Tide level monitoring method and device, electronic equipment, storage medium and program product

Abstract

The invention relates to the technical field of interference reflection remote sensing, in particular to a tide level monitoring method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring SNR data of a low elevation angle of a reflection region on a sea surface; carrying out atmospheric refraction correction and trend term removal operation on the data to obtain a high-frequency component of the data; performing wavelet transformation on the high-frequency component to obtain an instantaneous frequency time sequence and a peak power time sequence at different moments, screening the instantaneous frequency time sequence and the peak power time sequence respectively, and finally performing average value calculation on frequency screening results to obtain a main frequency of the high-frequency component; the antenna height is calculated based on the dominant frequency to obtain an initial tide level sequence, the initial tide level sequence is decomposed into an astronomical tide sequence and a residual water level sequence which are arranged at equal intervals through quasi-tide harmonic analysis, and the astronomical tide sequence is corrected through local weighted linear regression to obtain a final tide level sequence. Therefore, the problem that the precision of a tide level monitoring result obtained in the prior art is low is solved.

Description

Technical Field

[0001] The present application relates to the field of interference reflection remote sensing technology, and in particular to a tide monitoring method, device, electronic equipment, storage medium and program product. Background Art

[0002] With human activities, the content of greenhouse gases in the atmosphere continues to increase. The global warming caused by it leads to the melting of glaciers, which causes a large amount of fresh water to be injected into the ocean. The thermal expansion of seawater and the changes in atmospheric and ocean circulation will affect the global climate, environment, ecology and the economy and security of coastal areas. At the same time, the annual extreme weather, such as typhoons, storm surges, extreme waves and extreme rainfall, will also affect the tide level, threatening the economic activities of coastal areas and the lives of residents. Therefore, high-precision and real-time tide level monitoring is of great significance.

[0003] Among the related technologies, GNSS-IR (Global Navigation Satellite System InterferometryReflectometry) tide inversion technology has a relatively complete set of classic tide inversion principles and technical processes. The main frequency of the multipath signal in SNR (Signal to Noise Ratio) is related to the vertical distance from the antenna phase center to the sea surface, and is usually determined using the LSP (Line Spectrum Pair) spectrum analysis method. Subsequently, a method of extracting the main frequency using wavelet transform was proposed. Using wavelet transform to perform spectral analysis on the SNR sequence can obtain the instantaneous frequency and perform a visual analysis of sea surface changes.

[0004] However, this method is greatly affected by noise, and the instantaneous frequency results obtained contain many outliers, that is, the accuracy of tide monitoring results is low, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a tide monitoring method, device, electronic device, storage medium and program product to solve the problem of low accuracy of tide monitoring results obtained in the prior art, effectively reduce the noise of tide sequence monitoring results, and obtain high-precision tide sequence with arbitrary time resolution.

[0006] To achieve the above object, the first embodiment of the present application proposes a tide level monitoring method, comprising the following steps:

[0007] Obtaining SNR data of the reflection area within a preset altitude angle range of the sea surface;

[0008] Performing an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data;

[0009] Based on a preset wavelet transform strategy, the instantaneous frequency time series of the high-frequency component at different moments and the peak power time series at different moments are obtained, and the instantaneous frequency time series and the peak power time series are respectively screened to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a moment corresponding to the screened peak power time series, and an average value is calculated based on the intersection of the first frequency screening result and the second frequency screening result to obtain the dominant frequency of the high-frequency component;

[0010] A vertical height change sequence of the receiving antenna relative to the sea surface is calculated based on the main frequency, and an initial tide level sequence is obtained according to the vertical height change sequence, and the initial tide level sequence is decomposed into an equally spaced astronomical tide sequence and a first residual water level sequence using a preset quasi-tidal harmonic analysis strategy, and a local weighted average calculation is performed on the first residual water level sequence to obtain a second residual water level sequence at a time corresponding to the equally spaced astronomical tide sequence, and the equally spaced astronomical tide sequence and the second residual water level sequence are added to obtain a final tide level sequence.

[0011] According to one embodiment of the present application, performing an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data includes:

[0012] Performing an atmospheric refraction correction operation on the SNR data to obtain corrected SNR data;

[0013] Fitting the direct signal component in the corrected SNR data using a quadratic polynomial to obtain a fitting result;

[0014] The corrected SNR data is subtracted from the fitting result to obtain a high-frequency component of the SNR data.

[0015] According to an embodiment of the present application, the method of obtaining the instantaneous frequency time series of the high-frequency component at different times and the peak power time series at different times based on a preset wavelet transform strategy includes:

[0016] Using a preset wavelet function as a basis function, performing wavelet transform on the high frequency component to obtain wavelet spectra at different times;

[0017] Based on the wavelet spectra at different moments, the frequency corresponding to the peak power at each moment is selected as the instantaneous frequency, and the instantaneous frequency time series at different moments and the peak power time series at different moments are obtained.

[0018] According to an embodiment of the present application, the instantaneous frequency time series and the peak power time series are respectively screened to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a time corresponding to the screened peak power time series, including:

[0019] Determine the current short window length and the current long window length;

[0020] Calculating a short window average frequency according to the current short window length, calculating a long window average frequency according to the current long window length, calculating a ratio of the short window average frequency to the long window average frequency, and calculating a difference between the ratio and a first preset value;

[0021] Obtaining a first frequency screening result corresponding to the instantaneous frequency time series according to the current short window length, the current long window length, the difference value and a first preset reference value;

[0022] Normalizing the peak power time series, sorting the normalized peak powers according to a preset arrangement rule, and selecting peak powers that meet preset conditions to calculate the average value to obtain a second preset reference value;

[0023] Filtering peak powers greater than or equal to the second preset reference value from the peak power time series to obtain a filtered peak power time series;

[0024] The instantaneous frequency at the time corresponding to the filtered peak power time series is selected from the instantaneous frequency time series to obtain the second frequency filtering result.

[0025] According to an embodiment of the present application, the calculating the vertical height change sequence of the receiving antenna relative to the sea surface based on the main frequency, and obtaining the initial tide level sequence according to the vertical height change sequence, includes:

[0026] Based on the main frequency, using a preset SNR multipath signal expression, calculating a vertical height change sequence of the receiving antenna relative to the sea surface;

[0027] The second preset value is subtracted from the vertical height change sequence of the receiving antenna relative to the sea surface to obtain the initial tide level sequence.

[0028] According to an embodiment of the present application, the decomposition of the initial tide level sequence into an astronomical tide sequence and a first residual water level sequence at equal intervals using a preset quasi-tidal harmonic analysis strategy includes:

[0029] Using the preset tidal model, the amplitude and delay angle relationship of tidal components at multiple target locations are predicted;

[0030] Based on the relationship between the amplitude and the retardation angle, the initial tide level sequence is decomposed into the equally spaced astronomical tide sequence and the first residual water level sequence using the preset quasi-tidal harmonic analysis strategy.

[0031] According to the tide monitoring method proposed in the embodiment of the present application, by obtaining the SNR data of the reflection area at a low altitude angle on the sea surface, the data can be corrected for atmospheric refraction and detrended to obtain its high-frequency component; by using wavelet transform on the high-frequency component, the instantaneous frequency time series and peak power time series at different times can be obtained, and the instantaneous frequency time series and peak power time series are screened respectively, and finally the average value of the frequency screening results is calculated to obtain the main frequency of the high-frequency component; the antenna height is calculated based on the main frequency to obtain the initial tide sequence, and the initial tide sequence is decomposed into an astronomical tide sequence and a residual water level sequence at equal intervals using quasi-tidal harmonic analysis, and the astronomical tide sequence is corrected using local weighted linear regression to obtain the final tide sequence. Thus, the problem of low precision of the tide monitoring results obtained by the prior art is solved, the noise of the tide sequence monitoring results is effectively reduced, and a high-precision tide sequence with arbitrary time resolution is obtained.

[0032] To achieve the above-mentioned purpose, a second embodiment of the present application provides a tide level monitoring device, comprising:

[0033] An acquisition module is used to acquire SNR data of the reflection area within a preset altitude angle range of the sea surface;

[0034] A first acquisition module is used to perform an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data;

[0035] A second acquisition module is used to obtain the instantaneous frequency time series of the high-frequency component at different moments and the peak power time series at different moments based on a preset wavelet transform strategy, and to screen the instantaneous frequency time series and the peak power time series respectively to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a moment corresponding to the screened peak power time series, and to calculate an average value based on the intersection of the first frequency screening result and the second frequency screening result to obtain the main frequency of the high-frequency component;

[0036] The third acquisition module is used to calculate the vertical height change sequence of the receiving antenna relative to the sea surface based on the main frequency, and obtain an initial tide level sequence according to the vertical height change sequence, and use a preset quasi-tidal harmonic analysis strategy to decompose the initial tide level sequence into an equally spaced astronomical tide sequence and a first residual water level sequence, perform local weighted average calculation on the first residual water level sequence to obtain a second residual water level sequence at a time corresponding to the equally spaced astronomical tide sequence, and add the equally spaced astronomical tide sequence and the second residual water level sequence to obtain a final tide level sequence.

[0037] According to an embodiment of the present application, the first obtaining module is specifically used to:

[0038] Performing an atmospheric refraction correction operation on the SNR data to obtain corrected SNR data;

[0039] Fitting the direct signal component in the corrected SNR data using a quadratic polynomial to obtain a fitting result;

[0040] The corrected SNR data is subtracted from the fitting result to obtain a high-frequency component of the SNR data.

[0041] According to an embodiment of the present application, the second obtaining module is specifically used to:

[0042] Using a preset wavelet function as a basis function, performing wavelet transform on the high frequency component to obtain wavelet spectra at different times;

[0043] Based on the wavelet spectra at different moments, the frequency corresponding to the peak power at each moment is selected as the instantaneous frequency, and the instantaneous frequency time series at different moments and the peak power time series at different moments are obtained.

[0044] According to an embodiment of the present application, the second obtaining module is specifically used to:

[0045] Determine the current short window length and the current long window length;

[0046] Calculating a short window average frequency according to the current short window length, calculating a long window average frequency according to the current long window length, calculating a ratio of the short window average frequency to the long window average frequency, and calculating a difference between the ratio and a first preset value;

[0047] Obtaining a first frequency screening result corresponding to the instantaneous frequency time series according to the current short window length, the current long window length, the difference value and a first preset reference value;

[0048] Normalizing the peak power time series, sorting the normalized peak powers according to a preset arrangement rule, and selecting peak powers that meet preset conditions to calculate the average value to obtain a second preset reference value;

[0049] Filtering peak powers greater than or equal to the second preset reference value from the peak power time series to obtain a filtered peak power time series;

[0050] The instantaneous frequency at the time corresponding to the filtered peak power time series is selected from the instantaneous frequency time series to obtain the second frequency filtering result.

[0051] According to one embodiment of the present application, the third obtaining module is specifically used to:

[0052] Based on the main frequency, using a preset SNR multipath signal expression, calculating a vertical height change sequence of the receiving antenna relative to the sea surface;

[0053] The second preset value is subtracted from the vertical height change sequence of the receiving antenna relative to the sea surface to obtain the initial tide level sequence.

[0054] According to one embodiment of the present application, the third obtaining module is specifically used to:

[0055] Using the preset tidal model, the amplitude and delay angle relationship of tidal components at multiple target locations are predicted;

[0056] Based on the relationship between the amplitude and the retardation angle, the initial tide level sequence is decomposed into the equally spaced astronomical tide sequence and the first residual water level sequence using the preset quasi-tidal harmonic analysis strategy.

[0057] According to the tide monitoring device proposed in the embodiment of the present application, by obtaining the SNR data of the reflection area at a low altitude angle on the sea surface, the data can be corrected for atmospheric refraction and detrended to obtain its high-frequency component; by using wavelet transform on the high-frequency component, the instantaneous frequency time series and peak power time series at different times can be obtained, and the instantaneous frequency time series and peak power time series are screened respectively, and finally the average value of the frequency screening results is calculated to obtain the main frequency of the high-frequency component; the antenna height is calculated based on the main frequency to obtain the initial tide sequence, and the initial tide sequence is decomposed into an astronomical tide sequence and a residual water level sequence at equal intervals using quasi-tidal harmonic analysis, and the astronomical tide sequence is corrected using local weighted linear regression to obtain the final tide sequence. Thus, the problem of low precision of the tide monitoring results obtained by the prior art is solved, the noise of the tide sequence monitoring results is effectively reduced, and a high-precision tide sequence with arbitrary time resolution is obtained.

[0058] To achieve the above objectives, the third aspect of the present application proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the tide monitoring method as described in the above embodiments.

[0059] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the tide monitoring method as described in the above embodiments.

[0060] To achieve the above objectives, a fifth aspect of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the tide monitoring method as described in the above embodiments.

[0061] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0063] Figure 1 A flow chart of a tide monitoring method provided according to an embodiment of the present application;

[0064] Figure 2 A schematic diagram of a multipath effect geometric model according to an embodiment of the present application;

[0065] Figure 3 A flowchart of another tide monitoring method provided according to an embodiment of the present application;

[0066] Figure 4 A schematic diagram of spectrum analysis and screening results according to an embodiment of the present application;

[0067] Figure 5 is a schematic diagram of a tide inversion result according to an embodiment of the present application;

[0068] Figure 6 A schematic block diagram of a tide monitoring device provided according to an embodiment of the present application;

[0069] Figure 7 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0070] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0071] The following describes a tide monitoring method, device, electronic device, storage medium, and program product according to embodiments of the present application with reference to the accompanying drawings.

[0072] Figure 1 It is a flow chart of a tide monitoring method according to an embodiment of the present application.

[0073] Before introducing the tide monitoring method proposed in the embodiment of the present application, the relevant technical background and the principles involved in the tide monitoring method are first introduced.

[0074] The Global Navigation Satellite System (GNSS) was originally used for navigation, positioning, and timing (PNT). In addition to PNT services, with the continuous development of GNSS technology and the in-depth promotion of its application, GNSS is also widely used in remote sensing, communications, aerospace, etc., and the use of GNSS reflected signals for remote sensing applications has also become an important part of GNSS applications. When the navigation signal contacts the surface, reflection usually occurs, and the receiving antenna will simultaneously receive the interference signal generated by the reflected signal and the direct signal, resulting in multipath errors in conventional navigation positioning. From the basic theory of electromagnetic wave propagation, the navigation signal reflected by the surface will carry the characteristic information of the surface. Compared with the direct signal, the reflected signal can reflect the physical characteristics of the surface through changes in phase, frequency, amplitude, and polarization.

[0075] GNSS-R (Global Navigation Satellite System Reflectometry) technology is a technology that uses the L-band signal emitted by global navigation satellites as an external radiation source to obtain the physical parameters of the reflecting surface by extracting and analyzing the reflected GNSS signal. It is one of the main technologies for GNSS remote sensing applications. According to the different carriers of the received signals, GNSS-R is divided into three types: satellite-borne, airborne, and ground-based; according to the different signal receiving methods, GNSS-R is divided into reflection mode and interference mode. GNSS-IR technology is the ground-based branch of GNSS-R, which refers to the use of ground measurement receivers to complete the inversion of geophysical parameters of the surrounding environment of the measuring station based on its signal-to-noise ratio or the interference characteristics in other observation data. GNSS-IR technology has the characteristics of low cost, all-weather and multiple signal sources, and is widely used in sea breeze inversion, snow parameter inversion, soil moisture monitoring and water level measurement.

[0076] Among the related technologies, the traditional tide gauge is currently the most commonly used stable and continuous tide monitoring method, but its installation and maintenance costs are high; the satellite altimetry method can obtain the absolute change of tide level over a large range, but its accuracy is low in offshore areas, and it is difficult to achieve high-time-resolution tide monitoring. However, by using GNSS-IR tide monitoring technology, it is possible to achieve fully automatic, all-weather, and high-time-resolution monitoring of tide levels by using nearshore GNSS stations. This technology can improve the spatial resolution or accuracy of existing tide monitoring technologies, provide more tide monitoring data, and is very beneficial to marine research such as sea level research and ocean analysis.

[0077] At present, GNSS-IR tide inversion technology has a relatively complete set of classic tide inversion principles and technical processes. The main frequency of the multipath signal in SNR is related to the vertical distance from the antenna phase center to the sea surface, which is usually determined by the LSP spectrum analysis method. Subsequently, a method of extracting the main frequency by wavelet transform was proposed. The instantaneous frequency can be obtained by spectral analysis of the SNR sequence using wavelet transform, and the sea surface changes can be visualized. However, since the wavelet transform is greatly affected by noise, the instantaneous frequency results obtained contain many outliers, and it is necessary to design additional feasible solutions to screen the results. In addition, the GNSS-IR tide monitoring results are non-equally spaced time series and contain large noise. Therefore, the GNSS-IR tide results need to be refined.

[0078] Based on the above problems, the embodiment of the present application proposes a tide monitoring method, which aims to eliminate wavelet transform outliers and improve the accuracy of wavelet transform tide inversion; at the same time, combined with astronomical tides, the noise of GNSS-IR tide sequence is reduced, and GNSS-IR tide results of arbitrary time resolution can be obtained.

[0079] The principles involved in the tide level monitoring method proposed in the embodiment of the present application will be combined with Figure 2 Provide detailed explanation.

[0080] like Figure 2 As shown, Figure 2 This is a geometric model diagram of the multipath effect of satellite direct signals and reflected signals during the reception process. A GNSS receiver is placed vertically upward on the shore. The height from the phase center of the receiver antenna point A to the water surface is H. The receiver can simultaneously receive the interference signal generated by the GNSS direct signal and the reflected signal from the water surface. According to the optical economy and approximation principles of high-frequency electromagnetic waves and the principle of mirror reflection, the reflected signal mainly comes from the mirror reflection point B in the first Fresnel zone, where the energy of the reflected signal is the largest and the path delay is the shortest. Therefore, when establishing the GNSS-IR height measurement geometric model, only the reflected signal from the mirror reflection point is considered. Compared with the direct signal, there is a path delay Δ(t) in the journey of the reflected signal to the receiver, from Figure 2 Triangle AA in ′ It can be seen from C that the path delay Δ(t) of the reflected signal has the following geometric relationship with the antenna height H:

[0081] Δ(t)=2Hsinθ(t); (1)

[0082] Among them, θ(t) is the satellite altitude angle, which is related to time. From the path delay, the phase difference between the direct signal and the reflected signal can be obtained.

[0083]

[0084] Where λ is the wavelength of the GNSS incident signal. By taking the derivative of the phase, we can get the angular frequency ω(t) of the reflected signal. In addition, ω(t) is also related to the frequency f of the reflected signal. Therefore, the GNSS reflected signal has the following relationship:

[0085]

[0086] The above formula can be used to obtain the relationship between the height of the reflecting surface and the frequency of the reflected signal and the wavelength of the direct signal, which is the GNSS-IR height measurement geometric model:

[0087]

[0088] The tide level monitoring method proposed in the embodiment of the present application will be described in detail below.

[0089] For example, Figure 1 As shown, the tide level monitoring method comprises the following steps:

[0090] In step S101, SNR data of the reflection area within a preset altitude angle range of the sea surface is obtained.

[0091] Among them, SNR data refers to signal-to-noise ratio data, which reflects the proportional relationship between signal and noise.

[0092] Specifically, combined Figure 1 and Figure 3 As shown, when monitoring tide level, monitoring stations are set up on the shore, and standard geodetic GNSS receivers are usually used to collect data to obtain navigation messages and GNSS raw observations, and convert them into RINEX (Receiver Independent Exchange Format, data exchange format independent of the receiver) format. You can also directly select the IGS (International Global Navigation Satellite System Service, International Global Satellite Positioning and Navigation Service Organization) station near the shore to find the daily RINEX format observation value file and broadcast ephemeris file. After obtaining the observation value file and broadcast ephemeris file, the on-orbit position coordinates of each satellite, the monitoring station coordinates (i.e., the user receiver position coordinates) and SNR data can be calculated through this information, and the satellite's altitude angle, azimuth angle and SNR information of different bands can be determined based on the satellite and receiver position coordinate information. According to the satellite's ascending and descending process, the SNR data can be divided into two sections. By limiting the azimuth angle, the reflection signal from the sea surface in the reflection area (i.e., SNR data) can be effectively obtained. Since GNSS receivers generally use right-hand polarized antennas, this type of antenna has a significant inhibitory effect on left-hand polarized electromagnetic waves. Therefore, when the satellite elevation angle is small, the intensity of the reflected component of the right-hand electromagnetic wave is greater than the left-hand component. However, if the satellite elevation angle is too low, the impact of the reflected signal on the direct signal will be too significant, which can easily lead to satellite loss of lock and cycle slip, thus affecting the accuracy and reliability of the data. In order to ensure that the GNSS observation value contains reflected signal information of sufficient strength and avoid data problems caused by excessively strong reflected signals, the satellite's elevation angle can be limited to a preset range, such as between 5° and 25°. Such an elevation angle range can balance the intensity of the reflected signal and the stability of the direct signal, ensuring the accuracy and reliability of the tide monitoring data.

[0093] In step S102, atmospheric refraction correction and detrending operations are performed on the SNR data to obtain high-frequency components of the SNR data.

[0094] Among them, atmospheric refraction correction is a correction operation used to correct the refraction effect caused by factors such as changes in atmospheric density when the signal propagates in the atmosphere to ensure the accuracy of the data. The detrending operation refers to the process of removing long-term trends from the data. This is usually to eliminate the slowly changing trends in the data caused by factors such as environmental changes, so as to more accurately analyze the short-term changes in the data. The high-frequency component refers to the higher-frequency part of the data, which contains the rapidly changing information of the signal. In signal processing, analyzing the high-frequency component helps to identify the rapidly changing characteristics in the signal.

[0095] It is understandable that the SNR sequence is a low altitude sequence. The lower the receiving altitude of the satellite signal, the greater the atmospheric refraction effect of the signal when it passes through the atmosphere. This effect will cause the signal to bend more during propagation, resulting in a greater deviation between the calculated altitude and the actual altitude. This deviation not only affects the propagation path of the signal, but also has an adverse effect on the final inversion result. Therefore, in order to ensure the accuracy of the observation data, it is necessary to correct the altitude deviation caused by the atmospheric refraction effect, thereby reducing the error caused by the atmospheric refraction effect and improving the accuracy of the final inversion result.

[0096] Next, how to perform atmospheric refraction correction and detrending operations on SNR data is explained in detail.

[0097] As a possible implementation method, in some embodiments, atmospheric refraction correction and detrending operations are performed on the SNR data to obtain high-frequency components of the SNR data, including: performing an atmospheric refraction correction operation on the SNR data to obtain corrected SNR data; fitting the direct signal component in the corrected SNR data using a quadratic polynomial to obtain a fitting result; and subtracting the corrected SNR data from the fitting result to obtain the high-frequency components of the SNR data.

[0098] Specifically, after obtaining the corresponding SNR data, the SNR data needs to be preprocessed. During the preprocessing process, the SNR data at the same altitude angle needs to be averaged first to reduce the impact of random errors, that is, multiple observations are statistically averaged to obtain a more stable and reliable representative value. Next, in order to more accurately reflect the true characteristics of the signal, the atmospheric refraction effect needs to be corrected. The temperature and air pressure data of the monitoring station can be collected through the tropospheric model or co-located sensors. After obtaining these data, the altitude angle is corrected using the improved Bennett formula to eliminate the impact of atmospheric refraction on the signal path length, thereby improving the accuracy of the SNR data.

[0099] Under standard meteorological parameter conditions, that is, atmospheric pressure of 1010 kpa and temperature of 10°C, the altitude correction value R of standard meteorological conditions is M The improved Bennett formula can be used for calculation:

[0100] R M =R ′ M -0.06sin(14.7R ′ M +13); (5)

[0101]

[0102] When considering different temperature and air pressure conditions, the altitude angle correction value R can be calculated using the following formula M :

[0103]

[0104] Where T is temperature, P is air pressure, R M It is the altitude angle correction value under standard meteorological conditions.

[0105] After completing the above operations, the corrected SNR data needs to be detrended to extract the high-frequency components.

[0106] It can be understood that the SNR data measured by the GNSS receiver contains direct signal components and reflected signal components, which can be expressed as:

[0107]

[0108] Among them, A d is the direct signal amplitude, A r is the reflected signal amplitude, is the phase difference between the direct signal and the reflected signal.

[0109] Due to the gain mode of the GNSS-IR receiving antenna, the reflected signal is suppressed, and the change trend of the SNR data is dominated by the direct signal. The amplitude of the direct signal is much larger than the reflected signal. Therefore, a low-order polynomial (such as a quadratic polynomial) can be used to fit the direct component of the corrected SNR data, and then the corrected SNR data is subtracted from the fitted direct component to obtain the detrended SNR sequence, that is, the high-frequency component:

[0110]

[0111] It should be noted that the detrended SNR sequence is the measured SNR multipath error sequence, which can more truly reflect the impact of multipath effects on the signal during the propagation process. By processing the detrended term, the multipath components in the signal can be effectively separated, providing a more accurate data basis for further analysis and processing.

[0112] In step S103, based on the preset wavelet transform strategy, the instantaneous frequency time series of the high-frequency component at different times and the peak power time series at different times are obtained, and the instantaneous frequency time series and the peak power time series are filtered respectively to obtain a first frequency filtering result corresponding to the instantaneous frequency time series and a second frequency filtering result at a time corresponding to the filtered peak power time series. The average value is calculated based on the intersection of the first frequency filtering result and the second frequency filtering result to obtain the dominant frequency of the high-frequency component.

[0113] Among them, wavelet transform (WT) is a mathematical transformation analysis method used to analyze signals with different frequency components. It can provide frequency information of signals at different time points. It inherits and develops the idea of ​​short-time Fourier transform localization, while overcoming the shortcomings of window size not changing with frequency. It is very useful in signal processing.

[0114] Its generating function ψ(t) is defined as:

[0115]

[0116] Among them, m and n are scaling factor and translation factor respectively.

[0117] Any function f(t)∈L 2 The continuous wavelet transform of (R) is:

[0118]

[0119] Instantaneous frequency refers to the frequency component of a signal at any moment, which describes the frequency change of a signal at a specific moment. Peak power refers to the maximum power value of a signal at a certain moment or in a certain period of time, which can reflect the strength of the signal. The dominant frequency refers to the highest frequency part of a signal, or the most important frequency component of a signal. In the embodiment of the present application, the dominant frequency refers to the main frequency of the high-frequency component.

[0120] That is to say, in the embodiment of the present application, a wavelet transform strategy can be used to analyze the instantaneous frequency and peak power of the high-frequency component at different times, and to construct an instantaneous frequency time series, which records the frequency changes of the high-frequency component at different time points; at the same time, a peak power time series is also constructed, which reflects the energy distribution of the high-frequency component at different time points. Next, the two time series are screened to identify representative frequency and power values. For the instantaneous frequency time series, the first frequency screening results can be obtained according to a specific standard or algorithm, and these results represent the main frequency change trend of the high-frequency component at different time points. For the peak power time series, the same screening is performed, and the peak power time series after screening is obtained. Based on the peak power time series after screening and the instantaneous frequency time series, the second frequency screening result can be screened from the instantaneous frequency time series. Finally, the intersection of the first frequency screening result and the second frequency screening result is taken, and the average value is calculated based on the intersection, so that the main frequency of the high-frequency component can be obtained, so as to perform tide inversion. Through this method, the high-frequency dynamic characteristics of the signal can be more deeply understood, and an important reference basis can be provided for further signal processing and analysis.

[0121] The following is a detailed explanation of how to obtain the instantaneous frequency time series at different times and the peak power time series at different times.

[0122] As a possible implementation method, in some embodiments, based on a preset wavelet transform strategy, the instantaneous frequency time series of the high-frequency component at different moments and the peak power time series at different moments are obtained, including: taking a preset wavelet function as a basis function, performing a wavelet transform on the high-frequency component to obtain the wavelet spectrum at different moments; based on the wavelet spectrum at different moments, selecting the frequency corresponding to the peak power at each moment as the instantaneous frequency, to obtain the instantaneous frequency time series at different moments and the peak power time series at different moments.

[0123] Specifically, using a preset wavelet function (such as Morlet function) as the basis function, the high-frequency component can be transformed by wavelet transform, and the signal can be decomposed into a two-dimensional representation of time and frequency, thereby obtaining the wavelet spectrum information at different time points, such as Figure 4 As shown in (a), in these spectrum information, by extracting the frequency points corresponding to the power reaching the maximum value (i.e., frequency peak) at different times, it is regarded as the instantaneous frequency, thereby obtaining an instantaneous frequency time series corresponding to the original SNR data. This sequence reflects the instantaneous frequency change of the signal at different time points. At the same time, by extracting the power value corresponding to each peak, a peak power time series corresponding to the original SNR data can be formed.

[0124] Since continuous wavelet transform is sensitive to noise, it is also necessary to screen abnormal instantaneous frequencies. The specific method is as follows:

[0125] As a possible implementation method, in some embodiments, the instantaneous frequency time series and the peak power time series are screened respectively to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result corresponding to the filtered peak power time series, including: determining the current short window length and the current long window length; calculating the short window average frequency according to the current short window length, and calculating the long window average frequency according to the current long window length, and calculating the ratio of the short window average frequency to the long window average frequency, and calculating the difference between the ratio and the first preset value; obtaining the first frequency screening result corresponding to the instantaneous frequency time series according to the current short window length, the current long window length, the difference and the first preset reference value; normalizing the peak power time series, sorting the normalized peak power according to a preset arrangement rule, and selecting the peak power that meets the preset conditions to calculate the average value to obtain a second preset reference value; screening the peak power greater than or equal to the second preset reference value from the peak power time series to obtain the filtered peak power time series; selecting the instantaneous frequency at the moment corresponding to the filtered peak power time series from the instantaneous frequency time series to obtain the second frequency screening result.

[0126] Specifically, from the perspective of stable changes in instantaneous frequency, the STA (Short-Term Average) / LTA (Long-Term Average) method can be used to filter the instantaneous frequency time series to filter out frequency points with drastic changes. The principle of the STA / LTA algorithm is as follows:

[0127]

[0128] Where n is the sampling time of the signal, sw is the window length of the short window, lw is the window length of the long window, δ is the set trigger threshold, and CF(i) is the characteristic function value.

[0129] The embodiment of the present application only averages the values ​​within the window. First, the current short window length and the current long window length are determined. The short window average frequency is calculated according to the current short window length (as shown in formula (12)), and the long window average frequency is calculated according to the current long window length (as shown in formula (13)). By calculating the ratio of the short window average frequency to the long window average frequency (as shown in formula (14)), it is determined whether the instantaneous frequency changes stably. The difference between the ratio of the short window average frequency to the long window average frequency and the first preset value (such as 1) is calculated. When the difference result exceeds the first preset reference value (such as 0.005), it is considered that there is an abnormal value, and the value in the center of the window needs to be deleted. Otherwise, it means that the instantaneous frequency changes stably, and these data can be retained for subsequent analysis and processing. Figure 4 (b) is the result of STA / LTA method screening of G08 L1. The solid line in the figure is the ratio of the short window average frequency to the long window average frequency, and the dotted line represents the first preset reference value. The weather on the selected date is good and the tide level is stable, so the ratio of the short window average frequency to the long window average frequency changes stably.

[0130] Starting from the signal quality, the peak power time series is processed. In order to obtain instantaneous power with high observation quality, a method based on normalized power threshold can be adopted. First, the peak power time series is normalized (normalization is achieved by dividing each power value by the maximum power value in the sequence) to ensure that all power values ​​are compared at a uniform magnitude. After the normalization is completed, the first 20 power values ​​are selected from them in order from large to small for average calculation, and 70% of the average value is used as the second preset reference value for subsequent judgment. Each power value in the peak power time series is compared with the second preset reference value. If a power value is less than the second preset reference value, it is considered that the power value is abnormal and is deleted from the sequence. On the contrary, if the power value is greater than or equal to the second preset reference value, it is considered that the power value is normal and can be retained, thereby obtaining the filtered peak power time series. Figure 4 (c) is the normalized power threshold screening result of G08 L1. The solid line in the figure is the power sequence, and the dotted line is the second preset reference value. The results show that the instantaneous frequency observation quality in the second half is higher, which is consistent with Figure 4 (a) The energy concentration area corresponds to the area where the SNR data is concentrated. The main reason is that this section of SNR data is the star drop stage. The altitude angle of the area later in time is relatively low, so the interference effect is more obvious, and a higher peak frequency can be obtained. This method can effectively identify and remove outliers in the power sequence, thereby ensuring the accuracy and reliability of the data. Based on the screened peak power time series and instantaneous frequency time series, the instantaneous frequency corresponding to the screened peak power time series is selected from the instantaneous frequency time series to obtain the second frequency screening result.

[0131] In step S104, a vertical height change sequence of the receiving antenna relative to the sea surface is calculated based on the main frequency, and an initial tide level sequence is obtained according to the vertical height change sequence. The initial tide level sequence is decomposed into an equally spaced astronomical tide sequence and a first residual water level sequence using a preset quasi-tidal harmonization analysis strategy. The first residual water level sequence is locally weighted averaged to obtain a second residual water level sequence at a time corresponding to the equally spaced astronomical tide sequence. The equally spaced astronomical tide sequence and the second residual water level sequence are added to obtain a final tide level sequence.

[0132] Among them, the astronomical tide sequence refers to the simulated or calculated periodic change sequence of tidal phenomena caused by the gravitational force between the earth, the moon and the sun, which is composed of a finite number of waves with different amplitudes. The residual water level sequence refers to the remaining tidal level change sequence after removing the main astronomical tide influence, which is related to meteorological factors.

[0133] That is to say, by calculating the vertical height change sequence of the phase center of the receiving antenna relative to the sea surface through the extracted main frequency, and subtracting the vertical height change sequence of the phase center of the receiving antenna relative to the sea surface from a certain value, it can be converted into an initial tide level sequence. At this time, the tide level sequence is a non-equally spaced sequence containing large noise. Therefore, the initial tide level sequence can be processed using the preset quasi-tidal harmonic analysis strategy to obtain an equally spaced astronomical tide sequence and a first residual water level sequence (the difference between the astronomical tide and the true tide level can be regarded as a constant) of arbitrary time resolution. The first residual water level sequence contains large noise and is still a non-equally spaced sequence. In order to convert this non-equally spaced first residual water level sequence into an equally spaced sequence, a time window is first set with a length of 3 hours. This time window is used to traverse the entire first residual water level sequence. During the traversal process, the distance from each data point to the center of the current time window is paid attention to, and the weight of each data point is determined based on this distance. In order to determine the weight, a specific weight function can be used, such as the Gaussian function. This function can assign weights based on the distance between the data point and the center of the window. The closer the distance, the greater the weight, and vice versa. The weight of each data point and the corresponding residual water level prediction value are calculated using the following formula:

[0134]

[0135] d res =(B T PB) -1 B T PL (17)

[0136] Assuming that the residual water level change caused by meteorological factors in the window remains unchanged or is very small, weighted linear regression is performed on all data points in the window, and the weighted result of the residual water level is calculated and used as the estimated value of the residual water level at the prediction point. In this way, the first residual water level sequence can be effectively converted into a smooth equally spaced second residual water level sequence. By adding the equally spaced astronomical tide sequence to the second residual water level sequence, an improved GNSS-IR tide level sequence can be obtained. Such a sequence has higher practical value and accuracy for subsequent analysis and research.

[0137] Next, the process of the above step S104 is described in detail.

[0138] As a possible implementation method, in some embodiments, a vertical height change sequence of a receiving antenna relative to the sea surface is calculated based on the main frequency, and an initial tide level sequence is obtained according to the vertical height change sequence, including: based on the main frequency, using a preset SNR multipath signal expression to calculate the vertical height change sequence of the receiving antenna relative to the sea surface; and performing a difference calculation between a second preset value and the vertical height change sequence of the receiving antenna relative to the sea surface to obtain the initial tide level sequence.

[0139] Specifically, using Figure 2 The geometric model shown can be used to calculate the vertical height of the receiving antenna relative to the sea surface through the main frequency calculation, as shown in formula (4). After obtaining the vertical height change sequence of the receiving antenna relative to the sea surface, the initial tide level sequence can be calculated through a fixed reference value. This reference value is usually the average height of the sea level, or a pre-set reference height. The initial tide level sequence can be obtained by subtracting the vertical height change sequence of the receiving antenna relative to the sea surface from this reference value. This sequence reflects the change of the sea surface height over time. In order to ensure the accuracy of the tide level data, a preliminary anomaly detection can be performed on the initial tide level sequence. By setting a window size, the data in the window is analyzed. In this process, the median absolute deviation (MAD) of the data in the window is calculated. MAD is a robust statistic used to measure the degree to which a data point deviates from the median. If the deviation of a data point exceeds 2 times the MAD, then this data point is considered an outlier. The outlier may be caused by measurement error, environmental interference or other abnormal factors. Therefore, these outliers need to be deleted from the initial tide level sequence. On the contrary, if the deviation of the data point is less than or equal to 2 times the MAD, the data point is considered normal and retained. In this way, the outliers in the initial tide level sequence can be filtered out to obtain a more accurate and reliable initial tide level sequence, providing a solid foundation for subsequent analysis and research.

[0140] As a possible implementation method, in some embodiments, a preset quasi-tidal harmonic analysis strategy is used to decompose the initial tide level sequence into an equally spaced astronomical tide sequence and a first residual water level sequence, including: using a preset tidal model to predict the amplitude and retardation relationship of tidal components at multiple target locations; based on the amplitude and retardation relationship, using a preset quasi-tidal harmonic analysis strategy, the initial tide level sequence is decomposed into an equally spaced astronomical tide sequence and a first residual water level sequence.

[0141] It can be understood that the tide level sequence includes two parts: an astronomical tide sequence and a residual water level sequence. The embodiment of the present application processes the astronomical tide sequence and the residual water level sequence in the tide level sequence separately to obtain a higher precision, equally spaced tide level sequence.

[0142] The tide level at any tide station can be expressed as:

[0143]

[0144] Among them, S0 is the mean sea level, r is the non-periodic water level change caused by non-astronomical factors, σ j is the tidal frequency, H j is the tidal amplitude, g j It is the initial phase angle of the tidal bore.

[0145] Considering only the astronomical tide, linearizing equation (9) yields:

[0146]

[0147] in,

[0148]

[0149] For a tidal sequence of N moments, the following set of equations can be obtained:

[0150]

[0151] Based on the least squares principle, by solving the above equation group (21), we can obtain the various parameters in the equation, and then obtain the amplitude and phase of each partial tide, thereby separating the astronomical tide from the residual water level.

[0152] Since the initial tidal level sequence is a non-equally spaced sequence, harmonic analysis cannot be performed directly. Therefore, the embodiment of the present application is based on a tidal model developed based on satellite observations (these models are usually based on theoretical analysis of tides (such as tidal force theory) and statistical fitting of long-term observation data, such as FES (Finite Element Solution, tidal load model), GOT (Global Ocean Tide, global ocean model) and other models) to obtain information on tidal components of multiple target locations around the world, including the amplitude and retardation relationship of the main tides (such as semi-diurnal tide M2, diurnal tide K1, O1, etc.), and then use the preset quasi-tidal harmonic analysis strategy to use the amplitude and retardation relationship for quasi-harmonic analysis of the initial tidal level sequence, and by setting the time resolution, separate and obtain any equally spaced astronomical tide sequence and the first residual water level sequence.

[0153] For example, if Figure 5 As shown, the GNSS data of the CALC station, an IGS station located in the Gulf of Mexico of the United States, for the whole year of 2020 was used to compare the results of tide monitoring with the data of the parallel tide gauge station. The experimental results show that the tide monitoring results of the tide monitoring method of the embodiment of the present application were processed for one year of nearshore IGS station data, and the error between them and the actual measured data of the tide gauge station was within 5 cm, which significantly improved the tide monitoring accuracy of the wavelet transform.

[0154] In summary, (1) the embodiment of the present application aims at the problem that the continuous wavelet transform result is sensitive to noise and has some outliers, and proposes an improvement method for the wavelet transform result. The specific improvement methods are as follows: from the perspective of the stable change of instantaneous frequency, the STA / LTA method is used to screen out outliers with drastic changes; from the perspective of observation quality, a normalized power threshold is set to retain instantaneous frequencies with higher power. (2) In view of the problem that the GNSS-IR tide inversion result is a non-equally spaced time series and contains a certain amount of noise, the embodiment of the present application proposes to use a quasi-harmonic analysis strategy to obtain an astronomical tide sequence with equal intervals and high temporal resolution, and to separate the residual water level sequence from the GNSS-IR tide sequence, and then use local weighted regression to obtain a smooth residual water level sequence at the corresponding moment of the astronomical tide sequence, and restore it to the tide level, thereby obtaining GNSS-IR tide results with equal intervals and high precision at any temporal resolution.

[0155] According to the tide monitoring method proposed in the embodiment of the present application, by obtaining the SNR data of the reflection area at a low altitude angle on the sea surface, the data can be corrected for atmospheric refraction and detrended to obtain its high-frequency component; by using wavelet transform on the high-frequency component, the instantaneous frequency time series and peak power time series at different times can be obtained, and the instantaneous frequency time series and peak power time series are screened respectively, and finally the average value of the frequency screening results is calculated to obtain the main frequency of the high-frequency component; the antenna height is calculated based on the main frequency to obtain the initial tide sequence, and the initial tide sequence is decomposed into an astronomical tide sequence and a residual water level sequence at equal intervals using quasi-tidal harmonic analysis, and the astronomical tide sequence is corrected using local weighted linear regression to obtain the final tide sequence. Thus, the problem of low precision of the tide monitoring results obtained by the prior art is solved, the noise of the tide sequence monitoring results is effectively reduced, and a high-precision tide sequence with arbitrary time resolution is obtained.

[0156] Next, the tide monitoring device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0157] Figure 6 It is a block diagram of a tide monitoring device according to an embodiment of the present application.

[0158] like Figure 6 As shown, the tide monitoring device 10 includes: an acquisition module 100 , a first acquisition module 200 , a second acquisition module 300 and a third acquisition module 400 .

[0159] The acquisition module 100 is used to acquire SNR data of the reflection area within a preset altitude angle range of the sea surface;

[0160] The first acquisition module 200 is used to perform an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data;

[0161] The second acquisition module 300 is used to obtain the instantaneous frequency time series of the high-frequency component at different times and the peak power time series at different times based on a preset wavelet transform strategy, and to screen the instantaneous frequency time series and the peak power time series respectively to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a time corresponding to the screened peak power time series, and to calculate the average value based on the intersection of the first frequency screening result and the second frequency screening result to obtain the main frequency of the high-frequency component;

[0162] The third acquisition module 400 is used to calculate the vertical height change sequence of the receiving antenna relative to the sea surface based on the main frequency, and obtain the initial tide level sequence according to the vertical height change sequence, and use the preset quasi-tidal harmonic analysis strategy to decompose the initial tide level sequence into an equally spaced astronomical tide sequence and a first residual water level sequence, perform local weighted average calculation on the first residual water level sequence, obtain a second residual water level sequence at a time corresponding to the equally spaced astronomical tide sequence, add the equally spaced astronomical tide sequence and the second residual water level sequence, and obtain a final tide level sequence.

[0163] Furthermore, in some embodiments, the first obtaining module 200 is specifically configured to:

[0164] Perform atmospheric refraction correction on the SNR data to obtain corrected SNR data;

[0165] The direct signal component in the corrected SNR data is fitted using a quadratic polynomial to obtain a fitting result;

[0166] Subtract the corrected SNR data from the fitting result to obtain the high-frequency component of the SNR data.

[0167] Further, in some embodiments, the second obtaining module 300 is specifically configured to:

[0168] Using the preset wavelet function as the basis function, the high-frequency component is subjected to wavelet transformation to obtain the wavelet spectrum at different times;

[0169] Based on the wavelet spectra at different moments, the frequency corresponding to the peak power at each moment is selected as the instantaneous frequency, and the instantaneous frequency time series at different moments and the peak power time series at different moments are obtained.

[0170] Further, in some embodiments, the second obtaining module 300 is specifically configured to:

[0171] Determine the current short window length and the current long window length;

[0172] Calculating the short window average frequency according to the current short window length, calculating the long window average frequency according to the current long window length, and calculating the ratio of the short window average frequency to the long window average frequency, and calculating the difference between the ratio and the first preset value;

[0173] Obtaining a first frequency screening result corresponding to the instantaneous frequency time series according to the current short window length, the current long window length, the difference value and the first preset reference value;

[0174] Normalizing the peak power time series, sorting the normalized peak powers according to a preset arrangement rule, and selecting peak powers that meet preset conditions to calculate the average value to obtain a second preset reference value;

[0175] Filtering peak powers greater than or equal to a second preset reference value from the peak power time series to obtain a filtered peak power time series;

[0176] The instantaneous frequency at the time corresponding to the filtered peak power time series is selected from the instantaneous frequency time series to obtain a second frequency screening result.

[0177] Further, in some embodiments, the third obtaining module 400 is specifically used to:

[0178] Based on the main frequency, the vertical height change sequence of the receiving antenna relative to the sea surface is calculated using the preset SNR multipath signal expression;

[0179] The second preset value is calculated by difference with the vertical height change sequence of the receiving antenna relative to the sea surface to obtain an initial tide level sequence.

[0180] Further, in some embodiments, the third obtaining module 400 is specifically used to:

[0181] Using the preset tidal model, the amplitude and delay angle relationship of tidal components at multiple target locations are predicted;

[0182] Based on the relationship between amplitude and retardation angle, the initial tidal level sequence is decomposed into equally spaced astronomical tide sequence and the first residual water level sequence using the preset quasi-tidal harmonic analysis strategy.

[0183] It should be noted that the above explanations and descriptions of the tide level monitoring method embodiment are also applicable to the tide level monitoring device of this embodiment, and will not be repeated here.

[0184] According to the tide monitoring device proposed in the embodiment of the present application, by obtaining the SNR data of the reflection area at a low altitude angle on the sea surface, the data can be corrected for atmospheric refraction and detrended to obtain its high-frequency component; by using wavelet transform on the high-frequency component, the instantaneous frequency time series and peak power time series at different times can be obtained, and the instantaneous frequency time series and peak power time series are screened respectively, and finally the average value of the frequency screening results is calculated to obtain the main frequency of the high-frequency component; the antenna height is calculated based on the main frequency to obtain the initial tide sequence, and the initial tide sequence is decomposed into an astronomical tide sequence and a residual water level sequence at equal intervals using quasi-tidal harmonic analysis, and the astronomical tide sequence is corrected using local weighted linear regression to obtain the final tide sequence. Thus, the problem of low precision of the tide monitoring results obtained by the prior art is solved, the noise of the tide sequence monitoring results is effectively reduced, and a high-precision tide sequence with arbitrary time resolution is obtained.

[0185] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0186] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0187] When the processor 702 executes the program, the tide level monitoring method provided in the above embodiment is implemented.

[0188] Furthermore, the electronic device further comprises:

[0189] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0190] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0191] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0192] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0193] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0194] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0195] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above tide level monitoring method is implemented.

[0196] An embodiment of the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the above tide monitoring method is implemented.

[0197] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0198] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0199] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A tidal level monitoring method, characterized in that: The following steps are involved: Obtaining SNR data of the reflection area within a preset altitude angle range of the sea surface; Performing an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data; Based on a preset wavelet transform strategy, the instantaneous frequency time series of the high-frequency component at different moments and the peak power time series at different moments are obtained, and the instantaneous frequency time series and the peak power time series are respectively screened to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a moment corresponding to the screened peak power time series, and an average value is calculated based on the intersection of the first frequency screening result and the second frequency screening result to obtain the dominant frequency of the high-frequency component; A vertical height change sequence of the receiving antenna relative to the sea surface is calculated based on the main frequency, and an initial tide level sequence is obtained according to the vertical height change sequence, and the initial tide level sequence is decomposed into an equally spaced astronomical tide sequence and a first residual water level sequence using a preset quasi-tidal harmonic analysis strategy, and a local weighted average calculation is performed on the first residual water level sequence to obtain a second residual water level sequence at a time corresponding to the equally spaced astronomical tide sequence, and the equally spaced astronomical tide sequence and the second residual water level sequence are added to obtain a final tide level sequence.

2. The method according to claim 1, characterized in that The performing an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data includes: Performing an atmospheric refraction correction operation on the SNR data to obtain corrected SNR data; Fitting the direct signal component in the corrected SNR data using a quadratic polynomial to obtain a fitting result; The corrected SNR data is subtracted from the fitting result to obtain a high-frequency component of the SNR data.

3. The method according to claim 1, characterized in that The method of obtaining the instantaneous frequency time series of the high-frequency component at different times and the peak power time series at different times based on the preset wavelet transform strategy includes: Using a preset wavelet function as a basis function, performing wavelet transform on the high frequency component to obtain wavelet spectra at different times; Based on the wavelet spectra at different moments, the frequency corresponding to the peak power at each moment is selected as the instantaneous frequency, and the instantaneous frequency time series at different moments and the peak power time series at different moments are obtained.

4. The method according to claim 1 or 3, characterized in that: The step of respectively screening the instantaneous frequency time series and the peak power time series to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a time corresponding to the screened peak power time series includes: Determine the current short window length and the current long window length; Calculating a short window average frequency according to the current short window length, calculating a long window average frequency according to the current long window length, calculating a ratio of the short window average frequency to the long window average frequency, and calculating a difference between the ratio and a first preset value; Obtaining a first frequency screening result corresponding to the instantaneous frequency time series according to the current short window length, the current long window length, the difference value and a first preset reference value; Normalizing the peak power time series, sorting the normalized peak powers according to a preset arrangement rule, and selecting peak powers that meet preset conditions to calculate the average value to obtain a second preset reference value; Filtering peak powers greater than or equal to the second preset reference value from the peak power time series to obtain a filtered peak power time series; The instantaneous frequency at the time corresponding to the filtered peak power time series is selected from the instantaneous frequency time series to obtain the second frequency filtering result.

5. The method according to claim 1, characterized in that: The step of calculating a vertical height change sequence of a receiving antenna relative to the sea surface based on the main frequency, and obtaining an initial tide level sequence according to the vertical height change sequence, includes: Based on the main frequency, using a preset SNR multipath signal expression, calculating a vertical height change sequence of the receiving antenna relative to the sea surface; The second preset value is subtracted from the vertical height change sequence of the receiving antenna relative to the sea surface to obtain the initial tide level sequence.

6. The method according to claim 1, characterized in that The method of decomposing the initial tide level sequence into an astronomical tide sequence and a first residual water level sequence at equal intervals by using a preset quasi-tidal harmonic analysis strategy includes: Using the preset tidal model, the amplitude and delay angle relationship of tidal components at multiple target locations are predicted; Based on the relationship between the amplitude and the retardation angle, the initial tide level sequence is decomposed into the equally spaced astronomical tide sequence and the first residual water level sequence using the preset quasi-tidal harmonic analysis strategy.

7. A tide level monitoring device, characterized in that: include: An acquisition module is used to acquire SNR data of the reflection area within a preset altitude angle range of the sea surface; A first acquisition module is used to perform an atmospheric refraction correction operation and a detrending operation on the SNR data to obtain a high-frequency component of the SNR data; A second acquisition module is used to obtain the instantaneous frequency time series of the high-frequency component at different moments and the peak power time series at different moments based on a preset wavelet transform strategy, and to screen the instantaneous frequency time series and the peak power time series respectively to obtain a first frequency screening result corresponding to the instantaneous frequency time series and a second frequency screening result at a moment corresponding to the screened peak power time series, and to calculate an average value based on the intersection of the first frequency screening result and the second frequency screening result to obtain the main frequency of the high-frequency component; The third acquisition module is used to calculate the vertical height change sequence of the receiving antenna relative to the sea surface based on the main frequency, and obtain an initial tide level sequence according to the vertical height change sequence, and use a preset quasi-tidal harmonic analysis strategy to decompose the initial tide level sequence into an equally spaced astronomical tide sequence and a first residual water level sequence, perform local weighted average calculation on the first residual water level sequence to obtain a second residual water level sequence at a time corresponding to the equally spaced astronomical tide sequence, and add the equally spaced astronomical tide sequence and the second residual water level sequence to obtain a final tide level sequence.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the tide monitoring method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the tide level monitoring method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, is used to implement the tide monitoring method according to any one of claims 1 to 7.

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