A numerical fitting method, system, device and storage medium for wave scatter diagram

Abstract

The application discloses a numerical fitting method and system of a wave scatter diagram, computer equipment and a computer readable storage medium, and belongs to the technical field of data processing. The method comprises the following steps: fitting the significant wave height by using a Weibull distribution; fitting the average zero-crossing period by using a lognormal distribution; and generating a wave scatter diagram according to the significant wave height and the average zero-crossing period. In the application, the significant wave height is fitted by using the Weibull distribution, the large significant wave height data can be considered, the wave scatter diagram is more accurate, and the characteristics of wave statistics are met. The average zero-crossing period is fitted by using the lognormal distribution, the problem of possible period deviation in the original data can be considered, the wave scatter diagram obtained by fitting is avoided to make the calculated wave load ratio larger than the wave load obtained by using the un-fitted data, and then a more accurate wave scatter diagram is generated according to the significant wave height and the average zero-crossing period.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and specifically relates to a numerical fitting method, system, computer device, and computer-readable storage medium for wave scatter plots. Background Technology

[0002] Wave loads are critical loads for assessing the structural safety of a ship. Extreme wave load predictions are generally obtained through long-term wave load forecasts. However, if the wave scatter diagram used in the calculation is not specifically specified when making long-term wave load forecasts for a ship, the results will be highly uncertain. The wave scatter diagram is related to the possibility of partial or complete structural damage when a ship encounters severe sea conditions, leading to casualties and economic losses. Both classification societies and design firms need to pay attention to wave scatter diagrams during the specification development and design phases to ensure structural safety. Therefore, wave scatter diagrams are a key focus for classification societies, ship design firms, and ship owners.

[0003] Wave scatter plots are composed of the meaningful wave heights and the average zero-crossing period of waves. They can be obtained directly through statistical analysis of wave data, or through numerical fitting based on statistical data. However, both methods have certain drawbacks.

[0004] The wave scatter plot obtained directly from wave data statistics has the disadvantage that the occurrence frequency of the highest-level meaningful wave height is higher than that of the lower-level meaningful wave height, which makes the wave scatter plot inconsistent with the characteristics of wave statistics.

[0005] The wave scatter plot obtained by numerical fitting method has the disadvantage of only correcting the average cross-zero period offset of the original data. Since the sea state with large meaningful wave height plays an important role in the long-term wave load forecast of ships, the wave scatter plot obtained by numerical fitting will make the calculated wave load larger than the wave load obtained by using unfitted data. Summary of the Invention

[0006] The purpose of this invention is to provide a numerical fitting method, system, computer device, and computer-readable storage medium for wave scatter plots. This addresses the shortcomings of current wave scatter plots obtained directly from wave data statistics, where the highest-level significant wave height occurs more frequently than the lower-level significant wave height, making the wave scatter plot inconsistent with the characteristics of wave statistics. Furthermore, the numerical fitting method for wave scatter plots only corrects for the average zero-cycle offset of the original data, resulting in a calculated wave load that is larger than that obtained using unfitted data.

[0007] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0008] In a first aspect, the present invention provides a numerical fitting method for wave scatter plots, comprising:

[0009] Obtain wave data;

[0010] By fitting the wave data using the Weibull distribution, the meaningful wave height of the wave data is obtained;

[0011] By fitting the wave data to a log-normal distribution, the average zero-crossing period of the wave data is obtained;

[0012] A wave scatter plot is generated based on the defined meaningful wave height and the average zero-crossing period.

[0013] Secondly, the present invention provides a numerical fitting system for wave scatter plots, comprising:

[0014] The acquisition module is used to acquire wave data;

[0015] The first fitting module is used to fit the wave data using a Weibull distribution to obtain the meaningful wave height of the wave data;

[0016] The second fitting module is used to fit the wave data using a log-normal distribution to obtain the average zero-crossing period of the wave data.

[0017] The generation module is used to generate a wave scatter plot based on the significant wave height and the average zero-crossing period.

[0018] Thirdly, the present invention provides a computer device, characterized in that it includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method described in the first aspect.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the method described in the first aspect.

[0020] The above technical solution has at least the following advantages compared with the existing technology:

[0021] In this embodiment of the invention, fitting the significant wave height with a Weibull distribution can focus on large significant wave height data, ensuring that the occurrence frequency of the highest-level significant wave height is lower than that of the lower-level significant wave height, making the wave scatter plot more accurate and consistent with the characteristics of wave statistics. Fitting the mean zero-crossing period with a log-normal distribution can take into account the period offset that may exist in the original data, avoiding the wave load calculated by the fitted wave scatter plot being larger than the wave load obtained using unfitted data. Then, based on the significant wave height and the mean zero-crossing period, a more accurate wave scatter plot is generated, which helps to understand and utilize ocean wave data more deeply and makes the distribution of wave characteristics visible. Attached Figure Description

[0022] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0023] Figure 1 A flowchart illustrating a numerical fitting method for wave scatter plots provided in an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a numerical fitting method for wave scatter plots provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a numerical fitting system for wave scatter plots provided in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0028] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0029] Reference Figure 1 The diagram shows a flowchart of a numerical fitting method for wave scatter plots provided by an embodiment of the present invention.

[0030] Reference Figure 2The diagram shows a structural schematic of a numerical fitting method for wave scatter plots provided by an embodiment of the present invention.

[0031] In one embodiment, the present invention provides a numerical fitting method for wave scatter plots. It should be noted that the wave scatter plot is composed of the meaningful wave height and the average zero-crossing period of the wave, and is essentially a two-dimensional probability density function with the meaningful wave height and the average zero-crossing period as variables.

[0032] Significant wave height (Hs) is a key parameter describing the height of waves in an ocean wave. It represents the average height of the largest half of the waves over a certain period of time.

[0033] The Average Zero-Crossing Period (Tz) is a parameter that describes the temporal characteristics of waves. It represents the average time it takes for a wave to cross the zero line (usually sea level) within a certain time period.

[0034] This invention provides a numerical fitting method for wave scatter plots, comprising:

[0035] S1: Obtain wave data.

[0036] Specifically, specially designed buoys can be used to measure the height, period, and direction of waves in the ocean to obtain wave data. Satellite remote sensing technology can also be used to monitor wave conditions over large areas of the ocean. Satellite sensors can acquire ocean surface height data, thereby obtaining wave data.

[0037] S2: By fitting the wave data using the Weibull distribution, the meaningful wave height of the wave data is obtained.

[0038] The Weibull distribution is a continuous probability distribution commonly used to describe the distribution characteristics of phenomena such as event lifetime, reliability, and failure rate. By fitting data to a Weibull distribution, we can better understand and describe the distribution characteristics of the data, supporting applications such as reliability engineering and risk analysis.

[0039] In this invention, the Weibull distribution is used for fitting the meaningful wave height, eliminating the need to determine the probability density function or cumulative probability distribution function, thus shortening the computation time. Furthermore, the Weibull distribution can more accurately describe the probability distribution characteristics of the meaningful wave height.

[0040] In one possible implementation, S2 specifically includes sub-steps S201 to S206:

[0041] S201: Using the Weibull distribution, the marginal density function with significant wave height as the variable is obtained according to the following formula:

[0042]

[0043] Where, f Hs (h) denotes the marginal density function with the significant wave height as the variable, where h represents the significant wave height, α represents the scale parameter of the Weibull distribution, β represents the shape parameter of the Weibull distribution, γ represents the location parameter of the Weibull distribution, and exp represents the exponential function with base e.

[0044] In this invention, by fitting and determining the scale parameter α, shape parameter β, and position parameter γ of the Weibull distribution, the Weibull distribution can be adapted to the characteristics of different datasets. Appropriate selection of these parameters can better fit the data and provide a more accurate probability model.

[0045] S202: Setting the position parameter γ = 0, the edge density function with the significant wave height as the variable yields Formula 2:

[0046]

[0047] In this invention, the location parameter of the Weibull distribution is set to zero to avoid the fitting calculation terminating due to the independent variable exceeding the function's domain. This also reduces the number of solution parameters and simplifies the problem.

[0048] S203: Parameter estimation is performed using the maximum likelihood estimation method, resulting in Formula 3:

[0049]

[0050] Where L represents the likelihood function, h i Let y represent the i-th meaningful wave height. i This represents the probability of the i-th meaningful wave height occurring.

[0051] Maximum Likelihood Estimation (MLE) is a method for estimating parameters of a statistical model. Based on the principles of statistical inference, MLE aims to find the values ​​of the model parameters that maximize the probability (likelihood function) of the observed data.

[0052] S204: Taking the logarithm of both sides of equation 3, we get equation 4:

[0053]

[0054] In this invention, taking the log-likelihood function helps to transform the maximum likelihood estimation problem into a more manageable optimization problem, because the log-function is generally easier to handle and can reduce computational complexity.

[0055] S205: Taking partial derivatives with respect to the scale parameter α and the shape parameter β respectively, we obtain Equations 5 and 6:

[0056]

[0057]

[0058] S206: Solve for the scale parameter α and shape parameter β according to formulas 5 and 6.

[0059] In this invention, the optimal estimates of the scale parameter α and shape parameter β of the Weibull distribution can be found through maximum likelihood estimation. These parameters maximize the likelihood function L. The estimated values ​​of these parameters will enable the model to best fit the actual observed data, which helps to determine the shape and scale of the Weibull distribution so as to better describe the distribution of meaningful wave heights.

[0060] In one possible implementation, S2 further includes sub-step S207:

[0061] S207: Based on the scale parameter α and shape parameter β obtained by the maximum likelihood estimation method, and with the objective function value being minimized, the least squares method parameter solution is searched through trial and error. The specific objective function is:

[0062]

[0063] Here, E(α,β) represents the objective function.

[0064] The least squares method is a commonly used mathematical and statistical method for fitting data and estimating model parameters. The main goal of the least squares method is to find a mathematical model that minimizes the sum of squared residuals between the predicted values ​​and the observed data.

[0065] In this invention, based on the parameters of the probability function obtained by the maximum likelihood estimation method, the minimum value of the objective function of the least squares method is searched by trial and error. The parameters that minimize the objective function are the solution of the least squares method, which solves the problem that it is not easy to solve nonlinear equations when directly using the least squares method for fitting.

[0066] In one possible implementation, S2 further includes sub-step S208:

[0067] S208: Based on the least squares parameter solution obtained by the search, the shape parameter β is refitted with the probability of the occurrence of large wave height.

[0068] It should be noted that while large wave heights are generally rare, their occurrence is crucial in certain applications, particularly in marine engineering and safety risk assessment. Refitting the shape parameter β using large wave height data can better adapt the model to extreme situations and improve its ability to model such events.

[0069] In this invention, the focus is on large meaningful wave height data to ensure that the occurrence frequency of the highest-level meaningful wave height is lower than that of the lower-level meaningful wave height, so that the wave scatter plot is more accurate and conforms to the characteristics of wave statistics.

[0070] In one possible implementation, the large wave height is determined as follows:

[0071] Select meaningful wave height data with a probability of occurrence of less than 1 / 1000.

[0072] It should be noted that by filtering meaningful wave height data with a probability of less than 1 / 1000, the method will focus on extreme events, i.e., extreme cases of large wave heights, rather than regular cases.

[0073] Data that shows a higher probability of occurrence with higher meaningful wave heights are removed.

[0074] It should be noted that removing data with higher meaningful wave heights and higher probability of occurrence can eliminate outliers, thereby improving data quality and model robustness. This helps the model better adapt to real-world situations and avoid interference from extreme values.

[0075] Determine if the remaining large wave height data is less than 3. If so, increase the data in the low wave height direction until the large wave height data is not less than 3.

[0076] It should be noted that determining whether there are fewer than 3 remaining large wave height data points, and adding data in the low wave height direction as needed, to ensure sufficient large wave height data for reliable analysis, helps to more accurately estimate the probability and nature of extreme events.

[0077] In this invention, the advantages of determining large wave heights using the above method include improved model adaptability, enhanced modeling capabilities for extreme events, ensured data quality, and more reliable risk estimation. This has practical significance for fields that need to consider extreme ocean conditions, such as marine engineering and the planning and safety of maritime activities.

[0078] S3: By fitting the wave data to a log-normal distribution, the average zero-crossing period of the wave data is obtained.

[0079] The log-normal distribution is a probability distribution typically used to describe the distribution characteristics of positive random variables. Unlike the normal distribution, the log-normal distribution is characterized by its logarithmic value following a normal distribution.

[0080] In this invention, fitting the average zero-crossing period with a log-normal distribution can improve the model's adaptability, better consider the properties of the data, reduce skewness, and improve the robustness of parameter estimation.

[0081] In one possible implementation, S3 specifically includes sub-steps S301 to S307:

[0082] S301: Using the log-normal distribution, the conditional density function with the average zero-crossing period as the variable is obtained according to the following formula:

[0083]

[0084] Where, f Tz|Hs (t|h) represents the conditional density function with the average zero-crossing period as the variable, where t represents the average zero-crossing period, σ represents the root mean square error, and μ represents the mean.

[0085] The mean μ is calculated as follows:

[0086]

[0087] Where E represents the expected value, and a0, a1, and a2 represent the data fitting parameters.

[0088] The mean square error σ is calculated as follows:

[0089]

[0090] Where std represents the standard deviation, and b0, b1, and b2 represent the data fitting parameters.

[0091] S302: Set a0 = 0.7, and fit the data fitting parameters a1 and a2 using the least squares method:

[0092]

[0093] Where E(a1,a2) represents the error function of the least squares method, h i μ represents the height of the i-th meaningful wave. i Let represent the mean of the i-th meaningful wave height.

[0094] In this invention, by employing a log-normal distribution and the least squares method, the model's adaptability to the mean zero-crossing period can be improved, enabling the model to better capture the distribution characteristics of actual data, thereby enhancing the model's prediction accuracy. Furthermore, estimating parameters using the least squares method helps improve the robustness of parameter estimation, reduces the uncertainty of parameter estimation, and enhances the reliability of the model.

[0095] S303: Taking the partial derivatives of the data fitting parameters a1 and a2 respectively, we obtain Formula 12 and Formula 13:

[0096]

[0097]

[0098] S304: Based on formulas 12 and 13, the data fitting parameters a1 and a2 are solved using the bisection method.

[0099] S305: Set b0 = 0.07, and fit the data fitting parameters b1 and b2 using the least squares method:

[0100]

[0101] Where E(b1,b2) represents the error function of the least squares method, h i μ represents the height of the i-th meaningful wave. i Let represent the mean of the i-th meaningful wave height.

[0102] S306: Taking the partial derivatives of the data fitting parameters b1 and b2 respectively, we obtain Equations 15 and 16:

[0103]

[0104]

[0105] S307: Based on formulas 15 and 16, the bisection method is used to solve for the data fitting parameters b1 and b2.

[0106] In this invention, by setting a0 = 0.7 and b0 = 0.07, the data fitting parameters a1, a2, b1 and b2 are solved using the least squares method and the bisection method. The parameter estimation is more robust and has less impact on outliers, thereby improving the reliability of the model.

[0107] S4: Generate a wave scatter plot based on the significant wave height and the average zero-crossing period.

[0108] In one possible implementation, S4 specifically involves generating a wave scatter plot based on the significant wave height and the average zero-crossing period using the following formula:

[0109] p(h,t)=f Hs (h)f Tz|Hs (t|h) Formula 17

[0110] Where p(h,t) represents the wave scatter plot, f Hs (h) represents the marginal density function with significant wave height as the variable, f Tz|Hs (t|h) represents the conditional density function with the average zero-crossing period as the variable.

[0111] In this invention, the size of the fitted mathematical model can be effectively reduced and the computational efficiency improved while ensuring the accuracy of the calculation.

[0112] The above technical solution has at least the following advantages compared with the existing technology:

[0113] In this embodiment of the invention, fitting the significant wave height with a Weibull distribution can focus on large significant wave height data, ensuring that the occurrence frequency of the highest-level significant wave height is lower than that of the lower-level significant wave height, making the wave scatter plot more accurate and consistent with the characteristics of wave statistics. Fitting the mean zero-crossing period with a log-normal distribution can take into account the period offset that may exist in the original data, avoiding the wave load calculated by the fitted wave scatter plot being larger than the wave load obtained using unfitted data. Then, based on the significant wave height and the mean zero-crossing period, a more accurate wave scatter plot is generated, which helps to understand and utilize ocean wave data more deeply and makes the distribution of wave characteristics visible.

[0114] Reference Figure 3 The diagram shows a structural schematic of a numerical fitting system for wave scatter plots provided in an embodiment of the present invention.

[0115] In one embodiment, the present invention provides a numerical fitting system 20 for wave scatter plots, comprising:

[0116] Module 201 is used to acquire wave data;

[0117] The first fitting module 202 is used to fit the wave data using the Weibull distribution to obtain the meaningful wave height of the wave data;

[0118] The second fitting module 203 is used to fit the wave data using a log-normal distribution to obtain the average zero-crossing period of the wave data.

[0119] The generation module 204 is used to generate a wave scatter plot based on the significant wave height and the average zero-crossing period.

[0120] In one possible implementation, the first fitting module 202 is specifically used for:

[0121] Using the Weibull distribution, the marginal density function with significant wave height as the variable is obtained according to the following formula:

[0122]

[0123] Where, f Hs (h) represents the marginal density function with the significant wave height as the variable, where h represents the significant wave height, α represents the scale parameter of the Weibull distribution, β represents the shape parameter of the Weibull distribution, γ represents the location parameter of the Weibull distribution, and exp represents the exponential function with base e.

[0124] With the position parameter γ = 0, the edge density function with the significant wave height as the variable yields Formula 2:

[0125]

[0126] By using the maximum likelihood estimation method, parameter estimation is performed, resulting in Formula 3:

[0127]

[0128] Where L represents the likelihood function, h i Let y represent the i-th meaningful wave height. i This represents the probability of the i-th meaningful wave height occurring;

[0129] Taking the logarithm of both sides of equation 3, we get equation 4:

[0130]

[0131] Taking partial derivatives with respect to the scale parameter α and the shape parameter β, we obtain Equations 5 and 6:

[0132]

[0133]

[0134] Based on formulas 5 and 6, the scale parameter α and shape parameter β are solved.

[0135] In one possible implementation, the first fitting module 202 is further configured to:

[0136] Based on the scale parameter α and shape parameter β obtained by the maximum likelihood estimation method, and with the objective function of minimizing its value, the least squares method is searched for through trial and error. The specific objective function is as follows:

[0137]

[0138] Here, E(α,β) represents the objective function.

[0139] In one possible implementation, the first fitting module 202 is further configured to:

[0140] Based on the least squares parameter solution obtained by the search, the shape parameter β is refitted with the probability of the occurrence of large wave height.

[0141] In one possible implementation, the large wave height is determined as follows:

[0142] Select meaningful waveform data with a probability of occurrence of less than 1 / 1000;

[0143] Data where the higher the significance level, the higher the probability of occurrence;

[0144] Determine if the remaining large wave height data is less than 3. If so, increase the data in the low wave height direction until the large wave height data is not less than 3.

[0145] In one possible implementation, the second fitting module 203 is specifically used for:

[0146] Using the log-normal distribution, the conditional density function with the average zero-crossing period as the variable is obtained according to the following formula:

[0147]

[0148] Where, f Tz|Hs (t|h) represents the conditional density function with the average zero-crossing period as the variable, where t represents the average zero-crossing period, σ represents the root mean square error, and μ represents the mean.

[0149] The mean μ is calculated as follows:

[0150]

[0151] Where E represents the expected value, and a0, a1, and a2 represent the data fitting parameters;

[0152] The mean square error σ is calculated as follows:

[0153]

[0154] Where std represents the standard deviation, and b0, b1, and b2 represent the data fitting parameters;

[0155] Set a0 = 0.7, and use the least squares method to fit the data to the fitting parameters a1 and a2:

[0156]

[0157] Where E(a1,a2) represents the error function of the least squares method, h i μ represents the height of the i-th meaningful wave. iThis represents the mean of the i-th meaningful wave height;

[0158] Taking the partial derivatives of the data fitting parameters a1 and a2 respectively, we obtain Equations 12 and 13:

[0159]

[0160]

[0161] Based on formulas 12 and 13, the data fitting parameters a1 and a2 are solved using the bisection method.

[0162] Set b0 = 0.07, and use the least squares method to fit the data to the fitting parameters b1 and b2:

[0163]

[0164] Where E(b1,b2) represents the error function of the least squares method, h i μ represents the height of the i-th meaningful wave. i This represents the mean of the i-th meaningful wave height;

[0165] Taking the partial derivatives of the data fitting parameters b1 and b2 respectively, we obtain Equations 15 and 16:

[0166]

[0167]

[0168] Based on formulas 15 and 16, the data fitting parameters b1 and b2 are solved using the bisection method.

[0169] In one possible implementation, the generation module 204 is specifically used to: generate a wave scatter plot based on the significant wave height and the average zero-crossing period using the following formula:

[0170] p(h,t)=f Hs (h)f Tz|Hs (t|h) Formula 17

[0171] Where p(h,t) represents the wave scatter plot, f Hs (h) represents the marginal density function with significant wave height as the variable, f Tz|Hs (t|h) represents the conditional density function with the average zero-crossing period as the variable.

[0172] The numerical fitting system for wave scatter plots in this embodiment of the invention can implement each step in the numerical fitting method for wave scatter plots described above, and can achieve the same technical effect as the numerical fitting method for wave scatter plots described above. To avoid repetition, the invention will not elaborate further.

[0173] The virtual system in this embodiment of the invention may be a device, or a component, integrated circuit, or chip in a terminal.

[0174] Furthermore, it should be noted that the device embodiments described above are merely illustrative and do not constitute a limitation on the scope of protection of the present invention. In practical applications, those skilled in the art can select some or all of the modules to achieve the purpose of the embodiment according to actual needs, and no restrictions are imposed here.

[0175] The above technical solution has at least the following advantages compared with the existing technology:

[0176] In this embodiment of the invention, fitting the significant wave height with a Weibull distribution can focus on large significant wave height data, ensuring that the occurrence frequency of the highest-level significant wave height is lower than that of the lower-level significant wave height, making the wave scatter plot more accurate and consistent with the characteristics of wave statistics. Fitting the mean zero-crossing period with a log-normal distribution can take into account the period offset that may exist in the original data, avoiding the wave load calculated by the fitted wave scatter plot being larger than the wave load obtained using unfitted data. Then, based on the significant wave height and the mean zero-crossing period, a more accurate wave scatter plot is generated, which helps to understand and utilize ocean wave data more deeply and makes the distribution of wave characteristics visible.

[0177] Reference Figure 4 The diagram shows a structural schematic of a computer device provided in an embodiment of the present invention.

[0178] In one embodiment, the present invention provides a computer device, including: a processor 301;

[0179] Memory 302 is used to store processor-executable instructions;

[0180] The processor 301 is configured to call instructions stored in the memory 302 to execute the numerical fitting method for the wave scatter plot described above.

[0181] The above technical solution has at least the following advantages compared with the existing technology:

[0182] In this embodiment of the invention, fitting the significant wave height with a Weibull distribution can focus on large significant wave height data, ensuring that the occurrence frequency of the highest-level significant wave height is lower than that of the lower-level significant wave height, making the wave scatter plot more accurate and consistent with the characteristics of wave statistics. Fitting the mean zero-crossing period with a log-normal distribution can take into account the period offset that may exist in the original data, avoiding the wave load calculated by the fitted wave scatter plot being larger than the wave load obtained using unfitted data. Then, based on the significant wave height and the mean zero-crossing period, a more accurate wave scatter plot is generated, which helps to understand and utilize ocean wave data more deeply and makes the distribution of wave characteristics visible.

[0183] In one embodiment, the present invention provides a computer-readable storage medium storing computer program instructions, characterized in that the computer program instructions, when executed by a processor, implement the above-described numerical fitting method for wave scatter plots.

[0184] In this embodiment of the invention, fitting the significant wave height with a Weibull distribution can focus on large significant wave height data, ensuring that the occurrence frequency of the highest-level significant wave height is lower than that of the lower-level significant wave height, making the wave scatter plot more accurate and consistent with the characteristics of wave statistics. Fitting the mean zero-crossing period with a log-normal distribution can take into account the period offset that may exist in the original data, avoiding the wave load calculated by the fitted wave scatter plot being larger than the wave load obtained using unfitted data. Then, based on the significant wave height and the mean zero-crossing period, a more accurate wave scatter plot is generated, which helps to understand and utilize ocean wave data more deeply and makes the distribution of wave characteristics visible.

[0185] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0186] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0187] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0188] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A numerical fitting method for wave scatter plots, characterized in that, include: Obtain wave data; By fitting the wave data using the Weibull distribution, the meaningful wave height of the wave data is obtained; By fitting the wave data to a log-normal distribution, the average zero-crossing period of the wave data is obtained; A wave scatter plot is generated based on the defined wave height and the average zero-crossing period. Specifically, the step of fitting the wave data using a Weibull distribution to obtain the meaningful wave height of the wave data includes: Using the Weibull distribution, the marginal density function with significant wave height as the variable is obtained according to the following formula: Official 1 in, f Hs ( h ) represents the marginal density function with significant wave height as the variable. h Indicates a meaningful wave height, α The scale parameter of the Weibull distribution is denoted by . β This represents the shape parameter of the Weibull distribution. γ Let exp represent the location parameter of the Weibull distribution. e An exponential function with base 0; Set position parameters Then, taking the meaningful wave height as the marginal density function, we get Formula 2: Official 2 By using the maximum likelihood estimation method, parameter estimation is performed, resulting in Formula 3: Official 3 in, L Represents the likelihood function. h i Indicates the first i A person with high moral character, y i Indicates the first i The probability of a meaningful wave occurring; Taking the logarithm of both sides of equation 3, we get equation 4: Official 4 For scale parameters α and shape parameters β Taking the partial derivatives, we obtain formulas 5 and 6: Official 5 Official 6 The scale parameters are obtained by solving formulas 5 and 6. α and shape parameters β ; Specifically, fitting the wave data to a log-normal distribution to obtain the average zero-crossing period of the wave data includes: S201: Using the log-normal distribution, the conditional density function with the average zero-crossing period as the variable is obtained according to the following formula: Official 8 in, This represents the conditional density function with the average zero-crossing period as the variable. t Indicates the average zero-crossing period. σ Indicates the mean squared error. μ This represents the mean. μ The mean is calculated as follows: Official 9 Where E represents the expected value, a 0、 a 1 and a 2 represents the data fitting parameters; Root mean square σ The calculation method is as follows: Official 10 Where std represents the standard deviation, b 0、 b 1 and b 2 represents the data fitting parameters; S202: Settings By using the least squares method, the parameters are fitted to the data. a 1 and a 2. Perform fitting: Official 11 in, E ( a 1, a 2) Represents the error function of the least squares method. h i Indicates the first i A person with high moral character, μ i Indicates the first i The mean of a meaningful wave height; S203: Fitting parameters to the data a 1 and a 2. Taking the partial derivatives respectively, we obtain Formula 12 and Formula 13: Official 12 Official 13 S204: Based on formulas 12 and 13, the data fitting parameters are solved using the bisection method. a 1 and a 2; S205: Settings By using the least squares method, the parameters are fitted to the data. b 1 and b 2. Perform fitting: Official 14 in, E ( b 1, b 2) Represents the error function of the least squares method. h i Indicates the first i A person with high moral character, μ i Indicates the first i The mean of a meaningful wave height; S206: Fitting parameters to the data b 1 and b 2. Taking the partial derivatives respectively, we obtain Formula 15 and Formula 16: Official 15 Official 16 S207: Based on formulas 15 and 16, the data fitting parameters are solved using the bisection method. b 1 and b 2.

2. The numerical fitting method for wave scatter plots according to claim 1, characterized in that, The step of fitting the wave data using the Weibull distribution to obtain the meaningful wave height of the wave data further includes: The scale parameter obtained by the maximum likelihood estimation method α and shape parameters β Based on this, with the objective function being minimized, the least squares method is used to search for the parameter solution through trial and error. The specific objective function is: Official 7 in, E ( α , β ) represents the objective function.

3. The numerical fitting method for wave scatter plots according to claim 2, characterized in that, The step of fitting the wave data using the Weibull distribution to obtain the meaningful wave height of the wave data further includes: Based on the least squares parameter solution obtained through the search, the shape parameter is adjusted according to the probability of the occurrence of large wave heights. β Refit.

4. The numerical fitting method for wave scatter plots according to claim 3, characterized in that, The method for determining the large wave height is as follows: Select meaningful waveform data with a probability of occurrence of less than 1 / 1000; Data where the higher the significance level, the higher the probability of occurrence; Determine if the remaining large wave height data is less than 3. If so, increase the data in the low wave height direction until the large wave height data is not less than 3.

5. The numerical fitting method for wave scatter plots according to claim 1, characterized in that, The specific steps for generating the wave scatter plot based on the significant wave height and the average zero-crossing period are as follows: Based on the significant wave height and the average zero-crossing period, a wave scatter plot is generated using the following formula: Official 17 in, p ( h , t () represents a wave scattering diagram. f Hs ( h ) represents the marginal density function with significant wave height as the variable. This represents the conditional density function with the average zero-crossing period as the variable.

6. A numerical fitting system for wave scatter plots, characterized in that, include: The acquisition module is used to acquire wave data; The first fitting module is used to fit the wave data using a Weibull distribution to obtain the meaningful wave height of the wave data; The second fitting module is used to fit the wave data using a log-normal distribution to obtain the average zero-crossing period of the wave data. The generation module is used to generate a wave scatter plot based on the significant wave height and the average zero-crossing period; Specifically, the first fitting module is used for: Using the Weibull distribution, the marginal density function with significant wave height as the variable is obtained according to the following formula: Official 1 in, f Hs ( h ) represents the marginal density function with significant wave height as the variable. h Indicates a meaningful wave height, α The scale parameter of the Weibull distribution is denoted by . β This represents the shape parameter of the Weibull distribution. γ Let exp represent the location parameter of the Weibull distribution. e An exponential function with base 0; Set position parameters Then, taking the meaningful wave height as the marginal density function, we get Formula 2: Official 2 By using the maximum likelihood estimation method, parameter estimation is performed, resulting in Formula 3: Official 3 in, L Represents the likelihood function. h i Indicates the first i A person with high moral character, y i Indicates the first i The probability of a meaningful wave occurring; Taking the logarithm of both sides of equation 3, we get equation 4: Official 4 For scale parameters α and shape parameters β Taking the partial derivatives, we obtain formulas 5 and 6: Official 5 Official 6 The scale parameters are obtained by solving formulas 5 and 6. α and shape parameters β ; Specifically, the second fitting module is used for: S201: Using the log-normal distribution, the conditional density function with the average zero-crossing period as the variable is obtained according to the following formula: Official 8 in, This represents the conditional density function with the average zero-crossing period as the variable. t Indicates the average zero-crossing period. σ Indicates the mean squared error. μ This represents the mean. μ The mean is calculated as follows: Official 9 Where E represents the expected value, a 0、 a 1 and a 2 represents the data fitting parameters; Root mean square σ The calculation method is as follows: Official 10 Where std represents the standard deviation, b 0、 b 1 and b 2 represents the data fitting parameters; S202: Settings By using the least squares method, the parameters are fitted to the data. a 1 and a 2. Perform fitting: Official 11 in, E ( a 1, a 2) Represents the error function of the least squares method. h i Indicates the first i A person with high moral character, μ i Indicates the first i The mean of a meaningful wave height; S203: Fitting parameters to the data a 1 and a 2. Taking the partial derivatives respectively, we obtain Formula 12 and Formula 13: Official 12 Official 13 S204: Based on formulas 12 and 13, the data fitting parameters are solved using the bisection method. a 1 and a 2; S205: Settings By using the least squares method, the parameters are fitted to the data. b 1 and b 2. Perform fitting: Official 14 in, E ( b 1, b 2) Represents the error function of the least squares method. h i Indicates the first i A person with high moral character, μ i Indicates the first i The mean of a meaningful wave height; S206: Fitting parameters to the data b 1 and b 2. Taking the partial derivatives respectively, we obtain Formula 15 and Formula 16: Official 15 Official 16 S207: Based on formulas 15 and 16, the data fitting parameters are solved using the bisection method. b 1 and b 2.

7. A computer device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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