Marine environment dynamic element frequency analysis method and device, medium and product

Abstract

The invention discloses a marine environment dynamic element frequency analysis method and device, a medium and a product, and relates to the field of coast and ocean engineering.The method comprises the steps that a marine environment dynamic element continuous time sequence of a research area is obtained; using a super-threshold method and a time window for screening extreme events to construct sample groups corresponding to different thresholds; fitting a sample group corresponding to each threshold value by using a generalized Pareto distribution function, and determining a shape parameter and a scale parameter corresponding to each threshold value by taking a minimum tail residual quadratic sum between a sample value and a theoretical value in the sample group as a parameter fitting target; and taking a threshold value corresponding to the minimum tail residual quadratic sum between a sample value and a theoretical value as an optimal threshold value, further determining an optimal shape parameter, an optimal scale parameter and an optimal sample group, and finally calculating the extreme value marine environment dynamic element. According to the method, through a bidirectional support mechanism of a sampling method and a parameter estimation method, the accuracy of extreme value marine environment dynamic element calculation is improved.

Description

Technical Field

[0001] The present application relates to the field of coastal and marine engineering, and in particular to a method, device, medium and product for frequency analysis of dynamic elements of marine environment. Background Art

[0002] Coastal and marine engineering often face complex, changeable and extreme marine dynamic environments, such as huge waves, rapids and storm surges, which pose severe challenges to the integrity of engineering structures and operational safety. In order to scientifically respond to the adverse effects of extreme marine environments on engineering safety, the design process usually uses multi-year extreme marine environmental dynamic elements based on frequency analysis as design standards. These extreme elements are key parameters in engineering design, which directly determine the safety and reliability of the project and are an important basis for evaluating whether it can meet safety requirements. Therefore, it is crucial to establish a reliable frequency analysis method for marine environmental dynamic elements and accurately deduce extreme marine environmental dynamic elements for the design and safe operation of coastal and marine engineering.

[0003] Existing frequency analysis methods for marine environmental dynamic elements are usually based on the time series of marine environmental dynamic elements. Extreme samples are extracted through different sampling methods (such as annual extreme value method, annual N-maximum value method and super-threshold method); then, different parameter estimation methods (such as least squares method, moment estimation method and maximum likelihood estimation method) are used to fit the unknown parameters of the theoretical frequency distribution of extreme samples (such as extreme value type I distribution, Pearson type III distribution, generalized extreme value distribution and generalized Pareto distribution); finally, the theoretical frequency distribution function of extreme samples is determined based on these parameters, and then the extreme value of marine environmental dynamic elements that occurs once in many years is estimated. However, in terms of sampling methods, the annual extreme value method has a low utilization rate of samples and it is difficult to fully reflect the occurrence characteristics of extreme events; the annual N-maximum value method and super-threshold method have greater empiricism and subjectivity in determining the screening criteria for extreme events. These defects will have a significant impact on the accuracy of frequency distribution parameter estimation. In terms of frequency distribution parameter estimation methods, the least squares method is sensitive to outliers and tends to minimize the overall error, which may ignore the characteristics of the tail data, resulting in insufficient fitting of extreme events; the moment estimation method has low estimation accuracy and is sensitive to the tail data, which may lead to unstable parameter estimation; the maximum likelihood estimation method is sensitive to extreme events or data outliers, and usually requires numerical optimization algorithms (such as Newton's method, gradient descent method, etc.) to solve the optimal parameters. If the initial parameters are not properly selected, the algorithm may converge to the local optimal solution rather than the global optimal solution, thereby affecting the accuracy of parameter estimation. In the extreme value analysis of marine environmental dynamic elements, the distribution characteristics of extreme events and outliers are the focus of research. However, when conducting frequency analysis of marine environmental dynamic elements based on existing sampling methods and frequency distribution parameter estimation methods, there are still deficiencies in the accuracy of estimating extreme marine environmental dynamic elements. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, medium and product for frequency analysis of marine environmental dynamic elements, which can accurately calculate extreme marine environmental dynamic elements.

[0005] To achieve the above purpose, this application provides the following solutions: In the first aspect, this application provides a method for frequency analysis of marine environmental dynamic elements, including: Obtain the continuous time series of marine environmental dynamic elements in the research area; According to the continuous time series of marine environmental dynamic elements, use the over-threshold method and the time window for screening extreme events to construct sample groups corresponding to different thresholds; each sample in the sample group corresponding to each threshold is independently and identically distributed; For each sample group corresponding to each threshold, use the generalized Pareto distribution function to fit the sample group, and take the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting target, and determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold; the theoretical value is determined according to the threshold, shape parameter, scale parameter and the empirical exceedance probability of the corresponding sample value; Compare the sum of squared tail residuals between the sample values and the theoretical values corresponding to each threshold, take the threshold with the minimum sum of squared tail residuals as the optimal threshold, and take the shape parameter, scale parameter and sample group corresponding to the optimal threshold as the optimal shape parameter, optimal scale parameter and optimal sample group; According to the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group, apply the generalized Pareto distribution function to calculate extreme marine environmental dynamic elements.

[0006] In the second aspect, this application provides a device for frequency analysis of marine environmental dynamic elements, including: A data acquisition module for obtaining the continuous time series of marine environmental dynamic elements in the research area; A sampling module for constructing sample groups corresponding to different thresholds according to the continuous time series of marine environmental dynamic elements by using the over-threshold method and the time window for screening extreme events; each sample in the sample group corresponding to each threshold is independently and identically distributed; A parameter fitting module for using the generalized Pareto distribution function to fit each sample group corresponding to each threshold, and taking the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting target, and determining the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold; the theoretical value is determined according to the threshold, shape parameter, scale parameter and the empirical exceedance probability of the corresponding sample value; An optimal parameter determination module, configured to compare the sum of squared tail residuals between the sample values corresponding to each threshold and the theoretical values, and use the threshold with the minimum sum of squared tail residuals as the optimal threshold, and use the shape parameter, scale parameter, and sample group corresponding to the optimal threshold as the optimal shape parameter, optimal scale parameter, and optimal sample group; An extrapolation module, configured to extrapolate the extreme marine environmental dynamic elements by applying the generalized Pareto distribution function according to the optimal threshold, optimal shape parameter, optimal scale parameter, and optimal sample group.

[0007] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned frequency analysis method for marine environmental dynamic elements.

[0008] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned frequency analysis method for marine environmental dynamic elements.

[0009] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above-mentioned frequency analysis method for marine environmental dynamic elements.

[0010] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application: The present application provides a method, apparatus, medium and product for frequency analysis of marine environmental dynamic elements, specifically a method, apparatus, medium and product for frequency analysis of marine environmental dynamic elements based on two-way support of sampling and parameter estimation. Through the two-way support mechanism of the sampling method and the parameter estimation method, a more scientific and reliable frequency analysis system is constructed: the sampling method screens samples by combining the over-threshold method and the time window for screening extreme events. For each threshold, the screened samples are independently and identically distributed, ensuring the requirement of independent and identical distribution of samples for frequency analysis and providing a high-quality data basis for parameter estimation; the parameter estimation method optimizes the threshold based on the criterion of minimizing the fitting error between the sample and the tail of the theoretical distribution (the sum of the squared tail residuals between the sample value and the theoretical value) to ensure the objectivity and uniqueness of the threshold selection, and then ensure the objectivity of parameter estimation and sampling. Moreover, focusing on the tail characteristics of extreme events improves the characterization accuracy of the frequency distribution model for extreme events, thus more accurately reflecting the distribution law of extreme marine environmental dynamic elements and improving the accuracy of extrapolating extreme marine environmental dynamic elements. Therefore, in the present application, by providing a data basis for parameter estimation through sampling, and then through the two-way feedback process of optimizing the threshold and sampling by parameter estimation, the objectivity of parameter estimation and sampling can be ensured, thereby effectively reducing the subjectivity and uncertainty in the characterization of extreme events and significantly improving the robustness and accuracy of extreme value extrapolation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is an application environment diagram of a method for frequency analysis of marine environmental dynamic elements in an embodiment of the present application; Figure 2 It is a flowchart of a method for frequency analysis of marine environmental dynamic elements provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the technical concept of a method for frequency analysis of marine environmental dynamic elements provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the functional modules of a device for frequency analysis of marine environmental dynamic elements provided in another embodiment of the present application; Figure 5 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0014] Currently, when performing frequency analysis of marine environmental dynamic elements based on existing sampling methods and frequency distribution parameter estimation methods, there are still deficiencies in the accuracy of calculating extreme marine environmental dynamic elements. In this regard, the purpose of the present application is to provide a method, device, medium, and product for frequency analysis of marine environmental dynamic elements, specifically a method, device, medium, and product for frequency analysis of marine environmental dynamic elements based on the two-way support of sampling and parameter estimation. Through the two-way support mechanism of the sampling method and the parameter estimation method, a more scientific and reliable frequency analysis system is constructed, which can accurately calculate extreme marine environmental dynamic elements.

[0015] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0016] The method for frequency analysis of marine environmental dynamic elements provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the continuous time series of ocean environmental dynamic elements in the research area to the server. After receiving the continuous time series of ocean environmental dynamic elements in the research area, the server constructs sample groups corresponding to different thresholds according to the continuous time series of ocean environmental dynamic elements, using the exceedance threshold method and the time window for screening extreme events; each sample in the sample group corresponding to each threshold is independently and identically distributed; for each sample group corresponding to each threshold, the sample group is fitted using the generalized Pareto distribution function, and the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold are determined with the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting objective; the theoretical value is determined according to the empirical exceedance probability of the threshold, shape parameter, scale parameter, and the corresponding sample value; compare the sum of squared tail residuals between the sample values and the theoretical values corresponding to each threshold, take the threshold with the minimum sum of squared tail residuals as the optimal threshold, and take the shape parameter, scale parameter, and sample group corresponding to the optimal threshold as the optimal shape parameter, optimal scale parameter, and optimal sample group; according to the optimal threshold, optimal shape parameter, optimal scale parameter, and optimal sample group, apply the generalized Pareto distribution function to estimate the extreme ocean environmental dynamic elements. The server can feedback the estimated extreme ocean environmental dynamic elements to the terminal. In addition, in some embodiments, the ocean environmental dynamic element frequency analysis method can also be implemented separately by the server or the terminal. For example, the terminal can directly perform the ocean environmental dynamic element frequency analysis on the continuous time series of ocean environmental dynamic elements, or the server can obtain the continuous time series of ocean environmental dynamic elements from the data storage system and perform the ocean environmental dynamic element frequency analysis.

[0017] Among them, the terminal can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablets, Internet of Things devices, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0018] In an exemplary embodiment, as Figure 2 and Figure 3 shown, a method for analyzing the frequency of ocean environmental dynamic elements is provided. This method is executed by a computer device, and specifically can be executed separately by a computer device such as a terminal or a server, or jointly executed by the terminal and the server. In the embodiments of the present application, taking this method applied to Figure 1 the server in

[0019] Step 101, obtaining a continuous time series of marine environmental dynamic elements in the study area.

[0020] Step 102, based on the continuous time series of the marine environment dynamic elements, using the super-threshold method and the time window for screening extreme events to construct sample groups corresponding to different thresholds; each sample in the sample group corresponding to each threshold is independent and identically distributed.

[0021] Step 103, for each sample group corresponding to a threshold, a generalized Pareto distribution function is used to fit the sample group, and the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold are determined with the minimum sum of squares of the tail residuals between the sample values ​​in the sample group and the theoretical values ​​as the parameter fitting target; the theoretical value is determined based on the threshold, shape parameter, scale parameter and the empirical exceedance probability of the corresponding sample value.

[0022] Step 104, compare the tail residual sum of squares between the sample values ​​corresponding to each threshold and the theoretical value, take the threshold with the smallest tail residual sum of squares as the optimal threshold, and take the shape parameter, scale parameter and sample group corresponding to the optimal threshold as the optimal shape parameter, optimal scale parameter and optimal sample group.

[0023] Step 105, based on the optimal threshold, the optimal shape parameter, the optimal scale parameter and the optimal sample group, the generalized Pareto distribution function is applied to estimate the extreme ocean environment dynamic factors.

[0024] Implementing the above steps 101 to 105 has the following technical effects: (1) Sample screening is performed by combining the super-threshold method with the time window for screening extreme events. For each threshold, the screened samples are independent and identically distributed. The sample screening method not only improves the sample utilization rate, but also ensures the requirement of frequency analysis for independent and identically distributed samples, thereby ensuring the accuracy of the frequency analysis results.

[0025] (2) Threshold optimization is performed based on the criterion of minimizing the tail fitting error between the sample and theoretical distribution (the sum of squares of the tail residuals between the sample value and the theoretical value is minimized), which ensures the objectivity and uniqueness of the threshold selection, thereby effectively improving the accuracy of the calculation of extreme marine environmental dynamic factors.

[0026] (3) Taking the minimization of tail error as the core optimization goal, we focus on the tail characteristics of extreme events and improve the accuracy of the frequency distribution model in representing extreme events, thereby more accurately reflecting the distribution law of extreme marine environmental dynamic elements and improving the accuracy of the calculation of extreme marine environmental dynamic elements.

[0027] (4) A more scientific and reliable frequency analysis system is constructed through a two-way support mechanism of sampling methods and parameter estimation methods: the sampling method provides a high-quality data basis for parameter estimation by scientifically screening extreme samples; the parameter estimation result is based on the criterion of minimizing the fitting error between the sample and the tail of the theoretical distribution as the parameter fitting goal, and through model verification and feedback, dynamically optimizes the rationality and self-adaptability of the sampling standard. The synergistic effect of the two effectively reduces the subjectivity and uncertainty in the characterization of extreme events, and significantly improves the robustness and accuracy of extreme value calculation.

[0028] In another exemplary embodiment of the present application, in step 101, obtaining the continuous time series of marine environmental dynamic elements in the study area specifically includes: (1-1) Obtaining multi-source marine environmental dynamic element data in the study area.

[0029] For the marine environmental dynamic elements in the study area (such as waves, flow velocity, storm surge level, etc.), collect multi-source data, including on-site observation data, reanalysis data, numerical simulation results, etc., to construct a comprehensive data basis.

[0030] (1-2) Perform preprocessing and integration processing on the multi-source marine environmental dynamic element data to obtain the integrated continuous time series of marine environmental dynamic elements; the preprocessing includes format conversion, outlier removal, and missing value filling.

[0031] Convert data from different sources into a unified format (such as NetCDF, CSV, etc.) to ensure the consistency of various data (on-site observation data, reanalysis data, numerical simulation results, etc.) in terms of time stamps, spatial positions, physical quantity units, etc. Conduct a preliminary inspection of the data, identify and remove outliers (such as values beyond a reasonable range). Mark the missing values to provide a basis for subsequent filling. Concatenate data from different sources in chronological order. For the time-overlapping part, select data according to the priority of "on-site observation data > reanalysis data > numerical simulation results". For time periods without overlap, extract information from multi-source data to fill in the missing values according to the same priority. For time periods still with missing values, use interpolation methods (such as linear interpolation, spline interpolation) to fill in the blanks to ensure the continuity of the time series. Integrate multi-source data through the above operations to form a complete and continuous time series. Statistically analyze the integrated time series to obtain the total number (Nt) of marine environmental dynamic element values in the time series.

[0032] In another exemplary embodiment of the present application, in step 102, according to the continuous time series of marine environmental dynamic elements, use the over-threshold method and the time window for screening extreme events to construct sample groups corresponding to different thresholds, specifically including: (2-1) Determine the extreme events in the continuous time series of the marine environmental dynamic elements according to the meteorological processes (such as typhoons, cold snaps, etc.) and marine hydrological processes (such as storm surges, large waves, etc.) in the target area.

[0033] (2-2) Calculate the duration of each extreme event.

[0034] Based on the duration of the impact of the meteorological process on the selected location (such as from 24 hours before the impact to 24 hours after the end of the impact of a typhoon or cold snap, etc.), calculate the duration of each extreme event.

[0035] (2-3) Take the mean value of the durations of each extreme event as the time window for screening extreme events.

[0036] Statistically analyze the distribution characteristics of the durations (such as mean value, maximum value, minimum value). Take the mean value of the durations of extreme events as the time window for screening extreme events.

[0037] (2-4) Conduct a statistical analysis on the continuous time series of the marine environmental dynamic elements to determine the statistical distribution characteristics of the series, including the mean value ( h avg ), maximum value ( h max ), and minimum value ( h min ).

[0038] (2-5) Determine the upper and lower boundary values of the threshold according to the statistical distribution characteristics of the series.

[0039] Take the maximum value ( h max ) and mean value ( h avg ) of the time series as the upper and lower boundary values for setting the threshold respectively.

[0040] (2-6) Determine the interval for setting the threshold according to the upper and lower boundary values of the threshold.

[0041] According to the difference between the maximum value ( h max ) and mean value ( h avg ) of the time series, determine the interval (Δ μ ) for setting the threshold. The calculation formula is as shown in the following formula.

[0042] ; In the formula, N μ is the number of threshold settings, which can be determined according to actual needs.

[0043] (2-7) Set different thresholds according to the intervals set by the threshold and the upper and lower boundary values of the threshold.

[0044] Based on the above intervals, calculate different thresholds through the following formula ( μ i ).

[0045] ; In the formula, μ i is the i th threshold, i is the threshold serial number.

[0046] (2-8) For each threshold, screen out the element values in the continuous time series of the marine environmental dynamic elements that exceed the threshold; the time interval between adjacent element values that exceed the threshold is not less than the time window for screening extreme events.

[0047] For each threshold ( μ i ), screen out the element values in the time series that exceed the threshold ( μ i ), and perform secondary screening using the time window determined in step (2-3) to ensure that the time interval between adjacent events that exceed the threshold is not less than the time window of the extreme events determined in step (2-3), forming an independent and identically distributed sample group corresponding to different thresholds.

[0048] (2-9) The element values that exceed the threshold screened out form a sample group corresponding to the threshold.

[0049] In another exemplary embodiment of the present application, in step 103, for each sample group corresponding to a threshold, use the generalized Pareto distribution function to fit the sample group, and take the minimum sum of the squared tail residuals between the sample values in the sample group and the theoretical values as the parameter fitting target to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold, specifically including: For each sample group corresponding to a threshold, sort each sample according to the sample values in the sample group to obtain a sorted sample group.

[0050] For different thresholds μ i ( i = 1, 2, 3,..., N μ ), and their corresponding sample groups ( X i ), count the total number of samples contained in the sample group ( N i ). For the sample group X iThe sample values in it are sorted in descending order.

[0051] (3-2) Calculate the empirical exceedance probability of each sample according to the arrangement serial number and the number of samples in the sorted sample group.

[0052] Empirical exceedance probability ( P ij ) is calculated according to the following formula.

[0053] ; In the formula, P ij is the i th threshold μ i corresponding to the sample group X i and is the empirical exceedance probability of the sample ranked as the j th largest value in the sorted sample group; i is the threshold serial number, j is the i th serial number of the samples in the sample group X i arranged in descending order corresponding to the threshold.

[0054] (3-3) Determine the formula for calculating the sum of squared tail residuals between each sample value and the corresponding theoretical value according to the empirical exceedance probability.

[0055] In order to improve the representation accuracy of the frequency distribution model for extreme events, this application focuses on the tail characteristics of extreme events and takes the minimization of the sum of squared tail residuals between sample values and theoretical values as the core optimization goal. The sum of squared errors between sample values with empirical exceedance probability not greater than a preset value (such as 0.05) and theoretical values is used as the sum of squared tail residuals between sample values and theoretical values, and the calculation formula is shown as follows.

[0056]

[0057] Among them, ; In the formula, TRSS i is the sum of squared tail residuals between the sample value corresponding to the threshold μ i and the theoretical value; N it is the μ i corresponding number of samples with empirical exceedance probability not greater than the preset value after the sample group X i is arranged in descending order; x ij is the μ iThe corresponding sample group X i The j sample value of the y ij th sample after descending order; x ij is the theoretical value corresponding to the sample value P ij is the threshold μ i corresponding to the sample group X i The empirical exceedance probability of the sample ranked j in the sample group; σ i and ξ i are the scale parameter and shape parameter of the generalized Pareto distribution corresponding to the threshold μ i .

[0058] (3 - 4) Fit the sample group using the generalized Pareto distribution function, and take the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting objective to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold.

[0059] Use the generalized Pareto distribution function to fit the independently and identically distributed sample group screened in step 102. The generalized Pareto distribution function is shown as follows. Substitute different thresholds ( μ i , i = 1, 2, 3,..., N μ ) and the corresponding sample values in their corresponding sample groups X i into the generalized Pareto distribution function of the following formula, and take the minimum calculated value of the sum of squared tail residuals as the judgment basis to determine the scale parameter σ i , shape parameter ξ i .

[0060] ; In the formula, G () is the generalized Pareto distribution function; μ i is the threshold; x ij is the μ i th sample value after descending order of the sample group corresponding to the threshold X i ;( j x ​ij -[[]]END]] μ i ) > 0.

[0061] After that, compare the sum of squared tail residuals between the sample values and the theoretical values at different thresholds, and select the threshold that minimizes the sum of squared tail residuals as the optimal threshold ( μ opt ). μ opt The samples corresponding to the optimal threshold ( X opt ) are used as the optimal samples ( σ opt ), and the corresponding optimal scale parameter ( ξ opt ) and optimal shape parameter (

[0062] In another exemplary embodiment of the present application, in step 105, calculate the extreme marine environmental dynamic element value with a recurrence interval of many years: Based on the optimal threshold ( μ opt ), the optimal scale parameter ( σ opt ), the optimal shape parameter ( ξ opt ), and the optimal samples ( X opt ), calculate the extreme marine environmental dynamic element value with a recurrence interval of many years. According to the above generalized Pareto distribution function, the calculation of the extreme marine environmental dynamic element with a recurrence period of T rp years is shown in the following formula.

[0063]

[0064] In the formula, y rp is T rp the extreme marine environmental dynamic element value with a recurrence interval of λ opt years; N opt is the ratio of the number of samples ( N t ) in the optimal samples to the total number of samples, that is ; μ opt is the optimal threshold; σ opt is the optimal scale parameter; ξ opt is the optimal shape parameter.

[0065] This application addresses the difficulties in the frequency analysis of ocean dynamic environmental elements, especially the deficiencies of existing sampling methods and parameter estimation methods in capturing the distribution characteristics of extreme events. An improved frequency analysis method is proposed, which constructs a more scientific and reliable frequency analysis system through a two-way support mechanism between the sampling method and the parameter estimation method. The method of this application not only optimizes the screening and characterization capabilities of extreme events but also provides solid technical support for in-depth research on the distribution characteristics of ocean environmental dynamic elements and accurate calculation of extreme ocean environmental dynamic elements.

[0066] This application also provides an application scenario that applies the above-mentioned frequency analysis method for ocean environmental dynamic elements. Specifically: The frequency analysis method for ocean environmental dynamic elements provided in this embodiment can be applied in the safety assessment scenario of ocean engineering structures. This scenario includes a data acquisition link, a frequency analysis link, and a safety assessment link; the data acquisition link is used to collect multi-source ocean environmental dynamic element data; the frequency analysis link is used to calculate the extreme value of ocean environmental dynamic elements based on the multi-source ocean environmental dynamic element data; the safety assessment link is used to evaluate the safety of ocean engineering structures based on the calculated value of ocean environmental dynamic elements. The frequency analysis method for ocean environmental dynamic elements provided in this embodiment belongs to the frequency analysis link.

[0067] Based on the same inventive concept, the embodiment of this application also provides an ocean environmental dynamic element frequency analysis device for implementing the above-mentioned frequency analysis method for ocean environmental dynamic elements. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the ocean environmental dynamic element frequency analysis device provided below can refer to the limitations of the frequency analysis method for ocean environmental dynamic elements in the above text and will not be elaborated here.

[0068] In an exemplary embodiment, as Figure 4 shown, an ocean environmental dynamic element frequency analysis device is provided, including: A data acquisition module M1, configured to acquire a continuous time series of ocean environmental dynamic elements in a research area.

[0069] A sampling module M2, configured to construct sample groups corresponding to different thresholds according to the continuous time series of ocean environmental dynamic elements by using the over-threshold method and a time window for screening extreme events; each sample in the sample group corresponding to each threshold is independently and identically distributed.

[0070] The parameter fitting module M3 is used to fit each sample group corresponding to each threshold value by using the generalized Pareto distribution function, and determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold value with the goal of minimizing the sum of squared tail residuals between the sample values and the theoretical values in the sample group; the theoretical value is determined according to the empirical exceedance probability of the threshold value, shape parameter, scale parameter, and the corresponding sample value.

[0071] The optimal parameter determination module M4 is used to compare the sum of squared tail residuals between the sample values and the theoretical values corresponding to each threshold value, take the threshold value with the minimum sum of squared tail residuals as the optimal threshold value, and take the shape parameter, scale parameter, and sample group corresponding to the optimal threshold value as the optimal shape parameter, optimal scale parameter, and optimal sample group.

[0072] The extrapolation module M5 is used to extrapolate the extreme marine environmental dynamic elements by applying the generalized Pareto distribution function according to the optimal threshold value, optimal shape parameter, optimal scale parameter, and optimal sample group.

[0073] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, memory, and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the intermediate data and result data of the frequency analysis of marine environmental dynamic elements. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for frequency analysis of marine environmental dynamic elements.

[0074] Those skilled in the art can understand, Figure 5The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0075] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0076] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0079] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0080] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0081] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for frequency analysis of marine environmental dynamic elements, characterized in that, Including: Obtaining a continuous time series of marine environmental dynamic elements in the study area; According to the continuous time series of the marine environmental dynamic elements, using the exceedance threshold method and the time window for screening extreme events to construct sample groups corresponding to different thresholds; each sample in the sample group corresponding to each threshold is independently and identically distributed; For each sample group corresponding to each threshold, fitting the sample group using the generalized Pareto distribution function, and taking the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting objective to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold; the theoretical value is determined according to the threshold, shape parameter, scale parameter, and the empirical exceedance probability of the corresponding sample value; Comparing the sum of squared tail residuals between the sample values and the theoretical values corresponding to each threshold, taking the threshold with the minimum sum of squared tail residuals as the optimal threshold, and taking the shape parameter, scale parameter, and sample group corresponding to the optimal threshold as the optimal shape parameter, optimal scale parameter, and optimal sample group; According to the optimal threshold, optimal shape parameter, optimal scale parameter, and optimal sample group, applying the generalized Pareto distribution function to estimate the extreme marine environmental dynamic elements.

2. The method for analyzing the frequency of ocean environmental dynamic elements according to claim 1, wherein Obtaining a continuous time series of marine environmental dynamic elements in the study area specifically includes: Obtaining multi-source marine environmental dynamic element data in the study area; Performing preprocessing and integration processing on the multi-source marine environmental dynamic element data to obtain an integrated continuous time series of marine environmental dynamic elements; the preprocessing includes format conversion, outlier removal, and missing value filling.

3. The method for analyzing the frequencies of marine environmental dynamic factors according to claim 1, wherein According to the continuous time series of the marine environmental dynamic elements, using the exceedance threshold method and the time window for screening extreme events to construct sample groups corresponding to different thresholds specifically includes: Determining extreme events in the continuous time series of the marine environmental dynamic elements according to the meteorological process and ocean hydrological process in the target area; Calculating the duration of each extreme event; Taking the mean of the durations of each extreme event as the time window for screening extreme events; Performing statistical analysis on the continuous time series of the marine environmental dynamic elements to determine the statistical distribution characteristics of the series; Determining the upper and lower boundary values of the threshold according to the statistical distribution characteristics of the series; Determining the interval for threshold setting according to the upper and lower boundary values of the threshold; Setting different thresholds according to the interval for threshold setting and the upper and lower boundary values of the threshold; For each threshold, screening out the element values in the continuous time series of the marine environmental dynamic elements that exceed the threshold; the time interval between adjacent screened element values that exceed the threshold is not less than the time window for screening extreme events; The screened element values that exceed the threshold form the sample group corresponding to the threshold.

4. The method for analyzing the frequency of marine environmental dynamic elements according to claim 1, wherein For each sample group corresponding to each threshold, fitting the sample group using the generalized Pareto distribution function, and taking the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting objective to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold specifically includes: For each sample group corresponding to each threshold, sorting each sample according to the sample values in the sample group to obtain a sorted sample group; Calculating the empirical exceedance probability of each sample according to the arrangement serial number and sample quantity of each sample in the sorted sample group; Determine the formula for calculating the sum of squared tail residuals between each sample value and the corresponding theoretical value according to the exceedance probability of experience; Use the generalized Pareto distribution function to fit the sample group, and take the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting target to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold.

5. The method for analyzing the frequencies of marine environmental dynamic elements according to claim 4, wherein The expression for the sum of squared tail residuals between the sample value and the theoretical value is: ； Among them, ; In the formula, TRSS i is the sum of squared tail residuals between the sample value and the theoretical value corresponding to the threshold μ i ; N it is the number of samples whose empirical exceedance probability is not greater than the preset value after the sample group μ i corresponding to the threshold X i is sorted in descending order; x ij is the sample value of the μ i -th sample after the sample group X i corresponding to the threshold j is sorted in descending order; y ij is the theoretical value corresponding to the sample value x ij ; P ij is the threshold μ i corresponding to the X i -th sample in the sample group j in terms of empirical exceedance probability; σ i and ξ i are the scale parameter and shape parameter of the generalized Pareto distribution corresponding to the threshold μ i .

6. The method for analyzing the frequency of marine environmental dynamic elements according to claim 1, wherein The expression for extrapolating the extreme marine environmental dynamic elements is: ； In the formula, y rp is T rp the extreme value of the marine environmental dynamic element with a recurrence period of λ opt the ratio of the number of samples in the optimal sample group to the total number of samples; μ opt is the optimal threshold; σ opt is the optimal scale parameter; ξ opt is the optimal shape parameter; T rp is the recurrence period.

7. An apparatus for frequency analysis of marine environmental dynamic elements, characterized in that, including: A data acquisition module for acquiring the continuous time series of marine environmental dynamic elements in the study area; A sampling module for constructing sample groups corresponding to different thresholds according to the continuous time series of the marine environmental dynamic elements by using the threshold exceedance method and the time window for screening extreme events; each sample in the sample group corresponding to each threshold is independently and identically distributed; A parameter fitting module for using the generalized Pareto distribution function to fit the sample group for each threshold corresponding sample group, and taking the minimum sum of squared tail residuals between the sample values and the theoretical values in the sample group as the parameter fitting target to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold; the theoretical value is determined according to the threshold, shape parameter, scale parameter and the exceedance probability of experience of the corresponding sample value; An optimal parameter determination module for comparing the sum of squared tail residuals between the sample values and the theoretical values corresponding to each threshold, taking the threshold with the minimum sum of squared tail residuals as the optimal threshold, and taking the shape parameter, scale parameter and sample group corresponding to the optimal threshold as the optimal shape parameter, optimal scale parameter and optimal sample group; An extrapolation module for extrapolating the extreme marine environmental dynamic elements by applying the generalized Pareto distribution function according to the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for frequency analysis of marine environmental dynamic elements according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for frequency analysis of marine environmental dynamic elements according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for frequency analysis of marine environmental dynamic elements according to any one of claims 1-6.

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