CN108225283B - Internal wave monitoring system and method based on nonlinear dynamics characteristics - Google Patents
Internal wave monitoring system and method based on nonlinear dynamics characteristics Download PDFInfo
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- CN108225283B CN108225283B CN201711364206.2A CN201711364206A CN108225283B CN 108225283 B CN108225283 B CN 108225283B CN 201711364206 A CN201711364206 A CN 201711364206A CN 108225283 B CN108225283 B CN 108225283B
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Abstract
The invention discloses an internal wave monitoring system and method based on nonlinear dynamics characteristics, which comprises a pressure data acquisition and transmission module: acquiring sea wave pressure data by using a pressure sensor, and transmitting the sea wave pressure data to a working platform;the data analysis and processing module: filtering tide level information contained in the data by using a high-pass filter to obtain a time sequence only containing sea wave pressure field information, intercepting the time sequence by using a sliding window, and associating dimension D by using a nonlinear dynamics characteristic parameter2Quantitatively characterizing its complexity; an internal wave discrimination module: when D is present2When the convergence is not caused along with the increase of the embedding dimension, the internal wave is judged not to be in accordance with the nonlinear dynamics characteristics of the internal wave; when D is present2And when the embedded dimension is increased and converged to a stable value, the nonlinear dynamics characteristics of the internal wave are met, and the system judges that the internal wave comes. The method of the scheme of the invention is simple, the internal wave judgment is accurate and effective, and especially the missing judgment of the weak energy internal wave can be avoided.
Description
Technical Field
The invention relates to the field of ocean monitoring, in particular to an ocean internal wave monitoring system and method based on the nonlinear dynamic characteristics of sea wave pressure signals.
Background
Ocean internal waves are a medium and small-scale fluctuation phenomenon which occurs in stratified oceans and play an important role in the transmission of mass, momentum and energy in the oceans and global climate change; energy exchange caused by the marine internal waves in the processes of generation, propagation and attenuation has important influence on the marine dynamics process; in addition, the ocean internal waves can cause strong shear flow, and have potential threats to the safety of ocean engineering related operations such as offshore oil exploitation and the like.
At present, the marine internal waves are mainly detected by a chain mooring system and a Synthetic Aperture Radar (SAR): the former describes the internal wave based on the temperature, salinity, speed and other parameters measured by sensors at different depths, and is a main observation means of the dynamic parameters of the internal wave; the latter is based on the phenomenon of amplitude convergence and amplitude dispersion of a surface flow field generated at the wave crest and the wave trough of the internal wave respectively, so that the roughness of the sea surface is changed, and a light and dark alternative stripe structure is formed in an SAR image, and the method is an important means for dynamically monitoring the internal wave.
The pressure sensor is mature equipment widely distributed in the current ocean observation system, and internal wave information can be obtained by utilizing a pressure observation network. At present, the method for extracting marine internal wave information by using a pressure sensor mainly relies on signal time domain waveform and frequency spectrum characteristics for analysis and extraction, and the method cannot effectively distinguish weak-strength internal waves.
The invention provides an internal wave monitoring system and method based on the nonlinear dynamic characteristics of a sea wave pressure signal, and solves the problems in the prior art.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an internal wave monitoring system and method based on the nonlinear dynamic characteristics of a sea wave pressure signal.
The invention is realized by the following technical scheme:
an internal wave monitoring system based on nonlinear dynamics characteristics comprises a pressure data acquisition and transmission module, a data analysis and processing module and an internal wave discrimination module; the pressure data acquisition and transmission module is used for realizing pressure data acquisition and transmission; the data analysis and processing module is used for filtering the tide level and analyzing the nonlinear dynamics characteristics of the data transmitted to the working platform by the pressure data acquisition and transmission module; and the internal wave judging module judges whether the internal wave comes or not according to the analysis result of the data analysis and processing module.
Furthermore, in the data analysis and processing module, sea wave pressure data is recorded through a pressure sensor arranged at a certain depth in the sea water, and then the sea wave pressure data is transmitted to the working platform by utilizing two modes of real-time data transmission or time-lapse data transmission.
Furthermore, in the data analysis and processing module, a high-pass digital filter is used for filtering.
Further, in the data analysis and processing module, the nonlinear dynamics analysis method uses a correlation dimension D2The correlation dimension D2The complexity of the reconstructed phase space can be characterized quantitatively.
Further, the internal wave discrimination module is based on the correlation dimension D2The abnormal change indicates the arrival of an internal wave.
Further, the correlation dimension D of the time series of the wave pressure signals when the internal wave passes through2Convergence to a lower stable value will occur as the embedding dimension increases.
The method of the internal wave monitoring system based on the nonlinear dynamic characteristics comprises the following steps:
step one): in the pressure data acquisition and transmission module, a pressure sensor in the tidal wave instrument is used for recording water pressure data, and the data transmission is transmitted to the working platform in a satellite real-time data transmission or time-lapse data centralized transmission mode;
step two): in the data processing and analyzing method module, tide level filtering is carried out on the data transmitted to the working platform in the step one), and a group of time series x (t) is formed by the filtered pressure signals1),x(t2),x(t3) …, where x (t)i)∈R,ti＝t0+ i Δ t, i ═ 0,1, …, ∞, Δ t as the sampling interval, reconstructing the phase space by the delay time series using a nonlinear dynamics analysis method, calculating the correlation dimension D from the reconstructed phase space vector2The specific analysis steps of the nonlinear parameters are as follows:
from a time series x (t)i) The reconstructed phase space X is obtainedi：
Xi＝[x(ti),x(ti-τ),...,x(ti-(d-1)τ)]T, (1)
Wherein τ is the delay time and d is the embedding dimension; the delay time is determined by a mutual information Method, the minimum embedding dimension D is determined by a Cao-Method, and the dimension D is related by utilizing nonlinear kinetic parameters2Quantitatively describing the complexity of the reconstructed phase space; computing the relevance dimension D by a time series of length N2The calculation method is as follows:
wherein r is the search radius taken in the calculation, and C (W, N, r) is the correlation integral, and the calculation method is as follows:
wherein, the value of W is the optimal delay tau; theta (X) is the Heaviside function, Xi+nIs a phase space XiTime delay transformation of (1); satisfies the following conditions:
d can be obtained by calculating the slope of the curve in the space of lnC (W, N, r) -lnr2When the time series length N is sufficiently large, D2The value of (D) increases with the increase of the embedding dimension and finally converges to a stable value, which is the desired correlation dimension D2；
Step three): for the pressure wave time sequence filtered in the step two), adopting a sliding window with the window width of 2000 points to intercept the time sequence, wherein the sliding step length is 1000 data points, and calculating a correlation dimension D2A change over time; in the absence of internal waves, the correlation dimension D2Continuously keeping a large value; when an internal wave arrives, dimension D is associated2Will converge to lower values as the embedding dimension increases.
Further, when taking the correlation dimension D2At 12 hours, normally without internal waves, the dimension D is associated2Greater than 8; when an internal wave arrives, dimension D is associated2Less than 4.
Further, in the second step, the high-pass filter for removing the tide level information is a second-order butterworth digital filter.
The invention has the beneficial effects that:
(1) the method is simple, the cost is low, the missing judgment can be reduced for weak energy internal waves, and the misjudgment can be reduced for strong energy disturbance; (2) the real-time on-line internal wave monitoring can be realized by combining a real-time infinite transmission technology; (3) in addition, in consideration of the problem of internal wave detection data transmission efficiency in the practical application of the pressure sensor, the internal wave generation time interval is judged by a nonlinear method, only the low-dimensional signals containing the internal waves in the time interval are subjected to harmonic analysis and are remotely transmitted after being compressed, the data transmission efficiency is greatly improved, and the method has potential application value in the aspect of utilizing the pressure observation network to organize the internal wave detection.
Drawings
FIG. 1 is a schematic diagram of an internal wave monitoring system based on nonlinear dynamics of a sea wave pressure signal according to the present invention;
FIG. 2 is a seawater pressure time signal with tide level information filtered;
FIG. 3 is a diagram of the association dimension D2A graph showing variation with embedded dimension;
FIG. 4 shows (a) a time-frequency diagram, (b) an energy spectrum, and (c) a correlation dimension D of a time series of pressure waves2Schematic as a function of sampling time.
Detailed Description
The objects, technical solutions and advantages of the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The scope of the invention is not limited thereto.
Examples
An ocean internal wave monitoring system based on the nonlinear dynamics characteristics of a sea wave pressure signal is shown in figure 1. The method specifically comprises the following steps: (1) a pressure data acquisition and transmission module; (2) a data analysis and processing module; (3) and an internal wave judging module.
The pressure data acquisition and transmission module records sea wave pressure data by using a pressure sensor and then transmits the sea wave pressure data to the working platform; preferably, a pressure sensor in the tidal wave instrument is used for recording water pressure data, and the sampling frequency is 1 Hz; preferably, the data transmission can be transmitted to the working platform by a satellite real-time data transmission or a historical data centralized transmission mode.
The specific implementation method of the data processing and analyzing method module is as follows:
the tidal level information is filtered by a filtering method, and the waveform diagram of the filtered pressure signal is shown in fig. 2. Preferably, the filtering is performed with a second order butterworth digital filter.
For the filtered pressure signal time series x (t)1),x(t2),x(t3) … (where x (t)i)∈R,ti＝t0+ i Δ t, i ═ 0,1, …, ∞, Δ t as the sampling interval), the phase space is reconstructed by a delay time series using a nonlinear dynamics analysis method, and the correlation dimension D is calculated from the reconstructed phase space vector2A non-linearity parameter. The specific analysis steps are as follows:
from a time series x (t)i) The reconstructed phase space is obtained:
Xi＝[x(ti),x(ti-τ),...,x(ti-(d-1)τ)]T, (1)
where τ is the delay time and d is the embedding dimension. Preferably, the delay time may be determined by a mutual information Method, and the minimum embedding dimension size is determined by the Cao-Method.
Further, using non-linear dynamics parameters to relate dimension D2The complexity of the reconstructed phase space is quantitatively described. Computing the relevance dimension D by a time series of length N2The calculation method is as follows:
wherein r is the search radius taken in the calculation, and C (W, N, r) is the correlation integral, and the calculation method is as follows:
wherein, the value of W is the optimal delay tau. θ (x) is a Heaviside function, satisfying:
d can be obtained by calculating the slope of the curve in the space of lnC (W, N, r) -lnr2When the time series length N is sufficiently large, D2The value of (a) increases with increasing embedding dimension and eventually converges to a stable value, which is the desired correlation dimension.
FIG. 3 shows the correlation dimension D of the wave pressure signals with and without internal waves2The rule of change with embedded dimension. As can be seen from the figure, when the pressure signal does not contain an internal wave, the complexity is higher, and the corresponding associated dimension does not converge as the embedded dimension increases; when the internal wave arrives, the complexity of the pressure wave signal is low, and the corresponding correlation dimension is low. In contrast, for an input random noise signal, the computed correlation dimension also appears to be non-converging as the embedding dimension increases. This is the basic reason for the present invention to realize the inner wave discriminationAnd (6) processing.
In addition, the implementation method of the internal wave discrimination module comprises the following steps: for the filtered pressure wave time sequence, adopting a sliding window with the window width of 2000 points to intercept the time sequence, wherein the sliding step length is 1000 data points, and calculating an association dimension D2Change over time. When the internal wave does not exist normally, the correlation dimension keeps a larger value continuously, and if the correlation dimension value when the embedding dimension is 12 is obtained, the correlation dimension value is always larger than 8; when an internal wave arrives, the value of the associated dimension will converge to a lower value, typically less than 4, as the embedded dimension increases. To illustrate that this method can be used for real-time online internal wave discrimination, fig. 4(c) shows the variation of the correlation dimension with time. As can be seen from fig. 4(c), the correlation dimension jumps from a higher dimension that does not converge (8.5 as the mean value when the embedding dimension is 12) to a lower converging dimension (2.7 as the mean value) as the internal wave passes. In order to show that the method is more effective than the traditional method, the analysis results of a time-frequency analysis method and an energy spectrum method are given, as shown in fig. 4(a) and 4 (b). As can be seen by comparison, the traditional method cannot accurately judge the second internal wave, but the nonlinear parameter D2The response to weak energy internal wave signals is very sensitive.
The present invention is not limited to the above-described embodiments, and any obvious modifications, substitutions or alterations can be made by those skilled in the art without departing from the design principle of the present invention.
Claims (7)
1. An internal wave monitoring system based on nonlinear dynamics characteristics is characterized in that: the device comprises a pressure data acquisition and transmission module, a data analysis and processing module and an internal wave discrimination module; the pressure data acquisition and transmission module is used for realizing pressure data acquisition and transmission; the data analysis and processing module is used for filtering the tide level and analyzing the nonlinear dynamics characteristics of the data transmitted to the working platform by the pressure data acquisition and transmission module; the internal wave judging module judges whether the internal wave comes or not according to the analysis result of the data analysis and processing module; in the data analysis and processing module, the nonlinear dynamics analysis method uses a correlation dimension D2The said association dimensionD2The complexity of the reconstructed phase space can be characterized quantitatively.
2. The system according to claim 1, wherein the system comprises: in the data analysis and processing module, sea wave pressure data is recorded through a pressure sensor arranged in the sea water at a certain depth, and then the sea wave pressure data is transmitted to the working platform by utilizing two modes of real-time data transmission or time-lapse data transmission.
3. The system according to claim 1, wherein the system comprises: and in the data analysis and processing module, a high-pass digital filter is used for filtering.
4. The system according to claim 1, wherein the system comprises: the internal wave discrimination module is based on the correlation dimension D2The abnormal change indicates the arrival of an internal wave.
5. The system according to claim 1, wherein the system comprises: correlation dimension D of wave pressure signal time sequence when internal wave passes through2Will converge to a lower stable value as the embedding dimension increases, i.e. the correlation dimension D2Less than 4.
6. The method of a nonlinear dynamical feature based internal wave monitoring system according to any one of claims 1 to 5, wherein: the method comprises the following steps:
step one): in the pressure data acquisition and transmission module, a pressure sensor in the tidal wave instrument is used for recording water pressure data, and the data transmission is transmitted to the working platform in a satellite real-time data transmission or time-lapse data centralized transmission mode;
step two): in the data processing and analyzing method module, tide level filtering is carried out on the data transmitted to the working platform in the step one), and a group of time series x (t) is formed by the filtered pressure signalsi),Wherein i is a positive integer, tiRepresents the ith sampling time point; when i is 1, t1Is the first sampling time point; obviously, ti＝t1+ (i-1) Δ t, Δ t being the sampling time interval of the pressure sensor, x (t) for a pressure wave time seriesi) Reconstructing a phase space by a delay time sequence by adopting a nonlinear dynamics analysis method, and calculating a correlation dimension D from a reconstructed phase space vector2The specific analysis steps of this nonlinear kinetic parameter are as follows:
from a time series x (t)i) The reconstructed phase space X is obtainedi：
Xi＝[x(ti),x(ti-τ),...,x(ti-(d-1)τ)]T, (1)
Wherein τ is the delay time and d is the embedding dimension; the delay time tau is determined by a mutual information Method, the minimum embedding dimension D is determined by a Cao-Method, and the dimension D is related by a nonlinear kinetic parameter2Quantitatively describing the complexity of the reconstructed phase space; computing the relevance dimension D by a time series of length N2The calculation method is as follows:
wherein r is the search radius taken in the calculation, and C (W, N, r) is the correlation integral, and the calculation method is as follows:
wherein, the value of W is the optimal delay tau; xi+nIs a phase space XiTime delay transformation of (1); θ (x) is a Heaviside function, satisfying:
d can be obtained by calculating the slope of the curve in the space of lnC (W, N, r) -lnr2When the time series length N is sufficiently large,D2the value of (D) increases with the increase of the embedding dimension and finally converges to a stable value, which is the desired correlation dimension D2；
Step three): for the pressure wave time sequence filtered in the step two), adopting a sliding window with the window width of 2000 points to intercept the time sequence, wherein the sliding step length is 1000 data points, and calculating a correlation dimension D2A change over time; normally without internal waves, the correlation dimension D2Keeping the value continuously larger, i.e. taking the embedded dimension D2When 12, the association dimension D2Greater than 8; when an internal wave arrives, dimension D is associated2Will converge to a lower value as the embedded dimension increases, associating dimension D2Less than 4.
7. The method for an internal wave monitoring system based on nonlinear dynamics features of claim 6, wherein in the second step, the high-pass filter for removing the tide level information is a second-order Butterworth digital filter.
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