Ocean internal wave data acquisition method, medium and system

Abstract

The invention provides an ocean internal wave data acquisition method, medium and system, and belongs to the technical field of ocean hydrological observation.The ocean internal wave data acquisition method comprises the steps that an acoustic Doppler flow velocity profiler is used for acquiring water body movement data, a temperature-salinity-depth profiler is used for acquiring seawater parameters, and meanwhile a satellite altimeter and drifting buoy data are combined. Performing time-frequency analysis on the water body motion data, and obtaining internal wave candidate data by using band-pass filtering; and constructing an internal wave mathematical equation set comprising a hydromechanics basic equation, buoyancy frequency, a frequency dispersion relation and the like, and verifying and screening out effective data. And inputting the effective data into a pre-trained classification model to determine an internal wave type, finally extracting a key waveform segment through a preset extraction equation set, and generating and outputting internal wave characteristic waveform data. According to the method, multi-source observation data and a mathematical physical model are combined, identification, classification and feature extraction of the ocean internal waves are realized, and the problem that a data acquisition method capable of effectively extracting effective signals of the ocean internal waves and accurately identifying the features of the internal waves is lacked at present is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean hydrological observation, and in particular, relates to an ocean internal wave data collection method, medium and system. Background Art

[0002] Ocean internal waves are an extremely important physical phenomenon in ocean dynamics. They are generated by seawater density stratification and buoyancy, and can propagate in the ocean in the form of waves. This wave phenomenon not only affects the transport process of matter and energy in the ocean, but also has a profound impact on marine ecosystems, marine engineering facilities, and underwater acoustics. In recent years, with the increase in global climate change and marine development activities, the demand for accurate monitoring and analysis of ocean internal waves has become increasingly urgent. Modern ocean internal wave monitoring technology has formed a multi-level and multi-dimensional observation system. In terms of field observation, the acoustic Doppler current profiler has become the core instrument for studying the dynamic characteristics of internal waves due to its high temporal and spatial resolution and continuous observation capabilities. It can simultaneously obtain velocity profiles in the horizontal and vertical directions, providing key data for the study of internal wave propagation characteristics. The temperature-salinity-depth profiler obtains information on the density stratification structure of seawater by measuring seawater temperature, salinity, and pressure. These data are the basis for calculating buoyancy frequency and analyzing internal wave propagation conditions.

[0003] However, the existing ocean internal wave monitoring technology faces a prominent technical problem in practical applications: the collected raw data contains a large amount of irrelevant information and noise interference, resulting in insufficient accuracy in internal wave signal recognition and feature extraction. This problem is mainly manifested in the following aspects: First, there are multi-scale motion phenomena in the marine environment, such as tides, vortices, and turbulence, and their signals are often mixed with internal wave signals, increasing the difficulty of separating effective signals. Secondly, the measurement errors and environmental noise of various observation instruments themselves will also affect the data quality. Thirdly, the existing data processing methods often analyze a single data source and lack a comprehensive utilization mechanism for multi-source data, which results in the acquisition of internal wave feature information that is not comprehensive and accurate enough.

[0004] These problems have seriously restricted the in-depth development of ocean internal wave research. In practical applications, inaccurate internal wave feature identification may lead to deviations in marine engineering design parameters and affect the safe operation of offshore platforms; in scientific research, it may cause deviations in the understanding of the internal wave propagation mechanism and affect the construction and verification of relevant theoretical models. Therefore, it is of great practical significance to establish a data acquisition and processing method that can effectively extract effective signals of ocean internal waves and accurately identify internal wave features. Summary of the invention

[0005] In view of this, the present invention provides an ocean internal wave data acquisition method, medium and system, which can solve the technical problem that the prior art lacks a data acquisition method that can effectively extract effective ocean internal wave signals and accurately identify internal wave characteristics.

[0006] The present invention is achieved in that:

[0007] A first aspect of the present invention provides a method for collecting ocean internal wave data, comprising the following steps:

[0008] S10, continuously and in real time, using an acoustic Doppler current profiler to collect ocean water movement data, wherein the ocean water movement data includes horizontal current velocity data and vertical current velocity data; at the same time, using a temperature-salinity-depth profiler to collect seawater temperature, salinity and depth data, and calculating density gradient data and background density data according to the seawater state equation; using satellite altimeter data to invert sea surface wave number direction data; using a deployed drifting buoy to measure and obtain group velocity data;

[0009] S20, performing time-frequency analysis on the ocean water body motion data to obtain ocean internal wave frequency range data;

[0010] S30, filtering the ocean water body motion data using a bandpass filter according to the ocean internal wave frequency range data to obtain ocean internal wave candidate data;

[0011] S40, constructing a mathematical equation group of ocean internal waves, wherein the mathematical equation group of ocean internal waves includes a basic equation of fluid mechanics, a buoyancy frequency equation, a dispersion relation equation, a density perturbation equation, an energy propagation equation, and a phase velocity equation;

[0012] S50, substituting the ocean internal wave candidate data into the ocean internal wave mathematical equation group for verification, and screening out the ocean internal wave valid data that satisfies the ocean internal wave mathematical equation group;

[0013] S60, inputting the ocean internal wave valid data into a pre-trained ocean internal wave classification model for classification to obtain ocean internal wave type data;

[0014] S70. Utilizing a preset extraction equation group, extracting key waveform segments in the ocean internal wave valid data according to the ocean internal wave type data, generating and outputting ocean internal wave characteristic waveform data.

[0015] On the basis of the above technical solution, the method for collecting ocean internal wave data of the present invention can also be improved as follows:

[0016] The basic equation of fluid mechanics is used to calculate the motion state of seawater particles, the input includes horizontal velocity data and vertical velocity data, and the output is the motion trajectory data of seawater particles;

[0017] The buoyancy frequency equation is used to calculate the seawater stratification intensity, the input includes density gradient data and gravity acceleration data, and the output is the buoyancy frequency value;

[0018] The dispersion relation equation is used to verify the internal wave propagation characteristics, the input includes angular frequency data and wave number data, and the output is the internal wave dispersion relation verification result;

[0019] The density perturbation equation is used to calculate the density field change, the input includes background density data and perturbation amplitude data, and the output is density perturbation distribution data;

[0020] The energy propagation equation is used to calculate the energy propagation direction, the input includes group velocity data and wave number direction data, and the output is energy propagation vector data;

[0021] The phase velocity equation is used to calculate the wave propagation velocity, the input includes angular frequency data and wave number data, and the output is the phase velocity value.

[0022] Furthermore, in the step of screening out valid ocean internal wave data that satisfies the ocean internal wave mathematical equations, the screening criteria are that the verification results of the internal wave dispersion relationship meet the theoretical prediction value, the density perturbation distribution data is within a preset threshold range, and the energy propagation vector data points reasonably.

[0023] Among them, the ocean internal wave classification model adopts a convolutional neural network structure, specifically including four convolution layers, two pooling layers and three fully connected layers.

[0024] Furthermore, the ocean internal wave classification model uses labeled historical ocean internal wave data to train the deep neural network model, specifically using historical ocean internal wave data with labeled information for supervised learning training.

[0025] Furthermore, the preset extraction equation group includes Fourier transform equation, wavelet transform equation, Hilbert transform equation and empirical mode decomposition equation.

[0026] Furthermore, the Fourier transform equation is used to extract the main frequency waveform segment, the input is the effective data of the ocean internal wave, and the output is the main frequency band waveform data;

[0027] The wavelet transform equation is used to extract energy concentrated waveform segments, the input is effective ocean internal wave data, and the output is energy concentrated segment waveform data;

[0028] The Hilbert transform equation is used to extract the envelope mutation waveform segment, the input is the effective data of the ocean internal wave, and the output is the envelope mutation segment waveform data;

[0029] The empirical mode decomposition equation is used to extract characteristic mode waveform segments, the input is effective ocean internal wave data, and the output is characteristic mode segment waveform data.

[0030] Furthermore, the preset threshold range of the density perturbation distribution data is ±30% of the background density gradient.

[0031] The seawater state equation is specifically expressed as follows:

[0032] ρ=ρ0[1-α(T-T0)+β(S-S0)-γ(P-P0)];

[0033]

[0034] Where ρ is the density of seawater, kg / m 3 ; ρ0 is the reference density, taken as 1025kg / m 3 ; P is temperature, ℃; T0 is reference temperature, 15℃; S is salinity; S0 is reference salinity, 35; P is pressure, Pa; P0 is reference pressure, 1.013×10 5 Pa; α is the thermal expansion coefficient, which is 2.1×10 -4 / ℃; β is the salinity contraction coefficient, which is 7.6×10 -4 ; γ is the compression coefficient, which is 4.5×10 -10 / Pa; z is the depth, m.

[0035] The parameter acquisition method is:

[0036] T, S, and P are obtained by direct measurement of the temperature-salinity-depth profiler;

[0037] Background density ρ b The calculated density is obtained by time averaging:

[0038]

[0039] Where N is the number of measurements; ρ i The density value calculated for the i-th measurement.

[0040] The equation for inverting the sea surface wave number direction using satellite altimeter data is specifically expressed as follows:

[0041]

[0042] In the formula, is the wave number vector; η is the sea surface height anomaly, m; k is the wave number size, m -1 ; g is the acceleration due to gravity, take 9.81m / s 2 ; c is phase velocity, m / s; h is water depth, m.

[0043] The parameter acquisition method is:

[0044] η is obtained by direct measurement of satellite altimeter;

[0045] h is obtained by sounding.

[0046] The equations of the ocean internal wave mathematical equations are specifically described as follows:

[0047] (1) Basic equations of fluid mechanics:

[0048]

[0049] Where u is the horizontal velocity, m / s; w is the vertical velocity, m / s; p is the pressure, Pa; ρ′ is the density disturbance, kg / m 3 ; ν is the kinematic viscosity coefficient, which is 1.01×10 -6 m 2 / s.

[0050] (2) Buoyancy frequency equation:

[0051]

[0052] Where N is the buoyancy frequency, rad / s.

[0053] (3) Dispersion relation equation:

[0054]

[0055] Where ω is the angular frequency, rad / s; k x is the horizontal wave number, m -1 ;k z is the vertical wave number, m -1 .

[0056] (4) Density perturbation equation:

[0057]

[0058] Where A is the amplitude, m; φ(z) is the vertical mode function.

[0059] (5) Energy propagation equation:

[0060]

[0061] In the formula, is the group velocity vector, m / s.

[0062] (6) Phase velocity equation:

[0063]

[0064] In the formula, cp is the phase velocity, m / s.

[0065] The equations of the extraction equation group are specifically described as follows:

[0066] (1) Fourier transform equation:

[0067] F=W F X;

[0068] W F =[ω mn ],ω mn =e -2πi(m-1)(n-1) / N ;

[0069] P=diag(|F| 2 );

[0070]

[0071] In the formula, is the input data matrix, each column represents an observation time series; is the Fourier transform coefficient matrix; is the Fourier transform matrix; is the power spectrum matrix; The matrix is ​​selected for the main frequency band, according to the power spectrum threshold λ F Sure; It is the output main frequency band waveform data matrix.

[0072] (2) Wavelet transform equation:

[0073] W = ΨX;

[0074]

[0075] E=|W| 2 ;

[0076]

[0077] In the formula, is the wavelet coefficient matrix; is the wavelet basis matrix, L is the scale number; is the wavelet energy matrix; The matrix is ​​selected for the energy concentration segment, which is determined according to the energy threshold λW; It is the output energy concentration segment waveform data matrix.

[0078] (3) Hilbert transform equation:

[0079] H=W H X;

[0080]

[0081] In the formula, is the Hilbert transform matrix; is the Hilbert transform kernel matrix; is the envelope matrix; Select the matrix for the envelope mutation segment, according to the envelope change rate threshold λ H Sure; It is the output envelope mutation segment waveform data matrix.

[0082] (4) Empirical mode decomposition equation:

[0083] C k =S k X;

[0084]

[0085] In the formula, is the kth internal model function matrix; is the screening operator matrix; is the envelope mean matrix; They are the upper and lower envelope matrices respectively; Select the matrix for the characteristic mode segment according to the modal energy ratio threshold λ E Sure; It is the output characteristic mode segment waveform data matrix.

[0086] Parameter acquisition method:

[0087] 1. Lambda F Determined by power spectrum distribution, generally 10%-30% of the maximum power;

[0088] 2. Lambda W Determined by wavelet energy distribution, generally 60%-80% of the total energy;

[0089] 3. Lambda H Determined by envelope change rate statistics, usually taking 2 times the standard deviation;

[0090] 4. Lambda E Determined by the modal energy ratio, generally 30%-50%.

[0091] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned ocean internal wave data collection method.

[0092] A third aspect of the present invention provides an ocean internal wave data acquisition system, which includes the above-mentioned computer-readable storage medium.

[0093] Compared with the prior art, the beneficial effects of the ocean internal wave data collection method, medium and system provided by the present invention are:

[0094] 1. The fusion and analysis of multi-sensor data is realized. This method makes full use of the data collected by different sensor equipment such as acoustic Doppler current profilers, temperature-salinity-depth profilers, and satellite remote sensing, and organically combines these data through mathematical modeling and intelligent algorithms, providing a solid data foundation for the comprehensive analysis of internal wave parameters.

[0095] 2. A method for extracting internal wave characteristics based on a mathematical equation group is proposed. The present invention constructs a mathematical equation group that describes the physical characteristics of internal waves, substitutes monitoring data for verification, and extracts key parameters such as the frequency, amplitude, and propagation speed of internal waves, providing a reliable mathematical basis for internal wave characteristic analysis.

[0096] 3. Deep learning is used to realize intelligent identification of internal wave types. This method uses a pre-trained convolutional neural network model to automatically classify the screened effective internal wave data, realizes rapid identification of internal wave types, and significantly improves the efficiency of internal wave research.

[0097] 4. Comprehensively use a variety of signal processing algorithms to generate internal wave characteristic waveforms. The present invention uses methods such as Fourier transform, wavelet transform, Hilbert transform and empirical mode decomposition to extract key waveform features such as main frequency band, energy concentration segment, envelope mutation segment and characteristic mode segment from the effective data of internal waves, providing rich information for internal wave dynamic analysis.

[0098] Therefore, the present invention solves the technical problem that the prior art lacks a data acquisition method that can effectively extract effective signals of ocean internal waves and accurately identify internal wave characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 A flow chart of the method provided by the present invention;

[0100] Figure 2 It is a Fourier transform main frequency band waveform analysis diagram in the embodiment;

[0101] Figure 3 Graphs showing typical wave characteristics of three different types of internal waves in the embodiment;

[0102] Figure 4 1 is a performance comparison chart of the method of the present invention and the traditional method in the embodiment. DETAILED DESCRIPTION

[0103] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0104] like Figure 1 FIG. 1 is a flow chart of a method for collecting ocean internal wave data provided by the present invention. The method comprises the following steps:

[0105] S10. Continuously and in real time, collect ocean water movement data using an acoustic Doppler current profiler, which includes horizontal and vertical current velocity data; collect seawater temperature, salinity and depth data using a temperature-salinity-depth profiler, and calculate density gradient data and background density data based on the seawater state equation; obtain sea surface wave number direction data by inverting satellite altimeter data; and obtain group velocity data by measuring a deployed drifting buoy;

[0106] S20, performing time-frequency analysis on the ocean water movement data to obtain ocean internal wave frequency range data;

[0107] S30, filtering the ocean water body motion data using a bandpass filter according to the ocean internal wave frequency range data to obtain ocean internal wave candidate data;

[0108] S40, constructing a mathematical equation group of ocean internal waves, wherein the mathematical equation group of ocean internal waves includes a basic equation of fluid mechanics, a buoyancy frequency equation, a dispersion relation equation, a density perturbation equation, an energy propagation equation, and a phase velocity equation;

[0109] S50, substituting the ocean internal wave candidate data into the ocean internal wave mathematical equation group for verification, and screening out the ocean internal wave valid data that satisfies the ocean internal wave mathematical equation group;

[0110] S60, inputting the ocean internal wave valid data into a pre-trained ocean internal wave classification model for classification, and obtaining ocean internal wave type data;

[0111] S70. Utilizing a preset extraction equation group, extracting key waveform segments in the effective ocean internal wave data according to the ocean internal wave type data, generating and outputting ocean internal wave characteristic waveform data.

[0112] The specific implementation methods of the above steps are described in detail below:

[0113] The specific implementation method of step S10 is: first, use an acoustic Doppler current profiler to continuously and in real time collect the movement data of the ocean water body, including horizontal current velocity data and vertical current velocity data. At the same time, use a temperature-salinity-depth profiler to collect the temperature, salinity and depth data of the seawater. Then, density gradient data and background density data are calculated based on the seawater state equation. In addition, the sea surface wave number direction data is inverted using satellite altimeter data. Finally, the group velocity data of the ocean internal waves is measured using a deployed drifting buoy. The purpose of this step is to obtain various basic data required to describe the movement and propagation characteristics of ocean internal waves.

[0114] Among them, the specific expression of the seawater state equation is:

[0115] ρ=ρ0[1-α(T-T0)+β(S-S0)-γ(P-P0)];

[0116]

[0117] Where ρ is the density of seawater, in kg / m 3 ; ρ0 is the reference density, which is 1025kg / m 3 ; T is temperature, unit is ℃; T0 is reference temperature, value is 15℃; S is salinity; S0 is reference salinity, value is 35; P is pressure, unit is Pa; P0 is reference pressure, value is 1.013×10 5 Pa; α is the thermal expansion coefficient, which is 2.1×10 -4 / ℃; β is the salinity contraction coefficient, which is 7.6×10 -4 ; γ is the compression coefficient, which is 4.5×10 -10 / Pa; z is the depth, the unit is m.

[0118] Among these parameters, T, S and P can be directly measured by temperature-salinity-depth profiler. b It is obtained by averaging the calculated density values ​​over time:

[0119]

[0120] Where N is the number of measurements, ρ i The density value calculated for the i-th measurement.

[0121] The equation for inverting the sea surface wave number direction using satellite altimeter data is as follows:

[0122]

[0123]

[0124] In the formula, is the wave number vector; η is the sea surface height anomaly, unit is m; k is the wave number size, unit is m -1 ; g is the acceleration due to gravity, and its value is 9.81m / s 2 ; c is the phase velocity, in m / s; h is the water depth, in m. Among them, η can be directly measured by satellite altimeter, and h can be obtained by depth measurement.

[0125] The specific implementation of step S20 is: performing time-frequency analysis on the ocean water body motion data obtained in step S10 to obtain frequency range data of ocean internal waves. The purpose of this step is to determine the frequency characteristics of ocean internal waves.

[0126] The specific implementation of step S30 is: according to the ocean internal wave frequency range data obtained in step S20, the ocean water body motion data obtained in step S10 is filtered using a bandpass filter to obtain candidate data of ocean internal waves. The purpose of this step is to extract the part that matches the internal wave frequency from the original data as candidate data of internal waves.

[0127] The specific implementation of step S40 is: constructing a set of mathematical equations describing the motion and propagation characteristics of ocean internal waves, including the following equations:

[0128] (1) Basic equations of fluid mechanics:

[0129]

[0130] Where n is the horizontal velocity in m / s; w is the vertical velocity in m / s; p is the pressure in Pa; ρ′ is the density disturbance in kg / m 3 ; v is the kinematic viscosity coefficient, which is 1.01×10 -6 m 2 / s. These equations describe the motion state of seawater particles.

[0131] (2) Buoyancy frequency equation:

[0132]

[0133] Where N is the buoyancy frequency in rad / s. This equation is used to calculate the intensity of seawater stratification.

[0134] (3) Dispersion relation equation:

[0135]

[0136] Where ω is the angular frequency in rad / s; k x is the horizontal wave number, in m -1 ;k z is the vertical wave number, in m -1 This equation is used to verify the propagation characteristics of internal waves.

[0137] (4) Density perturbation equation:

[0138]

[0139] Where A is the internal wave amplitude, in m; φ(z) is the vertical mode function. This equation describes the density field changes caused by internal waves.

[0140] (5) Energy propagation equation:

[0141]

[0142] In the formula, is the group velocity vector in m / s. This equation is used to calculate the direction of propagation of internal wave energy.

[0143] (6) Phase velocity equation:

[0144]

[0145] In the formula, c p is the phase velocity in m / s. This equation is used to calculate the propagation speed of internal waves.

[0146] This set of mathematical equations comprehensively describes the movement and propagation characteristics of ocean internal waves, laying the foundation for subsequent internal wave identification and feature extraction.

[0147] The specific implementation method of step S50 is: substitute the ocean internal wave candidate data obtained in step S30 into the mathematical equation group constructed in step S40 for verification, and screen out the ocean internal wave valid data that meets the equation group. The specific screening criteria include: the internal wave dispersion relationship verification result meets the theoretical prediction value, the density perturbation distribution data is within the preset threshold range, and the energy propagation vector data points reasonably. The purpose of this step is to screen out valid data that meets the theoretical characteristics of internal waves from the candidate data. Among them, the preset threshold range of the density perturbation distribution data is generally taken as ±30% of the background density gradient.

[0148] The specific implementation method of step S60 is: input the effective ocean internal wave data screened in step S50 into a pre-trained deep neural network model for classification to obtain the type data of ocean internal waves. This deep neural network model adopts a convolutional neural network structure, including 4 convolutional layers, 2 pooling layers and 3 fully connected layers. During model training, the labeled historical ocean internal wave data is used for supervised learning. The purpose of this step is to use deep learning technology to automatically identify and classify internal waves.

[0149] The specific implementation of step S70 is: using the preset extraction equation group, according to the internal wave type data obtained in step S60, extracting the key waveform segment from the effective ocean internal wave data screened in step S50, generating and outputting the characteristic waveform data of the ocean internal wave. These extraction equation groups include:

[0150] (1) Fourier transform equation:

[0151] F=W F X;

[0152] W F =[ω mn ],ω mn =e -2πi(m-1)(n-1) / N ;

[0153] P=diag(|F| 2 );

[0154]

[0155] in, is the input data matrix; is the Fourier transform coefficient matrix; is the Fourier transform matrix; is the power spectrum matrix; The matrix is ​​selected for the main frequency band, according to the power spectrum threshold λ F (generally 10%-30% of the maximum power) to determine; It is the output main frequency band waveform data matrix.

[0156] (2) Wavelet transform equation:

[0157] W = ΨX;

[0158]

[0159] E=|W| 2 ;

[0160]

[0161] in, is the wavelet coefficient matrix; is the wavelet basis matrix, L is the scale number; is the wavelet energy matrix; The matrix is ​​selected for the energy concentration segment according to the energy threshold λ W (generally 60%-80% of the total energy) determined; It is the output energy concentration segment waveform data matrix.

[0162] (3) Hilbert transform equation:

[0163] H=W H X;

[0164]

[0165] in, is the Hilbert transform matrix; is the Hilbert transform kernel matrix; is the envelope matrix; Select the matrix for the envelope mutation segment according to the envelope change rate threshold λ H (usually 2 times the standard deviation) to determine; It is the output envelope mutation segment waveform data matrix.

[0166] (4) Empirical mode decomposition equation:

[0167] C k =S k X;

[0168]

[0169] in, is the kth internal model function matrix; is the screening operator matrix; is the envelope mean matrix; They are the upper and lower envelope matrices respectively; The matrix is ​​selected for the characteristic mode segment according to the modal energy ratio threshold λ E (Usually 30%-50%) to determine; It is the output characteristic mode segment waveform data matrix.

[0170] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned ocean internal wave data collection method.

[0171] A third aspect of the present invention provides an ocean internal wave data acquisition system, which includes the above-mentioned computer-readable storage medium.

[0172] Specifically, the principle of the present invention is:

[0173] First, from the perspective of physical mechanism, ocean internal waves, as a special wave phenomenon, have their unique physical characteristics and propagation laws. The present invention comprehensively describes the physical characteristics of internal waves by constructing a complete set of mathematical equations for ocean internal waves. Among them, the basic equations of fluid mechanics describe the motion state of water particles under the action of internal waves, the buoyancy frequency equation reflects the influence of seawater stratification on the propagation of internal waves, the dispersion relationship equation characterizes the relationship between the frequency and wave number of internal waves, the density perturbation equation describes the density field changes caused by internal waves, the energy propagation equation describes the propagation characteristics of internal wave energy, and the phase velocity equation reflects the propagation speed of waves. These equations constitute a complete physical framework, which provides a theoretical basis for identifying real internal wave signals.

[0174] From the perspective of data processing, the present invention adopts a progressive processing flow of "rough screening-fine verification-intelligent classification-feature extraction". First, the frequency range in which internal waves may exist is determined through time-frequency analysis, and then the candidate data is preliminarily screened out using a bandpass filter. This step can effectively remove obvious non-internal wave signals. Then, a mathematical equation group is used for strict verification to ensure that the data meets the physical characteristics of internal waves. This verification method based on physical mechanisms has a reliable theoretical basis and can effectively identify real internal wave signals.

[0175] In the intelligent classification link, the present invention selects convolutional neural network as the core architecture of the classification model based on the following considerations: first, internal wave data has obvious spatiotemporal characteristics, and the convolution layer can effectively extract these features; second, the introduction of the pooling layer can reduce the dimension of the data and extract more representative features; finally, the multi-layer fully connected layer can establish nonlinear associations between features and achieve accurate classification. By using annotated historical data for training, the model can learn the typical characteristics of internal waves, thereby achieving accurate classification of new data.

[0176] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below: A marine scientific research team conducted a one-month ocean internal wave observation experiment in a certain sea area in the South China Sea. The experimental area is located in the central part of the South China Sea, with a water depth of about 1,200 meters and a relatively flat seabed terrain. During the experiment, the team used acoustic Doppler current profilers, temperature-salinity-depth profilers, satellite remote sensing, drifting buoys and other equipment to continuously observe and measure parameters such as ocean water movement, temperature, salinity and density in the sea area.

[0177] First, the team deployed four acoustic Doppler current profilers, fixed at water depths of 100 meters, 400 meters, 800 meters, and 1100 meters, to obtain three-dimensional velocity distribution data of ocean water bodies. These instruments took measurements every 10 minutes and worked continuously for 30 days, obtaining a total of 4032 sets of velocity data (4 units x 30 days x 24 hours / 0.17 hours). Taking the velocity data at a depth of 100 meters as an example, the time series of horizontal velocity u and vertical velocity w are shown in Table 1:

[0178] Table 1. Horizontal and vertical velocity data at a depth of 100 meters

[0179] time u(m / s) w(m / s) 00:00 0.213 0.031 00:10 0.196 0.028 00:20 0.234 0.026 … … … 23:50 0.202 0.033

[0180] At the same time, the team deployed three temperature-salinity-depth profilers in the experimental area, located at water depths of 300 meters, 700 meters, and 1,100 meters. These instruments took measurements every 2 hours and worked continuously for 30 days, obtaining a total of 360 sets (3 units x 30 days / 0.083 days) of temperature-salinity-depth data. Taking the temperature, salinity, and pressure data at a depth of 700 meters as an example, as shown in Table 2:

[0181] Table 2. Temperature, salinity and pressure data at a depth of 700 meters

[0182] time T(℃) S P(Pa) 00:00 5.12 34.52 7.0E+6 02:00 5.09 34.49 7.0E+6 04:00 5.06 34.46 7.0E+6 … … … … 22:00 5.18 34.57 7.0E+6

[0183] Based on these temperature-salinity-depth data, the density gradient can be calculated by the seawater state equation. and background density ρ b , as shown in Table 3:

[0184] Table 3 Density gradient and background density at 700 m depth

[0185]

[0186] In addition, the team also used satellite altimeter data to invert the sea surface height anomaly η in the experimental area and obtained the sea surface wave number direction and wave number size k, as shown in Table 4:

[0187] Table 4. Direction and magnitude of sea surface wave numbers

[0188]

[0189] At the same time, the team deployed five drifting buoys in the experimental area and measured the group velocity of the ocean internal waves. As shown in Table 5:

[0190] Table 5. Group velocity of ocean internal waves

[0191]

[0192] With these basic data, the research team began to extract and analyze the characteristics of ocean internal waves.

[0193] First, the team conducted a time-frequency analysis on the flow velocity, temperature-salinity depth and group velocity data obtained in step S10 and found that the frequency range of the internal wave was 0.001-0.01rad / s.

[0194] Then, according to this frequency range, the velocity and temperature-salinity-depth data are filtered using a bandpass filter to extract candidate data of internal waves.

[0195] Next, the team constructed a set of mathematical equations that describe the motion and propagation characteristics of ocean internal waves, including the basic equations of fluid mechanics, the buoyancy frequency equation, the dispersion relationship equation, the density perturbation equation, the energy propagation equation, and the phase velocity equation. The candidate internal wave data were substituted into these equations for verification, and the results showed:

[0196] (1) Internal wave dispersion relation The angular frequency ω and wave number k in x, k z meets theoretical predictions;

[0197] (2) Density perturbation The amplitude a in the background density gradient Within the range of ±30%;

[0198] (3) Energy propagation direction The measured group velocity Basically consistent.

[0199] Through this verification process, the team screened out valid data that met the characteristics of internal wave theory.

[0200] Next, the team input these valid internal wave data into a pre-trained convolutional neural network model for classification. This model contains 4 convolutional layers, 2 pooling layers and 3 fully connected layers, and uses labeled historical internal wave data for supervised learning training. After classification, the internal wave data was divided into 3 types: the first type is free internal waves, accounting for 45%; the second type is forced internal waves, accounting for 35%; and the third type is vortex internal waves, accounting for 20%.

[0201] Finally, the team used Fourier transform, wavelet transform, Hilbert transform and empirical mode decomposition to extract waveform features such as main frequency band, energy concentration segment, envelope mutation segment and characteristic mode segment from the effective internal wave data. Figure 2 As shown, the main frequency is about 0.005rad / s. The upper part shows the time domain waveform, showing the amplitude change of the internal wave over time; the lower part shows the frequency domain analysis results, which clearly shows that the main frequency is around 0.005rad / s. Figure 3 The figure shows the comparison of typical waveform characteristics of three different types of internal waves. From top to bottom, they are the time domain waveforms of free internal waves, forced internal waves, and vortex internal waves. This figure shows the significant differences in the waveform structures of the three types of internal waves;

[0202] Figure 4 This is a performance comparison chart of the method of the present invention and the traditional method. The performance of different methods in three dimensions, recognition accuracy, computing time and memory usage, is shown in the form of a bar chart. This chart clearly shows that the method of the present invention is superior to the traditional method in all aspects, especially in terms of computing efficiency and accuracy;

[0203] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for collecting ocean internal wave data, characterized in that: The following steps are involved: S10, continuously and in real time, using an acoustic Doppler current profiler to collect ocean water movement data, wherein the ocean water movement data includes horizontal current velocity data and vertical current velocity data; at the same time, using a temperature-salinity-depth profiler to collect seawater temperature, salinity and depth data, and calculating density gradient data and background density data according to the seawater state equation; using satellite altimeter data to invert sea surface wave number direction data; using a deployed drifting buoy to measure and obtain group velocity data; S20, performing time-frequency analysis on the ocean water body motion data to obtain ocean internal wave frequency range data; S30, filtering the ocean water body motion data using a bandpass filter according to the ocean internal wave frequency range data to obtain ocean internal wave candidate data; S40, constructing a mathematical equation group of ocean internal waves, wherein the mathematical equation group of ocean internal waves comprises a basic equation of fluid mechanics, a buoyancy frequency equation, a dispersion relation equation, a density perturbation equation, an energy propagation equation, and a phase velocity equation; S50, substituting the ocean internal wave candidate data into the ocean internal wave mathematical equation group for verification, and screening out the ocean internal wave valid data that satisfies the ocean internal wave mathematical equation group; S60, inputting the ocean internal wave valid data into a pre-trained ocean internal wave classification model for classification to obtain ocean internal wave type data; S70, using a preset extraction equation group, extracting key waveform segments in the ocean internal wave valid data according to the ocean internal wave type data, generating and outputting ocean internal wave characteristic waveform data.

2. The method for collecting ocean internal wave data according to claim 1, characterized in that: The basic fluid mechanics equation is used to calculate the motion state of seawater particles, the input includes horizontal velocity data and vertical velocity data, and the output is the motion trajectory data of seawater particles; The buoyancy frequency equation is used to calculate the seawater stratification intensity, the input includes density gradient data and gravity acceleration data, and the output is the buoyancy frequency value; The dispersion relation equation is used to verify the internal wave propagation characteristics, the input includes angular frequency data and wave number data, and the output is the internal wave dispersion relation verification result; The density perturbation equation is used to calculate the density field change, the input includes background density data and perturbation amplitude data, and the output is density perturbation distribution data; The energy propagation equation is used to calculate the energy propagation direction, the input includes group velocity data and wave number direction data, and the output is energy propagation vector data; The phase velocity equation is used to calculate the wave propagation velocity, the input includes angular frequency data and wave number data, and the output is the phase velocity value.

3. The method for collecting ocean internal wave data according to claim 2, characterized in that: In the step of screening out valid ocean internal wave data that satisfies the ocean internal wave mathematical equation group, the screening criteria are that the verification results of the internal wave dispersion relationship meet the theoretical prediction value, the density disturbance distribution data is within the preset threshold range, and the energy propagation vector data points reasonably.

4. The method for collecting ocean internal wave data according to claim 1, characterized in that: The ocean internal wave classification model adopts a convolutional neural network structure, specifically including four convolutional layers, two pooling layers and three fully connected layers.

5. The method for collecting ocean internal wave data according to claim 4, characterized in that: The ocean internal wave classification model uses labeled historical ocean internal wave data to train the deep neural network model, specifically using historical ocean internal wave data with labeled information for supervised learning training.

6. The method for collecting ocean internal wave data according to claim 5, characterized in that: The preset extraction equation group includes Fourier transform equation, wavelet transform equation, Hilbert transform equation and empirical mode decomposition equation.

7. The method for collecting ocean internal wave data according to claim 6, characterized in that: The Fourier transform equation is used to extract the main frequency waveform segment, the input is the effective data of the ocean internal wave, and the output is the main frequency waveform data; The wavelet transform equation is used to extract energy concentrated waveform segments, the input is effective ocean internal wave data, and the output is energy concentrated segment waveform data; The Hilbert transform equation is used to extract the envelope mutation waveform segment, the input is the effective data of the ocean internal wave, and the output is the envelope mutation segment waveform data; The empirical mode decomposition equation is used to extract characteristic mode waveform segments, the input is effective ocean internal wave data, and the output is characteristic mode segment waveform data.

8. The method for collecting ocean internal wave data according to claim 3, characterized in that: The preset threshold range for density perturbation distribution data was ±30% of the background density gradient.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the ocean internal wave data collection method according to any one of claims 1 to 8.

10. An ocean internal wave data acquisition system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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