A method and system for extracting wind waves and swells based on GNSS and anemometers

Abstract

The present invention discloses a method and system for extracting wind waves and swells based on GNSS and an anemometer, including: obtaining water level height data and wind data detected by a buoy multiple times according to a reference time and a detection time series respectively; performing data preprocessing on the water level height data and wind data of the buoy, preprocessing the water level height and wind data to obtain the preprocessed water level height and wind data; calculating the water level height and wind data per unit time according to the piecewise interpolation function based on the preprocessed water level height and wind data, and calculating the wave height data corresponding to the detection time series according to the water level height and wind data per unit time; classifying the feature set according to the wind speed level, and performing IFFT transformation after screening to obtain the final wind wave and swell data. The present invention effectively improves the extraction accuracy of wind wave and swell data on the water surface, and can realize low-cost and long-term detection of wind waves and swells on the water surface.

Description

Technical Field

[0001] The present invention relates to wind wave extraction technology, and in particular to a method and system for extracting wind waves and swells based on GNSS and anemometer. Background Art

[0002] Waves in the ocean are divided into wind waves and swell waves. Wind waves are waves generated when the sea surface is continuously affected by wind; while swell waves are waves that remain on the sea surface or waves from other sea areas when the waves propagate beyond the wind zone. Swell waves can cause great harm to ships and marine engineering. At present, the research methods of swell waves are mainly carried out through theoretical research combined with field measured data. The main methods of field measurement are manual observation and instrument automatic observation. Among them, instrument observation is divided into three types: surface buoys, underwater devices and remote sensing satellites. The accuracy of manual observation results is highly correlated with the experience of the observers, and it is difficult to achieve continuous observation; conventional surface buoys observe gravity acceleration, which is expensive. Although they can achieve long-term automatic observation, they can generally only obtain data at the hour, and are prone to loss, and generally do not collect wind data; underwater devices can generally only be placed above a stable seabed, and have high requirements for the selection of deployment points. At the same time, data is difficult to transmit in real time. At the same time, since it is difficult to provide continuous power supply underwater, batteries need to be replaced regularly for maintenance, and long-sequence observations cannot be achieved, and the working status of the instrument cannot be obtained. It is easy to lose data and wind data cannot be obtained. Remote sensing methods can only obtain data when satellites pass by, and continuous observation data cannot be obtained, and they are greatly affected by various weather conditions. Summary of the invention

[0003] One of the inventive purposes of the present invention is to provide a method and system for extracting wind waves and surges based on GNSS and anemometer, wherein the method and system utilize GNSS (Global Positioning Satellite System) and buoys to collect water level height data of different positioned water surfaces at high frequency, wherein the present invention also configures a wind speed and direction sensor on the buoy, utilizes the GNSS and wind speed sensor to analyze the obtained water level height data and wind speed and direction data, and uses the wind speed data as an evaluation basis to extract wind wave and surge data of the detected water surface, thereby effectively improving the extraction accuracy of wind wave and surge data of the detected water surface, and can realize low-cost and long-term detection of wind waves and surges on the water surface.

[0004] Another object of the present invention is to provide a method and system for extracting wind waves and swells based on GNSS and an anemometer. The method and system also perform interpolation processing on missing data of water level height data and wind speed and direction data detected by a surface buoy at different time series, and perform smoothing processing on abnormal point data of water level height data and wind speed and direction data within different detection time ranges, so as to effectively avoid deviations in the extraction of the wind wave data and swell data caused by missing data or abnormal point data, and improve the accuracy of the wind wave data and swell data.

[0005] Another object of the present invention is to provide a method and system for extracting wind waves and swells based on GNSS and an anemometer. After processing the missing data and abnormal data of the vertical water level height data detected by the GNSS, the method and system use a piecewise interpolation function to process the data to obtain wave height data, and use wind speed data of different levels to screen the wave height data, and propose a separation algorithm for wind waves, swells and tidal data based on Fourier transform from the wind speed and its duration. The algorithm is easy to implement, has low requirements for observation equipment, and the position information feedback by the GNSS can be used to monitor the buoy state.

[0006] In order to achieve at least one of the above-mentioned objects of the invention, the present invention further provides a method for extracting wind waves and swells based on GNSS and an anemometer, the method comprising:

[0007] Setting the reference time and detection time series of the buoy, and respectively obtaining the water level height data and wind data detected by the buoy multiple times according to the reference time and detection time series;

[0008] Performing data preprocessing on the water level height data and wind data of the buoy, filling the missing data of the water level height data and wind data of the buoy, and removing the abnormal data in the water level height data and wind data of the buoy to obtain the preprocessed water level height data and wind data;

[0009] Calculating the water level height data and wind data per unit time from the preprocessed water level height data and wind data according to a piecewise interpolation function, and calculating the wave height data corresponding to the detection time series according to the water level height data and wind data per unit time;

[0010] Classifying the calculated wave height data corresponding to the detection time series according to the wind speed level, classifying and screening the wave height data corresponding to the detection time series to obtain the spectral data sets of wind waves and swells, and further screening the spectral data sets of wind waves and swells according to the frequency magnitude, and performing IFFT transformation after further screening to obtain the final separated and screened wind wave and swell data.

[0011] According to one preferred embodiment of the present invention, the wind data includes wind speed data and wind direction data. The preprocessing method for the missing data of the wind speed data and the wind direction data includes: obtaining multiple consecutive time series of the wind speed data and the wind direction data, respectively obtaining the wind speed data and the wind direction data of at least one time series before and after the time series corresponding to the missing data, and calculating the average values of the wind speed data and the wind direction data of at least one time series before and after the time series corresponding to the missing data as the corresponding missing data for interpolation.

[0012] According to another preferred embodiment of the present invention, the preprocessing method for the missing data in the buoy water level height data includes: obtaining the water level height data of multiple adjacent time series corresponding to the missing data in the buoy water level height data, selecting the water level height data of at least 3 consecutive adjacent time series before and after the time series corresponding to the missing data, obtaining at least 6 consecutive adjacent time series of water level height data and calculating the average value as the missing data of the buoy water level height data for interpolation.

[0013] According to another preferred embodiment of the present invention, the method for removing abnormal data from the buoy water level height data includes: presetting the lengths of multiple detection time series of the buoy water level height data, and presetting a proportionality coefficient for each detection time series length. Taking the currently selected detection time t as a reference, calculate the average value of all detected buoy water level height data within each detection time series length before and after the detection time t, establish a constraint value range for abnormal value removal based on the average value of the buoy water level height data within each detection time series length before and after and the corresponding proportionality coefficient. If the actually detected buoy water level height data Z t of the currently selected detection time t is within the constraint value range for abnormal value removal, then the actually detected buoy water level height data Z t of the currently selected detection time t is normal data, otherwise it is removed as abnormal data; for the abnormal data determined to be the buoy water level height data, further collect the normal data of multiple adjacent time series corresponding to the abnormal data for average value calculation, which is used for interpolation processing after the abnormal data is removed.

[0014] According to another preferred embodiment of the present invention, the method for removing outliers from wind speed data and wind direction data includes: The method for removing outliers from wind speed data and wind direction data includes: presetting multiple detection time series lengths for wind speed data and wind direction data, and presetting a proportionality coefficient for each detection time series length. Based on the currently selected detection time t, calculate the average value of all detected wind speed data and wind direction data within each detection time series length before and after the detection time t. Establish the constraint value range for outlier removal respectively according to the average value of wind speed data and wind direction data within each detection time series length before and after and the corresponding proportionality coefficient. If the actually detected wind speed data V t and wind direction data α t at the currently selected detection time t are both within the constraint value range for outlier removal, then the actually detected wind speed data V t and wind direction data α t at the currently selected detection time t are normal data, otherwise they are removed as abnormal data; for the determined abnormal data of wind speed data V t and wind direction data α t , further collect the normal data of multiple adjacent time series corresponding to the abnormal data for average value calculation, which is used for interpolation processing after the abnormal data is removed.

[0015] According to another preferred embodiment of the present invention, the method for calculating the water level height data and wind data per unit time by the piecewise interpolation function includes: The buoy water level height data after preprocessing the missing data and abnormal data is as follows: Define the 1-minute average value, 5-minute average value, 15-minute average value, 30-minute average value, and 60-minute average value respectively corresponding to and

[0016] Using and as characteristic points, select the data of 6 known points, with a time length of 1 hour and 30 minutes. Denote the node characteristics as (x i , z i ), where i ranges from 0 to 5, x represents the detection time, i represents the subscript of different detection times, and z represents the water level height corresponding to the detection time;

[0017] For i = 0, 1,..., 5, calculate h i = x i+1 - x i

[0018] Calculate α i and β i (i = 0, 1,..., 5), where α i and β i are the calculated relevant parameters respectively;

[0019]

[0020] For i = 1, …, 4, calculate:

[0021]

[0022] Calculate a i and b i (i = 0, 1, …, 5)

[0023]

[0024] For i = 1, …, 5, calculate:

[0025]

[0026] Calculate m i (i = 0, 1, …, 5), m i is a relevant parameter in the calculation process;

[0027] m n = b n ;

[0028] m i = a i m i+1 + b i (i = n - 1, …, 1, 0);

[0029] Then calculate all 1-minute data values through this interpolation function:

[0030]

[0031] where x` represents each moment for which interpolation is required, and z` represents the water level height interpolated at each moment;

[0032] Calculate the 1-minute average value within this time span through this piecewise interpolation function and denote it as

[0033] For each 1-minute average data, calculate:

[0034] If then directly use as the water level value at this point, otherwise use as the water level height value at this point. After processing all the data, the obtained 1-minute average value is used as the wave height data.

[0035] According to another preferred embodiment of the present invention, the method for classifying the wave height data under the corresponding detection time series into feature sets includes the following steps: Define the following different sea conditions according to the wind speed:

[0036] 1. Define the sea condition where the wave level changes from level 2 to level 3 or above as the sea condition affected by wind waves;

[0037] 2. Define the sea condition where the wave level changes from level 3 or above to below level 3 and the duration does not exceed 24 hours as the sea condition mainly affected by swell;

[0038] 3. Define other sea conditions with wave levels from 0 to 2 as the sea conditions mainly affected by tides and containing other short - period accidental factors;

[0039] Divide the calculated wave height data into 3 feature sets according to the sea conditions defined above. The three feature sets are defined as: D 风浪 、D 涌浪 and D 潮汐 , where perform FFT transformation on the data of D 风浪 、D 涌浪 and D 潮汐 , convert the time - domain data to the frequency domain, and obtain three data sets, namely the wind - wave data set, the swell data set, and the tide data set. The corresponding spectral data are denoted as DF 风浪 、DF 涌浪 and DF 潮汐 .

[0040] According to another preferred embodiment of the present invention, extract the data of the low - frequency part less than 0.002 Hz of DF 潮汐 as the spectrum mainly affected by tides, denoted as F 潮汐 ; Subtract the spectrum of this part of the spectral data from the spectrum of DF 涌浪 , and retain the low - frequency part less than 20 Hz as the influence spectrum of swell, denoted as F 涌浪 ; Subtract the data of F 潮汐 and F 涌浪 from the spectrum of DF 风浪 , and retain the data with a frequency less than 1 Hz as the influence spectrum of wind waves, denoted as F 风浪 ; Perform IFFT transformation on the obtained spectral data F 风浪 、F 涌浪 and F 潮汐 to convert the frequency - domain data to the time - domain and obtain the separated wind - wave and swell data that affect each other.

[0041] To achieve at least one of the above - mentioned invention purposes, the present invention further provides a wind - wave and swell extraction system based on GNSS and an anemometer. The system executes the above - mentioned method for extracting wind - wave and swell based on GNSS and anemometer.

[0042] The present invention further provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the above-mentioned method for extracting wind waves and swells based on GNSS and an anemometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure shows a schematic flowchart of a method for extracting wind waves and swells based on GNSS and an anemometer according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and other obvious variations can be conceived by those skilled in the art. The basic principles defined in the following description can be applied to other embodiments, variations, improvements, equivalent embodiments, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0045] It can be understood that the term "a" should be construed as "at least one" or "one or more". That is, in one embodiment, the number of an element can be one, while in other embodiments, the number of the element can be multiple. The term "a" should not be construed as a limitation on the number.

[0046] Please refer to Figure 1, the present invention discloses a method and system for extracting wind waves and swell based on GNSS and an anemometer. The method mainly includes the following steps: First, a wind sensor and a GNSS sensor need to be set in the buoy. The wind sensor includes a wind speed sensor and a wind direction sensor, which are respectively used to detect the wind speed and wind direction of the buoy. The GNSS (Global Navigation Satellite System) sensor is used for positioning in the horizontal direction and water surface height positioning in the vertical direction. In the present invention, the use of the GNSS sensor to obtain the water surface height data in the vertical direction can eliminate the need for a buoy with an expensive accelerometer, making the buoy of the present invention have a high cost-saving effect in height measurement and wind speed measurement. The present invention uses the GNSS sensor and the wind sensor to obtain water level height data, wind speed data, and wind direction data, fills in missing data, eliminates and fills abnormal data to obtain clean and complete data, and uses a piecewise interpolation function to extract the water level height data in the clean and complete data per unit time. After the present invention processes the water level height data using the piecewise interpolation function, it can effectively smooth and filter the influence of accidental factors in the actual water level height detection process on the water level height data to obtain wave height data. Further, the wave height is classified into feature sets according to the wind speed magnitude. After processing the wave height feature sets based on FFT transform (Fast Fourier Transform) and IFFT transform (Inverse Fast Fourier Transform), the final wind wave and swell data are separated by frequency screening. The technical solution of the present invention is easier to implement in technical means and has higher accuracy.

[0047] Specifically, the method for collecting and preprocessing the water surface height data in the present invention includes: setting a time reference, where the time reference can be the standard time of the corresponding time zone; further presetting a collection frequency, where the collection frequency can be once every 1 - 60 seconds. In the present invention, the collection frequency is preferably higher than once every minute, and the time series of collection is determined. Define the collected water surface height data as Z t , t is the corresponding time series. Since the water surface height may have missing data in the corresponding time series t due to environmental or equipment factors during the collection process, when there is missing data, the present invention needs to fill in the missing data. The method for filling the missing water surface height data includes: when there is missing data in the water surface height data of the corresponding time series t, at least 3 time series before and at least 3 time series after are obtained based on the time series t. In the present invention, preferably 3 time series before and after are used, that is, Z t-3 , Z t-2 , Z t-1 , Z t+1 , Z t+2 , Z t+3; The average value of the first 3 and last 3 data is used to interpolate the missing values, and its calculation formula is as follows:

[0048]

[0049] The calculated average value is used as the interpolation data for the missing data.

[0050] For wind speed and direction data, if the observed data at time t is missing, the present invention preferably uses the average value of the first 1 and last 1 data to interpolate the missing data, and its calculation formula is as follows:

[0051] Wind speed:

[0052] Wind direction:

[0053] For the outliers of the water surface height data, wind speed and wind direction, the present invention performs the following data processing: preset multiple detection time series lengths of the buoy water level height data, and preset a proportionality coefficient for each detection time series length. Based on the currently selected detection time t, calculate the average value of all detected buoy water level height data within each detection time series length before and after the detection time t, and establish a constraint value range for outlier rejection according to the average value of the buoy water level height data within each detection time series length before and after and the corresponding proportionality coefficient. For example: in the present invention, the constraint value ranges of 1 minute, 5 minutes and 30 minutes are preset. Among them, the present invention first calculates the 1-minute average value, 5-minute average value and 30-minute average value, which are respectively denoted as and Taking the calculation of the 1-minute average value as an example, if the sampling period is 1 hour, its calculation method is:

[0054]

[0055] In the above formula, it represents taking the average value by continuously detecting 60 times with each minute as the sampling time series point (the actual detection frequency is higher than 1 minute, and only the corresponding data is obtained with a 1-minute time series). Similarly, the 5-minute average value and 30-minute average value can be obtained simultaneously according to the above average value principle; among them, the above average values include the average values before and after the reference time t.

[0056] Since the average value with a longer sampling time is less affected by outliers and is smoother, for the detection value Z t , at each time t, it is judged whether it is an abnormal detection value according to different time series lengths and corresponding proportionality coefficients according to the following steps:

[0057] 1. If Z t is located in the interval If it is within, that is, between the average values of 1 minute before and after time t, then this detected value is normal data; otherwise, proceed to the next discrimination.

[0058] 2. If the value of Z t is within the interval that is, between 0.9 times the proportionality coefficient of the average values of 5 minutes before and after time t, then this detected value is normal data; otherwise, proceed to the next discrimination.

[0059] 3. If the value of Z t is within the interval that is, between 0.8 times the proportionality coefficient of the average values of 30 minutes before and after time t, then this detected value is normal.

[0060] For the abnormal detection data of the horizontal height data at time t, the present invention preferably interpolates the missing data by using the average value of the three data before and after time t as a reference, and its calculation formula is as follows:

[0061]

[0062] The method for removing and filling abnormal points for wind speed and wind direction includes:

[0063] Define the wind speed as V and the wind direction as α, preset the lengths of multiple detection time series of wind speed data and wind direction data, and preset the proportionality coefficients for each detection time series length. Based on the currently selected detection time t, calculate the average values of all detected wind speed data and wind direction data within each detection time series length before and after detection time t, and establish the constraint value ranges for removing abnormal values respectively according to the average values of the wind speed data and wind direction data within each detection time series length before and after and the corresponding proportionality coefficients:

[0064] For example: Calculate the average values of wind speed and wind direction for 15 minutes, 30 minutes, and 1 hour respectively, and denote them as and

[0065] 1. If the values of V t and α t are respectively within the intervals and that is, between the sum of the average wind speeds of 15 minutes before and after time t and the angular change, then this detected value is normal data; otherwise, proceed to the next discrimination.

[0066] 2. If the values of V t and α t are within the intervals and that is, between 0.9 times the proportionality coefficient of the average wind speeds of 30 minutes before and after time t and the angular change, then this detected value is normal data; otherwise, proceed to the next discrimination.

[0067] 3. If V t and α t are within the intervals and , that is, between 0.8 times the proportional coefficient of the average wind speed in the 1 hour before and after time t and the angle change, then the detected value is normal data; otherwise, it is abnormal data;

[0068] For the abnormal detection data at time t, the present invention preferably interpolates the missing data with the average value of one data before and after time t as the reference, and its calculation formula is as follows:

[0069] Wind speed:

[0070] Wind direction:

[0071] After filling in the missing data, removing and filling the abnormal data for the water level height data, wind speed data, and wind direction data by using the above method, the present invention further updates the average time of the above different time series lengths, and performs data smoothing processing according to the piecewise interpolation function to solve the influence of accidental factors on the above data, specifically including the following steps:

[0072] Define the 1-minute average value, 5-minute average value, 15-minute average value, 30-minute average value, and 60-minute average value after the above data preprocessing as and

[0073] Use and as feature points, select 6 known point data, with a time length of 1 hour and 30 minutes, and record the node features (x i , z i ), where i takes values from 0 to 5, x represents the detection time, i represents the subscript of different detection times, and z represents the water level height at the corresponding detection time;

[0074] For i = 0, 1,..., 5, calculate h i = x i+1 - x i

[0075] Calculate α i and β i (i = 0, 1,..., 5), where α i and β i are the calculated relevant parameters respectively;

[0076]

[0077] For i = 1,..., 4, calculate:

[0078]

[0079] Calculate a i and b i (i = 0, 1, …, 5)

[0080]

[0081] For i = 1, …, 5, calculate:

[0082]

[0083] Calculate m i (i = 0, 1, …, 5), m i is a relevant parameter in the calculation process;

[0084] m n = b n ;

[0085] m i = a i m i+1 + b i (i = n - 1, …, 1, 0);

[0086] Then calculate all 1 - minute data values through this interpolation function:

[0087] where x` represents each moment for which interpolation is required, and z` represents the water level height interpolated at each moment;

[0088] Calculate the 1 - minute average value within this time span through this piecewise interpolation function and denote it as

[0089] For each 1 - minute average data, calculate:

[0090] If then directly use as the water level value at this point, otherwise use as the water level height value at this point. After processing all the data, the obtained 1 - minute average value is used as the wave height data.

[0091] Since the influence of different wind speeds on the sea conditions is different, the wave height under the continuous action of the wind is affected by tides, wind waves and swells. After the wind weakens, the wave height is mainly affected by tides and swells. Under continuous calm conditions, it is mainly affected by tides. Therefore, the relevant algorithm of the present invention screens the wave height data through two criteria: wind speed and its duration, and defines the following different sea conditions according to the wind speed:

[0092] 1. The situation where the sea state changes from wave level 2 to wave level 3 or above is defined as the sea state affected by wind waves.

[0093] 2. The situation where the sea state changes from wave level 3 or above to wave level below 3 and the duration does not exceed 24 hours is defined as the sea state mainly affected by swell.

[0094] 3. Other sea states with wave levels 0 to 2 are defined as the sea states mainly affected by tides and containing other short - period accidental factors.

[0095] According to the sea states defined above, the calculated wave height data are divided into 3 feature sets, and the three feature sets are defined as: D 风浪 、D 涌浪 and D 潮汐 , where the data of D 风浪 、D 涌浪 and D 潮汐 are subjected to FFT transformation to convert the time - domain data to the frequency domain, and three data sets are obtained as the wind - wave data set, the swell data set and the tide data set, and the corresponding spectral data are denoted as DF 风浪 、DF 涌浪 and DF 潮汐 . Further, the data of the low - frequency part less than 0.002 Hz of DF 潮汐 is extracted as the spectrum mainly affected by tides, denoted as F 潮汐 ; the difference between this part of the spectral data and the spectrum of DF 涌浪 is taken, and the low - frequency part less than 20 Hz is retained as the influence spectrum of swell, denoted as F 涌浪 ; after subtracting the data of F 潮汐 and F 涌浪 from the spectrum of DF 风浪 , the data with frequencies less than 1 Hz are retained as the influence spectrum of wind waves, denoted as F 风浪 ; the obtained spectral data F 风浪 、F 涌浪 and F 潮汐 are subjected to IFFT transformation to convert the frequency - domain data to the time domain, and the wind - wave and swell data with separated mutual influences are obtained.

[0096] Embodiments disclosed in the present invention. The processes described above with reference to the flowcharts can be implemented as computer software programs. Embodiments disclosed in the present invention include a computer program product that includes a computer program carried on a computer-readable medium. The computer program contains program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above functions defined in the methods of the present application are performed. It should be noted that the computer-readable medium described above in the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or combined with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or combined with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless segments, wire segments, optical cables, RF, etc., or any suitable combination of the above.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0098] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the said principles, the embodiments of the present invention may have any variations or modifications.

Claims

1. A method for extracting wind waves and swells based on GNSS and anemometer, characterized in that: The method comprises: Setting a reference time and a detection time sequence for the buoy, and obtaining water level height data and wind data detected by the buoy for multiple times respectively according to the reference time and the detection time sequence; Preprocessing the buoy water level data and wind data, filling in missing data of the buoy water level data and wind data, and removing abnormal data in the buoy water level data and wind data to obtain preprocessed water level data and wind data; The pre-processed water level height data and wind data are used to calculate the water level height data and wind data within a unit time according to a piecewise interpolation function, and the wave height data under the corresponding detection time series is calculated according to the water level height data and wind data within the unit time; The wave height data corresponding to the detection time series obtained by calculation are classified into feature sets according to the wind speed level, and the frequency spectrum data set of wind waves and surges is obtained after classification and screening of the wave height data corresponding to the detection time series. The frequency spectrum data set of wind waves and surges is further screened according to the frequency size, and after further screening, IFFT transformation is performed to obtain the final wind wave and surge data after separation and screening.

2. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 1, characterized in that: The wind data includes wind speed data and wind direction data, wherein a method for preprocessing missing data of the wind speed data and wind direction data includes: obtaining multiple continuously detected time series of the wind speed data and wind direction data, and respectively obtaining the wind speed data and wind direction data of at least one time series before and after the time series corresponding to the missing data, and calculating the average value of the wind speed data and wind direction data of at least one time series before and after the time series corresponding to the missing data as interpolation of the corresponding missing data.

3. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 1, characterized in that: The method for preprocessing missing data in the buoy water level height data includes: obtaining water level height data of multiple adjacent time series corresponding to the time series of the missing data in the buoy water level height data, selecting water level height data of at least 3 consecutive adjacent time series before and after the time series corresponding to the missing data, and obtaining the water level height data of at least 6 consecutive adjacent time series to calculate the average value as the missing data of the buoy water level height data for interpolation.

4. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 1, characterized in that: The method for removing abnormal data of buoy water level height data comprises: presetting a plurality of detection time series lengths of buoy water level height data, and presetting a proportional coefficient for each detection time series length, taking the currently selected detection time t as a reference, calculating the average value of all detected buoy water level height data in each detection time series length before and after the detection time t, and establishing a constraint value range for removing abnormal values ​​according to the average value of the buoy water level height data in each detection time series length before and after the detection time t and the corresponding proportional coefficient, if the buoy water level height data Z actually detected at the currently selected detection time t is t If all of them are within the constraint value range of the abnormal value removal, then the buoy water level height data Z actually detected at the currently selected detection time t t If it is normal data, it will be eliminated as abnormal data; for abnormal data determined to be buoy water level height data, further collect normal data of multiple adjacent time series corresponding to the time series of the abnormal data to calculate the average value, which is used for interpolation processing after the abnormal data is eliminated.

5. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 1, characterized in that: The method for removing abnormal values ​​of wind speed data and wind direction data comprises: presetting a plurality of detection time series lengths of wind speed data and wind direction data, and presetting a proportional coefficient for each detection time series length, taking the currently selected detection time t as a reference, calculating the average value of all detected wind speed data and wind direction data in each detection time series length before and after the detection time t, and establishing a constraint value range for removing abnormal values ​​according to the average value of the wind speed data and wind direction data in each detection time series length before and after and the corresponding proportional coefficient, if the wind speed data V actually detected at the currently selected detection time t is t and wind direction data α t If all of them are within the constraint value range of the abnormal value removal, then the wind speed data V actually detected at the currently selected detection time t t and wind direction data α t is normal data, otherwise it is removed as abnormal data; for wind speed data V t and wind direction data α t For abnormal data, normal data of multiple adjacent time series corresponding to the time series of the abnormal data are further collected to calculate the average value, which is used for interpolation processing after the abnormal data is removed.

6. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 1, characterized in that: The method for calculating the water level height data and wind data in unit time by piecewise interpolation function includes: the buoy water level height data after preprocessing the vacant data and abnormal data is as follows: define the 1-minute average value, 5-minute average value, 15-minute average value, 30-minute average value and 60-minute average value as respectively corresponding to and use and As feature points, select 6 known point data, the time length is 1 hour and 30 minutes, and record the node coordinates (x i ,z i ), i ranges from 0 to 5, x represents the detection time, i represents the subscript of different detection times, and z represents the water level at the corresponding detection time; For i = 0, 1, ..., 5, calculate h i =x i+1 -x i Calculate α i and β i (i=0,1,…,5), α i and β i are the relevant parameters of the calculation respectively; For i=1,…,4, calculate: Calculate a i and b i (i=0,1,…,5) For i=1,…,5, calculate: Calculate m i (i=0,1,…,5),m i are the relevant parameters in the calculation process; m n =b n ; m i =a i m i+1 +b i (i=n-1,…,1,0); Then use the interpolation function to calculate all 1-minute data values: Where x` represents each moment that needs to be interpolated, and z` represents the interpolated water level height at each moment; The 1-minute average value within the time span is calculated by the piecewise interpolation function and recorded as For each 1-minute average data, calculate: if Then use directly As the water level value at this point, otherwise use As the water level height value at that point, after processing all the data, the 1-minute average value is obtained as the wave height data.

7. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 6, characterized in that: The method for classifying the feature set of the wave height data corresponding to the detection time series comprises the following steps: defining different sea conditions according to wind speed as follows:

1. The sea conditions that change from level 2 to level 3 or above are defined as sea conditions affected by wind and waves; 2. The situation where the sea condition changes from wave level 3 or above to wave level 3 or below and lasts for no more than 24 hours is defined as the sea condition dominated by swell waves; 3. Other sea conditions with wave magnitudes of 0 to 2 are defined as those that are mainly affected by tides and contain other short-period occasional factors; According to the above defined sea conditions, the calculated wave height data is divided into three feature sets, and the three feature sets are defined as: 风浪 , D 涌浪 and D 潮汐 , where D 风浪 , D 涌浪 and D 潮汐 The data is transformed by FFT, and the time domain data is converted to the frequency domain. Three data sets are obtained, namely the wind wave data set, the surge data set and the tide data set. The corresponding spectrum data is recorded as DF 风浪 DF 涌浪 and DF 潮汐 .

8. The method for extracting wind waves and swells based on GNSS and anemometer according to claim 7, characterized in that: Extract DF 潮汐 The low-frequency data of less than 0.002 Hz is taken as the spectrum mainly affected by tides and is recorded as F 潮汐 ; Combine this part of spectrum data with DF 涌浪 The frequency spectrum of the surge wave is obtained by subtracting the frequency spectrum of the surge wave, and the low-frequency part less than 20 Hz is retained, which is the frequency spectrum of the surge wave, denoted as F 涌浪 ; F 潮汐 and F 涌浪 Data and DF 风浪 After subtracting the frequency spectrum of the wind and waves, the data with a frequency less than 1 Hz is retained as the frequency spectrum of wind and waves, denoted as F 风浪 ; The obtained spectrum data F 风浪 、F 涌浪 and F 潮汐 Perform IFFT transformation to convert the frequency domain data into the time domain to obtain the separated wind wave and swell data that influence each other.

9. A wind wave and swell extraction system based on GNSS and anemometer, characterized in that: The system executes a method for extracting wind waves and swells based on GNSS and anemometer as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the wind wave and swell extraction method based on GNSS and anemometer as described in any one of claims 1 to 8.

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