", and the reliability review results are summarized.

[0025] The present invention comprehensively guarantees the authenticity and accuracy of the rainstorm dataset through a multi-level and multi-dimensional reliability review system; relies on strict verification of equipment information and data sources to ensure the integrity of monitoring sites and collection equipment, laying a solid foundation for data quality; unifies observation time accuracy and data format, effectively standardizes data standards, and improves the consistency and compatibility of data processing; through extreme value rationality review and logical judgment, it timely identifies and eliminates abnormal extreme values to prevent abnormal data from misleading the analysis results; multi-year trend fluctuation analysis and abnormal year identification help to discover long-term data change patterns and potential anomalies, and ensure the stability and continuity of time series; combined with the spatial consistency comparison of regional meteorological stations, it realizes the spatial rationality test of data and enhances the regional representativeness and reliability of data; finally, through the comprehensive identification of abnormal data entries through multiple indicators, it realizes the comprehensive screening and identification of rainstorm datasets, improves the overall quality of the dataset, and provides high-reliability data guarantee for subsequent frequency analysis.

[0026] Preferably, the representativeness review of multiple series of short-duration rainstorm datasets in step S2 includes: Statistical analysis of the number of observation years, wet and dry seasons, and time period coverage at each station was conducted on multiple series of short-duration rainstorm datasets to obtain the results of sample time integrity analysis. Based on the results of sample time integrity analysis, the short series data and long series data in the multi-series short-duration rainstorm data set are identified, and the mean, coefficient of variation and skewness coefficient are compared and analyzed to obtain the statistical parameter proximity discrimination results; Based on the results of statistical parameter proximity discrimination, abnormal station samples in multiple series of short-duration rainstorm datasets are identified and marked to generate representative review results.

[0027] In the embodiment of the present invention, the rainstorm records of each station are first grouped and counted according to the duration type, and the number of observation years, observation start and end time and number of years with complete data under each duration are calculated respectively. The number of years is not less than 15 years as the time integrity qualification standard. At the same time, the model ratio coefficient is constructed according to the maximum annual rainstorm value. Sequence, draw the year-by-year difference value graph to identify whether there is a unilateral trend of continuous high or low rainfall for more than 3 years. If the difference curve changes upward or downward by more than 0.3, it is determined that the change of high and low rainfall at this station is uneven. After completing the time integrity analysis, the samples are divided into two categories: short series (observation years less than 20 years) and long series (observation years greater than or equal to 30 years), and the mean of each group of samples is extracted. , coefficient of variation , skewness coefficient , and compare the parameter differences of the short series samples and the long series samples under the same duration, calculate the mean error, error, Error, the upper limit of the error is set to 5%, 10% and 15% respectively. The site samples with any parameter exceeding the limit value are judged to be unrepresentative; then the samples with these parameter errors exceeding the limit are marked, and a "representative abnormal sample list" is constructed, in which each record includes the site number, duration type, number of observation years, comparison parameter value and error rate, and is assigned an identification field "REP_FLAG=1", and the remaining data is assigned to "REP_FLAG=0". Finally, the identification fields of all sites are summarized to generate the representative review results.

[0028] The present invention ensures the integrity and representativeness of the rainstorm data samples in the time dimension through a comprehensive statistical analysis of the number of observation years, changes in wet and dry seasons, and time period coverage of each monitoring station; by comparing the mean, coefficient of variation, and coefficient of skewness of data series of different lengths, the statistical consistency between samples is scientifically evaluated, and potential deviations in the data set are effectively identified; further, the timely identification and elimination of abnormal station samples improves the overall balance and representativeness of the data set, ensuring that the frequency analysis results can more truly reflect the regional rainfall characteristics, enhancing the scientific nature and engineering applicability of the analysis, and providing a solid statistical foundation and reliable sample guarantee for subsequent rainstorm frequency research.

[0029] Preferably, the consistency review of multiple series of short-duration rainstorm datasets in step S2 includes: Identify the annual maximum rainfall sequence in multiple series of short-duration rainfall data sets, and reconstruct the time series to obtain the rainfall annual extreme value time series data set; Based on the extreme value time series data set of heavy rain years, the rank construction and order sequence difference accumulation of each heavy rain series are carried out to obtain the UFk-UBk trend test statistic series; Based on the UFk-UBk trend test statistic sequence, the positive and reverse order statistical curves are constructed and superimposed to obtain the rainstorm sequence consistency trend identification map; Based on the rainstorm sequence consistency trend identification diagram, the rainstorm sequence with significant mutation points is identified, and the rationality of the abnormal trend is verified to obtain the trend abnormality rationality checklist; Based on the rainstorm sequence consistency trend identification diagram and trend anomaly rationality check table, multiple series of short-duration rainstorm datasets are classified and labeled to generate consistency review results.

[0030] In the embodiment of the present invention, firstly, a multi-series short-duration rainstorm dataset is classified and extracted according to the station number and duration type, and the annual maximum rainstorm value corresponding to each duration is selected and arranged in chronological order to construct the annual maximum rainstorm time series. It is required that the time series length is not less than 15 years and the records of each year are complete, forming a rainstorm annual extreme value time series dataset; then, for each time series, a rank construction process is performed according to the time positive order. Let the rainstorm amount in the i-th year be , and the amount of heavy rainfall in all subsequent years is less than The number of, in turn build the positive order sequence , cumulatively generate UFk sequence, the calculation formula is ,in for The expected value of for Variance estimation; then reorder the same time series in reverse order and perform the same steps to construct the UBk series; plot UFk and UBk against the time axis to form a UFk-UBk trend test statistic sequence diagram. If the intersection of the two curves falls within the confidence interval =0.05, and the year corresponding to the intersection is close to the middle of the sequence (i.e., the 10%-90% position interval), it is marked as a suspected mutation year; the mean rainfall, coefficient of variation, and maximum value of the 10 years before and after the mutation year are compared for the sequence; if the change rate of any parameter before and after the mutation exceeds 30%, the mutation trend is determined to be a structural change and included in the trend anomaly rationality verification table, and the duration type, mutation year, and judgment index change value are recorded; all verified stations are compared one by one to see if they have consistent mutation behavior over multiple durations; if a station has structural mutations in more than five durations, the sample of this station is determined to be an unstable distribution sample as a whole, and the field "CON_FLAG=1" is marked, and the remaining stations are marked as "CON_FLAG=0". Finally, the classification identification of all durations and stations is summarized and output as the consistency review result.

[0031] Through systematic reconstruction and rank analysis of the extreme value series of heavy rain years, the present invention scientifically reveals the inherent sorting rules and changing trends of heavy rain data of different durations; uses the superposition and drawing of positive and reverse statistical curves to intuitively reflect the consistency and potential mutation characteristics of the heavy rain series, effectively improving the detection sensitivity of abnormal trends; combined with rationality verification, it accurately identifies and judges the rationality and abnormality of trend mutations, avoiding misjudgment and omission; by classifying and labeling different types of heavy rain series, it realizes hierarchical management and precise screening of data quality, significantly enhancing the consistency and stability of the data set; overall improves the temporal continuity and trend reliability of heavy rain data, provides a solid statistical basis and scientific basis for subsequent frequency analysis, and promotes the accuracy of heavy rain frequency analysis results and the credibility of engineering applications.

[0032] Preferably, step S3 includes the following steps: Step S31: performing parameter statistical calculation on the effective rainstorm frequency analysis sample sequence to obtain a rainstorm frequency analysis sample statistical parameter table, wherein the rainstorm frequency analysis sample statistical parameter table includes the mean, coefficient of variation, and skewness coefficient of each duration rainstorm sequence; Step S32: Pre-calculating the shape parameters, scale parameters, and location parameters of the Pearson III probability density function based on the statistical parameter table of the rainstorm frequency analysis samples to obtain a parameter set of the Pearson III distribution function; Step S33: sorting the effective rainstorm frequency analysis sample sequence in ascending order, and constructing the empirical frequency to obtain the empirical frequency point data set; Step S34: performing fitting and matching based on the Pearson III distribution function parameter set and the empirical frequency point data set to obtain a frequency distribution fitting function; Step S35: performing a KS test based on the frequency distribution fitting function to obtain a KS goodness of fit test report; Step S36: constructing a frequency distribution curve using a frequency distribution fitting function based on the KS goodness of fit test report.

[0033] In the embodiment of the present invention, firstly, the effective rainstorm frequency analysis sample sequence obtained in the above sample screening step is carried out, and the sample sequence is grouped by station and duration type, and the statistical parameters are calculated for each group. , coefficient of variation , standard deviation , skewness coefficient All parameter results are sorted into a table of sample statistical parameters for heavy rain frequency analysis, with three decimal places retained; then based on the table 、 and Parameters, respectively calculate the shape parameters of the Pearson type III probability density function , scale parameter , positional parameters , and obtain a complete set of Pearson type III distribution function parameters; then sort the sample sequences of the same group in ascending order, assign the mth sample in the sequence to the empirical frequency P = m / (n + 1), and form an empirical frequency point data set, where n is the number of samples, m is the sequence number of the sample in ascending order, and the frequency is rounded to three decimal places; match the theoretical cumulative distribution function F (x) of the Pearson type III distribution function with the above empirical frequency points, and use the maximum vertical difference between the theoretical cumulative probability value of each sample point and the empirical frequency As a test statistic, look up the KS distribution critical value table and limit the significance level =0.05, if D< , the fitting is judged to be passed, and a KS goodness of fit test report is generated; finally, based on the fitted frequency distribution function, the heavy rainfall values corresponding to the cumulative probability points such as P = 0.2, 0.1, 0.05, 0.02, and 0.01 are extracted to construct a frequency distribution curve coordinate point group. In the semi-logarithmic coordinate system, the horizontal axis is the return period T = 1 / (1-P), and the vertical axis is the heavy rainfall value H. The frequency distribution curve is drawn, and the corresponding duration, station, and statistical parameter source are marked respectively.

[0034] The present invention comprehensively reflects the basic distribution characteristics of rainstorms of various durations by systematically counting the key parameters of rainstorm sample sequences, providing a scientific basis for subsequent distribution fitting; accurately estimates the shape, scale and location parameters of the Pearson III distribution based on statistical parameters, thereby improving the accuracy and stability of model parameter estimation; combines empirical frequency data to enhance the fit of the probability distribution model to the actual observation data; effectively integrates theoretical distribution and empirical data through the fitting and matching process, thereby improving the expressive power and representativeness of the frequency distribution function; adopts a strict KS test to quantitatively evaluate the fitting effect, thereby ensuring the scientific nature and reliability of the distribution model; the frequency distribution curve finally constructed not only accurately reflects the frequency characteristics of rainstorms, but also has good statistical applicability, providing a solid mathematical foundation and precise frequency analysis tool for the calculation of the design return period and engineering applications.

[0035] Preferably, step S4 includes the following steps: Step S41: intercepting the high-value section in the frequency distribution curve, and performing deviation identification and abnormality judgment to obtain a preliminary judgment sample of heavy rainstorm; Step S42: Analyze and compare the list of suspected heavy rainstorm samples with the preset history to generate an estimated value of the heavy rainstorm recurrence period; Step S43: Based on the estimated value of the return period of the heavy rainstorm, the effective rainstorm frequency analysis sample sequence is reinforced with survey samples and reconstructed with frequency sorting to obtain an extended empirical frequency sequence containing survey samples; Step S44: performing frequency repositioning and re-weighting processing on the heavy rain value points based on the extended empirical frequency sequence to obtain a frequency distribution curve correction point sequence; Step S45: refitting the frequency distribution curve correction point sequence, and adjusting the Pearson III distribution function parameter set by optimizing the fitting method to obtain the corrected frequency parameters.

[0036] In the embodiment of the present invention, first, a high-value segment corresponding to a recurrence period greater than 50 years, that is, an empirical frequency less than 0.02, is selected from the frequency distribution curve, and the corresponding rainstorm sample points are extracted. The vertical deviation value of each high-value point from the Pearson III distribution fitting curve is calculated. If the deviation value is greater than 20% and the modulus ratio coefficient is greater than 0.02, the vertical deviation value of each high-value point is greater than 0.02. If the value is greater than 2.0, the point is determined to be a preliminary sample of heavy rain, and a "list of suspected heavy rain samples" is established; then, the occurrence time, site and magnitude recorded in the list are used as indexes to compare with historical heavy rain documents, hydrological yearbooks and extreme weather archives in the target area to confirm whether it occurred in a typical historical heavy rain event, and the corresponding recurrence period T value is determined based on the description of the disaster caused by the event in the historical data and the grade standard of the "Handbook of Heavy Rain and Flood Calculation", and then the cumulative frequency is calculated by P = 1-1 / T to generate an estimated value of the recurrence period of heavy rain; then the survey samples with estimated frequencies are included in the original sample sequence, and the heavy rain magnitude is calculated. The samples were rearranged in descending order, and the original measured samples and survey samples were assigned frequency weights respectively. The measured sample weight was set to 1.0 and the survey sample weight was set to 0.6. A weighted frequency sorting table was constructed to form an extended empirical frequency sequence containing the survey samples. The survey samples were then frequency-replaced based on the weighted frequency sorting results, and their empirical frequency values were adjusted. The interpolation smoothing method was used to ensure the continuity of the frequency point distribution, forming a frequency distribution curve correction point sequence, which was superimposed and compared with the original frequency point sequence to form a frequency difference distribution diagram. Finally, the frequency curve was refitted on the correction point sequence, and the minimum sum of squared deviations was used as the fitting criterion. The shape parameters of the Pearson III distribution function were adjusted successively. , scale parameters With positional parameters , make the fitting curve pass through the central distribution area of the correction point series, and determine the final correction frequency parameter set under the condition that the sum of squared residuals is reduced to within 5%.

[0037] The present invention accurately intercepts the high-value sections of the frequency distribution curve, scientifically identifies and eliminates abnormal deviation data, effectively screens out preliminary judgment samples of heavy rainstorms, and improves the accuracy of extreme event identification; combines historical data with the annotation list for in-depth comparison, realizes the reasonable estimation of the recurrence period of heavy rainstorms, and enhances the scientific judgment of the frequency of extreme rainstorms; through the reinforcement of survey samples and frequency sorting reconstruction, improves the integrity and representativeness of the sample sequence, and improves the coverage and depth of frequency analysis; frequency relocation and re-weighting processing effectively corrects the frequency deviation of heavy rainstorm values and optimizes the expression of frequency distribution; finally, the refitting and optimization fitting method is used to adjust the distribution parameters, realizes the fine correction of the frequency curve and the dynamic optimization of model parameters, significantly improves the accuracy and reliability of heavy rain frequency analysis in extreme event prediction, and promotes the scientific nature of the design recurrence period calculation and the practicality of engineering application.

[0038] Preferably, step S45 includes the following steps: Step S451: performing residual square value calculation and preliminary fitting of the distribution curve on the frequency distribution curve correction point sequence to obtain a preliminary frequency fitting residual sequence; Step S452: Calculate the squared deviation and minimized value of the shape parameter, scale parameter, and location parameter of the Pearson III distribution function based on the preliminary residual sequence of frequency fitting to obtain a preliminary optimal frequency parameter set; Step S453: Verify the preliminary optimal frequency parameter set and analyze the deviation trend between the fitting curve and the reliable rainstorm sample points to obtain the estimated adjustment parameter values; Step S454: Based on the preliminary optimal frequency parameter set and the estimated adjustment recommended parameter values, a weighted fusion is performed to construct a Pearson III distribution function correction parameter combination with the best fitting performance to obtain the corrected frequency parameters.

[0039] In the embodiment of the present invention, first, in step S451, based on the correction point sequence obtained after the reinforcement and reconstruction frequency sorting of the survey samples in the early stage, the vertical difference between the empirical frequency point and the initial Pearson type III distribution curve fitting value is calculated item by item using the point-by-point residual square value calculation method. Specifically, the residual sequence is constructed by summing the square of the ordinate deviation of each point. The residual sequence is used to quantify the distribution characteristics of the fitting error and generate a preliminary residual sequence of frequency fitting; in step S452, based on the preliminary residual sequence of frequency fitting, the three-parameter traversal method is used to perform fixed-amplitude equal-interval scanning on the shape parameter α, scale parameter β and location parameter a0 of the Pearson type III distribution. Under each set of parameter combinations, the frequency curve fitting value is recalculated, and the residual square sum under this set of parameters is calculated synchronously. Finally, the residual sum corresponding to all parameter combinations is compared. The sum of squares is used to determine the parameter combination corresponding to the minimum residual sum of squares as the preliminary optimal frequency parameter set; in step S453, the frequency curve is redrawn based on the preliminary optimal frequency parameter set, and compared with the rainstorm sample points in the sample that are determined to be credible through the "three-quality review" and empirical frequency analysis, and the relative deviation rate is calculated point by point. The deviation rate is used as an indicator to perform up and down trend classification analysis, and the phenomenon of large fitting deviation of parameters for high-value point segments is identified in combination with the rainstorm intensity level (for example, the point series with the annual maximum 60-minute rainfall greater than 100 mm), and the trend deviation type is summarized based on this to form the estimated adjustment recommended parameter value; in step S454, the linear weight method is used to assign numerical weights to the preliminary optimal frequency parameter set and the estimated adjustment recommended parameter value, for example, the preliminary optimal parameter set accounts for 70%, the estimated recommended parameter accounts for 30%, and the 、 、 The three groups of parameters are weighted and superimposed item by item to construct the final Pearson type III distribution function modified parameter combination with the best fitting performance. The final frequency distribution curve is further generated and used for the subsequent design rainstorm value calculation. The frequency point series used in this process must cover at least 8 durations (such as 10 minutes, 30 minutes, 1 hour, 3 hours, 6 hours, 12 hours, 24 hours, and 3 days), and the D value based on the KS test must be less than 0.1 as the goodness of fit judgment threshold.

[0040] The present invention effectively quantifies the fitting error between the frequency distribution curve and the observed data through the precise calculation and preliminary fitting of the residual square value, thereby improving the scientific nature of the fitting process; the squared deviation minimization optimization of the parameters based on the residual sequence is performed, which significantly improves the accuracy and stability of the parameter estimation of the Pearson type III distribution function; through the verification and deviation trend analysis of the preliminary parameters, combined with the expert visual adjustment suggestions, the effective fusion of objective data drive and subjective experience is achieved; finally, the weighted fusion strategy is adopted to optimize the parameter combination to ensure that the fitting performance reaches the optimal level, which significantly enhances the fitting accuracy and applicability of the frequency distribution curve, provides high-precision and reliable distribution model parameters for heavy rain frequency analysis, and enhances the scientific support capabilities for extreme event prediction and engineering design.

[0041] Preferably, step S5 includes the following steps: Step S51: performing unified format conversion and distribution standardization processing on the Pearson III distribution parameters in the corrected frequency parameters to obtain a standardized frequency parameter data set; Step S52: Calculate the frequency factor of the return period probability of each combination based on the standardized frequency parameter data set and the preset design return period to obtain a return period-frequency factor matching table; Step S53: Calculate the rainstorm data of each station and duration based on the modified frequency parameters and the recurrence period-frequency factor matching table to obtain the design rainstorm value matrix; Step S54: restructure and format the design rainstorm value matrix according to the duration dimension to obtain a design rainstorm value result table under multiple durations and frequencies.

[0042] In the embodiment of the present invention, first, in step S51, the shape parameter of the Pearson III type distribution in the modified frequency parameter obtained in the previous step S454 is , scale parameters and positional parameters Perform unified format conversion. Keep the three parameter values of each station and duration to five decimal places, use a unified unit (such as mm) and establish a standardized frequency parameter data table to ensure that all parameter column names and value arrangement structures are consistent. 、 、 The frequency parameter standard structure matrix is entered in a fixed field order to form a standardized frequency parameter data set; then in step S52, combined with the non-exceedance probability values corresponding to the preset 10 design return periods (5 years, 10 years, 20 years, 50 years, 100 years, 200 years, 500 years, 1000 years, 5000 years, 10000 years), the standard normal distribution function value Z is obtained by table lookup or calculation (for example, if the return period T = 100 years, the corresponding non-exceedance probability P = 0.01, Z is approximately 2.326), and then based on the Pearson III distribution frequency factor calculation formula ,,in is the skewness coefficient, accurate to three decimal places, and the The recurrence period-frequency factor matching table is constructed, and then in step S53, the matching table is matched with the standardized frequency parameter data set station by station and time by time. Each calculation point is calculated according to the design value calculation formula Perform operations, where is the mean, is the standard deviation, and That is, the design rainstorm value of the corresponding site, corresponding duration, and corresponding recurrence period, and all calculation results are stored in the design rainstorm value matrix in the order of site number, duration category and recurrence period; finally, in step S54, based on the three-dimensional data structure of the design rainstorm value matrix, the site number dimension is fixed and the duration dimension (such as 10min, 30min, 1h, 3h, 6h, 12h, 24h, 3d) is the main axis, and the two-dimensional structure output format is reconstructed. The column items are 10 recurrence periods and the row items are 8 duration design rainstorm values. The abnormal jump points in the matrix are eliminated according to the adjacent duration difference ratio, and finally a multi-duration and multi-frequency design rainstorm value result table with unified fields, consistent units and sequence specifications is output. The table output is in Excel spreadsheet mode and saved in CSV format. The first line of the file is the field label, including site number, duration type, recurrence period T and corresponding design value XT, and the data accuracy is uniformly retained to two decimal places.

[0043] The present invention realizes the standardized management of parameter data through unified format conversion and standardized processing of the correction frequency parameters, thereby improving the compatibility of data and the efficiency of subsequent calculations; combines the standardized parameters with the preset design recurrence period, scientifically calculates the frequency factor, ensures the accurate matching of the recurrence period and the frequency factor, and enhances the reliability of the design indicators; based on the comprehensive calculation of the correction parameters and the frequency factor, the system generates a multi-site, multi-duration design rainstorm value matrix, which comprehensively covers different time scales and spatial ranges and meets the diverse engineering design needs; finally, through structural reorganization and formatted output, the expression form of the design rainstorm value is optimized, which is convenient for engineering application and data sharing, significantly improves the scientificity, accuracy and practicality of the rainstorm design value, and supports more refined and multi-level planning and design of water conservancy and flood control projects.

[0044] Preferably, step S6 includes the following steps: Step S61: performing incremental comparison analysis on different duration values at the same site and the same frequency in the design rainstorm value result table to obtain a duration consistency verification result; Step S62: Based on the temporal consistency check mark table, the station data with temporal crossover, mutation, and reversal trends are identified and retrospectively corrected to obtain the optimized design rainstorm value dataset; Step S63: performing regional trend comparison on the design rainstorm values of different stations with the same duration and return period in the optimized design rainstorm value data set to obtain spatial rationality test results; Step S64: Identify unreasonable values based on the optimized design rainstorm value dataset and the spatial rationality test results, and perform fitting adjustments to obtain the final design result output dataset.

[0045] In the embodiment of the present invention, first, in step S61, based on the multi-duration and multi-frequency design rainstorm value result table obtained in step S54, the station number and the recurrence period are fixed, and the different duration design rainstorm values (such as 10min, 30min, 1h, 3h, 6h, 12h, 24h, 3d) under the frequency of the station are sequenced from short to long according to the duration, and the rainstorm value difference and the incremental increase between adjacent durations are calculated in turn. The data points that do not meet the incremental rule (that is, the design rainstorm value of the next duration is less than or equal to the previous duration value) are marked as violating the duration rule. The consistency rules are followed, and the site number, recurrence period, duration type, abnormal position and difference are recorded in the duration consistency check mark table; then in step S62, the data rows marked with duration crossover, mutation or reversal trend are retrospectively corrected, specifically, the design rainstorm value of the current abnormal duration is re-estimated based on the average value of the two durations before and after the mark point, and the difference between adjacent durations is recalculated to see whether the increasing trend is restored. After confirming that the correction is effective, the modified value is updated to the design rainstorm value matrix to form an optimized design rainstorm value data set; in step S63, the design rainstorm value matrix is updated based on the average value of the two durations before and after the mark point, and ... design rainstorm value matrix Based on the optimized design rainstorm value dataset, each duration and each recurrence period are fixed respectively, and the design rainstorm values under this combination of all stations are extracted, and spatial trend comparison is performed. The method is to calculate the relative deviation rate of each station value and the average value of each station under this duration and frequency. At the same time, the values are sorted according to the spatial distribution position of the station to observe whether there is regional distribution discontinuity or sudden deviation. If the deviation rate between adjacent stations exceeds 25%, the station value is marked as a spatial outlier, forming a spatial rationality test result; finally, in step S64, based on the optimized design rainstorm value dataset and the spatial rationality test result, all design values identified as unreasonable are reconstructed with double-point means according to the adjacent spatial station values, and the value is scaled with the original skewness coefficient so that it still retains the frequency parameter characteristics of the station. Finally, all corrected values are merged into the main table, and the final design result output dataset with the same field structure as step S54 is output. The dataset adopts a four-field structure of station number, duration type, recurrence period and design rainstorm value, with two decimal places of precision, and the output format is a standard CSV format file with field labels.

[0046] The present invention scientifically verifies the temporal consistency of the design rainstorm values by comparing the incrementality of the design rainstorm values in different temporal dimensions, ensuring the temporal logical rationality of the data; timely identification and retrospective correction of temporal intersections, mutations and reversal trends effectively eliminates abnormal fluctuations and data contradictions, and improves the stability and credibility of the design rainstorm values; through regional spatial trend comparison, the spatial rationality of the design rainstorm values at different sites is accurately evaluated, ensuring the regional representativeness and engineering applicability of the results; finally, fitting and adjusting the unreasonable data realizes the scientific optimization and refined processing of the design results, greatly improves the accuracy and reliability of the rainstorm design parameters, and provides solid data support and decision-making basis for water conservancy projects and flood control planning.

[0047] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited by the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the application documents are included in the present invention.

[0048] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A frequency analysis method for multiple series of short-duration rainstorm data, characterized in that: The following steps are involved: Step S1: Acquire multiple series of short-duration rainfall original data; perform missing data interpolation and sequence extension processing on the rainfall original data to obtain completed rainfall series data; The completed rainfall series data were checked for consistency and rainfall intensity, and multiple series of short-duration rainstorm data sets were obtained. Step S2: Conduct reliability review, representativeness review, and consistency review on multiple series of short-duration rainstorm datasets, and integrate the results of the three-quality review; Based on the results of the three-property review, the validity of samples of multiple series of short-duration rainstorm datasets was screened to obtain a valid rainstorm frequency analysis sample sequence; Step S3: construct a Pearson III probability density function model based on the effective rainstorm frequency analysis sample sequence, perform probability distribution fitting and verify the goodness of fit, and generate a frequency distribution curve; Step S4: Perform heavy rain value analysis and empirical frequency fitting analysis on the frequency distribution curve to generate corrected frequency parameters; Step S5: Calculate the design rainstorm value of each design return period based on the modified frequency parameter, and obtain a result table of design rainstorm values under multiple durations and multiple frequencies; Step S6: Check the temporal consistency and spatial rationality of the design rainstorm value result table to obtain the final design result output data set.

2. The frequency analysis method for multiple series of short-duration rainstorm data according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting long-term rainfall monitoring data from automatic rain gauges, self-recording rain gauges, and remotely sensed rainfall monitoring stations deployed in the target area to obtain multiple series of short-term rainfall raw data; Step S12: Identify the rainfall sequence with missing data in the original rainfall data, and perform interpolation and sliding sequence synthesis processing of adjacent stations to obtain the completed rainfall sequence data; Step S13: reconstructing the time correspondence relationship and regularizing the data time axis of the completed rainfall series data to obtain a multi-duration rainfall time series matrix; Step S14: Based on the multi-duration rainfall time series matrix, the consistency of the total rainfall relationship in each duration period is judged to verify the regularity that the short-duration rainfall is not greater than the long-duration rainfall, and the duration consistency verification result is obtained; Step S15: Based on the multi-duration rainfall time series matrix, the gradient of the average rainfall intensity of each duration is judged, and the proportional relationship is calculated to verify whether it conforms to the intensity decreasing law that the short-duration rainfall intensity is higher than the long-duration rainfall intensity, and obtain the rainfall intensity law verification result; Step S16: Based on the duration consistency verification results and the rainfall intensity law verification results, the validity of the supplemented rainfall series data is screened to obtain multiple series of short-duration rainstorm data sets.

3. The frequency analysis method of multiple series of short-duration rainstorm data according to claim 1, characterized in that: The reliability review of the multi-series short-duration rainstorm dataset in step S2 includes: Data source identification verification and collection equipment information verification were performed on multiple series of short-duration rainstorm datasets to obtain site equipment integrity confirmation results; Based on the site equipment integrity confirmation results, a consistency check of observation time accuracy, decimal uniformity, and unit standardization was performed on multiple series of short-duration rainstorm datasets to obtain format integrity verification results. Based on the format integrity verification results, the maximum and minimum values in the multi-series short-duration rainstorm data set are logically judged, and the rationality of the extreme values is reviewed to obtain the extreme value validity identification results; Multi-year trend fluctuation analysis was conducted on multiple series of short-duration rainstorm data sets to identify years with sudden changes and years with abnormal missing data, and to obtain stability test results for daily rainfall series. Based on the comparative analysis of multiple series of short-duration rainstorm datasets and the preset meteorological station data in the same area, the spatial consistency deviation is determined and the station spatial consistency test results are obtained; Based on the site equipment integrity confirmation results, format integrity verification results, extreme value validity identification results, daily rainfall series stability detection results and site spatial consistency test results, abnormal data entries in multiple series of short-duration rainstorm data sets are identified and marked to generate reliability review results.

4. The frequency analysis method for multiple series of short-duration rainstorm data according to claim 1, characterized in that: The representativeness review of the multi-series short-duration rainstorm dataset in step S2 includes: Statistical analysis of the number of observation years, wet and dry seasons, and time period coverage at each station was conducted on multiple series of short-duration rainstorm datasets to obtain the results of sample time integrity analysis. Based on the results of sample time integrity analysis, the short series data and long series data in the multi-series short-duration rainstorm data set are identified, and the mean, coefficient of variation and skewness coefficient are compared and analyzed to obtain the statistical parameter proximity discrimination results; Based on the results of statistical parameter proximity discrimination, abnormal station samples in multiple series of short-duration rainstorm datasets are identified and marked to generate representative review results.

5. The frequency analysis method of multiple series of short-duration rainstorm data according to claim 1, characterized in that: The consistency review of multiple series of short-duration rainstorm datasets in step S2 includes: Identify the annual maximum rainfall sequence in multiple series of short-duration rainfall data sets, and reconstruct the time series to obtain the rainfall annual extreme value time series data set; Based on the extreme value time series data set of heavy rain years, the rank construction and order sequence difference accumulation of each heavy rain series are carried out to obtain the UFk-UBk trend test statistic series; Based on the UFk-UBk trend test statistic sequence, the positive and reverse order statistical curves are constructed and superimposed to obtain the rainstorm sequence consistency trend identification map; Based on the rainstorm sequence consistency trend identification diagram, the rainstorm sequence with significant mutation points is identified, and the rationality of the abnormal trend is verified to obtain the trend abnormality rationality checklist; Based on the rainstorm sequence consistency trend identification diagram and trend anomaly rationality check table, multiple series of short-duration rainstorm datasets are classified and labeled to generate consistency review results.

6. The frequency analysis method for multiple series of short-duration rainstorm data according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing parameter statistical calculation on the effective rainstorm frequency analysis sample sequence to obtain a rainstorm frequency analysis sample statistical parameter table, wherein the rainstorm frequency analysis sample statistical parameter table includes the mean, coefficient of variation, and skewness coefficient of each duration rainstorm sequence; Step S32: Pre-calculating the shape parameters, scale parameters, and location parameters of the Pearson III probability density function based on the statistical parameter table of the rainstorm frequency analysis samples to obtain a parameter set of the Pearson III distribution function; Step S33: sorting the effective rainstorm frequency analysis sample sequence in ascending order, and constructing the empirical frequency to obtain the empirical frequency point data set; Step S34: performing fitting and matching based on the Pearson III distribution function parameter set and the empirical frequency point data set to obtain a frequency distribution fitting function; Step S35: performing a KS test based on the frequency distribution fitting function to obtain a KS goodness of fit test report; Step S36: constructing a frequency distribution curve using a frequency distribution fitting function based on the KS goodness of fit test report.

7. The frequency analysis method for multiple series of short-duration rainstorm data according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: intercepting the high-value section in the frequency distribution curve, and performing deviation identification and abnormality judgment to obtain a preliminary judgment sample of heavy rainstorm; Step S42: Analyze and compare the list of suspected heavy rainstorm samples with the preset history to generate an estimated value of the heavy rainstorm recurrence period; Step S43: Based on the estimated value of the return period of the heavy rainstorm, the effective rainstorm frequency analysis sample sequence is reinforced with survey samples and reconstructed with frequency sorting to obtain an extended empirical frequency sequence containing survey samples; Step S44: performing frequency repositioning and re-weighting processing on the heavy rain value points based on the extended empirical frequency sequence to obtain a frequency distribution curve correction point sequence; Step S45: refitting the frequency distribution curve correction point sequence, and adjusting the Pearson III distribution function parameter set by optimizing the fitting method to obtain the corrected frequency parameters.

8. The frequency analysis method for multiple series of short-duration rainstorm data according to claim 7, characterized in that: Step S45 includes the following steps: Step S451: performing residual square value calculation and preliminary fitting of the distribution curve on the frequency distribution curve correction point sequence to obtain a preliminary frequency fitting residual sequence; Step S452: Calculate the squared deviation and minimized value of the shape parameter, scale parameter, and location parameter of the Pearson III distribution function based on the preliminary residual sequence of frequency fitting to obtain a preliminary optimal frequency parameter set; Step S453: Verify the preliminary optimal frequency parameter set and analyze the deviation trend between the fitting curve and the reliable rainstorm sample points to obtain the estimated adjustment parameter values; Step S454: Based on the preliminary optimal frequency parameter set and the estimated adjustment recommended parameter values, a weighted fusion is performed to construct a Pearson III distribution function correction parameter combination with the best fitting performance to obtain the corrected frequency parameters.

9. The frequency analysis method for multiple series of short-duration rainstorm data according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing unified format conversion and distribution standardization processing on the Pearson III distribution parameters in the corrected frequency parameters to obtain a standardized frequency parameter data set; Step S52: Calculate the frequency factor of the return period probability of each combination based on the standardized frequency parameter data set and the preset design return period to obtain a return period-frequency factor matching table; Step S53: Calculate the rainstorm data of each station and duration based on the modified frequency parameters and the recurrence period-frequency factor matching table to obtain the design rainstorm value matrix; Step S54: restructure and format the design rainstorm value matrix according to the duration dimension to obtain a design rainstorm value result table under multiple durations and frequencies.

10. The frequency analysis method of multiple series of short-duration rainstorm data according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: performing incremental comparison analysis on different duration values at the same site and the same frequency in the design rainstorm value result table to obtain a duration consistency verification result; Step S62: Based on the temporal consistency check mark table, the station data with temporal crossover, mutation, and reversal trends are identified and retrospectively corrected to obtain the optimized design rainstorm value dataset; Step S63: performing regional trend comparison on the design rainstorm values of different stations with the same duration and return period in the optimized design rainstorm value data set to obtain spatial rationality test results; Step S64: Identify unreasonable values based on the optimized design rainstorm value dataset and the spatial rationality test results, and perform fitting adjustments to obtain the final design result output dataset.

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