A method, device, medium and product for frequency analysis of marine environmental dynamic elements

Abstract

The present application discloses a method, device, medium and product for frequency analysis of marine environmental dynamic elements, which relates to the field of coastal and marine engineering. The method comprises obtaining a continuous time series of marine environmental dynamic elements in a study area; constructing sample groups corresponding to different threshold values using a superthreshold method and a time window for screening extreme events; fitting the sample groups corresponding to each threshold value using a generalized Pareto distribution function, and determining the shape parameters and scale parameters corresponding to each threshold value with the minimum sum of squared tail residuals between the sample values in the sample group and the theoretical values as the parameter fitting target; taking the threshold value corresponding to the minimum sum of squared tail residuals between the sample values and the theoretical values as the optimal threshold value, and then determining the optimal shape parameters, optimal scale parameters and optimal sample group, and finally inferring the extreme marine environmental dynamic elements. In the present application, the accuracy of inferring the extreme marine environmental dynamic elements is improved through a two-way support mechanism of sampling method and parameter estimation method.

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 projects often face complex, changing, and extreme marine dynamic environments, such as huge waves, rapids, and storm surges. These factors pose severe challenges to the integrity and operational safety of engineering structures. To scientifically address the potential adverse effects of extreme marine environments on engineering safety, the design process often uses multi-year extreme marine environmental dynamic elements based on frequency analysis as design criteria. These extreme elements are key parameters in engineering design, directly determining the safety and reliability of the project and providing an important basis for assessing whether it can meet safety requirements. Therefore, establishing a reliable frequency analysis method for marine environmental dynamic elements and accurately deriving extreme marine environmental dynamic elements are crucial to the design and safe operation of coastal and marine engineering projects.

[0003] Existing frequency analysis methods for marine environmental dynamics are typically based on time series of marine environmental dynamics. Extreme samples are extracted using various sampling methods (such as the annual extreme value method, the annual maximum N value method, and the over-threshold method). Subsequently, various parameter estimation methods (such as the least squares method, the method of moments, and the maximum likelihood method) are used to fit the unknown parameters of the theoretical frequency distributions of these extreme samples (such as the extreme value type I distribution, the Pearson type III distribution, the generalized extreme value distribution, and the generalized Pareto distribution). Finally, based on these parameters, the theoretical frequency distribution function of the extreme samples is determined, and the multi-year extreme value of the marine environmental dynamics is then estimated. However, in terms of sampling methods, the annual extreme value method has a low sample utilization rate and cannot fully reflect the occurrence characteristics of extreme events. The annual maximum N value method and the over-threshold method also have high empirical and subjective requirements in determining the screening criteria for extreme events. These shortcomings significantly affect the accuracy of frequency distribution parameter estimates. Regarding frequency distribution parameter estimation methods, the least squares method is sensitive to outliers and tends to minimize the overall error, potentially ignoring the characteristics of the tail data, leading to inadequate fitting of extreme events. The moment estimation method suffers from low estimation accuracy and is sensitive to the tail data, potentially leading to unstable parameter estimates. The maximum likelihood estimation method is sensitive to extreme events or data outliers and typically requires numerical optimization algorithms (such as Newton's method and gradient descent) to find the optimal parameters. If the initial parameters are not properly selected, the algorithm may converge to a local optimum rather than a global optimum, thus affecting the accuracy of parameter estimation. In the analysis of extreme values of marine environmental dynamics, the distribution characteristics of extreme events and outliers are a key research topic. However, current sampling methods and frequency distribution parameter estimation methods for frequency analysis of marine environmental dynamics still have shortcomings in accurately estimating extreme values of marine environmental dynamics. 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 objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for frequency analysis of dynamic elements of the marine environment, comprising:

[0007] Obtain continuous time series of marine environmental dynamic elements in the study area;

[0008] Based on the continuous time series of the marine environmental dynamic factors, the super-threshold method and the time window for screening extreme events are used to construct sample groups corresponding to different thresholds; each sample in the sample group corresponding to each threshold is independent and identically distributed;

[0009] 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 squared tail residuals between the sample values in the sample group and the theoretical value as the parameter fitting objective; the theoretical value is determined based on the threshold, shape parameter, scale parameter, and the empirical exceedance probability of the corresponding sample value;

[0010] 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;

[0011] According to the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group, the generalized Pareto distribution function is applied to infer the extreme marine environmental dynamic factors.

[0012] In a second aspect, the present application provides a frequency analysis device for marine environment dynamic elements, comprising:

[0013] Data acquisition module, used to obtain continuous time series of marine environmental dynamic elements in the study area;

[0014] A sampling module is used to construct sample groups corresponding to different thresholds based on the continuous time series of the marine environmental dynamic elements using a super-threshold method and a time window for screening extreme events; each sample in the sample group corresponding to each threshold is independent and identically distributed;

[0015] A parameter fitting module is used to fit the sample group corresponding to each threshold using a generalized Pareto distribution function, with the parameter fitting objective being to minimize the sum of squared tail residuals between the sample values in the sample group and the theoretical value, and to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold; the theoretical value is determined based on the threshold, shape parameter, scale parameter, and the empirical exceedance probability of the corresponding sample value;

[0016] The optimal parameter determination module is used to compare the tail residual sum of squares between the sample values corresponding to each threshold and the theoretical value, and 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;

[0017] The inference module is used to infer the extreme marine environmental dynamic factors based on the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group using the generalized Pareto distribution function.

[0018] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for frequency analysis of dynamic elements of the marine environment.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for frequency analysis of dynamic elements of the marine environment.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned method for frequency analysis of dynamic elements of the marine environment when executed by a processor.

[0021] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0022] The present application provides 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 two-way support of sampling and parameter estimation. Through the two-way support mechanism of sampling method and parameter estimation method, a more scientific and reliable frequency analysis system is constructed: the sampling method performs sample screening 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, which ensures the frequency analysis requirement of independent and identically distributed samples and provides 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 theoretical distribution tail (minimizing the sum of squares of the tail residuals between the sample value and the theoretical value), ensuring the objectivity and uniqueness of the threshold selection, and thus ensuring the objectivity of parameter estimation and sampling. In addition, by focusing on the tail characteristics of extreme events, the frequency distribution model's characterization accuracy of extreme events is improved, 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. Therefore, in this application, by providing a data basis for parameter estimation through sampling, and then optimizing the threshold value and sampling through the two-way feedback process of parameter estimation, the objectivity of parameter estimation and sampling can be guaranteed, thereby effectively reducing the subjectivity and uncertainty in the characterization of extreme events, and significantly improving the robustness and accuracy of extreme value estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is an application environment diagram of a method for frequency analysis of dynamic elements of an ocean environment in one embodiment of the present application;

[0025] Figure 2 A flow chart of a method for frequency analysis of dynamic elements of an ocean environment provided in one embodiment of the present application;

[0026] Figure 3 A schematic diagram of the technical concept of a method for frequency analysis of dynamic elements of an ocean environment provided in one embodiment of the present application;

[0027] Figure 4 A schematic diagram of the functional modules of a frequency analysis device for dynamic elements of an ocean environment provided by another embodiment of the present application;

[0028] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] At present, 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 estimating extreme marine environmental dynamic elements. In response to this, the purpose of this 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 bidirectional support of sampling and parameter estimation. Through the bidirectional support mechanism of sampling method and parameter estimation method, a more scientific and reliable frequency analysis system is constructed, which can accurately estimate extreme marine environmental dynamic elements.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] The frequency analysis method of marine environment dynamic elements provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal communicates with the server via a network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated with the server, or placed in the cloud or on other servers. The terminal can send a continuous time series of marine environmental dynamic elements in a study area to a server. After receiving the continuous time series of marine environmental dynamic elements in the study area, the server constructs sample groups corresponding to different thresholds based on the continuous time series of marine environmental dynamic elements using a superthreshold method and a time window for screening extreme events. Each sample in the sample group corresponding to each threshold is independent and identically distributed. For each sample group corresponding to the 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 squared tail residuals between the sample values in the sample group and the theoretical value as the parameter fitting target. The theoretical value is determined based on the threshold, shape parameter, scale parameter, and empirical exceedance probability of the corresponding sample value. The sum of squared tail residuals between the sample values corresponding to each threshold and the theoretical value are compared, and the threshold with the minimum sum of squared tail residuals is determined as the optimal threshold. The shape parameter, scale parameter, and sample group corresponding to the optimal threshold are determined as the optimal shape parameter, optimal scale parameter, and optimal sample group. Based on the optimal threshold, optimal shape parameter, optimal scale parameter, and optimal sample group, the generalized Pareto distribution function is used to infer the extreme marine environmental dynamic elements. The server can provide feedback to the terminal on the calculated extreme ocean environment dynamics. Furthermore, in some embodiments, the ocean environment dynamics frequency analysis method can also be implemented independently by the server or the terminal. For example, the terminal can directly perform ocean environment dynamics frequency analysis on the continuous time series of ocean environment dynamics, or the server can obtain the continuous time series of ocean environment dynamics from a data storage system and perform ocean environment dynamics frequency analysis.

[0033] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or as a cloud server.

[0034] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a method for analyzing the frequency of dynamic elements of the marine environment is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server in is used as an example to illustrate, including the following steps 101 to 105.

[0035] Step 101: Obtain a continuous time series of marine environmental dynamic elements in the study area.

[0036] Step 102: Based on the continuous time series of the marine environmental dynamic elements, sample groups corresponding to different thresholds are constructed using the super-threshold method and the time window for screening extreme events; each sample in the sample group corresponding to each threshold is independent and identically distributed.

[0037] Step 103, for each sample group corresponding to each threshold, the generalized Pareto distribution function is used to fit the sample group, and the sum of squares of the tail residuals between the sample values in the sample group and the theoretical value is minimized 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 based on the threshold, shape parameter, scale parameter and the empirical exceedance probability of the corresponding sample value.

[0038] 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.

[0039] 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.

[0040] Implementing the above steps 101 to 105 has the following technical effects:

[0041] (1) By combining the super-threshold method with the time window for screening extreme events, sample screening is performed. 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 identical distribution of samples, and ensures the accuracy of the frequency analysis results.

[0042] (2) Threshold optimization is performed based on the criterion of minimizing the fitting error between the sample and theoretical distribution tails (minimizing the sum of squares of the tail residuals between the sample value and the theoretical value), which ensures the objectivity and uniqueness of the threshold selection, thereby effectively improving the accuracy of the calculation of extreme marine environmental dynamic factors.

[0043] (3) Taking the minimization of tail error as the core optimization goal, focusing on the tail characteristics of extreme events, the frequency distribution model’s representation accuracy of extreme events is improved, thereby more accurately reflecting the distribution law of extreme ocean environment dynamic elements and improving the accuracy of the calculation of extreme ocean environment dynamic elements.

[0044] (4) Through the two-way support mechanism of sampling methods and parameter estimation methods, a more scientific and reliable frequency analysis system was constructed: the sampling method provides a high-quality data basis for parameter estimation by scientifically screening extreme samples; the parameter estimation results are based on the parameter fitting target of minimizing the fitting error between the sample and the theoretical distribution tail. Through model verification and feedback, the rationality and adaptability of the sampling standard are dynamically optimized. 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 estimation.

[0045] In another exemplary embodiment of the present application, in step 101, obtaining a continuous time series of marine environmental dynamic elements in the study area specifically includes:

[0046] (1-1) Obtain multi-source marine environmental dynamic factor data in the study area.

[0047] For the dynamic elements of the marine environment in the study area (such as waves, current velocity, storm tide level, etc.), multi-source data are collected, including field observation data, reanalysis data and numerical simulation results, to build a comprehensive data foundation.

[0048] (1-2) Preprocessing and integrating multi-source marine environmental dynamic factor data to obtain integrated marine environmental dynamic factor continuous time series; the preprocessing includes format conversion, outlier removal and missing value filling.

[0049] Convert data from different sources into a unified format (such as NetCDF or CSV), ensuring consistency across all data types (field observations, reanalysis data, numerical simulation results, etc.) in terms of timestamps, spatial locations, and physical units. Perform a preliminary data check to identify and remove outliers (e.g., values outside a reasonable range). Mark missing values to facilitate subsequent filling. Combine data from different sources in chronological order. For overlapping time periods, prioritize data selection based on field observations > reanalysis data > numerical simulation results. For non-overlapping time periods, extract information from multiple data sources using the same priority to fill missing values. For remaining missing time periods, use interpolation methods (e.g., linear interpolation or spline interpolation) to fill gaps and ensure the continuity of the time series. Through these operations, integrate multi-source data to form a complete and continuous time series. Statistically analyze the integrated time series to obtain the total number (Nt) of marine environmental dynamics element values in the time series.

[0050] In another exemplary embodiment of the present application, in step 102, based on the continuous time series of the marine environmental dynamic elements, a super-threshold method and a time window for screening extreme events are used to construct sample groups corresponding to different thresholds, specifically including:

[0051] (2-1) Based on the meteorological processes (such as typhoons, cold waves, etc.) and ocean hydrological processes (such as storm surges, high waves, etc.) in the target area, determine the extreme events in the continuous time series of the marine environmental dynamic elements.

[0052] (2-2) Calculate the duration of each extreme event.

[0053] The duration of each extreme event is calculated based on the duration of the meteorological process's impact on the selected location (such as a typhoon or cold wave from 24 hours before the impact to 24 hours after the impact ends).

[0054] (2-3) The mean duration of each extreme event is used as the time window for screening extreme events.

[0055] Statistically analyze the distribution characteristics of duration (such as mean, maximum, and minimum values). Use the mean of extreme event duration as the time window for screening extreme events.

[0056] (2-4) Statistically analyze the continuous time series of the marine environmental dynamic elements to determine the statistical distribution characteristics of the series, including the mean ( h avg ), maximum value ( h max ) and minimum value ( h min ).

[0057] (2-5) Determine the upper and lower boundary values of the threshold based on the statistical distribution characteristics of the sequence.

[0058] Take the maximum value of the time series ( h max ) and mean ( h avg ) are used as the upper and lower boundary values of the threshold setting.

[0059] (2-6) Determine the threshold setting interval based on the upper and lower boundary values of the threshold.

[0060] According to the maximum value of the time series ( h max ) and the mean ( h avg ) to determine the threshold setting interval (Δ m ), the calculation formula is shown below.

[0061] ;

[0062] Where, N μ The number of thresholds set can be determined based on actual needs.

[0063] (2-7) Set different thresholds according to the threshold setting interval and the upper and lower boundary values of the threshold.

[0064] Based on the above intervals, different thresholds are calculated using the following formula ( m i ).

[0065] ;

[0066] Where, m i For the i thresholds, i is the threshold number.

[0067] (2-8) For each threshold, the element values exceeding the threshold in the continuous time series of the marine environmental dynamic elements are screened out; the time interval between the adjacent element values exceeding the threshold is not less than the time window for screening extreme events.

[0068] For each threshold ( m i ), filter out the time series that exceed the threshold ( m i ) and perform secondary screening using the time window determined in step (2-3) to ensure that the time interval between adjacent threshold-crossing events is no less than the time window of the extreme event determined in step (2-3), thus forming independent and identically distributed sample groups corresponding to different thresholds.

[0069] (2-9) The filtered element values exceeding the threshold constitute the sample group corresponding to the threshold.

[0070] In another exemplary embodiment of the present application, in step 103, for each sample group corresponding to a threshold, a generalized Pareto distribution function is used to fit the sample group, and the sum of squared tail residuals between the sample values in the sample group and the theoretical values is minimized as the parameter fitting objective. The shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold are determined, specifically including:

[0071] (3-1) For each sample group corresponding to a threshold, sort each sample according to its value to obtain a sorted sample group.

[0072] For different thresholds m i ( i =1,2,3,..., N μ ) and its corresponding sample group ( X i ), the total number of samples contained in the statistical sample group ( N i ). For the sample group Xi The sample values in are sorted in descending order.

[0073] (3-2) Calculate the empirical exceedance probability of each sample based on the arrangement sequence number and sample quantity of each sample in the sorted sample group.

[0074] Empirical Exceedance Probability ( P ij ) is calculated according to the following formula.

[0075] ;

[0076] Where, P ij For the i Threshold m i Corresponding sample group X i Sort by j Empirical exceedance probability of samples with large values; i is the threshold number, j For the i Threshold value corresponds to sample group X i The serial numbers of the samples after sorting in descending order.

[0077] (3-3) Based on the empirical exceedance probability, the formula for calculating the sum of squares of the tail residuals between each sample value and the corresponding theoretical value is determined.

[0078] To improve the accuracy of frequency distribution models in representing extreme events, this application focuses on the tail characteristics of extreme events, with minimizing the sum of squared tail residuals between sample values and theoretical values as the core optimization objective. The sum of squared tail residuals between sample values and theoretical values for which the empirical probability of exceeding the theoretical value is no greater than a preset value (e.g., 0.05) is calculated as the sum of squared tail residuals. The calculation formula is shown below.

[0079]

[0080] in, ;

[0081] Where, TRSS i With the threshold m i The sum of squares of the tail residuals between the corresponding sample value and the theoretical value; N it With the threshold m i Corresponding sample group X i The number of samples whose empirical exceedance probability is not greater than the preset value after sorting in descending order; xij With the threshold m i Corresponding sample group X i After descending order j The sample value of samples; y ij For the sample value x ij The corresponding theoretical value; P ij is the threshold m i Corresponding sample group X i Sort by j The empirical exceedance probability of the sample ; s i and x i is the threshold m i The scale and shape parameters of the corresponding generalized Pareto distribution.

[0082] (3-4) The generalized Pareto distribution function is used to fit the sample group, and the parameter fitting target is to minimize the sum of squares of the tail residuals between the sample values in the sample group and the theoretical values, and the shape parameters and scale parameters of the generalized Pareto distribution corresponding to each threshold are determined.

[0083] The generalized Pareto distribution function is used to fit the independent and identically distributed sample group selected in step 102. The generalized Pareto distribution function is shown in the following formula. m i , i =1,2,3,..., N μ ) and its corresponding sample group X i Substitute the sample values in the generalized Pareto distribution function of the following formula, and determine the scale parameter corresponding to each threshold based on the minimum calculated value of the sum of squares of the tail residuals s i , shape parameters x i .

[0084] ;

[0085] Where, G () is the generalized Pareto distribution function; m i is the threshold; x ij With the threshold m i Corresponding sample group Xi After descending order j Sample values; ( x ij - m i )>0.

[0086] Then compare the tail residual sum of squares of the sample value and the theoretical value under different thresholds, and select the threshold that minimizes the tail residual sum of squares as the optimal threshold ( m opt ). Will be combined with the optimal threshold ( m opt ) corresponding to the sample as the optimal sample ( X opt ), and extract the corresponding optimal scale parameter ( s opt ) and the optimal shape parameter ( x opt ).

[0087] In another exemplary embodiment of the present application, in step 105, the multi-year extreme ocean environment dynamic factor value is calculated: based on the optimal threshold ( m opt ), optimal scale parameter ( s opt ), optimal shape parameters ( x opt ), optimal sample ( X opt ), calculate the extreme ocean environment dynamic factor value that occurs once every many years. According to the generalized Pareto distribution function mentioned above, the recurrence period is T rp The calculation of the annual extreme ocean environmental dynamic factors is shown in the following formula.

[0088]

[0089] Where, y rp for T rp Extreme ocean environment dynamic factor values with a return period of one year; l opt is the number of samples in the optimal sample ( N opt ) and the total number of samples ( N t ), that is, ; m opt is the optimal threshold; s opt is the optimal scale parameter; x opt is the optimal shape parameter.

[0090] This application addresses the difficulties in frequency analysis of marine dynamic environmental factors, particularly the shortcomings of existing sampling and parameter estimation methods in capturing the distribution characteristics of extreme events. This application proposes an improved frequency analysis method, which, through a two-way support mechanism of sampling and parameter estimation methods, constructs a more scientific and reliable frequency analysis system. This method 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 marine environmental dynamic factors and the accurate estimation of extreme marine environmental dynamic factors.

[0091] The present application also provides an application scenario, which applies the above-mentioned frequency analysis method of marine environment dynamic elements. Specifically: the frequency analysis method of marine environment dynamic elements provided in this embodiment can be applied in the safety assessment scenario of marine engineering structures. The scenario includes a data acquisition link, a frequency analysis link and a safety assessment link; the data acquisition link is used to collect marine environment dynamic element data from multiple sources; the frequency analysis link is used to infer extreme marine environment dynamic element values based on marine environment dynamic element data from multiple sources; the safety assessment link is used to assess the safety of marine engineering structures based on the inferred marine environment dynamic element values. The frequency analysis method of marine environment dynamic elements provided in this embodiment belongs to the frequency analysis link.

[0092] Based on the same inventive concept, the present application also provides an apparatus for analyzing the frequency of marine environment dynamic elements for implementing the aforementioned method for analyzing the frequency of marine environment dynamic elements. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the apparatus for analyzing the frequency of marine environment dynamic elements provided below can be found in the aforementioned limitations of the method for analyzing the frequency of marine environment dynamic elements, and will not be further elaborated here.

[0093] In an exemplary embodiment, Figure 4 As shown, a frequency analysis device for marine environment dynamic elements is provided, comprising:

[0094] The data acquisition module M1 is used to obtain the continuous time series of marine environmental dynamic elements in the study area.

[0095] The sampling module M2 is used to construct sample groups corresponding to different thresholds based on the continuous time series of the marine environmental dynamic elements using the super-threshold method and the time window for screening extreme events; each sample in the sample group corresponding to each threshold is independent and identically distributed.

[0096] The parameter fitting module M3 is used to fit the sample group corresponding to each threshold value using the generalized Pareto distribution function, and to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold value with the minimum sum of squares of the tail residuals between the sample values in the sample group and the theoretical value as the parameter fitting target; the theoretical value is determined based on the threshold value, shape parameter, scale parameter and the empirical exceedance probability of the corresponding sample value.

[0097] The optimal parameter determination module M4 is used to compare the sum of squared tail residuals between the sample values corresponding to each threshold and the theoretical value, and take the threshold with the smallest 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.

[0098] The estimation module M5 is used to estimate the extreme ocean environment dynamic factors by applying the generalized Pareto distribution function according to the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group.

[0099] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. 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. 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 intermediate data and result data of the frequency analysis of marine environment 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 an external terminal through a network connection. When the computer program is executed by the processor, a method for frequency analysis of marine environment dynamic elements is implemented.

[0100] Those skilled in the art will understand that Figure 5The structure shown in the figure is merely a block diagram of a portion of the structure related to the present application solution and does not constitute a limitation on the computer device to which the present application solution is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method embodiments are implemented.

[0101] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0102] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0103] 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 used 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 must comply with relevant regulations.

[0104] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the 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 above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0105] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0106] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0107] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A frequency analysis method for marine environment dynamic elements, characterized in that: include: Obtain continuous time series of marine environmental dynamic elements in the study area; Based on the continuous time series of the marine environmental dynamic factors, the super-threshold method and the time window for screening extreme events are used to construct sample groups corresponding to different thresholds; each sample in the sample group corresponding to each threshold is independent and identically distributed; 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 squared tail residuals between the sample values in the sample group and the theoretical value as the parameter fitting objective; the theoretical value is determined based on the threshold, shape parameter, scale parameter, and the empirical exceedance probability of the corresponding sample value; 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; According to the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group, the generalized Pareto distribution function is applied to infer the extreme marine environmental dynamic factors.

2. The method for frequency analysis of marine environment dynamic elements according to claim 1, characterized in that: Obtain a continuous time series of marine environmental dynamic elements in the study area, including: Obtain multi-source marine environmental dynamic factor data in the study area; Multi-source marine environment dynamic factor data are preprocessed and integrated to obtain an integrated marine environment dynamic factor continuous time series; the preprocessing includes format conversion, outlier removal and missing value filling.

3. The method for frequency analysis of marine environment dynamic elements according to claim 1, characterized in that: Based on the continuous time series of the marine environmental dynamic factors, the super-threshold method and the time window for screening extreme events are used to construct sample groups corresponding to different thresholds, including: Determine the extreme events in the continuous time series of the marine environmental dynamic elements based on the meteorological and oceanographic processes in the target area; Calculate the duration of each extreme event; The mean duration of each extreme event is used 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; Determine the upper and lower boundary values of the threshold according to the statistical distribution characteristics of the sequence; Determine the threshold setting interval based on the upper and lower boundary values of the threshold; Set different thresholds according to the threshold setting interval and the upper and lower boundary values of the threshold; For each threshold, the element values exceeding the threshold in the continuous time series of the marine environmental dynamic elements are screened out; the time interval between adjacent element values exceeding the threshold is not less than the time window for screening extreme events; The filtered element values exceeding the threshold constitute the sample group corresponding to the threshold.

4. The method for frequency analysis of marine environment dynamic elements according to claim 1, characterized in that: For each sample group corresponding to each threshold, the generalized Pareto distribution function is used to fit the sample group, and the parameter fitting target is to minimize the sum of squares of the tail residuals between the sample values in the sample group and the theoretical values. The shape parameters and scale parameters of the generalized Pareto distribution corresponding to each threshold are determined, specifically including: For each sample group corresponding to each threshold, sort each sample according to the sample value in the sample group to obtain a sorted sample group; Calculate the empirical exceedance probability of each sample according to the sequence number and sample quantity of each sample in the sorted sample group; Determine the formula for calculating the sum of squares of the tail residuals between each sample value and the corresponding theoretical value based on the empirical exceedance probability; The generalized Pareto distribution function is used to fit the sample group, and the minimum sum of squares of the tail residuals between the sample values in the sample group and the theoretical values is taken as the parameter fitting target to determine the shape parameters and scale parameters of the generalized Pareto distribution corresponding to each threshold.

5. The method for frequency analysis of marine environment dynamic elements according to claim 4, characterized in that: The expression of the sum of squares of the tail residuals between the sample value and the theoretical value is: ； in, ; Where, TRSS i With the threshold μ i The sum of squares of the tail residuals between the corresponding sample value and the theoretical value; N it With the threshold μ i Corresponding sample group X i The number of samples whose empirical exceedance probability is not greater than the preset value after sorting in descending order; x ij With the threshold μ i Corresponding sample group X i After descending order j The sample value of samples; y ij For the sample value x ij The corresponding theoretical value; P ij is the threshold μ i Corresponding sample group X i Sort by j The empirical exceedance probability of the sample ; σ i and ξ i is the threshold μ i The scale and shape parameters of the corresponding generalized Pareto distribution.

6. The method for frequency analysis of marine environment dynamic elements according to claim 1, characterized in that: The expression for calculating the extreme ocean environment dynamic factors is: ； Where, y rp for T rp Extreme ocean environment dynamic factor values with a return period of one year; λ opt is 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. A frequency analysis device for marine environment dynamic elements, characterized in that: include: Data acquisition module, used to obtain continuous time series of marine environmental dynamic elements in the study area; A sampling module is used to construct sample groups corresponding to different thresholds based on the continuous time series of the marine environmental dynamic elements using a super-threshold method and a time window for screening extreme events; each sample in the sample group corresponding to each threshold is independent and identically distributed; A parameter fitting module is used to fit the sample group corresponding to each threshold using a generalized Pareto distribution function, with the parameter fitting objective being to minimize the sum of squared tail residuals between the sample values in the sample group and the theoretical value, and to determine the shape parameter and scale parameter of the generalized Pareto distribution corresponding to each threshold; the theoretical value is determined based on the threshold, shape parameter, scale parameter, and the empirical exceedance probability of the corresponding sample value; The optimal parameter determination module is used to compare the tail residual sum of squares between the sample values corresponding to each threshold and the theoretical value, and 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; The inference module is used to infer the extreme marine environmental dynamic factors based on the optimal threshold, optimal shape parameter, optimal scale parameter and optimal sample group using the generalized Pareto distribution function.

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

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

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

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