Wind wave and surge extraction method and system based on GNSS and anemometer

Abstract

The invention discloses a wind wave and surge extraction method and system based on a GNSS and an anemometer. The method comprises the following steps: respectively obtaining water level height data and wind data detected by a buoy for multiple times according to a reference time sequence and a detection time sequence; the buoy water level height data and the wind data are subjected to data preprocessing, the water level height and the wind data are preprocessed, and the preprocessed water level height and the preprocessed wind data are obtained; calculating the water level height and the wind data in unit time by using the preprocessed water level height and the wind data according to a segmented interpolation function, and calculating wave height data under a corresponding detection time sequence according to the water level height and the wind data in the unit time; feature set classification is carried out according to wind speed grades, IFFT is carried out after screening, and final wind wave and surge data are obtained, the water surface wind wave and surge data extraction precision is effectively improved, and low-cost and long-time-efficiency water surface wind wave and surge detection can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind wave extraction, 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 anemometers, wherein the method and system utilize GNSS (Global Satellite Navigation 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 extracts wind wave and surge data of the detected water surface based on the wind speed data, 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 inventive object of the present invention is to provide a method and system for extracting wind waves and surges based on GNSS and anemometers. The method and system also perform interpolation processing on missing data of water level height data and wind speed and direction data detected by surface buoys 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, thereby effectively avoiding deviations in the extraction of wind wave data and surge data due to missing data or abnormal point data, and improving the accuracy of the wind wave data and surge data.

[0005] Another inventive object of the present invention is to provide a method and system for extracting wind waves and surges based on GNSS and anemometer. The method and system use the vertical water level height data obtained by the GNSS detection to process missing data and abnormal data, and then use a piecewise interpolation function to process the data to obtain wave height data, and use wind speed data of different levels to filter the wave height data, and propose a wind wave, surge and tidal data separation algorithm based on Fourier transform from the wind speed and its duration. The algorithm is easy to implement and has low requirements for observation equipment. The position information fed back by the GNSS can be used to monitor the buoy status.

[0006] In order to achieve at least one of the above-mentioned invention objects, the present invention further provides a method for extracting wind waves and swells based on GNSS and anemometer, the method comprising: S01. Setting a reference time and a detection time sequence for the buoy, and obtaining water level data and wind data detected by the buoy for multiple times according to the reference time and the detection time sequence; S02. Preprocessing the buoy water level data and wind data, filling the 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; S03. Calculate the water level data and wind data per unit time according to the piecewise interpolation function based on the preprocessed water level data and wind data, and calculate the wave height data corresponding to the detection time series according to the water level data and wind data per unit time; S04. 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, and the frequency spectrum data set of wind waves and surges is further screened according to the frequency size, and after further screening, IFFT transformation (inverse fast Fourier transform) is performed to obtain the final wind wave and surge data after separation and screening.

[0007] According to one of the preferred embodiments of the present invention, 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 the corresponding missing data for interpolation.

[0008] According to another preferred embodiment of the present invention, the method for preprocessing missing data in the buoy water level height data includes: obtaining water level height data of multiple adjacent time series of the time series corresponding to 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.

[0009] According to another preferred embodiment of the present invention, the method for removing abnormal data of the buoy water level height data comprises: presetting multiple detection time series lengths of the 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 the 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.

[0010] According to another preferred embodiment of the present invention, the method for removing abnormal values ​​of wind speed data and wind direction data includes: presetting multiple 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 wind speed data and wind direction data detected 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 tand 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.

[0011] According to another preferred embodiment of the present invention, 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 with a time length of 1 hour and 30 minutes, and record the node features (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 and (i=0,1,…,5), and 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 directly use 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.

[0012] According to another preferred embodiment of the present invention, the method for classifying the feature set of the wave height data corresponding to the detection time series comprises the following steps: defining the following different sea conditions according to the wind speed: 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 .

[0013] According to another preferred embodiment of the present invention, 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.

[0014] In order 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 anemometer, and the system executes the above-mentioned wind wave and swell extraction method based on GNSS and anemometer.

[0015] The present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the above-mentioned method for extracting wind waves and swells based on GNSS and anemometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a flow chart of a method for extracting wind waves and swells based on GNSS and anemometer according to the present invention. DETAILED DESCRIPTION

[0017] 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 those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0018] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0019] Please combine Figure 1The present invention discloses a method and system for extracting wind waves and swells based on GNSS and anemometer, wherein the method mainly comprises the following steps: S01. Setting a reference time and a detection time sequence for the buoy, and obtaining water level data and wind data detected by the buoy for multiple times according to the reference time and the detection time sequence; S02. Preprocessing the buoy water level data and wind data, filling the 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; S03. Calculate the water level data and wind data per unit time according to the piecewise interpolation function based on the preprocessed water level data and wind data, and calculate the wave height data corresponding to the detection time series according to the water level data and wind data per unit time; S04. 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, and 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.

[0020] In the present invention, it is first necessary to set a wind sensor and a GNSS sensor in the buoy, wherein 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; wherein the GNSS (Global Satellite Navigation System) sensor is used for positioning in the horizontal direction and the water surface height positioning in the vertical direction. In the present invention, the use of the GNSS sensor to obtain the vertical water surface height data can eliminate the need to use the buoy of the expensive gravity acceleration sensor, so that the buoy of the present invention has 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, including filling missing data and removing and filling 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 uses the piecewise interpolation function to process the water level height data, 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. The wave height is further classified into a feature set according to the wind speed, and the wave height feature set is processed based on FFT transformation (Fast Fourier Transform) and IFFT transformation (Inverse Fast Fourier Transform), and the final wind wave and surge data are obtained by frequency screening and separation. The technical solution of the present invention is easier to implement in terms of technical means and has higher accuracy.

[0021] Specifically, the method for collecting and preprocessing the water surface height data of the present invention includes: setting a time reference, wherein the time reference may be the standard time of the corresponding time zone; further presetting a collection frequency, wherein the collection frequency may be once every 1-60 seconds, and the collection frequency in the present invention is preferably higher than once every 1 minute, and determining the time sequence of the collection. The collected water surface height data is defined as Z t , t is the corresponding time series. Since the water surface height acquisition process may be affected by factors such as the environment or equipment, there may be missing data in the corresponding time series t. When there is missing data, the present invention needs to fill 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 corresponding to the 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, the three time series before and after are preferably obtained, that is, Z t-3 , Z t-2 、Z t-1 , Z t+1 , Z t+2 , Zt+3 ; The missing value is interpolated using the average value of the three preceding and following data. The calculation formula is as follows: ; The calculated average value is used as the interpolation data for the missing data.

[0022] For wind speed and direction data, if the observed data at time t is missing, the present invention can preferably use the average value of the data before and after it to interpolate the missing data, and the calculation formula is as follows: Wind speed: ; wind direction: .

[0023] For the abnormal values ​​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 proportional 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 in each detection time series length before and after the detection time t, and establish a constraint value range for eliminating abnormal values ​​based on the average value of the buoy water level height data in each detection time series length before and after and the corresponding proportional coefficient. For example: the present invention presets a constraint value range of 1 minute, 5 minutes and 30 minutes. The present invention first calculates the 1-minute average value, the 5-minute average value and the 30-minute average value, respectively, and records them as , and Taking the 1-minute average calculation as an example, if the sampling period is 1 hour, the calculation method is: ; The above formula indicates that every minute is used as a sampling time series point (the actual detection frequency is higher than 1 minute, and only the corresponding data is obtained in the 1-minute time series), and the detection is continued for 60 times to obtain the average value. Similarly, according to the above average value principle, the 5-minute average value and the 30-minute average value can be obtained at the same time; the above average value includes the average value before and after the reference time t.

[0024] Since the average value with longer sampling time is less disturbed by abnormal points and is smoother, the detection value Z at each time t is t According to the length of different time series and the corresponding proportional coefficient, the following steps are used to determine whether it is an abnormal detection value: 1. If Z t The value lies in the interval If the value is within the range of 1 minute before and after time t, that is, between the average values ​​of 1 minute before and after time t, then the detection value is normal data. Otherwise, proceed to the next step of judgment. 2. If Z t The value lies in the interval Within, that is, between 0.9 times the proportional coefficient of the 5-minute average value before and after time t, the detection value is normal data, otherwise it proceeds to the next step of judgment; 3. If Z t The value lies in the interval If the value is within the range of 0.8 times the proportional coefficient of the average value of the 30 minutes before and after time t, the test value is normal.

[0025] For abnormal detection data of the horizontal height data at time t, the present invention preferably uses the average value of the three data before and after time t as the reference to interpolate the missing data, and the calculation formula is as follows: ; The methods for removing and filling abnormal points of wind speed and wind direction include: Define wind speed as V, wind direction as α, preset multiple detection time series lengths of wind speed data and wind direction data, and preset a proportional coefficient for each detection time series length. Take the currently selected detection time t as the benchmark, calculate 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 establish the constraint value range for outlier removal according to the average value of wind speed data and wind direction data in each detection time series length before and after and the corresponding proportional coefficient: For example: Calculate the average wind speed and wind direction for 15 minutes, 30 minutes, and 1 hour respectively, and record them as , , , , and .

[0026] 1. If and The values ​​are in the interval and Within, that is, between the average wind speed and angle change of 15 minutes before and after time t, the detection value is normal data, otherwise it goes to the next step of judgment; 2. If and The value lies in the interval and Within, that is, between 0.9 times the proportional coefficient of the 30-minute average wind speed before and after time t and between the angle change, the detection value is normal data, otherwise it proceeds to the next step of judgment; 3. If and The value lies in the interval and Within, that is, between 0.8 times the proportional coefficient of the 1-hour average wind speed before and after time t and between the angle changes, the detection value is normal data, otherwise it is abnormal data; For the abnormal detection data at time t, the present invention preferably uses the average value of the data before and after time t as the benchmark to interpolate the missing data, and the calculation formula is as follows: Wind speed: ; wind direction: .

[0027] The present invention uses the above method to fill in missing data, remove abnormal data and fill in water level data, wind speed data and wind direction data, 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 occasional factors on the above data, which specifically includes the following steps: After the above data preprocessing, the following definitions are made: 1-minute average, 5-minute average, 15-minute average, 30-minute average, and 60-minute average, respectively. , , , and ; 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 ), 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 and (i=0,1,…,5), and are the relevant parameters for 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 directly use 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.

[0028] Due to the different effects of different wind speeds on sea conditions, the wave height under continuous wind action is affected by tides, wind waves and swells. After the wind weakens, the wave height is mainly affected by tides and swells. Under continuous windlessness, it is mainly affected by tides. Therefore, the relevant algorithm of the present invention uses wind speed and its duration as two standards to filter wave height data, for the following different sea conditions defined according to wind speed: The sea conditions that change from level 2 to level 3 and above are defined as sea conditions affected by wind and waves; The situation where the sea condition changes from wave condition level 3 or above to wave condition level 3 or below and lasts for no more than 24 hours is defined as the sea condition dominated by swell; 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 . Further extraction of 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.

[0029] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above functions defined in the method of the present application are executed. It should be noted that the computer-readable medium mentioned above in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with 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 may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0030] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0031] 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 functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

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 includes: presetting multiple 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 ,y i ), i takes the value of 0 to 5, and calculates the relevant parameters; Then calculate all 1-minute data values ​​through the interpolation function: , 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 piecewise interpolation function and recorded as ; For each 1-minute average data, calculate: ； if , then directly use 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: The sea conditions that change from level 2 to level 3 and above are defined as sea conditions affected by wind and waves; The situation where the sea condition changes from wave condition level 3 or above to wave condition level 3 or below and lasts for no more than 24 hours is defined as the sea condition dominated by swell; 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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