Sea surface surge digital twinning modeling method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of sea surface modeling, and provides a sea surface surge digital twinning modeling method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a surge parameter based on a surge factor and a hyperbolic tangent function, and determining an independent variable of the hyperbolic tangent function by a real-time wave value and a peak wave value of a target sea spectrum; determining a surge factor function based on the surge parameter and the wave number direction angle of the target sea spectrum; based on the basic direction spread function, the surge factor function and a first regularization function, a target direction spread function is determined, and the first regularization function is used for ensuring that the integral of the target direction spread function based on the wave number direction angle is 1; and constructing a sea surface surge model by taking the target sea spectrum as a bottom spectrum and combining the target direction spread function. According to the method, the sea surface surge model with relatively high trueness can be constructed under the condition of relatively low calculation amount.

Description

Technical Field

[0001] The present invention relates to the technical field of sea surface modeling, and in particular, to a digital twin modeling method, device, electronic device and storage medium for sea surface swells. Background Art

[0002] Sea surface modeling can be used for radar signal simulation and sea clutter analysis. However, in some challenging scenarios, such as in high sea states, the cost of collecting radar sea clutter data is relatively high, and it will be impossible to comprehensively measure and analyze the radar electromagnetic echoes with different wind speeds, wind directions or grazing angles. To reduce costs, a sea surface swell model can be used as the basis for radar electromagnetic echo simulation to conduct radar signal simulation and sea clutter analysis.

[0003] Currently, the methods for sea surface swell modeling are mainly divided into physical modeling methods and mathematical modeling methods. The models obtained by physical modeling methods have good authenticity and conform to physical laws. However, due to the extremely large amount of calculation, it is difficult to apply them to the simulation of large-area sea surface swells. Mathematical modeling methods have a fast calculation speed, but the fidelity of the sea surface swell models obtained by modeling is relatively poor.

[0004] In summary, the existing sea surface swell modeling methods cannot achieve a high-fidelity sea surface swell model with a relatively low amount of calculation. Summary of the Invention

[0005] The present invention provides a digital twin modeling method, device, electronic device and storage medium for sea surface swells, so as to solve the problem in the prior art that a high-fidelity sea surface swell model cannot be achieved with a relatively low amount of calculation.

[0006] The present invention provides a digital twin modeling method for sea surface swells, including the following steps: Determine swell parameters based on a swell factor and the hyperbolic tangent function, where the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of a target sea spectrum; Determine a swell factor function based on the swell parameters and the wave number direction angle of the target sea spectrum; Determine a target direction extension function based on a basic direction extension function, the swell factor function and a first regularization function, where the first regularization function is used to ensure that the integral of the target direction extension function based on the wave number direction angle is 1; Construct a sea surface swell model with the target sea spectrum as the base spectrum and in combination with the target direction extension function.

[0007] According to the digital twin modeling method for sea surface swells provided by the present invention, determining swell parameters based on a swell factor and the hyperbolic tangent function includes: determining the swell parameters according to the following formula : ; Among them, tanh(·) represents the hyperbolic tangent function, e denotes the swell factor, and its value range is [0, 1], k represents the real-time wave number value of the target sea spectrum, k p represents the peak wave number value of the target sea spectrum.

[0008] According to a sea surface swell digital twin modeling method provided by the present invention, based on the swell parameters and the wave number direction angle of the target sea spectrum, a swell factor function is determined, including: determining the swell factor function according to the following formula : ; Among them, k represents the real-time wave number value of the target sea spectrum, θ represents the wave number direction angle of the target sea spectrum, represents the swell parameter.

[0009] According to a sea surface swell digital twin modeling method provided by the present invention, based on the basic direction extension function, the swell factor function and the first regularization function, a target direction extension function is determined, including: determining the target direction extension function according to the following formula : ; ; Among them, represents the basic direction extension function, represents the swell factor function, represents the first regularization function, k represents the real-time wave number value of the target sea spectrum, θ represents the wave number direction angle of the target sea spectrum.

[0010] According to a sea surface swell digital twin modeling method provided by the present invention, the basic direction extension function is: ; ; ; ; Among them, k p represents the peak wave number value of the target sea spectrum, U represents the sea surface wind speed, represents the gravitational acceleration, represents the second regularization coefficient, represents the gamma function, s ands p is an intermediate variable.

[0011] A digital twin modeling method for ocean surface swells provided by the present invention constructs an ocean surface swell model with the target sea spectrum as the bottom spectrum and combines the target direction spreading function, including: Convert the target sea spectrum into a two-dimensional sea spectrum through the target direction spreading function; Construct a sea surface height model in the frequency domain based on the two-dimensional sea spectrum; Convert the sea surface height model in the frequency domain into a sea surface height model in the spatial domain; Determine that the sea surface height model in the spatial domain is the ocean surface swell model.

[0012] According to a digital twin modeling method for ocean surface swells provided by the present invention, the target sea spectrum adopts the Elfouhaily omnidirectional sea spectrum.

[0013] The present invention also provides a digital twin modeling device for ocean surface swells, including the following modules: A swell parameter determination module, configured to determine swell parameters based on a swell factor and a hyperbolic tangent function, where the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum; A swell factor function determination module, configured to determine a swell factor function based on the swell parameters and the wave number direction angle of the target sea spectrum; A target direction spreading function determination module, configured to determine a target direction spreading function based on a basic direction spreading function, a swell factor function, and a first regularization function, where the first regularization function is used to ensure that the integral of the target direction spreading function based on the wave number direction angle is 1; A swell model construction module, configured to construct an ocean surface swell model with the target sea spectrum as the bottom spectrum and combine the target direction spreading function.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor, where when the processor executes the program, it implements the digital twin modeling method for ocean surface swells as described in any one of the above.

[0015] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the digital twin modeling method for ocean surface swells as described in any one of the above.

[0016] The method, device, electronic device and storage medium for digital twin modeling of ocean surface swells provided by the present invention determine swell parameters by setting a swell factor and combining the hyperbolic tangent function to satisfy the property that the swell increases asymptotically, so as to subsequently determine a swell factor function with a superimposed swell factor according to the swell parameters, and combine a basic direction expansion function, the swell factor function and a first regularization function to obtain a target direction expansion function that reflects the properties of the swell. Thus, using the target sea spectrum as the bottom spectrum and combining the target direction expansion function of the present invention can construct a relatively realistic ocean surface swell model. Moreover, compared with the current ocean surface swell modeling method based on hydrodynamic equations (physical modeling), the ocean surface swell modeling method based on the target sea spectrum and the target direction expansion function proposed by the present invention has a lower computational complexity and can effectively solve the problem of difficult swell modeling in a large-scale sea area. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart of the method for digital twin modeling of ocean surface swells provided by the present invention.

[0019] Figure 2 It is a schematic diagram of the variance spectrum and curvature spectrum of the Elfouhaily omnidirectional sea spectrum in the method for digital twin modeling of ocean surface swells provided by the present invention.

[0020] Figure 3 It is a graph showing the change of the target direction expansion function with the swell factor in the method for digital twin modeling of ocean surface swells provided by the present invention.

[0021] Figure 4 It is a graph of the ocean surface morphology modeled in the method for digital twin modeling of ocean surface swells provided by the present invention when the swell factor is 0.

[0022] Figure 5 It is a graph of the ocean surface morphology modeled in the method for digital twin modeling of ocean surface swells provided by the present invention when the swell factor is 0.5.

[0023] Figure 6 It is a graph of the ocean surface morphology modeled in the method for digital twin modeling of ocean surface swells provided by the present invention when the swell factor is 0.8.

[0024] Figure 7It is the sea surface morphology map obtained by modeling in the sea surface swell digital twin modeling method provided by the present invention when the swell factor is 1.

[0025] Figure 8 It is the sea surface morphology map obtained by modeling in the sea surface swell digital twin modeling method provided by the present invention when the swell factor is 1 and the wind speed is 2 m / s.

[0026] Figure 9 It is the sea surface morphology map obtained by modeling in the sea surface swell digital twin modeling method provided by the present invention when the swell factor is 1 and the wind speed is 4 m / s.

[0027] Figure 10 It is the sea surface morphology map obtained by modeling in the sea surface swell digital twin modeling method provided by the present invention when the swell factor is 1 and the wind speed is 6 m / s.

[0028] Figure 11 It is the echo comparison map between the random sea surface and the sea surface swell in the sea surface swell digital twin modeling method provided by the present invention under sea state 1.

[0029] Figure 12 It is the echo comparison map between the random sea surface and the sea surface swell in the sea surface swell digital twin modeling method provided by the present invention under sea state 3.

[0030] Figure 13 It is the echo comparison map between the random sea surface and the sea surface swell in the sea surface swell digital twin modeling method provided by the present invention under sea state 5.

[0031] Figure 14 It is the distribution simulation map of sea state 1 in the sea surface swell digital twin modeling method provided by the present invention.

[0032] Figure 15 It is the distribution simulation map of sea state 3 in the sea surface swell digital twin modeling method provided by the present invention.

[0033] Figure 16 It is the distribution simulation map of sea state 5 in the sea surface swell digital twin modeling method provided by the present invention.

[0034] Figure 17 It is the distribution simulation map of measured sea clutter in the sea surface swell digital twin modeling method provided by the present invention.

[0035] Figure 18 It is the structural schematic diagram of the sea surface swell digital twin modeling device provided by the present invention.

[0036] Figure 19 It is the structural schematic diagram of the electronic device provided by the present invention. Detailed implementation manners

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0038] The method for digital twin modeling of ocean swells in the embodiments of the present invention, as Figure 1 shown, includes steps S110 to S140.

[0039] Step S110: Determine the swell parameters based on the swell factor and the hyperbolic tangent function, where the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum. Here, the wave number = 2π / wavelength.

[0040] The target sea spectrum is a pre-selected sea spectrum for generating the sea surface model. However, the sea surface model generated using the target sea spectrum is usually a linear sea surface model and cannot simulate the effect of ocean swells. Therefore, it is necessary to add a swell factor and combine the hyperbolic tangent function to satisfy the property of asymptotic increase in swell formation (elongation), so that a sea surface swell model can ultimately be constructed.

[0041] Optionally, in this embodiment, the target sea spectrum preferably adopts the Elfouhaily omnidirectional sea spectrum. As Figure 2 shown, it is the shape of the variance spectrum (left figure) and the curvature spectrum (right figure) of the Elfouhaily omnidirectional sea spectrum at a wind speed of 10 m / s. The first spectral peak can be clearly seen in the variance spectrum. Since the second spectral peak in the variance spectrum is not obvious at the wave number k = 370 rad / m, the wave number cubed is multiplied by the wave spectrum, so that the variance spectrum is converted into the curvature spectrum. The second spectral peak at the wave number k = 370 rad / m can be clearly seen from the curvature spectrum, which coincides with the double peak of the real sea area. Therefore, the Elfouhaily omnidirectional sea spectrum is selected as the target sea spectrum, making the ultimately constructed sea surface swell model more realistic.

[0042] Step S120: Determine the swell factor function based on the swell parameters and the wave number direction angle of the target sea spectrum. Exemplarily, a cosine power function with an independent variable determined by the wave number direction angle of the target sea spectrum can be used as the swell factor function. Specifically, the cosine power function takes the swell parameters as the power to obtain the swell factor function.

[0043] In this step, since the swell factor function contains swell parameters, and the swell parameters are obtained based on the swell factor combined with the hyperbolic tangent function, the swell factor function also satisfies the property of asymptotic increase in swell shaping (elongation). Based on the swell factor function, the swell effects caused by ultra-far fetch and gravity waves can be simulated. The swell factor function gradually elongates the wave as the wavelength increases and asymptotically approaches the maximum elongation value.

[0044] Step S130: Determine the target direction spread function based on the basic direction spread function, the swell factor function, and the first regularization function, where the first regularization function is used to ensure that the integral of the target direction spread function based on the wave number direction angle is 1, so as to ensure that the target direction spread function does not affect the total magnitude of the sea spectrum energy.

[0045] In this step, the basic direction spread function is combined with the swell factor function and the first regularization function to obtain the target direction spread function, which also satisfies the property of asymptotic increase in swell shaping (elongation).

[0046] Step S140: Construct a sea surface swell model with the target sea spectrum as the bottom spectrum and in combination with the target direction spread function. For example, using the Elfouhaily omnidirectional sea spectrum as the target sea spectrum and combining with a conventional basic direction spread function (such as the unilateral cosine form direction spread function proposed by Longuet-Higgins) can only generate a linear sea surface. However, using the target sea spectrum as the bottom spectrum and combining with the target direction spread function of this embodiment can construct a sea surface swell model with a higher degree of authenticity.

[0047] In the sea surface swell digital twin modeling method of this embodiment, by setting the swell factor and combining with the property that the hyperbolic tangent function satisfies asymptotic increase of the swell, the swell parameters are determined, so as to subsequently determine the swell factor function with the superimposed swell factor according to the swell parameters, and combine the basic direction spread function, the swell factor function, and the first regularization function to obtain the target direction spread function that reflects the swell property. Thus, using the target sea spectrum as the bottom spectrum and combining with the target direction spread function of this embodiment can construct a sea surface swell model with a higher degree of authenticity. Moreover, compared with the current sea surface swell modeling method based on the hydrodynamic equation (physical modeling), the sea surface swell modeling method based on the target sea spectrum combined with the target direction spread function proposed in this embodiment has a lower computational complexity and can effectively solve the problem of difficult swell modeling in a large-scale sea area.

[0048] In some embodiments, the swell parameters are determined based on the swell factor and the hyperbolic tangent function, including: determining the swell parameters according to the following formula : (1).

[0049] where tanh(·) represents the hyperbolic tangent function,e Denotes the swell factor, with a value range of [0, 1], k Denotes the real-time wave number value of the target sea spectrum, k p Denotes the peak wave number value of the target sea spectrum.

[0050] In this embodiment, the square of the swell factor generates the perceptual linearization of the swell parameter, thereby bringing about the swell effect, and introducing the hyperbolic tangent function tanh(·) to construct the swell parameter. The hyperbolic tangent function tanh(·) has the following characteristics: (1) Nonlinear saturation characteristic: Simulates the gradual attenuation process of swell energy when deviating from the main wave direction.

[0051] (2) Parameter adjustability: By introducing the swell factor ∈[0, 1] to regulate the distribution pattern of the swell.

[0052] (3) Directional bias ability: Realizes the asymmetric stretching of the energy distribution along the main wave direction axis.

[0053] When = 0, it degenerates into a classical model, that is, a linear sea surface model, corresponding to the fully developed wind wave state; →1, the distribution of the direction expansion function is significantly elongated, simulating the mature swell propagating over a long distance, and the intermediate value represents the mixed state of wind waves and swells. As Figure 3 shown, when the swell factor is closer to 1, the corresponding target direction expansion function has a more elongated and asymmetric effect, and the sea surface swell model constructed by combining with the bottom spectrum can better show the ductility and asymmetry of the swell, that is, the constructed sea surface swell model is more realistic; while the swell factor is closer to 0, the corresponding target direction expansion function is more symmetric, indicating a worse swell effect. It should be noted that: generally, when e > 0.5, the swell state can already be seen. Not all swells are in the most complete form. As long as e is not equal to 0, the finally constructed is the sea surface swell model.

[0054] In this embodiment, due to the hyperbolic tangent function tanh(·) having the above three characteristics, the target direction expansion function also has the above three characteristics. Therefore, the sea surface swell model constructed by combining the target direction expansion function with the target sea spectrum can more realistically simulate the sea surface swell.

[0055] In some embodiments, based on the swell parameter and the wave number direction angle of the target sea spectrum, a swell factor function is determined, including: determining the swell factor function according to the following formula : (2).

[0056] Wherein, kRepresents the real-time wave number value of the target sea spectrum, θ Represents the wave number direction angle of the target sea spectrum, Represents the swell parameter.

[0057] In this embodiment, the unilateral cosine form of the directional spreading function of Longuet-Higgins can be used as the basis, and the above-mentioned swell parameter is used as the power of the cosine function. Since the hyperbolic tangent function tanh(·) has the above three characteristics, the corresponding swell factor function also has the above three characteristics, making the finally constructed sea surface swell model able to more realistically simulate the sea surface swell.

[0058] In some embodiments, based on the basic directional spreading function, the swell factor function, and the first regularization function, the target directional spreading function is determined, including: determining the target directional spreading function according to the following formula : (3); (4).

[0059] Wherein, Represents the basic directional spreading function, Represents the swell factor function, Represents the first regularization function, k Represents the real-time wave number value of the target sea spectrum, θ Represents the wave number direction angle of the target sea spectrum.

[0060] Furthermore, the basic directional spreading function can adopt the unilateral cosine form of the directional spreading function of Longuet-Higgins. The unilateral cosine form of the directional spreading function has good scalability, fast integral calculation and power calculation, and is easy to calculate. Specifically, the basic directional spreading function Is: (5); (6); (7); (8).

[0061] Wherein, k p Represents the peak wave number value of the target sea spectrum, U Represents the sea surface wind speed, Represents the acceleration due to gravity, Represents the second regularization coefficient, Represents the gamma function, s And s p Are intermediate variables.

[0062] The Longuet - Higgins model adopts a cosine power form. Although it can characterize the concentration characteristics of the energy in the main wave direction, its symmetric bell - shaped distribution is difficult to describe the unique directional extension characteristics of swell waves. By analyzing the measured swell wave data, it is found that when the fetch is fully developed, the wave direction distribution shows a significant asymmetric elongated shape, and the energy decay shows a non - linear asymptotic characteristic. Therefore, the traditional Longuet - Higgins model cannot display the characteristics of swell waves. In this embodiment, through formula (3), on the basis of the direction - extension function of Longuet - Higgins, a swell - wave factor function and a first regularization function are superimposed to obtain a target direction - extension function that can describe the unique directional extension characteristics of swell waves, and it is ensured that the target direction - extension function does not affect the total magnitude of the sea - spectrum energy.

[0063] In some embodiments, taking the target sea - spectrum as the bottom spectrum and combining with the target direction - extension function to construct a sea - surface swell model, including: Converting the target sea - spectrum into a two - dimensional sea - spectrum through the target direction - extension function. For example: taking the Elfouhaily omnidirectional sea - spectrum as the target sea - spectrum , converting the target sea - spectrum (i.e., the Elfouhaily omnidirectional sea - spectrum) into a two - dimensional sea - spectrum through the following formula.

[0064] (9).

[0065] Among them, represents the function of the two - dimensional sea - spectrum, represents the wave - number vector, , , , n and m are respectively the sampling points in the x - dimensional and y - dimensional of the sea - surface, is the wave - number direction angle.

[0066] Based on the two - dimensional sea - spectrum, construct a sea - surface height model in the frequency domain .

[0067] (10).

[0068] Among them, t represents the time, represents a complex Gaussian random sequence with a mean of 0 and a variance of 1, and are respectively the constructed length and width of the sea - surface, represents taking the conjugate complex number, and both represent the inverse Fourier transform.

[0069] Convert the sea surface height model in the frequency domain to the sea surface height model in the spatial domain .

[0070] (11).

[0071] Among them, \(i\) represents the imaginary unit, represents the Fourier transform, v represents the \((x, y)\) coordinates in the sea level coordinate system.

[0072] Determine that the sea surface height model in the spatial domain is the sea surface swell model, that is is the finally constructed sea surface swell model.

[0073] In this embodiment, the Elfouhaily omnidirectional sea spectrum is used as the target sea spectrum, and the sea surface swell model that can truly reflect the characteristics of the swell is constructed through the above steps.

[0074] Such as Figure 4 , Figure 5 , Figure 6 and Figure 7 shown, for the above target direction extension function, under the conditions of a sea surface wind speed of 4 m / s, a wind direction of 0°, a sea surface length and width of 50 meters each, and 100 sampling points in both the \(x\) and \(y\) dimensions, different swell factors e . It can be seen that when the swell factor e is 0 (that is Figure 4 ), the sea surface shows a randomly undulating shape, and as the swell factor e increases, the sea surface undulation gradually shows the shape of a swell. When the swell factor e is 1 (that is Figure 7 ), the waves are strongly elongated, showing the maximum swell effect.

[0075] Such as Figure 8 , Figure 9 and Figure 10 shown, respectively, for the same swell factor e ( e = 1), the sea surface shapes at wind speeds of 2 m / s, 4 m / s, and 6 m / s. It can be seen that the swell effect can be well reproduced at low wind speeds. However, the higher the wind speed, the more the swell effect is driven by the breaking wave effect, and it becomes less obvious, but the presented is still the real sea surface shape.

[0076] Calculate the normalized radar cross section (RCS) of sea clutter based on specific radar parameters (such as operating frequency, polarization mode, and pulse repetition frequency, etc.) and environmental conditions (such as wind speed, wind direction, and sea state, etc.). Then, substitute these normalized RCS values into the far-field scattering model (TSC model), which takes into account factors such as the distance between the radar and the sea surface, radar height, and incident angle, etc., to generate sea clutter echo signals under different sea state conditions.

[0077] Such as Figure 11 、 Figure 12 and Figure 13 are respectively the comparison diagrams of the radar sea clutter echo signals (left figure) of the random sea surface and the radar sea clutter echo signals (right figure) of the sea swell model under sea state 1, sea state 3, and sea state 5. The specific situations of sea state 1, sea state 3, and sea state 5 are shown in Table 1 below. It can be clearly seen that, on the one hand, the influence of sea state on the echo still exists, and on the other hand, the radar echo of the sea surface under the condition of having sea swell is more stripe-shaped and more compressed in form, which can better reflect the sea swell effect. That is, the radar echo of the sea surface sea swell can better reflect the real radar sea clutter of the sea surface, without the need for electromagnetic simulation, with high speed and low simulation cost.

[0078] Table 1 Descriptions of Sea State 1, Sea State 3, and Sea State 5

[0079] Such as Figure 14 、 Figure 15 and Figure 16 show the statistical charts of the cumulative distribution function (CDF) of the sea swell models generated in the above embodiments under the conditions of sea state 1, sea state 3, and sea state 5. The fitting effects of the CDF for three distributions, namely the K distribution, Weibull distribution, and lognormal distribution, are respectively shown in each figure. It can be seen that under different sea states, the echoes generated by the sea swell models constructed in the above embodiments are more consistent with the amplitude distributions of the previous typical sea clutter.

[0080] At the same time, as Figure 17 shows, the measured sea clutter ( Figure 17 ) is also compared. Figure 17 is the fitting effect of the measured sea clutter collected in 2022 using the above three distributions. It can be seen that the process of generating the sea swell model in the above embodiments and performing electromagnetic scattering simulation can well simulate the situation of real sea clutter.

[0081] As shown in Table 2, the K-S test mainly reflects the maximum difference between the empirical distribution and the theoretical distribution. The smaller the value, the better the fitting effect. RMSE (Root Mean Square Error) measures the fitting accuracy of the Probability Density Function (PDF). The smaller the value, the smaller the fitting error. It can be seen that for the K-S test, the sea surface swell model constructed by the present invention performs excellently in the CDF distribution fitting compared with the measured sea clutter, and both are consistent with the classical sea clutter distribution. However, for RMSE, it performs worse than the measured sea clutter. The conflict between the RMSE and the K-S test results may stem from the contradiction between local fitting and global matching (a low RMSE reflects better fitting in the middle section of the PDF, but a higher value of K-S indicates differences in the CDF tail) and data heterogeneity.

[0082] Table 2 Distribution Fitting Test

[0083] The sea surface swell digital twin modeling device provided by the present invention will be described below. The sea surface swell digital twin modeling device described below can be correspondingly referred to the sea surface swell digital twin modeling method described above.

[0084] The sea surface swell digital twin modeling device according to the embodiment of the present invention, as Figure 18 shown, includes: A swell parameter determination module 1810, configured to determine swell parameters based on swell factors and the hyperbolic tangent function, wherein the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum.

[0085] A swell factor function determination module 1820, configured to determine a swell factor function based on the swell parameters and the wave number direction angle of the target sea spectrum.

[0086] A target direction expansion function determination module 1830, configured to determine a target direction expansion function based on a basic direction expansion function, a swell factor function, and a first regularization function, wherein the first regularization function is used to ensure that the integral of the target direction expansion function based on the wave number direction angle is 1.

[0087] A swell model construction module 1840, configured to construct a sea surface swell model with the target sea spectrum as the bottom spectrum and in combination with the target direction expansion function.

[0088] The sea swell digital twin modeling device of this embodiment determines the swell parameters by setting the swell factor and combining the hyperbolic tangent function to satisfy the property that the swell increases asymptotically, so as to subsequently determine the swell factor function with the superimposed swell factor according to the swell parameters, and combine the basic direction expansion function, the swell factor function, and the first regularization function to obtain the target direction expansion function that reflects the swell property. Thus, using the target sea spectrum as the bottom spectrum and combining the target direction expansion function of this embodiment can construct a sea swell model with a high degree of authenticity. Moreover, compared with the current sea swell modeling method based on the hydrodynamic equation (physical modeling), the sea swell modeling method based on the target sea spectrum combined with the target direction expansion function proposed in this embodiment has a lower computational complexity and can effectively solve the problem of difficult swell modeling in a large-scale sea area.

[0089] In some embodiments, the swell parameter determination module 1810 is specifically configured to determine the swell parameters according to the following formula : ; where tanh(·) represents the hyperbolic tangent function, e represents the swell factor, and its value range is [0, 1], k represents the real-time wave number value of the target sea spectrum, k p represents the peak wave number value of the target sea spectrum.

[0090] In some embodiments, the swell factor function determination module 1820 is specifically configured to determine the swell factor function according to the following formula : ; where, k represents the real-time wave number value of the target sea spectrum, θ represents the wave number direction angle of the target sea spectrum, represents the swell parameter.

[0091] In some embodiments, the target direction expansion function determination module 1830 is specifically configured to determine the target direction expansion function according to the following formula : ; ; where, represents the basic direction expansion function, represents the swell factor function, represents the first regularization function, k represents the real-time wave number value of the target sea spectrum, θ represents the wave number direction angle of the target sea spectrum.

[0092] In some embodiments, the basic direction expansion function is as follows: ; ; ; ; wherein, k p represents the peak wave number value of the target sea spectrum, U represents the sea surface wind speed, represents the acceleration due to gravity, represents the second regularization coefficient, represents the gamma function, s and s p are intermediate variables.

[0093] In some embodiments, the swell model construction module 1840 is specifically configured to: Convert the target sea spectrum into a two-dimensional sea spectrum through the target direction expansion function; Construct a sea surface height model in the frequency domain based on the two-dimensional sea spectrum; Convert the sea surface height model in the frequency domain into a sea surface height model in the spatial domain; Determine that the sea surface height model in the spatial domain is the sea surface swell model.

[0094] In some embodiments, the target sea spectrum adopts the Elfouhaily omnidirectional sea spectrum.

[0095] Figure 19 Illustrates a schematic diagram of the physical structure of an electronic device, as Figure 19 shown. The electronic device may include: a processor 1910, a communication interface 1920, a memory 1930, and a communication bus 1940. Among them, the processor 1910, the communication interface 1920, and the memory 1930 complete mutual communication through the communication bus 1940. The processor 1910 can call the logical instructions in the memory 1930 to execute the sea surface swell digital twin modeling method, and this method includes: Determine the swell parameters based on the swell factor and the hyperbolic tangent function, and the independent variable of the hyperbolic tangent function is determined by the real-time wave number value and the peak wave number value of the target sea spectrum.

[0096] Determine the swell factor function based on the swell parameters and the wave number direction angle of the target sea spectrum.

[0097] Based on a basic direction extension function, a surge factor function, and a first regularization function, a target direction extension function is determined, where the first regularization function is used to ensure that the integral of the target direction extension function based on the wave number direction angle is 1.

[0098] Using the target sea spectrum as the base spectrum, a sea surface surge model is constructed in combination with the target direction extension function.

[0099] In addition, when the logical instructions in the above-mentioned memory 1930 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0100] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the sea surface surge digital twin modeling method provided by the above-mentioned various methods. The method includes: Based on the surge factor and the hyperbolic tangent function, surge parameters are determined, and the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum.

[0101] Based on the surge parameters and the wave number direction angle of the target sea spectrum, a surge factor function is determined.

[0102] Based on a basic direction extension function, a surge factor function, and a first regularization function, a target direction extension function is determined, where the first regularization function is used to ensure that the integral of the target direction extension function based on the wave number direction angle is 1.

[0103] Using the target sea spectrum as the base spectrum, a sea surface surge model is constructed in combination with the target direction extension function.

[0104] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the sea surface surge digital twin modeling method provided by the above-mentioned various methods. The method includes: Based on the surge factor and the hyperbolic tangent function, surge parameters are determined, and the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum.

[0105] Based on the surge parameters and the wave number direction angle of the target sea spectrum, a surge factor function is determined.

[0106] Based on a basic direction spread function, a surge factor function, and a first regularization function, a target direction spread function is determined, where the first regularization function is used to ensure that the integral of the target direction spread function based on the wave number direction angle is 1.

[0107] Using the target sea spectrum as the base spectrum, a sea surface surge model is constructed in combination with the target direction spread function.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0109] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital twin modeling method for ocean swells, characterized in that, Including: Based on the surge factor and the hyperbolic tangent function, determine the surge parameters, where the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum; Based on the surge parameters and the wave number direction angle of the target sea spectrum, determine the surge factor function; Based on the basic direction extension function, the surge factor function, and the first regularization function, determine the target direction extension function, where the first regularization function is used to ensure that the integral of the target direction extension function based on the wave number direction angle is 1; Using the target sea spectrum as the base spectrum, construct a sea surface surge model in combination with the target direction extension function.

2. The digital twin modeling method for ocean swells according to claim 1, wherein Based on the surge factor and the hyperbolic tangent function, determine the surge parameters, including: determine the surge parameters according to the following formula : ； where, tanh(·) represents the hyperbolic tangent function, e represents the swell factor, and its value range is [0, 1], k represents the real-time wave value of the target sea spectrum, k p represents the peak wave value of the target sea spectrum.

3. The digital twin modeling method for ocean swells according to claim 1, wherein Determine a surge factor function based on the surge parameters and the wave number direction angle of the target sea spectrum, including: determining the surge factor function according to the following formula : ； Among them, k represents the real-time wave value of the target sea spectrum, θ represents the wave number direction angle of the target sea spectrum, represents the swell parameter.

4. The method for digital twin modeling of ocean swells according to claim 1, characterized in that, Determine a target direction expansion function based on a basic direction expansion function, a surge factor function, and a first regularization function, including: determining the target direction expansion function according to the following formula : ； ； Among them, represents the basic direction extension function, represents the swell factor function, represents the first regularization function, k represents the real-time wave number value of the target sea spectrum, θ represents the wave number direction angle of the target sea spectrum.

5. The method for digital twin modeling of ocean surface swells according to claim 4, characterized in that The basic direction extension function is as follows: ； ； ； ； Among them, k p represents the peak wave number value of the target sea spectrum, U represents the sea surface wind speed, represents the acceleration due to gravity, represents the second regularization coefficient, represents the gamma function, s and s p are intermediate variables.

6. The digital twin modeling method for ocean surface swells according to claim 1, characterized in that Using the target sea spectrum as the base spectrum, construct a sea surface surge model in combination with the target direction extension function, including: Convert the target sea spectrum into a two-dimensional sea spectrum through the target direction extension function; Based on the two-dimensional sea spectrum, construct a sea surface height model in the frequency domain; Convert the sea surface height model in the frequency domain into a sea surface height model in the spatial domain; Determine that the sea surface height model in the spatial domain is the sea surface surge model.

7. The digital twin modeling method for ocean swells according to any one of claims 1 to 6, characterized in that The target sea spectrum adopts the Elfouhaily omnidirectional sea spectrum.

8. A digital twin modeling device for ocean swells, characterized in that Including: A surge parameter determination module, configured to determine surge parameters based on the surge factor and the hyperbolic tangent function, where the independent variable of the hyperbolic tangent function is determined by the real-time wave value and the peak wave value of the target sea spectrum; A surge factor function determination module, configured to determine the surge factor function based on the surge parameters and the wave number direction angle of the target sea spectrum; A target direction extension function determination module, configured to determine the target direction extension function based on the basic direction extension function, the surge factor function, and the first regularization function, where the first regularization function is used to ensure that the integral of the target direction extension function based on the wave number direction angle is 1; A surge model construction module, configured to use the target sea spectrum as the base spectrum and construct a sea surface surge model in combination with the target direction extension function.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the sea surface surge digital twin modeling method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the sea surface surge digital twin modeling method according to any one of claims 1 to 7.

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