Ocean wave measurement method based on GNSS (Global Navigation Satellite System) wave buoy and atmosphere data fusion

Abstract

The invention relates to the technical field of ocean engineering and remote sensing, in particular to an ocean wave measurement method based on GNSS (global navigation satellite system) wave buoy and atmosphere data fusion, which comprises the following steps: S1, acquiring three-dimensional displacement data of a buoy in real time; s2, collecting atmospheric condition parameters over the target sea area; s3, performing time and space synchronization on the data acquired by the GNSS wave buoy and the data of the atmospheric sensor array; s4, mapping the GNSS wave buoy data and the atmosphere data to a unified feature space; s5, extracting wave characteristic parameters; and S6, establishing a coupling model for quantifying the dynamic influence of atmospheric factors on wave generation, development and propagation. According to the method, the GNSS wave buoy data and the atmospheric condition data are fused, the physical basic model and historical data optimization are combined, high-precision wave characteristic prediction can be achieved, and the defects of a traditional wave measurement method in the aspects of precision, real-time performance and coverage range are effectively overcome.

Description

Technical Field

[0001] The present invention relates to the fields of ocean engineering and remote sensing technology, and particularly to an ocean wave measurement method based on the fusion of GNSS wave buoys and atmospheric data. Background Art

[0002] With the in-depth study of the ocean, the monitoring and prediction of ocean waves have become important technical means to ensure the safety of maritime navigation, promote the development of ocean resources, and respond to ocean disasters; traditional ocean wave measurement methods usually rely on buoys, radars, and satellite remote sensing technology; however, these traditional methods have certain limitations; buoy monitoring is restricted by its deployment location, and the coverage area is narrow; although radar monitoring can achieve large-scale monitoring, its accuracy is relatively low, and it is greatly affected by weather conditions; although satellite remote sensing technology can provide a relatively wide range of data, its spatio-temporal resolution is limited and it cannot provide high-precision wave information in real time; therefore, the existing ocean wave monitoring methods cannot meet the requirements of high precision, real-time performance, and wide coverage.

[0003] The existing technology mainly faces two problems: on the one hand, traditional wave measurement methods cannot make full use of the dynamic relationship between the atmosphere and waves, resulting in the generation, development, and propagation process of waves being difficult to accurately predict; on the other hand, the application of existing multi-modal data fusion technology in wave monitoring is less, and there is still a lack of a mature technical solution for how to effectively fuse GNSS wave buoy data and atmospheric data. For this reason, there is an urgent need for an ocean wave measurement method based on the fusion of GNSS wave buoys and atmospheric data to solve the above problems. Summary of the Invention

[0004] Based on the above purpose, the present invention provides an ocean wave measurement method based on the fusion of GNSS wave buoys and atmospheric data.

[0005] The ocean wave measurement method based on the fusion of GNSS wave buoys and atmospheric data includes the following steps: S1: Deploy GNSS wave buoys in the target sea area to collect the three-dimensional displacement data of the buoys in real time, and record the instantaneous displacement and acceleration data of the buoys at each time step; S2: Collect the atmospheric condition parameters over the target sea area through a distributed atmospheric sensor array, including real-time wind speed, wind direction, and air pressure; S3: Use the spatio-temporal calibration algorithm to synchronize the data collected by the GNSS wave buoys and the data of the atmospheric sensor array in terms of time and space to ensure the spatio-temporal consistency of the data; S4: Through multi-modal data fusion technology, map the GNSS wave buoy data and atmospheric data to a unified feature space to generate a fusion data set; S5: Based on the fused dataset, use the spectral analysis method and wavelet transform algorithm to extract wave characteristic parameters, including wave height, period, propagation direction, and wave energy; S6: Based on the extracted wave characteristic parameters and atmospheric condition parameters, establish a coupling model for quantifying the dynamic impact of atmospheric factors on wave generation, development, and propagation.

[0006] Optionally, the specific steps of S1 include: S11: Deploy at least one GNSS wave buoy in the target sea area. The GNSS wave buoy is equipped with a receiver for converting satellite signals into position data of the buoy in three-dimensional space; S12: Real-time collect the three-dimensional displacement data of the buoy through the GNSS receiver. The three-dimensional displacement data includes the longitude, latitude, and depth of the buoy, and the collection frequency is set at a time interval of each second; S13: When collecting the three-dimensional displacement data of the buoy each time, use an accelerometer synchronized with the GNSS receiver to synchronously record the acceleration data of the buoy, and calculate the instantaneous displacement and velocity of the buoy according to the acceleration data through numerical integration methods; S14: Record the three-dimensional displacement data and acceleration data collected each time in sequence according to the time step to ensure the continuity and integrity of the data at each time point.

[0007] Optionally, the specific steps of S2 include: S21: Deploy multiple atmospheric sensors at different positions in the target sea area to form a distributed atmospheric sensor array. The atmospheric sensors include an anemometer, a wind vane, and a barometer; S22: The anemometer is used to collect real-time wind speed data over the target sea area. The anemometer can provide wind speed data at intervals of each second within a predetermined time interval, with the unit of meters per second; S23: Collect real-time wind direction data over the target sea area through the wind vane. The wind vane measures the wind direction angle through the air flow guiding or gyroscope principle; S24: Collect real-time air pressure data over the target sea area through the barometer. The barometer measures the change in atmospheric pressure using the static pressure sensor principle, with the unit of kilopascals; S25: Transmit the wind speed, wind direction, and air pressure data to the data center or local storage device through wireless communication technology, including GPRS, Wi-Fi, or satellite communication.

[0008] Optionally, the specific steps of S3 include: S31: Obtain the three-dimensional displacement data and acceleration data collected by the GNSS wave buoy, as well as the wind speed, wind direction, and air pressure data collected by the distributed atmospheric sensor array; S32: Perform preliminary time alignment on the data of the GNSS wave buoy and the data of the atmospheric sensor. The time alignment method is based on comparing the timestamps of each data source to determine the actual acquisition time of each data point, and dividing the time window according to the earliest and latest acquisition times to ensure that the time ranges of all data are exactly matched; S33: Use a spatio-temporal calibration algorithm to perform precise time synchronization on the data of the GNSS wave buoy and the data of the atmospheric sensor array. The spatio-temporal calibration algorithm uses interpolation to process the time differences in the acquired data, and interpolates the data with different time steps to a unified time interval to ensure that the time steps of each data point are consistent; S34: Synchronize the spatial positions through the spatio-temporal calibration algorithm. The spatial synchronization is aligned through the spatial coordinates of the reference point. Specifically, compare the three-dimensional position coordinates of the GNSS wave buoy with the geographical coordinates of the location where the atmospheric sensor array is located, and use the geographical coordinate transformation method to map the buoy data and the atmospheric data to a unified spatial coordinate system.

[0009] Optionally, the specific steps of S34 include: S341: Obtain the three-dimensional position coordinates of the GNSS wave buoy and the geographical coordinates of the atmospheric sensor array; S342: Convert the geographical coordinates of the GNSS wave buoy to spatial coordinates in the same projection coordinate system as the atmospheric sensor array for spatial coordinate synchronization; S344: Determine the spatial position synchronization error by calculating the position difference between the buoy and the atmospheric sensor array in the plane coordinate system, and perform fine-tuning after clarifying the error to make their spatial coordinates exactly synchronized.

[0010] Optionally, the specific steps of S4 include: S31: Obtain the GNSS wave buoy data after time and space synchronization; S32: Extract features from the GNSS wave buoy data and the atmospheric data respectively. For the GNSS wave buoy data, use Fourier transform or wavelet transform to extract the frequency domain features and time domain features of the wave signal; for the atmospheric data, extract the statistical features of wind speed, wind direction and air pressure; S33: Convert the features of the GNSS wave buoy data and the atmospheric data to a unified scale range by normalizing each feature so that its value range is between 0 and 1 to eliminate the dimensional differences between data sources; S34: Use the weighted average method to map the GNSS wave buoy data and the atmospheric data to a unified feature space; first assign weights to the GNSS wave buoy data and the atmospheric data and where is the weight of the GNSS wave buoy data, is the weight of the atmospheric data; then calculate the fused feature after weighted average , and the formula is: , where is the standardized feature of the GNSS wave buoy data, is the standardized feature of the atmospheric data, is the fused feature; S35: Generate a fused dataset based on the fused feature after weighted average.

[0011] Optionally, the S5 specifically includes: S51: Perform spectral analysis on the buoy displacement data and acceleration data in the fused dataset. The spectral analysis method uses the fast Fourier transform algorithm, and the specific steps include: S511: Convert the buoy displacement data and the acceleration data into frequency-domain signals, and calculate their spectra and ; S512: Extract the wave period T and wave height H from the spectrum. Determine the wave period and wave height by calculating the main peak frequency of the spectrum and its corresponding amplitude . The relationship between the wave period T and the frequency is: ; S513: Extract the wave height H by calculating the amplitude difference from the wave peak to the wave trough, which is the wave amplitude corresponding to the maximum amplitude in the spectrum; S52: Perform wavelet transform on the buoy displacement data in the fused dataset to extract the wave propagation direction and wave energy. The specific steps include: S521: Use the Morlet wavelet transform method to perform wavelet transform on the displacement data of the buoy, and calculate the distribution in time and frequency after wavelet transform; S522: Extract the wave propagation direction and wave energy from the wavelet transform result. The propagation direction is determined by the phase information of the signal, and the wave energy is calculated through the amplitude after wavelet transform. The formula is: , where is the energy density of the wavelet transform result, and E is the total energy of the wave.

[0012] Optionally, the S6 specifically includes: S61: Receive the wave characteristic parameters extracted in S5 and the atmospheric condition parameters obtained in S2; S62: Based on the theoretical framework of the physical basic model, select the Phelps - Pratt model as the basis to establish the coupling relationship between waves and atmospheric conditions and construct a coupling model; S63: Optimize the parameters of the coupling model using historical data; S64: Based on the coupling model optimized in S63, input the current wind speed, wind direction, and air pressure data to predict the wave characteristic parameters and quantify the specific impacts of different atmospheric conditions on wave generation, development, and propagation.

[0013] Optionally, the specific content of S62 includes: S621: Select the Phelps - Pratt model as the basis. Based on the dynamic impact of wind on ocean surface waves, describe the relationship between wind speed and wave generation. The formula is: , where, is the spectral density, is the model constant, is the density of water, g is the acceleration due to gravity, f is the frequency, and c is a constant in the model; S622: On the basis of the Phelps - Pratt model, consider the atmospheric conditions of wind speed , wind direction and air pressure . Combine the surface tension of the ocean and wave energy to establish the coupling relationship between wind speed and wave characteristics; The specific coupling relationships include: Coupling relationship 1: The relationship between the wave generation rate R and wind speed is expressed by the following formula: , where, is the coefficient, reflecting the sensitivity of wind speed to wave generation, is the wind speed, is the empirical constant, and f is the frequency; Coupling relationship 2: According to the change of wind direction , adjust the wave propagation direction. Specifically: , where, represents the offset of the wave propagation direction, is the original wave propagation direction, is the wind direction; Coupling relationship 3: The change in wave energy E is jointly affected by wind speed and air pressure. The formula is: , where, is a constant, is the wind speed, is the air pressure, and H is the wave height. Through this formula, the combined impact of wind speed and air pressure on wave energy can be calculated; S623: Integrate the above coupling relationships to finally construct a coupling model for wave generation, development, and propagation.

[0014] Optionally, S63 specifically includes: S631: Collect historical data sets, including known wave characteristic parameters and corresponding atmospheric condition parameters, for training the coupling model and optimizing the parameters in the model; S632: Determine the key parameters to be optimized in the coupling model, including wave generation rate, wave spectrum, propagation direction offset, and wave energy change; S633: Use the least squares method as the optimization method to optimize the parameters of the coupling model. By minimizing the sum of the squares of the errors between the model prediction values and the historical observation values, optimize the model parameters; S634: Through the model optimized by the least squares method, determine the optimal parameter values and use them as the final parameters of the coupling model.

[0015] Advantages of the present invention: In the present invention, through the multi-modal data fusion technology, it is possible to effectively integrate data from different sources, solving the limitations of traditional wave measurement methods in terms of accuracy and coverage; through the dynamic modeling of the wave generation, development, and propagation processes, it is possible to obtain higher-precision wave characteristic data in real time, thereby providing more accurate data support for marine environmental monitoring, maritime navigation safety, and climate change research.

[0016] In the present invention, by using historical data for training and optimization, the high accuracy and reliability of the coupling model in practical applications are ensured. By minimizing the prediction error, the optimized model can accurately quantify the dynamic influence of atmospheric factors on wave generation, development, and propagation, thereby improving the accuracy and real-time performance of wave prediction. 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 embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 Schematic diagram of the ocean wave measurement method according to the embodiment of the present invention; Figure 2 Schematic diagram of the coupling model parameter optimization method according to the embodiment of the present invention. Detailed Embodiments

[0019] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the accompanying drawings are only for more specifically describing the embodiments and are not intended to specifically limit the present invention.

[0020] As Figure 1 - Figure 2 shown, the ocean wave measurement method based on the fusion of GNSS wave buoys and atmospheric data includes the following steps: S1: Deploy GNSS wave buoys in the target sea area to collect the three-dimensional displacement data of the buoys in real time, and record the instantaneous displacement and acceleration data of the buoys at each time step; S2: Collect the atmospheric condition parameters over the target sea area through a distributed atmospheric sensor array, including real-time wind speed, wind direction, and air pressure; S3: Use the spatio-temporal calibration algorithm to synchronize the data collected by the GNSS wave buoys and the data of the atmospheric sensor array in terms of time and space to ensure the spatio-temporal consistency of the data; S4: Through multi-modal data fusion technology, map the GNSS wave buoy data and atmospheric data to a unified feature space to generate a fusion data set; S5: Based on the fusion data set, adopt the spectrum analysis method and wavelet transform algorithm to extract wave characteristic parameters, including wave height, period, propagation direction, and wave energy; S6: Based on the extracted wave characteristic parameters and atmospheric condition parameters, establish a coupling model to quantify the dynamic impact of atmospheric factors on wave generation, development, and propagation.

[0021] S1 specifically includes: S11: Deploy at least one GNSS wave buoy in the target sea area. The GNSS wave buoy is equipped with a receiver, and the receiver is used to convert the satellite signal into the position data of the buoy in three-dimensional space; S12: Collect the three-dimensional displacement data of the buoy in real time through the GNSS receiver. The three-dimensional displacement data includes the longitude, latitude, and depth (vertical position) of the buoy, and the collection frequency is set at a time interval of every second to ensure high spatio-temporal resolution of the data; S13: When collecting the three-dimensional displacement data of the buoy each time, use an accelerometer synchronized with the GNSS receiver to synchronously record the acceleration data of the buoy, and calculate the instantaneous displacement and velocity of the buoy according to the acceleration data through numerical integration methods to provide more accurate dynamic information; The specific calculation steps are as follows: S131: First record the acceleration data at each time step , where Denote the acceleration of the buoy at the k-th moment; S132: Calculate the instantaneous velocity by numerical integration method , and the formula is: , where is the instantaneous velocity of the buoy at the (k - 1)-th moment, is the acceleration of the buoy at the k-th moment, is the time step, representing the time interval between each data acquisition; S133: Continue to use the numerical integration method to calculate the instantaneous displacement , using the following formula: , where is the displacement of the buoy at the (k - 1)-th moment, is the instantaneous velocity of the buoy at the k-th moment, is the time step.

[0022] S14: Record the three-dimensional displacement data and acceleration data collected each time in sequence according to the time step, ensuring the continuity and integrity of the data at each time point, and providing high-precision raw data for subsequent wave analysis.

[0023] S2 specifically includes: S21: Deploy multiple atmospheric sensors at different positions in the target sea area to form a distributed atmospheric sensor array. The atmospheric sensors include an anemometer, a wind vane, and a barometer; S22: The anemometer is used to collect real-time wind speed data over the target sea area. The anemometer measures using the ultrasonic principle or the mechanical rotation principle to ensure high precision and real-time performance. The anemometer can provide wind speed data at one-second intervals within a predetermined time interval, with the unit of meters per second (m / s); S23: Collect real-time wind direction data over the target sea area through the wind vane. The wind vane measures the wind direction angle through the air flow guiding or gyroscope principle to accurately measure the dominant direction of the wind. The wind direction data is in degrees (°), reflecting the directionality of the wind speed; S24: Collect real-time air pressure data over the target sea area through the barometer. The barometer uses the static pressure sensor principle and can accurately measure the change in atmospheric pressure, with the unit of kilopascals (kPa). The data acquisition frequency of the barometer is synchronized with the wind speed and wind direction data to ensure real-time monitoring of atmospheric conditions; S25: Transmit the wind speed, wind direction, and air pressure data to the data center or local storage device through wireless communication technology. The wireless communication technology includes GPRS, Wi-Fi, or satellite communication to ensure the real-time performance and integrity of the data.

[0024] S3 specifically includes: S31: Obtain the three-dimensional displacement data and acceleration data collected by the GNSS wave buoy, as well as the wind speed, wind direction, and air pressure data collected by the distributed atmospheric sensor array; S32: Perform preliminary time alignment on the data of the GNSS wave buoy and the atmospheric sensor data. The time alignment method is based on comparing the timestamps of each data source to determine the actual collection time of each data point, and divide the time window according to the earliest and latest times collected to ensure that the time ranges of all data are exactly matched; S33: Use the spatio-temporal calibration algorithm to perform precise time synchronization on the GNSS wave buoy data and the atmospheric sensor array data. The spatio-temporal calibration algorithm uses the interpolation method to process the time differences in the collected data, and interpolates the data with different time steps to a unified time interval to ensure that the time steps of each data point are consistent; S34: Synchronize the spatial positions through the spatio-temporal calibration algorithm. The spatial synchronization is aligned through the spatial coordinates of the reference point. Specifically, compare the three-dimensional position coordinates (longitude, latitude, and depth) of the GNSS wave buoy with the geographical coordinates (longitude and latitude) of the location where the atmospheric sensor array is located, and use the geographical coordinate conversion method (such as geodetic coordinate conversion or projection coordinate conversion) to map the buoy data and the atmospheric data to a unified spatial coordinate system; The above steps achieve the precise synchronization of the GNSS wave buoy data and the atmospheric sensor data through the spatio-temporal calibration algorithm. Through the step-by-step calibration of time and space, it can ensure that the data from different sources are completely consistent in time and space, thereby providing reliable and accurate input data for subsequent multi-modal data fusion and wave characteristic analysis.

[0025] S34 specifically includes: S341: Obtain the three-dimensional position coordinates of the GNSS wave buoy, and the coordinates include the longitude of the buoy , latitude and depth , as well as the geographical coordinates of the atmospheric sensor array, including the longitude of the sensor array , latitude and altitude ; S342: Convert the geographical coordinates of the GNSS wave buoy into spatial coordinates in the same projection coordinate system as the atmospheric sensor array to perform spatial coordinate synchronization; The specific steps are as follows: S3421: Use the spherical coordinate conversion formula to convert the longitude and latitude into plane coordinates , and the formula is: ; , where R is the radius of the earth, and are the longitude and latitude of the buoy, in radians; and are the transformed planar coordinates of the buoy; S3422: Similarly, convert the longitude and latitude of the atmospheric sensor array to planar coordinates , and the formula is: ; .

[0026] S344: Determine the spatial position synchronization error between the buoy and the atmospheric sensor array by calculating the position difference in the planar coordinate system. After clarifying the error, perform fine-tuning to make their spatial coordinates completely synchronized; the specific calculation method is: ; ; . Among them, , and are the position differences (in meters) between the buoy and the atmospheric sensor array in the planar coordinate system, which are used to quantify the synchronization error of the spatial position; through the above steps using spherical coordinate transformation and difference calculation methods, the spatial positions of the buoy and the atmospheric sensor array can be precisely synchronized, thereby eliminating the deviation in the spatial position. This method ensures that the data is analyzed in the same coordinate system, further improving the accuracy of multi-modal data fusion and providing a more accurate spatial basis for subsequent wave characteristic analysis.

[0027] S4 specifically includes: S31: Obtain the GNSS wave buoy data after time and space synchronization; S32: Extract features from the GNSS wave buoy data and atmospheric data respectively. For the GNSS wave buoy data, use Fourier transform or wavelet transform to extract the frequency domain features and time domain features of the wave signal, such as wave height, wave period, wave energy, etc.; for the atmospheric data, extract the statistical features of wind speed, wind direction, and air pressure, such as the mean value and standard deviation of wind speed and the change trend of air pressure, etc.; S33: Convert the features of the GNSS wave buoy data and atmospheric data to a unified scale range. By normalizing each feature, make its value range between 0 and 1 to eliminate the dimensional difference between data sources; S34: Use the weighted average method to map the GNSS wave buoy data and atmospheric data to a unified feature space; first, assign weights and to the GNSS wave buoy data and atmospheric data, where is the weight of the GNSS wave buoy data, is the weight of the atmospheric data; then calculate the fused feature after weighted average, and the formula is: , where is the standardized feature of GNSS wave buoy data, is the standardized feature of atmospheric data, is the fused feature; S35: Generate a fused dataset based on the fused features after weighted averaging. The fused dataset combines the features of GNSS wave buoy data and atmospheric data in the same space, providing input data for subsequent wave characteristic analysis and coupling models.

[0028] S5 specifically includes: S51: Perform spectral analysis on the buoy displacement data and acceleration data in the fused dataset. The spectral analysis method uses the fast Fourier transform (FFT) algorithm. The specific steps include: S511: Convert the buoy displacement data and acceleration data into frequency-domain signals and calculate their spectra and . The calculation formula is: , where is the spectrum, is the time-domain signal, f is the frequency, t is the time. By calculating the spectrum through the FFT algorithm, the frequency components of the wave signal can be obtained; S512: Extract the wave period T and wave height H from the spectrum. Determine the wave period and wave height by calculating the main peak frequency of the spectrum and its corresponding amplitude . The relationship between the wave period T and the frequency is: ; S513: Extract the wave height H by calculating the amplitude difference from the wave peak to the wave valley, which is the wave amplitude corresponding to the maximum amplitude in the spectrum; S52: Perform wavelet transform on the buoy displacement data in the fused dataset to extract the wave propagation direction and wave energy. The specific steps include: S521: Use the Morlet wavelet transform method to perform wavelet transform on the displacement data of the buoy to obtain the time-frequency domain representation: , where is the data after wavelet transform, is the Morlet wavelet function, t is the time, f is the frequency, is the time variable, and calculate the distribution in time and frequency after wavelet transform; S522: Extract the wave propagation direction and wave energy from the wavelet transform results. The propagation direction is determined by the phase information of the signal, and the wave energy is calculated through the amplitude after wavelet transform. The formula is: , where is the energy density of the wavelet transform result, and E is the total energy of the wave; The wave height, period, propagation direction, and wave energy extracted through the above steps provide a basis for subsequent wave prediction and analysis.

[0029] S6 specifically includes: S61: Receive the wave characteristic parameters (including wave height, period, propagation direction, and wave energy) extracted in S5 and the atmospheric condition parameters (including wind speed, wind direction, and air pressure) obtained in S2; S62: Based on the theoretical framework of the physical basic model, select the Phelps - Pratt model as the basis to establish the coupling relationship between waves and atmospheric conditions and construct a coupling model; S63: Use historical data to optimize the parameters of the coupling model. By adjusting the coefficients in the equation, make the coupling model accurately reflect the dynamic influence of atmospheric conditions on wave generation, development, and propagation; S64: Based on the coupling model optimized in S63, input the current wind speed, wind direction, and air pressure data to predict the wave characteristic parameters (such as wave height, period, and propagation direction), and quantify the specific influence of different atmospheric conditions on wave generation, development, and propagation for applications such as actual ocean environment monitoring, offshore operation management, and wave warning.

[0030] S62 specifically includes: S621: Select the Phelps - Pratt model as the basis. Based on the dynamic influence of wind on ocean surface waves, describe the relationship between wind speed and wave generation, and the formula is: , where is the spectral density, is the model constant, is the density of water, g is the acceleration due to gravity, f is the frequency, and c is the constant in the model. This model reflects the influence of wind on wave generation through the relationship between wind speed and frequency; S622: On the basis of the Phelps - Pratt model, consider the atmospheric conditions of wind speed , wind direction and air pressure , and combine the surface tension of the ocean and wave energy to establish the coupling relationship between wind speed and wave characteristics (such as wave height, period, propagation direction, etc.); The coupling relationship specifically includes: Coupling relationship 1: The relationship between the wave generation rate R and wind speed is expressed by the following formula: , where is the coefficient, reflecting the sensitivity of wind speed to wave generation, is the wind speed, is an empirical constant and f is the frequency; Coupling relationship 2: According to the change of the wind direction adjust the propagation direction of the wave, specifically: wherein, represents the offset of the wave propagation direction, is the original wave propagation direction, is the wind direction; Coupling relationship 3: The change of wave energy E is jointly affected by wind speed and air pressure, and the formula is: wherein, is a constant, is the wind speed, is the air pressure, H is the wave height, and through this formula, the comprehensive influence of wind speed and air pressure on wave energy can be calculated; S623: Based on the above coupling relationships, finally construct a coupling model for wave generation, development and propagation, and ensure that the model can reflect the influence of atmospheric conditions (including wind speed, wind direction and air pressure) on the characteristics of each stage of the wave; The above steps can accurately reflect the influence of atmospheric factors on waves by combining the relationships between atmospheric parameters such as wind speed, wind direction and air pressure and wave generation and propagation.

[0031] S63 specifically includes: S631: Collect historical data sets, including known wave characteristic parameters (such as wave height, period, propagation direction, wave energy) and corresponding atmospheric condition parameters (such as wind speed, wind direction, air pressure), for training the coupling model and optimizing the parameters in the model; S632: Determine the key parameters to be optimized in the coupling model, including wave generation rate, wave spectrum, propagation direction offset and wave energy change; S633: Use the Least Squares Method as the optimization method to optimize the parameters of the coupling model. By minimizing the sum of the squares of the errors between the model prediction values and the historical observation values, the model parameters are optimized; The specific steps are as follows: S6331: Define the error function , which represents the difference between the model prediction value and the historical data, and the formula is as follows: wherein, is the prediction value of the coupling model, is the observed value of the historical data, and N is the number of data points; S6332: By minimizing the error function , optimize the coefficients in the coupling model (such as , , , etc.), minimizing the error between the model prediction value and the actual observed data; the optimization objective is to minimize the following loss function: , where , and are the wave generation rate, wave spectrum, propagation direction offset, and wave energy predicted by the model, respectively, , , and are the corresponding actual observed values in the historical data.

[0032] S634: Determine the optimal parameter values through the model optimized by the least squares method and use them as the final parameters of the coupled model. These optimized parameters enable the model to more accurately predict the impact of atmospheric conditions on wave generation, development, and propagation; by minimizing the error between the model prediction value and the historical observation value in the above steps, the prediction accuracy of the model can be effectively improved, making it more accurately reflect the dynamic impact of atmospheric conditions on waves. This method provides an efficient parameter optimization means for real-time monitoring and prediction of waves, significantly enhancing the reliability of wave prediction.

[0033] The present invention covers any alternatives, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without the description of these details. Additionally, well-known methods, processes, flows, components, and circuits, etc. are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0034] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. Ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data, characterized in that: The following steps are involved: S1: Deploy GNSS wave buoys in the target sea area to collect the 3D displacement data of the buoys in real time and record the instantaneous displacement and acceleration data of the buoys at each time step; S2: Collect atmospheric condition parameters over the target sea area, including real-time wind speed, wind direction and air pressure, through a distributed atmospheric sensor array; S3: Use the spatiotemporal calibration algorithm to synchronize the data collected by the GNSS wave buoy with the data of the atmospheric sensor array in time and space to ensure the spatiotemporal consistency of the data; S4: Through multimodal data fusion technology, GNSS wave buoy data and atmospheric data are mapped into a unified feature space to generate a fused data set; S5: Based on the fused data set, the spectrum analysis method and wavelet transform algorithm are used to extract wave characteristic parameters, including wave height, period, propagation direction and wave energy; S6: Based on the extracted wave characteristic parameters and atmospheric condition parameters, a coupling model is established to quantify the dynamic impact of atmospheric factors on wave generation, development and propagation.

2. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 1 is characterized in that: The S1 specifically includes: S11: deploying at least one GNSS wave buoy in the target sea area, wherein the GNSS wave buoy is equipped with a receiver, and the receiver is used to convert satellite signals into position data of the buoy in three-dimensional space; S12: collecting three-dimensional displacement data of the buoy in real time through a GNSS receiver, wherein the three-dimensional displacement data includes the longitude, latitude and depth of the buoy, and the collection frequency is set to a time interval of every second; S13: when collecting the three-dimensional displacement data of the buoy each time, using an accelerometer synchronized with the GNSS receiver to synchronously record the acceleration data of the buoy, and calculating the instantaneous displacement and velocity of the buoy according to the acceleration data by a numerical integration method; S14: Record the three-dimensional displacement data and acceleration data collected each time in sequence according to the time step to ensure that the data at each time point is continuous and complete.

3. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 1 is characterized in that: The S2 specifically includes: S21: deploying a plurality of atmospheric sensors at different locations in the target sea area to form a distributed atmospheric sensor array, wherein the atmospheric sensors include an anemometer, a wind vane, and a barometer; S22: The anemometer is used to collect real-time wind speed data over the target sea area. The anemometer can provide wind speed data at intervals of one second within a predetermined time interval, in meters per second; S23: collecting real-time wind direction data over the target sea area through a wind vane, wherein the wind vane measures the wind direction angle through airflow guidance or gyroscope principle; S24: collecting real-time air pressure data over the target sea area through a barometer, wherein the barometer uses a static pressure sensor principle to measure changes in atmospheric pressure in kilopascals; S25: Transmitting the wind speed, wind direction and air pressure data to a data center or a local storage device via wireless communication technology, wherein the wireless communication technology includes GPRS, Wi-Fi or satellite communication.

4. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 1 is characterized in that: The S3 specifically includes: S31: Acquire the three-dimensional displacement data and acceleration data collected by the GNSS wave buoy, and the wind speed, wind direction and air pressure data collected by the distributed atmospheric sensor array; S32: Perform preliminary time alignment on the GNSS wave buoy data and the atmospheric sensor data. The time alignment method compares the timestamps of each data source to determine the actual collection time of each data point, and divides the time window according to the earliest and latest collection times to ensure that the time range of all data is completely matched; S33: Accurately time-synchronize the GNSS wave buoy data and the atmospheric sensor array data using a time-space calibration algorithm, wherein the time-space calibration algorithm uses an interpolation method to process time differences in the collected data, and interpolates data with different time steps to a uniform time interval to ensure that the time step of each data point is consistent; S34: The spatial position is synchronized through a spatiotemporal calibration algorithm. The spatial synchronization is performed by aligning the spatial coordinates of the reference point. Specifically, the three-dimensional position coordinates of the GNSS wave buoy are compared with the geographic coordinates of the location of the atmospheric sensor array, and the buoy data and the atmospheric data are mapped to a unified spatial coordinate system using a geographic coordinate conversion method.

5. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 4 is characterized in that: The S34 specifically includes: S341: Obtain the three-dimensional position coordinates of the GNSS wave buoy and the geographic coordinates of the atmospheric sensor array; S342: converting the geographic coordinates of the GNSS wave buoy into spatial coordinates in the same projection coordinate system as the atmospheric sensor array, and performing spatial coordinate synchronization; S344: Determine the spatial position synchronization error between the buoy and the atmospheric sensor array by calculating the position difference between the buoy and the atmospheric sensor array in the plane coordinate system. After the error is identified, make fine adjustments to make their spatial coordinates completely synchronized.

6. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 1 is characterized in that: The S4 specifically includes: S31: Acquire GNSS wave buoy data after time and space synchronization; S32: extracting features from the GNSS wave buoy data and the atmospheric data respectively. For the GNSS wave buoy data, Fourier transform or wavelet transform is used to extract the frequency domain features and time domain features of the wave signal; for the atmospheric data, statistical features of wind speed, wind direction and air pressure are extracted; S33: Convert the features of GNSS wave buoy data and atmospheric data to a unified scale range, and normalize each feature so that its value range is between 0 and 1 to eliminate the dimensional differences between data sources; S34: Use weighted average method to map GNSS wave buoy data and atmospheric data into a unified feature space; first assign weights to GNSS wave buoy data and atmospheric data and ,in is the weight of the GNSS wave buoy data, is the weight of the atmospheric data; then the fusion feature after weighted average is calculated , the formula is: ,in, Standardized features for GNSS wave buoy data, is the standardized characteristic of atmospheric data, is the fused feature; S35: Generate a fused data set based on the weighted average fusion features.

7. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 1 is characterized in that: The S5 specifically includes: S51: performing spectrum analysis on the buoy displacement data and acceleration data in the fused data set, wherein the spectrum analysis method adopts a fast Fourier transform algorithm, and the specific steps include: S511: buoy displacement data and acceleration data Convert to frequency domain signal and calculate its spectrum and ; S512: Extract the wave period T and wave height H from the spectrum by calculating the main peak frequency of the spectrum and its corresponding amplitude Determine the period and height of waves, wave period T and frequency The relationship is: ; S513: By calculating the amplitude difference from the peak to the trough, the wave height H is extracted, which is the wave amplitude corresponding to the maximum amplitude in the spectrum; S52: performing wavelet transform on the buoy displacement data in the fused data set to extract the wave propagation direction and wave energy. The specific steps include: S521: Using the Morlet wavelet transform method, the displacement data of the buoy Perform wavelet transform and calculate the distribution of time and frequency after wavelet transform; S522: Extract the wave propagation direction and wave energy from the wavelet transform result. The propagation direction is determined by the phase information of the signal, and the wave energy is calculated by the amplitude after wavelet transform. The formula is: ,in, is the energy density of the wavelet transform result, and E is the total energy of the wave.

8. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 1 is characterized in that: The S6 specifically includes: S61: receiving the wave characteristic parameters extracted in S5 and the atmospheric condition parameters obtained in S2; S62: According to the theoretical framework of the physical foundation model, the Phelps-Pratt model is selected as the basis to establish the coupling relationship between waves and atmospheric conditions and construct a coupling model; S63: Optimize the parameters of the coupling model using historical data; S64: Based on the coupled model optimized by S63, the current wind speed, wind direction and air pressure data are input to predict wave characteristic parameters and quantify the specific impact of different atmospheric conditions on wave generation, development and propagation.

9. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 8, characterized in that: The S62 specifically includes: S621: The Phelps-Pratt model is chosen as the basis to describe the relationship between wind speed and wave generation based on the dynamic effect of wind on ocean surface waves. The formula is: ,in, is the spectral density, is the model constant, is the density of water, g is the acceleration due to gravity, f is the frequency, and c is a constant in the model; S622: Based on the Phelps-Pratt model, consider wind speed ,wind direction and air pressure atmospheric conditions, combined with ocean surface tension and wave energy, to establish a coupling relationship between wind speed and wave characteristics; The coupling relationship specifically includes: Coupling relationship 1: The relationship between the wave generation rate R and the wind speed is expressed by the following formula: ,in, is a coefficient reflecting the sensitivity of wind speed to wave generation, is the wind speed, is the empirical constant, f is the frequency; Coupling relationship 2: According to wind direction The change of adjusts the propagation direction of the wave, specifically: ,in, represents the offset in the wave propagation direction, is the original propagation direction of the wave, for wind direction; Coupling relationship 3: The change in wave energy E is caused by the combined effect of wind speed and air pressure, and the formula is: ,in, is a constant, is the wind speed, is the air pressure, H is the wave height, and this formula can be used to calculate the combined effect of wind speed and air pressure on wave energy; S623: Based on the above coupling relationships, a coupling model of wave generation, development and propagation is finally constructed.

10. The ocean wave measurement method based on fusion of GNSS wave buoy and atmospheric data according to claim 9, characterized in that: The S63 specifically includes: S631: Collect historical data sets, including known wave characteristic parameters and corresponding atmospheric condition parameters, for training the coupled model and optimizing the parameters in the model; S632: Determine the key parameters that need to be optimized in the coupled model, including wave generation rate, wave spectrum, propagation direction deviation, and wave energy change; S633: Least square method is used as the optimization method to optimize the parameters of the coupling model, and the model parameters are optimized by minimizing the sum of squares of the errors between the model prediction value and the historical observation value; S634: Determine the optimal parameter value of the model after optimization by the least square method, and use it as the final parameter of the coupling model.

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