A sea wave direction spectrum measurement method and system based on sea area measured characteristics

Abstract

The application provides a sea wave direction spectrum measurement method and system based on sea area measured characteristics, and relates to the technical field of ocean observation. The method comprises the following steps: obtaining a wave parameter set based on target sea area measured wave data; determining a direction concentration parameter value through regression analysis, including generating an initial direction concentration parameter based on a deep water wave steepness through a segmented regression model, and then calibrating based on an effective wave steepness; performing parameter correction on a plurality of preset direction distribution models based on the direction concentration parameter value to generate a candidate direction spectrum; and selecting a target direction spectrum according to the closeness of the candidate direction spectrum and a historical measured direction spectrum. The application improves the accuracy of direction spectrum measurement and the adaptability to sea area characteristics through measured data driving and segmented regression calibration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ocean observation, and in particular to a sea wave direction spectrum measurement method and system based on measured characteristics of a sea area. BACKGROUND

[0002] In the field of sea wave direction spectrum measurement, traditional methods often rely on theoretical models or simplified assumptions, failing to fully incorporate measured characteristics of the sea area, resulting in deviations between the measurement results and the actual sea wave characteristics. In the prior art, the measurement of the direction spectrum is usually based on a fixed direction distribution model, lacking adaptability to the actual sea wave parameters, thereby affecting the accuracy and reliability of the direction spectrum. In addition, how to dynamically determine the direction concentration parameter based on the measured data and effectively select the direction spectrum closest to the measured situation is a problem to be solved at present. The existing method often uses empirical values or a single model to determine the direction concentration parameter, which cannot adapt to the wave changes under different sea area conditions, resulting in systematic errors in the estimation of the direction spectrum.

[0003] Therefore, there is an urgent need for a method that can dynamically adjust parameters and optimize the selection of the direction spectrum based on the measured characteristics of the sea area, to improve the accuracy and practicality of the sea wave direction spectrum measurement. SUMMARY

[0004] To solve the above problems existing in the prior art, the present application provides a sea wave direction spectrum measurement method based on measured characteristics of a sea area, comprising:

[0005] S1: based on the measured wave data of the target sea area, obtaining the wave parameters, generating a set of wave parameters;

[0006] S2: based on the set of wave parameters, determining the direction concentration parameter value through regression analysis, generating the direction concentration parameter value; wherein S2 comprises:

[0007] S2-1: based on the deep water wave steepness in the set of wave parameters, determining the reference value and the rate of change of the direction concentration parameter through a piecewise regression model, generating an initial direction concentration parameter;

[0008] S2-2: based on the effective wave steepness in the set of wave parameters, determining a calibration coefficient, and scaling the initial direction concentration parameter based on the calibration coefficient and limiting it within a preset range, generating a calibrated direction concentration parameter;

[0009] S2-3: taking the calibrated direction concentration parameter as the direction concentration parameter value;

[0010] S3: based on the direction concentration parameter value, parameter correcting a plurality of preset direction distribution models, generating a plurality of candidate direction spectrums;

[0011] S4: selecting a target directional spectrum from the plurality of candidate directional spectra based on the closeness of the plurality of candidate directional spectra to the historical measured directional spectrum.

[0012] Compared with the prior art, the present application has the following beneficial effects:

[0013] The present application effectively solves the problem of disconnection between directional spectrum measurement and actual sea area characteristics in the background art. Firstly, step S1 obtains wave parameters based on the measured wave data of the target sea area, generates a wave parameter set, ensures that the basic data of the method directly comes from actual measurement, avoids the limitations of relying on theoretical assumptions in traditional methods, and provides real and reliable data support for subsequent analysis. Step S2 determines the directional concentration parameter based on the wave parameter set through regression analysis, and generates the directional concentration parameter value. The initial directional concentration parameter is determined by a segmented regression model, and a calibration coefficient is generated by using the effective wave steepness to scale and range limit, so that the directional concentration parameter can dynamically adapt to the measured characteristics of the sea area, improve the accuracy and adaptability of parameter estimation, and overcome the bias caused by the fixed parameter model. Step S3 corrects the parameters of a plurality of preset directional distribution models based on the directional concentration parameter value, and generates a plurality of candidate directional spectra. The flexibility of the directional distribution model is enhanced through parameter correction, which can cover the wave characteristics under different sea area conditions, and improve the diversity and representativeness of the directional spectrum. Step S4 selects a target directional spectrum from the plurality of candidate directional spectra based on the closeness of the plurality of candidate directional spectra to the historical measured directional spectrum. Through historical data verification and comparison, the consistency of the final directional spectrum with the measured data is ensured, and the reliability and practicality of the measurement result are improved.

[0014] In the whole method, each step cooperates with each other: S1 provides the basis of measured data, S2 generates key parameters based on data, S3 generates candidate spectra by using parameter correction model, and S4 selects the optimal result through historical data, forming a closed loop process from data collection to result optimization, which significantly improves the overall accuracy of sea wave directional spectrum measurement and the engineering application value. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0016] Figure 1 The flowchart of the sea wave directional spectrum measurement method based on the measured characteristics of the sea area provided by an embodiment of the present application is shown.

[0017] Figure 2 Fig. 1 shows a structural schematic diagram of a sea wave direction spectrum measurement system based on measured characteristics of a sea area according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0019] The specific embodiments of the present application will be described below.

[0020] Embodiment 1

[0021] As shown in the accompanying drawings, the present application provides a sea wave direction spectrum measurement method based on measured characteristics of a sea area, comprising: Figure 1

[0022] S1: based on the measured wave data of the target sea area, obtaining the wave parameters to generate a wave parameter set;

[0023] S2: based on the wave parameter set, determining the direction concentration parameter through regression analysis to generate the direction concentration parameter value; wherein S2 comprises:

[0024] S2-1: based on the deep water wave steepness in the wave parameter set, determining the reference value and the change rate of the direction concentration parameter through the piecewise regression model to generate the initial direction concentration parameter;

[0025] S2-2: based on the effective wave steepness in the wave parameter set, determining the calibration coefficient, and based on the calibration coefficient, scaling and limiting the initial direction concentration parameter within a preset range to generate the calibrated direction concentration parameter;

[0026] S2-3: taking the calibrated direction concentration parameter as the direction concentration parameter value;

[0027] S3: based on the direction concentration parameter value, performing parameter correction on a plurality of preset direction distribution models to generate a plurality of candidate direction spectrums;

[0028] S4: based on the closeness of the plurality of candidate direction spectrums and the historical measured direction spectrum, selecting the target direction spectrum from the plurality of candidate direction spectrums.

[0029] ​The sea wave direction spectrum measurement method based on the measured characteristics of the sea area realizes accurate estimation of the direction spectrum through systematic data processing and model optimization. The method first constructs a wave parameter set in step S1 by obtaining measured wave data of the target sea area. The measured wave data refers to the original wave information directly collected by wave buoys, wave radars and other marine observation equipment. The wave parameter set is a group of core parameters representing the statistical characteristics of the wave after preliminary extraction and calculation. This step ensures the authenticity and regional specificity of the data source, providing a reliable input basis for subsequent analysis.

[0030] Step S2 further utilizes the wave parameter set to determine the direction concentration parameter through regression analysis method. The direction concentration parameter is a key indicator describing the concentration degree of wave energy in different directions. Its determination process first calculates the initial direction concentration parameter based on the deep water wave steepness in the wave parameter set. The deep water wave steepness is the dimensionless ratio of deep water wave height to deep water wave length. The segmented regression model is a mathematical model that divides the value range of the deep water wave steepness into multiple intervals and establishes a regression relationship for each interval. Then, combined with the effective wave steepness in the wave parameter set, which is the ratio of effective wave height to effective wave length, the calibration coefficient is calculated to scale and limit the range of the initial value, generating the final direction concentration parameter value. This combination of segmented regression and calibration enables the direction concentration parameter to dynamically respond to the actual wave conditions of the sea area, significantly improving the adaptability of the parameter to complex sea conditions.

[0031] Step S3 uses the determined direction concentration parameter value to modify the parameters of multiple preset direction distribution models. The direction distribution model is a mathematical function describing the change of wave energy with direction. By modifying its parameters, the energy distribution pattern can be changed, thereby generating multiple candidate direction spectra that differ in energy distribution characteristics.

[0032] Step S4 selects the closest one from the candidate set as the final target direction spectrum by calculating the closeness of these candidate direction spectra to the historical measured direction spectrum, which is the direction spectrum data accumulated through long-term observation and considered to have high reliability. This selection mechanism based on historical data verification effectively guarantees the reliability and practicality of the output results.

[0033] The whole method is performed through the series connection of S1 to S4, forming a complete technical chain from raw data collection to key parameter calculation, to model correction and optimal result screening. The measured data provided by S1 step is the basis for all subsequent calculations, and its quality directly determines the accuracy of the final result. S2 step solves the problem of over-reliance on empirical formula or single model in traditional method through innovative segmented regression and calibration process, making it more accurately reflect the combined influence of deep water wave steepness and effective wave steepness. S3 step generates a diverse candidate directional spectrum through parameter correction, avoiding the systematic bias that may exist in a single model. S4 step realizes the objective optimization of the output result by introducing historical measured data as a reference. These four steps are closely linked, S2 relies on the high-quality parameter set provided by S1, S3 relies on the accurate directional concentration parameter output by S2, and S4 optimizes the selection of multiple results generated by S3. This close cooperation makes the whole method not only ensure the accuracy of the directional spectrum estimation, but also enhance its robustness and application value in different sea areas and different sea conditions.

[0034] In some implementations, S1 includes:

[0035] S1-1: Based on the measured wave data of the target sea area, extract the significant wave height, significant wave period and water depth to generate basic wave parameters;

[0036] S1-2: Based on the basic wave parameters, calculate the deep water wave height and deep water wavelength through linear wave theory to generate deep water wave parameters;

[0037] S1-3: Based on the basic wave parameters and deep water wave parameters, combine to generate a wave parameter set.

[0038] Step S1-1 extracts the significant wave height, significant wave period and water depth from the measured wave data of the target sea area, the significant wave height is the average wave height of the one-third largest wave in the wave height sequence, the significant wave period is the characteristic period corresponding to the significant wave height, and the water depth is the water depth at the observation point. These parameters are the most direct and core physical quantities to describe the state of sea waves and the environment of the water area, and their extraction provides accurate input for subsequent calculations.

[0039] Step S1-2 is based on these basic wave parameters and applies linear wave theory for calculation. Linear wave theory is a classical fluid mechanics theory that describes wave propagation and deformation, which assumes that waves are simple harmonic waves and the amplitude is much smaller than the wavelength. Under this theoretical framework, the deep water wave height is calculated from the significant wave height and the significant wave period, and the deep water wavelength is calculated from the significant wave period and the water depth, thereby obtaining a set of deep water wave parameters. Deep water wave parameters are parameters that characterize the characteristics of waves under ideal deep water conditions, excluding the influence of shallow water effects such as wave deformation and breaking caused by water depth.

[0040] Step S1-3 combines the basic wave parameters obtained in step S1-1 with the deep water wave parameters calculated in step S1-2 to form a final set of wave parameters. This step-by-step construction method has clear technical effects. First, by directly extracting the measured core parameters in S1-1, the objectivity of the data source is ensured. Second, S1-2 introduces linear wave theory for derivation, converting the measured parameters into deep water parameters with more universal physical meaning, which helps to eliminate the local influence of water depth at a specific observation point, so that the generated wave parameter set contains not only the measured information but also the deep water characteristic information corrected by theory. Finally, the combination operation of S1-3 makes the wave parameter set a more comprehensive and representative data set that contains both direct observations and theoretical derivations, providing a richer and more reliable feature dimension for the regression analysis of the direction concentration parameter in subsequent step S2. The entire process of generating the wave parameter set significantly improves the quality of the data foundation through the logic of "measured extraction-theoretical derivation-information fusion", making the subsequent analysis more accurately capture the essential characteristics of the sea waves, thereby laying a solid data foundation for the high-precision implementation of the entire measurement method.

[0041] In some implementations, S1-2 includes:

[0042] S1-2-1: based on the significant wave height and the significant wave period in the basic wave parameters, a deep water wave height is calculated and generated through linear wave theory;

[0043] S1-2-2: based on the significant wave period and the water depth in the basic wave parameters, a deep water wave length is calculated and generated through linear wave theory;

[0044] S1-2-3: based on the deep water wave height and the deep water wave length, deep water wave parameters are generated.

[0045] Step S1-2-1 calculates and generates a deep water wave height based on the significant wave height and the significant wave period in the basic wave parameters through linear wave theory. In the framework of linear wave theory, the deep water wave height can be considered equal to the significant wave height in ideal conditions, because the propagation of waves in deep water is not affected by seabed friction and energy decay is minimal. This calculation ensures that the wave height parameter can reflect the state of the wave in a standardized deep water environment. Step S1-2-2 is based on the significant wave period and the water depth, and also applies the dispersion relationship in linear wave theory to calculate the deep water wave length. The dispersion relationship describes the physical relationship between wave period, wave length and water depth, and through this relationship, the corresponding deep water wave length can be iteratively solved or obtained through an approximate formula from the known period and water depth, thereby standardizing the wave length parameter affected by local water depth to a theoretical value in deep water conditions. Step S1-2-3 combines the calculated deep water wave height and deep water wave length to generate a complete set of deep water wave parameters.

[0046] The technical effect of this series of steps is to realize the conversion of wave parameters from "field measurement conditions" to "standard deep water conditions". Although significant wave height and significant wave period are core parameters, they are measured in a specific water depth environment, and direct use may introduce interference of shallow water effect. Through the theoretical calculation of S1-2-1 and S1-2-2, the wave height and wavelength are uniformly converted to the standard reference environment of deep water, so that the characteristic parameters used for subsequent regression analysis (i.e. deep water wave parameters) have better comparability and universality. This conversion is crucial to improve the accuracy of directional concentration parameter estimation, because it strips away the complexity brought by the local water depth variable, so that the regression model can more purely reflect the influence of wave characteristics (such as wave steepness) on the directional distribution. Therefore, the deep water wave parameter calculation process refined in this embodiment essentially provides a high-quality input for the entire method that is standardized by theory and cleaned by data, greatly enhancing the reliability and physical consistency of the regression analysis results in subsequent step S2.

[0047] In some implementations, S2-1 includes:

[0048] S2-1-1: comparing the deep water wave steepness with a preset deep water wave steepness threshold to generate a comparison result;

[0049] S2-1-2: based on the comparison result, selecting a model segment from a plurality of preset regression model segments, and obtaining a reference value and a variation rate of the selected model segment;

[0050] S2-1-3: based on the reference value and the variation rate, calculating the initial directional concentration parameter through a linear relationship.

[0051] Step S2-1-1 compares the deep water wave steepness in the wave parameter set with a preset deep water wave steepness threshold to generate a comparison result, the deep water wave steepness threshold being one or more critical values set based on statistical analysis of a large amount of historical wave data or physical mechanism understanding, for dividing continuous wave steepness values into different intervals. Step S2-1-2 selects a model segment corresponding to the comparison result from a plurality of preset regression model segments, and obtains a reference value and a variation rate defined in the model segment, each regression model segment being independently established for a specific deep water wave steepness interval, and containing linear or nonlinear relationship parameters between the directional concentration parameter and the deep water wave steepness in the interval. Step S2-1-3 calculates the initial directional concentration parameter by linear relationship using the reference value and the variation rate provided by the selected model segment. This segmented regression processing method has significant technical advantages. The generation and evolution mechanism of sea waves is complex, and the relationship between the directional concentration parameter and the deep water wave steepness may present different rules in different wave steepness ranges, for example, the change is gentle at a smaller wave steepness, and the change is violent at a larger wave steepness. A single overall regression model is difficult to accurately depict such nonlinear and segmented dependent relationship. By introducing threshold comparison and model segment selection, the method can adaptively call the most appropriate local regression relationship for calculation according to deep water wave steepness of different sizes, which makes the estimation of the initial directional concentration parameter more accurate and more in line with the actual performance of sea wave physics.

[0052] This step provides a high-quality initial value for the determination of the entire directional concentration parameter, which has preliminarily reflected the main influence of the deep water wave steepness. After the effective wave steepness calibration in step S2-2, the final directional concentration parameter value obtained comprehensively reflects the macro interval characteristics of the deep water wave steepness and the micro calibration information of the effective wave steepness, thereby realizing more accurate and robust estimation of the key parameter of the directional concentration.

[0053] In some implementations, S3 includes:

[0054] S3-1: based on the directional concentration parameter value, correcting the light-type directional distribution function to generate a corrected directional distribution function;

[0055] S3-2: based on the wave parameter set, adjusting a preset spectral function to generate an adjusted spectral function;

[0056] S3-3: based on the corrected directional distribution function and the adjusted spectral function, generating a plurality of candidate directional spectra.

[0057] Step S3-1 modifies the Pierson-Moskowitz (PM) directional distribution function based on the directional concentration parameter value determined in step S2. The PM directional distribution function is an empirical model widely used in ocean engineering to describe the directional distribution of wave energy. The function form contains a parameter that controls the concentration of the distribution. The modification is to set or adjust the corresponding parameter in the function with the calculated directional concentration parameter value, thereby generating a modified directional distribution function that better fits the measured characteristics of the current sea area. Step S3-2 simultaneously adjusts the pre-set spectral function based on the wave parameter set. The spectral function is a model that describes the frequency distribution of wave energy, such as the JONSWAP spectrum or the PM spectrum. The parameter adjustment refers to setting the characteristic parameters of the spectral function, such as the spectral peak value and peak frequency, according to the information in the wave parameter set, such as the significant wave height and effective wave period, thereby generating an adjusted spectral function that fits the current sea wave energy frequency distribution. Step S3-3 combines the modified directional distribution function and the adjusted spectral function. Usually, a multiplication model is used, i.e., the directional spectrum is equal to the product of the spectral function and the directional distribution function, thereby generating multiple candidate directional spectra. Here, "multiple" can be derived from different modification strategies for the PM directional distribution function, or multiple different base spectral functions are pre-set for parameter adjustment and then combined.

[0058] The technical effect of this series of operations is to achieve "adaptation to local conditions" and "optimized combination" of the directional spectrum model. The modification in S3-1 ensures that the directional distribution part accurately reflects the directional characteristics of sea wave energy; the adjustment in S3-2 ensures that the spectral part accurately reflects the frequency characteristics of sea wave energy. The combination in S3-3 combines the two optimized parts into a complete two-dimensional directional spectrum. By generating multiple candidate directional spectra, this method acknowledges that for the same set of measured data, there may be multiple directional spectrum models that are physically reasonable, which avoids the arbitrariness and risks that may be brought about by a single model output. This multi-candidate set generation strategy provides the possibility and basis for step S4 to optimize based on the proximity to historical measured data, so that the final determined target directional spectrum is not only the result of model calculation, but also the relatively optimal solution verified by historical data, thereby greatly improving the accuracy and reliability of the final directional spectrum product.

[0059] In some implementations, S3-1 includes:

[0060] S3-1-1: determining a spectral peak frequency coupling term based on the directional concentration parameter value;

[0061] S3-1-2: segmenting the directional concentration parameter value based on the spectral peak frequency coupling term to generate a modified directional concentration parameter;

[0062] S3-1-3: Based on the modified directional concentration parameter, the parameter of the light-type directional distribution function is adjusted to generate a modified directional distribution function.

[0063] The technical content defined in this embodiment is the deepening of the directional distribution function modification process, and the core is to improve the physical accuracy of the directional distribution model through frequency coupling and segmented modification. The spectral peak frequency coupling term mentioned in step S3-1-1 is a key intermediate variable, which establishes a bridge between the directional concentration parameter and the wave frequency characteristics. In oceanography, the directional distribution is not consistent in the full frequency range, and the energy concentration is usually highest near the spectral peak frequency, and then spreads to the high and low frequency ends. The spectral peak frequency coupling term is an adjustment factor introduced to quantify this change rule, and its value is determined by the directional concentration parameter value through a specific function relationship, thereby dynamically relating the characteristics of the directional distribution to the peak characteristics of the frequency spectrum. The segmented modification in step S3-1-2 is based on the above coupling term to finely process a single directional concentration parameter value to generate a set of modified directional concentration parameters that vary with frequency. Here, "segmented" means dividing different intervals in the frequency domain, such as dividing the core frequency band, high frequency band, etc. around the spectral peak frequency, and applying different correction amounts to the original directional concentration parameter in each frequency band according to the spectral peak frequency coupling term. This makes the originally global constant parameter into a series of parameter values that reflect the frequency-dependent characteristics. Step S3-1-3 uses this set of modified, frequency-dependent parameter values to configure the light-type directional distribution function. The light-type directional distribution function is a commonly used parameterized model, and its mathematical expression contains parameters that control the distribution shape and concentration. In this step, the internal parameters of the function are replaced by the aforementioned modified directional concentration parameters that vary with frequency, so that the final modified directional distribution function can more finely describe the directional spread of different frequency wave components. The technical effect of this series of operations is to significantly improve the ability of the directional distribution model to describe real sea waves. Traditional directional distribution functions use fixed directional concentration parameters, which cannot reflect the physical fact that energy concentration changes with frequency, resulting in deviations in the estimation of directional distribution at non-spectral peak frequencies.

[0064] By introducing frequency coupling and segmented modification, this method enables the directional distribution model to adaptively adjust the concentration degree in different frequency bands, thereby more accurately simulating the actual distribution pattern of sea wave energy in the direction-frequency two-dimensional space. This refined description provides a crucial foundation for generating high-quality candidate directional spectra, ultimately making the target directional spectrum more reliable and practical in engineering applications and scientific research.

[0065] In some implementations, S3-2 includes:

[0066] S3-2-1: Determine the wave type feature based on the sea area type in the wave parameter set;

[0067] S3-2-2: Based on the wave type feature, select the corresponding spectral shape parameter and peak enhancement factor to parameterize the preset spectral function, and generate the adjusted spectral function.

[0068] The technical content described in this embodiment focuses on how to make the spectral function dynamically adapt to the wave generation mechanism of a specific sea area. In step S3-2-1, the sea area type is determined based on the wave parameter set. The sea area type is classified according to the main physical cause of the sea wave, for example, wind wave dominant type means that the wave of this sea area is mainly generated by the local wind field and is in the growth stage, and its spectral shape is usually sharp; the swell dominant type means that the wave energy is mainly derived from the wave transmitted from a distance and out of the wind area, and its spectral shape is relatively flat; the mixed type has both characteristics. The judgment of the sea area type can be analyzed according to the characteristic parameters in the wave parameter set, for example, by analyzing the relationship between the effective wave period and the spectral peak period, the wave age parameter (a dimensionless number representing the development degree of the wave), or by analyzing the width parameter of the frequency spectrum to infer.

[0069] Step S3-2-2 configures the corresponding spectral shape parameter and peak enhancement factor for the preset spectral function according to the identified wave type feature; the spectral shape parameter is a mathematical parameter that controls the overall shape of the spectrum (such as sharpness, symmetry), and the peak enhancement factor is a parameter used to control the sharpness of the spectral peak in models such as JONSWAP spectrum; for the sea area dominated by wind waves, the spectral shape parameter corresponding to the growth stage wind wave is selected and combined with a higher peak enhancement factor to generate a spectral shape with a prominent spectral peak and a rapid decay in the high frequency part; for the sea area dominated by swell, the spectral shape parameter corresponding to the fully developed or decay stage is selected and combined with a lower peak enhancement factor to generate a spectral shape with a smooth spectral peak and a wider energy distribution. The technical effect brought by this process is to realize the "adapt to local conditions" of spectral estimation. Sea waves of different physical sources have completely different spectral characteristics, and using a fixed spectral parameter cannot accurately describe this difference. By first identifying the sea area type and then dynamically selecting the most matched model parameter, the generated adjusted spectral function can more truly reflect the internal frequency structure of the wave energy of the target sea area. Compared with the method of using fixed parameters or global average parameters, this parameterization method based on physical causes can more accurately capture the detailed features of the spectrum, such as the position of the spectral peak, the sharpness, and the decay rate of the high frequency tail.

[0070] An adjusted frequency spectrum function highly consistent with the wave generation mechanism of the sea area is the cornerstone of constructing a high-fidelity directional spectrum, because it accurately represents the energy distribution of the sea wave in the frequency dimension. When it is combined with a frequency-dependent corrected directional distribution function, it can produce a more physically self-consistent and more realistic morphological candidate directional spectrum, thereby improving the accuracy and adaptability of the entire sea wave directional spectrum measurement system from the source.

[0071] In some implementations, S4 includes:

[0072] S4-1: Obtain a historical directional spectrum set based on historical measured directional spectrum data;

[0073] S4-2: Calculate the closeness value of each candidate directional spectrum to the historical directional spectrum set;

[0074] S4-3: Select the candidate directional spectrum with the highest closeness value as the target directional spectrum.

[0075] The embodiment defines the specific process of selecting the final result from the candidate directional spectrum, the core of which is to use historical measured data as an objective benchmark for optimization selection. The historical directional spectrum set obtained in step S4-1 is composed of directional spectrum data accumulated for a long time by professional directional spectrum observation equipment (such as directional buoy, coherent wave radar or underwater pressure sensor array) in the target sea area or adjacent sea area with similar hydro-meteorological conditions and after quality control, which represents the “true” statistical characteristics of the directional spectrum in the sea area under various sea conditions, and provides a reliable reference system for model verification. The closeness value calculated in step S4-2 is a comprehensive index for quantifying the overall similarity between two directional spectra (i.e. a candidate directional spectrum and a historical measured directional spectrum); calculating the closeness value usually needs to compare the two two-dimensional directional-frequency spectrum data as a whole, and the methods that can be used include calculating the root mean square error of all grid points, calculating their spatial correlation coefficient, or calculating a certain integral quantity based on the similarity of spectral energy distribution, the purpose of which is to objectively reflect the deviation of the candidate spectrum from the measured spectrum with a numerical value. The selection logic of step S4-3 is to select the one with the highest closeness value from all candidate directional spectra as the target directional spectrum when compared with the historical directional spectrum set as a whole; in actual operation, this may be manifested as calculating the average closeness of each candidate spectrum with all samples in the historical set, or calculating with a representative typical historical spectrum, and then selecting the best one. The technical advantage of this screening process lies in the effective integration of prior knowledge and model output. Although the multiple candidate directional spectra generated in step S3 have certain physical rationality, there are differences in their degree of agreement with the true situation. By introducing a historical directional spectrum set constructed based on a large amount of measured data and calculating the closeness of each candidate spectrum with this set, the selection of the final result is based on objective, data-driven comparison rather than subjective speculation. Essentially, selecting the candidate spectrum with the highest closeness value is to select a model solution that is statistically closest to the historical observation facts.

[0076] This method effectively uses valuable field observation data to constrain and correct the output of the numerical model, significantly reduces the estimation bias caused by the uncertainty of the model itself or the limitations of the parameterization scheme, and greatly enhances the credibility and practical value of the final target directional spectrum. It ensures that the output result is not only the product of mathematical calculation, but also an optimized solution verified by real data and consistent with the historical characteristics of the sea area, which is crucial for application scenarios such as marine engineering design and navigation safety assessment that require high reliability data.

[0077] In some implementations, S4-2 includes:

[0078] S4-2-1: Calculate the closeness of each candidate directional spectrum to the historical directional spectrum in the wind wave dominant frequency band, to generate a first closeness;

[0079] S4-2-2: Calculate the closeness of each candidate directional spectrum to the historical directional spectrum in the wave dominant direction, to generate a second closeness;

[0080] S4-2-3: Weighted average the first closeness and the second closeness based on a preset weight, to generate a closeness value.

[0081] The first closeness calculated in step S4-2-1 refers to the similarity of the candidate directional spectrum to the historical directional spectrum in the wind wave dominant frequency band; the wind wave dominant frequency band generally refers to the frequency range in the sea wave spectrum where the energy is most concentrated and mainly driven by local wind input, generally located near the spectral peak frequency and extending to the high frequency part; calculating the closeness in this frequency band can focus on comparing whether the directional distribution structures of the two in the energy core area are consistent, for example, the shape parameter difference of the directional distribution function in this frequency band or the matching degree of the energy concentration can be calculated. The second closeness calculated in step S4-2-2 refers to the closeness in the wave dominant direction; the wave dominant direction is a characteristic quantity describing the overall direction of the sea wave energy source, which is usually calculated by the first moment of the directional distribution function; this step compares the angle deviation or consistency between the main direction of the candidate directional spectrum and the main direction of the historical directional spectrum. Step S4-2-3 combines the first closeness and the second closeness, which reflect the frequency band characteristics and direction characteristics respectively, into a comprehensive closeness value through a preset weight; the preset weight reflects the different emphasis of the user on these two dimensions when evaluating the quality of the directional spectrum, for example, a higher weight can be given to the closeness of the wind wave dominant frequency band to emphasize the importance of matching in the energy core area, and a proper weight is given to the main direction closeness to ensure the accuracy of the wave direction.

[0082] This strategy of dimension-weighted evaluation has obvious technical benefits. The directional spectrum is a complex two-dimensional field containing both frequency and directional information, and its accuracy is reflected in multiple aspects. If only one general, global proximity indicator is used, it may mask the deficiencies of a candidate spectrum in some key features, for example, a candidate spectrum that is close in global energy but has a large deviation in dominant direction or a significantly different core frequency band distribution may also obtain a higher score. By independently calculating the proximity of the two core features, the dominant frequency band of wind waves and the dominant direction of waves, the differences between the candidate spectrum and the historical measured data in these key dimensions can be more sensitively captured. Subsequently, by using preset weights for weighted synthesis, it is realized that according to the actual application requirements (such as some applications pay more attention to the energy distribution form, while others pay more attention to the dominant direction), the "optimal" directional spectrum is defined flexibly. This refined evaluation mechanism makes the final selection process more comprehensive and reasonable, enabling the system to filter out the target directional spectrum that is highly consistent with historical observation data in the most important features, thereby optimizing the overall quality and applicability of the final output product as a whole.

[0083] Embodiment 2

[0084] As shown in Figure 2 the second aspect, the present application proposes a sea wave directional spectrum measurement system based on measured characteristics of sea areas. The system uses any of the above-mentioned embodiments to provide a sea wave directional spectrum measurement method based on measured characteristics of sea areas. The system includes:

[0085] A parameter extraction module is configured to obtain wave parameters based on measured wave data of a target sea area, and generate a set of wave parameters.

[0086] A regression analysis module is configured to determine a directional concentration parameter value by regression analysis based on the set of wave parameters. The regression analysis module includes:

[0087] An initial directional concentration parameter is determined based on the deep water wave steepness in the set of wave parameters through a piecewise regression model.

[0088] A calibration coefficient is determined based on the effective wave steepness in the set of wave parameters, and the initial directional concentration parameter is scaled based on the calibration coefficient and limited within a preset range to generate a calibrated directional concentration parameter.

[0089] The calibrated directional concentration parameter is used as the directional concentration parameter value.

[0090] A parameter correction module is configured to perform parameter correction on a plurality of preset directional distribution models based on the directional concentration parameter value, and generate a plurality of candidate directional spectra.

[0091] The selecting module is configured to select a target direction spectrum from the plurality of candidate direction spectra based on closeness of the plurality of candidate direction spectra to a historical measured direction spectrum.

[0092] The system corresponds to the method provided in Embodiment 1, and thus will not be described herein.

[0093] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area, characterized in that, include: S1: Based on the measured wave data of the target sea area, obtain wave parameters and generate a set of wave parameters; S2: Based on the wave parameter set, the directional concentration parameter is determined through regression analysis, and the directional concentration parameter value is generated; wherein, S2 includes: S2-1: Based on the deep-water wave steepness in the wave parameter set, the baseline value and rate of change of the directional concentration parameter are determined by a piecewise regression model to generate the initial directional concentration parameter; S2-2: Based on the effective wave steepness in the wave parameter set, determine the calibration coefficient, and scale the initial direction concentration parameter based on the calibration coefficient and limit it within a preset range to generate the calibration direction concentration parameter; S2-3: Use the calibration direction concentration parameter as the direction concentration parameter value; S3: Based on the directional concentration parameter value, perform parameter correction on multiple preset directional distribution models to generate multiple candidate directional spectra; wherein, S3 includes: S3-1: Based on the directional concentration parameter value, the light-easy type directional distribution function is modified to generate the modified directional distribution function; S3-2: Based on the set of wave parameters, adjust the parameters of the preset spectrum function to generate an adjusted spectrum function; S3-3: Generate multiple candidate direction spectra based on the modified direction distribution function and the adjusted spectrum function; S3-1 includes: S3-1-1: Based on the directional concentration parameter value, determine the spectral peak frequency coupling term. The value of the spectral peak frequency coupling term is determined by the directional concentration parameter value through a preset functional relationship, so as to dynamically associate the characteristics of the directional distribution with the peak characteristics of the spectrum. S3-1-2: Based on the spectral peak frequency coupling term, the directional concentration parameter value is piecewise corrected to generate the corrected directional concentration parameter; S3-1-3: Based on the modified directional concentration parameter, the parameters of the easy-to-read directional distribution function are adjusted to generate the modified directional distribution function; S4: Select the target direction spectrum from multiple candidate direction spectra based on the similarity between multiple candidate direction spectra and historical measured direction spectra.

2. The method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area according to claim 1, characterized in that, S1 includes: S1-1: Based on the measured wave data of the target sea area, extract the significant wave height, significant wave period and water depth to generate basic wave parameters; S1-2: Based on the basic wave parameters, deep-water wave height and deep-water wavelength are calculated using linear wave theory to generate deep-water wave parameters; S1-3: Based on basic wave parameters and deep-water wave parameters, generate a set of wave parameters by combining them.

3. The method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area according to claim 2, characterized in that, S1-2 includes: S1-2-1: Based on the effective wave height and effective wave period in the basic wave parameters, the deep-water wave height is calculated and generated by linear wave theory. S1-2-2: Based on the effective wave period and water depth in the basic wave parameters, the deep-water wavelength is calculated and generated using linear wave theory. S1-2-3: Generate deep-water wave parameters based on deep-water wave height and deep-water wavelength.

4. The method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area according to claim 1, characterized in that, S2-1 includes: S2-1-1: Based on the steepness of deep water waves, compare it with the preset steepness threshold of deep water waves and generate a comparison result; S2-1-2: Based on the comparison results, select one model segment from multiple preset regression model segments, and obtain the baseline value and rate of change of the selected model segment; S2-1-3: Calculate the initial directional concentration parameter based on the baseline value and the rate of change through a linear relationship.

5. The method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area according to claim 1, characterized in that, S3-2 includes: S3-2-1: Determine wave type characteristics based on the sea area type in the wave parameter set; S3-2-2: Based on wave type characteristics, select the corresponding spectral parameters and peak enhancement factor to set the parameters of the preset spectrum function and generate an adjusted spectrum function.

6. The method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area according to claim 1, characterized in that, S4 include: S4-1: Obtain the set of historical directional spectra based on historical measured directional spectrum data; S4-2: Calculate the proximity value between each candidate direction spectrum and the historical direction spectrum set; S4-3: Select the candidate direction spectrum with the highest proximity value as the target direction spectrum.

7. The method for measuring the direction spectrum of ocean waves based on measured characteristics of a sea area according to claim 6, characterized in that, S4-2 includes: S4-2-1: Calculate the proximity of each candidate directional spectrum to the historical directional spectrum in the wind and wave dominant frequency band, and generate the first proximity score; S4-2-2: Calculate the proximity of each candidate direction spectrum to the historical direction spectrum in the main wave direction, and generate a second proximity score; S4-2-3: Calculate a weighted average of the first and second proximity scores based on preset weights to generate a proximity score value.

8. A wave direction spectrum measurement system based on measured marine characteristics, characterized in that, The system employs a wave direction spectrum measurement method based on measured sea area characteristics as described in any one of claims 1 to 7, and the system comprises: The parameter extraction module is used to obtain wave parameters and generate a set of wave parameters based on measured wave data of the target sea area. The regression analysis module is used to determine the direction concentration parameter based on the wave parameter set through regression analysis and generate the direction concentration parameter value; it includes: Based on the deep-water wave steepness in the wave parameter set, the baseline value and rate of change of the directional concentration parameter are determined by a piecewise regression model, and the initial directional concentration parameter is generated. Based on the effective wave steepness in the wave parameter set, the calibration coefficient is determined, and the initial direction concentration parameter is scaled and limited to a preset range based on the calibration coefficient to generate the calibration direction concentration parameter. Use the calibration direction concentration parameter as the direction concentration parameter value; The parameter correction module is used to correct the parameters of multiple preset directional distribution models based on the directional concentration parameter value, and generate multiple candidate directional spectra. The selection module is used to select the target direction spectrum from multiple candidate direction spectra based on the similarity between multiple candidate direction spectra and historical measured direction spectra.

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